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| author | Chunseok Lee <chunseok.lee@samsung.com> | 2019-01-08 17:36:34 +0900 |
|---|---|---|
| committer | Chunseok Lee <chunseok.lee@samsung.com> | 2019-01-08 17:36:34 +0900 |
| commit | bd11b24234d7d43dfe05a81c520aa01ffad06e42 (patch) | |
| tree | 57d0d4044977e4fa0e50cd9ba40b32006dff19eb | |
| parent | 91f4ba45449f700a047a4aeea00b1a7c84e94c75 (diff) | |
| download | nnfw-bd11b24234d7d43dfe05a81c520aa01ffad06e42.tar.gz nnfw-bd11b24234d7d43dfe05a81c520aa01ffad06e42.tar.bz2 nnfw-bd11b24234d7d43dfe05a81c520aa01ffad06e42.zip | |
Imported Upstream version 0.3upstream/0.3
1988 files changed, 59129 insertions, 48291 deletions
diff --git a/.gitignore b/.gitignore index 96188f895..da6e28990 100644 --- a/.gitignore +++ b/.gitignore @@ -54,12 +54,6 @@ GPATH GTAGS GRTAGS -# acl build outputs -/externals/acl/..dblite -/externals/acl/build -/externals/acl/src/acl-stamp -/externals/acl/tmp - # Test cache for model download /tests/framework/cache @@ -90,3 +84,14 @@ GRTAGS /externals/gtest /externals/neon_2_sse /externals/tensorflow +/externals/acl +/externals/absl +/externals/nonius + +# tflkit info files +/tools/tflkit/*.info + +# Generated tests +/tests/nnapi/src/generated +/tests/runtime/neural_networks_test/generated +/runtimes/tests/neural_networks_test/generated diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index cc6da5068..000000000 --- a/.gitmodules +++ /dev/null @@ -1,4 +0,0 @@ -[submodule "acl"] - path = externals/acl - url = https://github.com/ARM-software/ComputeLibrary.git - ignore = dirty diff --git a/CMakeLists.txt b/CMakeLists.txt index fd9be5f30..34824ff71 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -38,16 +38,26 @@ link_directories(${CMAKE_INSTALL_PREFIX}/lib) # Download configuration option(DOWNLOAD_TENSORFLOW "Download Tensorflow source" ON) +option(DOWNLOAD_ABSL "Download Absl source" ON) option(DOWNLOAD_EIGEN "Download Eigen source" ON) option(DOWNLOAD_FARMHASH "Download farmhash source" ON) option(DOWNLOAD_GEMMLOWP "Download GEMM low precesion library source" ON) option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" ON) option(DOWNLOAD_FLATBUFFERS "Download FlatBuffers source" ON) +option(DOWNLOAD_ARMCOMPUTE "Download ARM Compute source" ON) +option(DOWNLOAD_NONIUS "Download nonius source" ON) # GTest support option(BUILD_GTEST "Download and build Google Test" ON) nnfw_find_package(GTest QUIET) +# NOTE Workaround to avoid build fail by tensorflow (or acl) package version mismatch on obs build +if(OBS_BUILD) + add_definitions(-DOBS_BUILD) +endif(OBS_BUILD) + +nnfw_include(ExtendCMakeFunction) + # TODO For now Android build is being enabled incrementally so not all subdirectories are added yet. # However we are going to have the same subdirectories with other OS eventually. if("${TARGET_OS}" STREQUAL "android") @@ -60,21 +70,22 @@ if("${TARGET_OS}" STREQUAL "android") add_subdirectory(libs) add_subdirectory(externals) - add_subdirectory(tools/nnapi_test) - add_subdirectory(tools/tflite_benchmark) + add_subdirectory(tests/tools/nnapi_test) + add_subdirectory(tests/tools/tflite_benchmark) + add_subdirectory(tests/nnapi) add_subdirectory(runtimes) else("${TARGET_OS}" STREQUAL "android") # General case (non-android build) - if (NOT OBS_BUILD) + if(NOT OBS_BUILD) add_subdirectory(externals) - endif() - add_subdirectory(libs) - add_subdirectory(tools) - add_subdirectory(runtimes) + endif(NOT OBS_BUILD) - add_subdirectory(benchmark) add_subdirectory(contrib) + add_subdirectory(libs) + add_subdirectory(runtimes) + add_subdirectory(tests) + add_subdirectory(tools) endif("${TARGET_OS}" STREQUAL "android") @@ -3,14 +3,13 @@ BUILD_TYPE?=Debug CROSS_BUILD?=0 HOST_OS?=linux TARGET_OS?=linux -NPROCS:=1 PARALLEL_BUILD?=1 ## TODO: fix obs build break OBS_BUILD?=OFF COVERAGE_BUILD?=0 BENCHMARK_ACL_BUILD?=0 OPTIONS?= -UPDATE_MODULE?=1 +GENERATE_FILES?=1 # make TARGET and TYPE to lowercase TARGET_ARCH_LC=$(shell echo $(TARGET_ARCH) | tr A-Z a-z) @@ -62,10 +61,11 @@ endif ifeq ($(PARALLEL_BUILD),1) # Get number of processors (linux only for now) ifeq ($(HOST_OS),linux) - NPROCS:=$(shell grep -c ^processor /proc/cpuinfo) + NPROCS?=$(shell grep -c ^processor /proc/cpuinfo) endif endif +NPROCS?=1 WORKHOME=Product WORKFOLDER=$(TARGET_ARCH_LC)-$(TARGET_OS).$(BUILD_TYPE_LC) WORKDIR=$(WORKHOME)/$(WORKFOLDER) @@ -111,10 +111,9 @@ endif ACL_FOLDER_NAME=$(ACL_BUILD_OUT).$(BUILD_TYPE_LC) ACL_COMMAND+= build_dir=$(ACL_FOLDER_NAME) ACL_FOLDER_BUILD=$(ACL_FOLDER)/build/$(ACL_FOLDER_NAME) - -ifeq ($(OBS_BUILD),ON) - UPDATE_MODULE=0 -endif +#ifneq ($(EXT_ACL_FOLDER),) +# ACL_FOLDER_BUILD=$(EXT_ACL_FOLDER) +#endif all: build @@ -129,6 +128,7 @@ install: $(TIMESTAMP_INSTALL) clean: rm -rf $(WORKDIR) + rm -rf externals/*.stamp acl: internal_acl_build internal_acl_install @@ -144,8 +144,8 @@ tflite: tflite_build_internal ### Command (internal) ### configure_internal: -ifeq ($(UPDATE_MODULE),1) - git submodule update --init --recursive +ifeq ($(GENERATE_FILES),1) + tests/nnapi/specs/generate_test.sh endif mkdir -p $(BUILD_ROOT) @@ -171,25 +171,24 @@ install_internal: ln -s $(INSTALL_FOLDER) $(INSTALL_ALIAS) touch $(TIMESTAMP_INSTALL) -internal_acl_build: -ifeq ($(UPDATE_MODULE),1) - git submodule update --init --recursive -endif - +internal_acl_build: configure_internal +#ifeq ($(EXT_ACL_FOLDER),) ifeq ($(TARGET_OS),android) cd $(ACL_FOLDER) && CXX=clang++ CC=clang PATH=$(ANDROID_GNUSTL_PATH) $(ACL_COMMAND) else cd $(ACL_FOLDER) && $(ACL_COMMAND) endif +#endif internal_acl_install: + @echo $(ACL_FOLDER_BUILD) @mkdir -vp $(INSTALL_PATH)/lib @cp -v $(ACL_FOLDER_BUILD)/libarm_compute_core.so $(INSTALL_PATH)/lib/. @cp -v $(ACL_FOLDER_BUILD)/libarm_compute_graph.so $(INSTALL_PATH)/lib/. @cp -v $(ACL_FOLDER_BUILD)/libarm_compute.so $(INSTALL_PATH)/lib/. external_acl: -ifneq ($(EXT_ACL_FOLDER),"") +ifneq ($(EXT_ACL_FOLDER),) @mkdir -vp $(INSTALL_PATH)/lib @cp -v $(EXT_ACL_FOLDER)/libarm_compute_core.so $(INSTALL_PATH)/lib/. @cp -v $(EXT_ACL_FOLDER)/libarm_compute_graph.so $(INSTALL_PATH)/lib/. @@ -207,20 +206,21 @@ test_internal_cross: @echo "Can't do tests on cross build" test_internal: - tools/test_driver/test_driver.sh . + tests/scripts/test_driver.sh . build_test_suite: install_internal @echo "packaging test suite" @rm -rf $(INSTALL_ROOT)/test-suite.tar.gz - @tar -zcf test-suite.tar.gz tests/ tools/test_driver $(INSTALL_ALIAS) --dereference + @tar -zcf test-suite.tar.gz tests/scripts tests/framework $(INSTALL_ALIAS) --dereference @mv test-suite.tar.gz $(INSTALL_ROOT)/. build_coverage_suite: install_internal @echo "packaging test-coverage suite" @rm -rf $(INSTALL_ROOT)/coverage-suite.tar.gz @find Product -name "*.gcno" > include_lists.txt - @tar -zcf coverage-suite.tar.gz tests/ tools/test_driver $(INSTALL_ALIAS) --dereference -T include_lists.txt - @rm -rf include_lists.txt + @pwd | grep -o '/' | wc -l > tests/scripts/build_path_depth.txt + @tar -zcf coverage-suite.tar.gz tests/scripts tests/framework $(INSTALL_ALIAS) --dereference -T include_lists.txt + @rm -rf include_lists.txt tests/scripts/build_path_depth.txt @mv coverage-suite.tar.gz $(INSTALL_ROOT)/. runtime_build_internal: $(BUILD_ROOT) @@ -232,8 +232,9 @@ runtime_build_internal: $(BUILD_ROOT) test_build_internal: $(BUILD_ROOT) # Build test cd $(BUILD_ROOT) && make -j $(NPROCS) nnapi_test + cd $(BUILD_ROOT) && make -j $(NPROCS) tflite_benchmark # Build unittest - cd $(BUILD_ROOT) && make -j $(NPROCS) runtime_run_android_nn_test + cd $(BUILD_ROOT) && make -j $(NPROCS) nnapi_gtest rm -rf $(BUILD_ALIAS) ln -s $(BUILD_FOLDER) $(BUILD_ALIAS) touch $(TIMESTAMP_BUILD) diff --git a/benchmark/CMakeLists.txt b/benchmark/CMakeLists.txt deleted file mode 100644 index e36a0ece2..000000000 --- a/benchmark/CMakeLists.txt +++ /dev/null @@ -1,3 +0,0 @@ -if(BUILD_BENCHMARK_ACL) - add_subdirectory(acl) -endif(BUILD_BENCHMARK_ACL) diff --git a/benchmark/acl/CMakeLists.txt b/benchmark/acl/CMakeLists.txt deleted file mode 100644 index 3bebc275e..000000000 --- a/benchmark/acl/CMakeLists.txt +++ /dev/null @@ -1,20 +0,0 @@ -nnfw_find_package(ARMCompute REQUIRED) - -add_library(arm_compute_benchmark SHARED "Benchmark.cpp") -target_include_directories(arm_compute_benchmark PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(arm_compute_benchmark arm_compute_graph) -install(TARGETS arm_compute_benchmark DESTINATION lib) - -# GoogLeNet benchmark -add_executable(benchmark_googlenet "benchmark_googlenet.cpp") -target_link_libraries(benchmark_googlenet arm_compute_benchmark) - -# GoogLeNet benchmark -add_executable(benchmark_inception_v3 "benchmark_inception_v3.cpp") -target_link_libraries(benchmark_inception_v3 arm_compute_benchmark) - -# MobileNet benchmark -add_executable(benchmark_mobilenet "benchmark_mobilenet.cpp") -target_link_libraries(benchmark_mobilenet arm_compute_benchmark) - -install(TARGETS benchmark_googlenet benchmark_inception_v3 benchmark_mobilenet DESTINATION bin) diff --git a/benchmark/acl/benchmark_googlenet.cpp b/benchmark/acl/benchmark_googlenet.cpp deleted file mode 100644 index ecefdcbea..000000000 --- a/benchmark/acl/benchmark_googlenet.cpp +++ /dev/null @@ -1,242 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/graph.h" - -#include "Benchmark.h" - -#include <cstdlib> -#include <tuple> - -using namespace arm_compute::graph::frontend; - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) -{ - return get_accessor<OutputAccessor>(); -} - -/** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -class GraphGooglenetExample -{ -public: - void do_setup(int argc, char **argv) - { - std::string data_path; /* Path to the trainable data */ - std::string image; /* Image data */ - std::string label; /* Label data */ - - const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - FastMathHint fast_math_hint = FastMathHint::DISABLED; - - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 2) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 3) - { - data_path = argv[2]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No image provided: using random values\n\n"; - } - else if(argc == 4) - { - data_path = argv[2]; - image = argv[3]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 5) - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; - std::cout << "No fast math info provided: disabling fast math\n\n"; - } - else - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; - } - - graph << target_hint - << fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), - get_input_accessor()) - << ConvolutionLayer( - 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"), - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"), - PadStrideInfo(2, 2, 3, 3)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) - << ConvolutionLayer( - 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << ConvolutionLayer( - 3U, 3U, 192U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"), - get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U); - graph << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U); - graph << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U); - graph << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); - graph << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); - graph << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) - << FullyConnectedLayer( - 1000U, - get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"), - get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy")) - << SoftmaxLayer() - << OutputLayer(get_output_accessor()); - - // Finalize graph - GraphConfig config; - config.use_tuner = (target == 2); - graph.finalize(target_hint, config); - } - void do_run() - { - run_benchmark(graph); - } - -private: - Stream graph{ 0, "GoogleNet" }; - - BranchLayer get_inception_node(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int> c_filters, - unsigned int d_filt) - { - std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "1x1_w.npy"), - get_weights_accessor(data_path, total_path + "1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"), - get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << ConvolutionLayer( - 3U, 3U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "3x3_w.npy"), - get_weights_accessor(data_path, total_path + "3x3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubStream i_c(graph); - i_c << ConvolutionLayer( - 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"), - get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << ConvolutionLayer( - 5U, 5U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "5x5_w.npy"), - get_weights_accessor(data_path, total_path + "5x5_b.npy"), - PadStrideInfo(1, 1, 2, 2)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))) - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "pool_proj_w.npy"), - get_weights_accessor(data_path, total_path + "pool_proj_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } -}; - -/** Main program for Googlenet - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -int main(int argc, char **argv) -{ - GraphGooglenetExample example; - - example.do_setup(argc, argv); - example.do_run(); - - return 0; -} diff --git a/benchmark/acl/benchmark_inception_v3.cpp b/benchmark/acl/benchmark_inception_v3.cpp deleted file mode 100644 index 7bb31fcdb..000000000 --- a/benchmark/acl/benchmark_inception_v3.cpp +++ /dev/null @@ -1,891 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/graph.h" - -#include "Benchmark.h" - -#include <cstdlib> -#include <tuple> - -using namespace arm_compute::graph::frontend; - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) -{ - return get_accessor<OutputAccessor>(); -} - -/** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) - */ -class InceptionV3Example -{ -public: - void do_setup(int argc, char **argv) - { - std::string data_path; /* Path to the trainable data */ - std::string image; /* Image data */ - std::string label; /* Label data */ - - // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - FastMathHint fast_math_hint = FastMathHint::DISABLED; - - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 2) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 3) - { - data_path = argv[2]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No image provided: using random values\n\n"; - } - else if(argc == 4) - { - data_path = argv[2]; - image = argv[3]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 5) - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; - std::cout << "No fast math info provided: disabling fast math\n\n"; - } - else - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; - } - - graph << target_hint - << fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), - get_input_accessor()) - << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) - .set_name("Conv2d_1a_3x3/convolution") - << BatchNormalizationLayer(get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") - << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_2a_3x3/convolution") - << BatchNormalizationLayer(get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") - - << ConvolutionLayer(3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) - .set_name("Conv2d_2b_3x3/convolution") - << BatchNormalizationLayer(get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") - - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") - - << ConvolutionLayer(1U, 1U, 80U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_3b_1x1/convolution") - << BatchNormalizationLayer(get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") - - << ConvolutionLayer(3U, 3U, 192U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_4a_3x3/convolution") - << BatchNormalizationLayer(get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") - - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); - - graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 32U) - .set_name("Mixed_5b/concat"); - graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 64U, true) - .set_name("Mixed_5c/concat"); - graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 64U) - .set_name("Mixed_5d/concat"); - - graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); - - graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U), - std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) - .set_name("Mixed_6b/concat"); - graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U), - std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) - .set_name("Mixed_6c/concat"); - graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U), - std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) - .set_name("Mixed_6d/concat"); - graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U), - std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) - .set_name("Mixed_6e/concat"); - - graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U), - std::make_tuple(192U, 192U, 192U, 192U)) - .set_name("Mixed_7a/concat"); - - graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U), - std::make_tuple(448U, 384U, 384U, 384U), 192U) - .set_name("Mixed_7b/concat"); - graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U), - std::make_tuple(448U, 384U, 384U, 384U), 192U, true) - .set_name("Mixed_7c/concat"); - - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool") - << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"), - get_weights_accessor(data_path, - "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"), - PadStrideInfo(1, 1, 0, 0)) - .set_name("Logits/Conv2d_1c_1x1/convolution") - << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") - << SoftmaxLayer().set_name("Predictions/Softmax") - << OutputLayer(get_output_accessor()); - - // Finalize graph - GraphConfig config; - config.use_tuner = (target == 2); - graph.finalize(target_hint, config); - } - - void do_run() - { - run_benchmark(graph); - } - -private: - Stream graph{ 0, "InceptionV3" }; - -private: - BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int> c_filters, - unsigned int d_filt, - bool is_name_different = false) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - - // This is due to a naming issue in the tf model - std::string conv_id0 = "_0a_"; - std::string conv_id1 = "2d_0b_"; - if(is_name_different) - { - conv_id0 = "_0b_"; - conv_id1 = "_1_0c_"; - } - - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu") - << ConvolutionLayer( - 5U, 5U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 2, 2)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu"); - - SubStream i_c(graph); - i_c << ConvolutionLayer( - 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } - - BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 3U, 3U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu"); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu"); - - SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); - } - - BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters, - unsigned int d_filt) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 7U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") - << ConvolutionLayer( - 1U, 7U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu"); - - SubStream i_c(graph); - i_c << ConvolutionLayer( - 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 1U, 7U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu") - << ConvolutionLayer( - 7U, 1U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu") - << ConvolutionLayer( - 1U, 7U, std::get<3>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu") - << ConvolutionLayer( - 7U, 1U, std::get<4>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } - - BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, - std::tuple<unsigned int, unsigned int> a_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, std::get<0>(a_filters), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<1>(a_filters), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu"); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 7U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") - << ConvolutionLayer( - 1U, 7U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<3>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu"); - - SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); - } - - BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters, - unsigned int d_filt, - bool is_name_different = false) - { - // This is due to a naming issue in the tf model - std::string conv_id = "_0b_"; - if(is_name_different) - { - conv_id = "_0c_"; - } - - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - i_b << ConvolutionLayer( - 1U, 1U, std::get<0>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu"); - - SubStream i_b1(static_cast<IStream &>(i_b)); - i_b1 << ConvolutionLayer( - 3U, 1U, std::get<1>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu"); - - SubStream i_b2(static_cast<IStream &>(i_b)); - i_b2 << ConvolutionLayer( - 1U, 3U, std::get<2>(b_filters), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 1)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu"); - - // Merge b1 and b2 - i_b << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat"); - - SubStream i_c(graph); - i_c << ConvolutionLayer( - 1U, 1U, std::get<0>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") - << ConvolutionLayer( - 3U, 3U, std::get<1>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu"); - - SubStream i_c1(static_cast<IStream &>(i_c)); - i_c1 << ConvolutionLayer( - 3U, 1U, std::get<2>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu"); - - SubStream i_c2(static_cast<IStream &>(i_c)); - i_c2 << ConvolutionLayer( - 1U, 3U, std::get<3>(c_filters), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu"); - - // Merge i_c1 and i_c2 - i_c << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), - get_random_accessor(1.f, 1.f), - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), - 0.001f) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } -}; - -/** Main program for Inception V3 - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -int main(int argc, char **argv) -{ - InceptionV3Example example; - - example.do_setup(argc, argv); - example.do_run(); - - return 0; -} diff --git a/benchmark/acl/benchmark_mobilenet.cpp b/benchmark/acl/benchmark_mobilenet.cpp deleted file mode 100644 index 765606e94..000000000 --- a/benchmark/acl/benchmark_mobilenet.cpp +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/graph.h" - -#include "Benchmark.h" - -#include <cstdlib> - -using namespace arm_compute::graph::frontend; - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) -{ - return get_accessor<OutputAccessor>(); -} - -/** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) - */ -class GraphMobilenetExample -{ -public: - void do_setup(int argc, char **argv) - { - std::string data_path; /* Path to the trainable data */ - std::string image; /* Image data */ - std::string label; /* Label data */ - - // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - ConvolutionMethod convolution_hint = ConvolutionMethod::GEMM; - DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3; - FastMathHint fast_math_hint = FastMathHint::DISABLED; - - // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160) - int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0; - ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)"); - int layout_id = (argc > 3) ? std::strtol(argv[3], nullptr, 10) : 0; - ARM_COMPUTE_ERROR_ON_MSG(layout_id > 1, "Invalid layout ID. Layout must be 0 (NCHW) or 1 (NHWC)"); - - float depth_scale = (model_id == 0) ? 1.f : 0.75; - unsigned int spatial_size = (model_id == 0) ? 224 : 160; - std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; - TensorDescriptor input_descriptor_nchw = TensorDescriptor(TensorShape(spatial_size, spatial_size, 3U, 1U), DataType::F32); - TensorDescriptor input_descriptor_nhwc = TensorDescriptor(TensorShape(3U, spatial_size, spatial_size, 1U), DataType::F32).set_layout(DataLayout::NHWC); - TensorDescriptor input_descriptor = (layout_id == 0) ? input_descriptor_nchw : input_descriptor_nhwc; - - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; - std::cout << "No data layout provided: using NCHW\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 2) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; - std::cout << "No data layout provided: using NCHW\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 3) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data layout provided: using NCHW\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 4) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 5) - { - data_path = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n"; - std::cout << "No image provided: using random values\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 6) - { - data_path = argv[4]; - image = argv[5]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 7) - { - data_path = argv[4]; - image = argv[5]; - label = argv[6]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; - std::cout << "No fast math info provided: disabling fast math\n\n"; - } - else - { - data_path = argv[4]; - image = argv[5]; - label = argv[6]; - fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; - } - - // Add model path to data path - if(!data_path.empty()) - { - data_path += model_path; - } - - graph << target_hint - << convolution_hint - << depthwise_convolution_hint - << fast_math_hint - << InputLayer(input_descriptor, - get_input_accessor()) - << ConvolutionLayer( - 3U, 3U, 32U * depth_scale, - get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) - .set_name("Conv2d_0") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_0/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); - graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") - << ConvolutionLayer( - 1U, 1U, 1001U, - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), - PadStrideInfo(1, 1, 0, 0)) - .set_name("Logits/Conv2d_1c_1x1") - << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") - << SoftmaxLayer().set_name("Softmax") - << OutputLayer(get_output_accessor()); - - // Finalize graph - GraphConfig config; - config.use_tuner = (target == 2); - graph.finalize(target_hint, config); - } - void do_run() - { - run_benchmark(graph); - } - -private: - Stream graph{ 0, "MobileNetV1" }; - - BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, - unsigned int conv_filt, - PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) - { - std::string total_path = param_path + "_"; - SubStream sg(graph); - sg << DepthwiseConvolutionLayer( - 3U, 3U, - get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - dwc_pad_stride_info) - .set_name(total_path + "depthwise/depthwise") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), - 0.001f) - .set_name(total_path + "depthwise/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") - << ConvolutionLayer( - 1U, 1U, conv_filt, - get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - conv_pad_stride_info) - .set_name(total_path + "pointwise/Conv2D") - << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), - 0.001f) - .set_name(total_path + "pointwise/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); - - return BranchLayer(std::move(sg)); - } -}; - -/** Main program for MobileNetV1 - * - * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), - * [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160), - * [optional] Path to the weights folder, - * [optional] image, - * [optional] labels, - * [optional] data layout, - * [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -int main(int argc, char **argv) -{ - GraphMobilenetExample example; - - example.do_setup(argc, argv); - example.do_run(); - - return 0; -} diff --git a/cmake/CfgOptionFlags.cmake b/cmake/CfgOptionFlags.cmake index 1a9d7244e..f14033592 100644 --- a/cmake/CfgOptionFlags.cmake +++ b/cmake/CfgOptionFlags.cmake @@ -1,16 +1,19 @@ # # Configuration flags # -option(BUILD_ACL "Build ARM Compute Library" OFF) option(BUILD_PURE_ARM_COMPUTE "Build pure_arm_compute runtime" ON) -option(BUILD_ACL_STATIC_LIB "Build ARM Comput Static Library" OFF) -option(BUILD_BENCHMARK_ACL "Build ARM Compute Library Benchmarks" OFF) option(BUILD_NEURUN "Build neurun" OFF) #if implementation is done, it would replace nn runtime. -option(BUILD_LABS "Build lab projects" ON) -option(BUILD_ANDROID_NN_RUNTIME_TEST "Build Android NN Runtime Test" ON) -option(BUILD_DETECTION_APP "Build detection example app" OFF) -option(BUILD_NNAPI_QUICKCHECK "Build NN API Quickcheck tools" OFF) +option(BUILD_RUNTIME_NNAPI_TEST "Build Runtime NN API Generated Test" ON) option(BUILD_TFLITE_BENCHMARK_MODEL "Build tflite benchmark model" OFF) +option(BUILD_NNAPI_QUICKCHECK "Build NN API Quickcheck tools" OFF) +# +# Configuration flags for contrib +# +option(BUILD_BENCHMARK_ACL "Build ARM Compute Library Benchmarks" OFF) +option(BUILD_DETECTION_APP "Build detection example app" OFF) +option(BUILD_LABS "Build lab projects" OFF) +option(BUILD_TFLITE_TEST "Build tensorflow lite test" OFF) +option(BUILD_TFLITE_CLASSIFY_APP "Build tflite_classify app" OFF) if("${TARGET_ARCH}" STREQUAL "armv7l" AND NOT "${TARGET_OS}" STREQUAL "tizen") set(BUILD_PURE_ARM_COMPUTE ON) @@ -19,5 +22,5 @@ endif() # On x86, disable pureacl/new runtine build which depends on arm compute library if("${TARGET_ARCH}" STREQUAL "x86_64") set(BUILD_PURE_ARM_COMPUTE OFF) - set(BUILD_NEW_RUNTIME OFF) + set(BUILD_NEURUN OFF) endif() diff --git a/cmake/modules/ExtendCMakeFunction.cmake b/cmake/modules/ExtendCMakeFunction.cmake new file mode 100644 index 000000000..06b7c768d --- /dev/null +++ b/cmake/modules/ExtendCMakeFunction.cmake @@ -0,0 +1,27 @@ +function(list_subdirectories OUTPUT_VARIABLE) + cmake_parse_arguments(ARG "" "" "EXCLUDES" ${ARGN}) + + file(GLOB PROJECT_FILES + RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} + "*/CMakeLists.txt") + + foreach(PROJECT_FILE IN ITEMS ${PROJECT_FILES}) + get_filename_component(PROJECT_DIR ${PROJECT_FILE} DIRECTORY) + list(FIND ARG_EXCLUDES ${PROJECT_DIR} PROJECT_INDEX) + if(${PROJECT_INDEX} EQUAL -1) + list(APPEND PROJECT_LIST ${PROJECT_DIR}) + endif(${PROJECT_INDEX} EQUAL -1) + endforeach(PROJECT_FILE) + + set(${OUTPUT_VARIABLE} ${PROJECT_LIST} PARENT_SCOPE) +endfunction(list_subdirectories) + +function(add_subdirectories) + cmake_parse_arguments(ARG "" "" "EXCLUDES" ${ARGN}) + + list_subdirectories(PROJECT_DIRS EXCLUDES ${ARG_EXCLUDES}) + + foreach(PROJECT_DIR IN ITEMS ${PROJECT_DIRS}) + add_subdirectory(${PROJECT_DIR}) + endforeach(PROJECT_DIR) +endfunction(add_subdirectories) diff --git a/cmake/modules/ExternalProjectTools.cmake b/cmake/modules/ExternalProjectTools.cmake index 7dde65f35..71d74e5a6 100644 --- a/cmake/modules/ExternalProjectTools.cmake +++ b/cmake/modules/ExternalProjectTools.cmake @@ -1,5 +1,10 @@ macro(add_extdirectory DIR TAG) - add_subdirectory(${DIR} "${CMAKE_BINARY_DIR}/externals/${TAG}") + cmake_parse_arguments(ARG "EXCLUDE_FROM_ALL" "" "" ${ARGN}) + if(ARG_EXCLUDE_FROM_ALL) + add_subdirectory(${DIR} "${CMAKE_BINARY_DIR}/externals/${TAG}" EXCLUDE_FROM_ALL) + else(ARG_EXCLUDE_FROM_ALL) + add_subdirectory(${DIR} "${CMAKE_BINARY_DIR}/externals/${TAG}") + endif(ARG_EXCLUDE_FROM_ALL) endmacro(add_extdirectory) set(ExternalProjectTools_FOUND TRUE) diff --git a/cmake/modules/ExternalSourceTools.cmake b/cmake/modules/ExternalSourceTools.cmake index cb1851413..76ebb5500 100644 --- a/cmake/modules/ExternalSourceTools.cmake +++ b/cmake/modules/ExternalSourceTools.cmake @@ -12,7 +12,16 @@ function(ExternalSource_Download PREFIX URL) file(MAKE_DIRECTORY "${CACHE_DIR}") endif(NOT EXISTS "${CACHE_DIR}") - if(NOT EXISTS "${STAMP_PATH}") + # TODO Check MD5 for correctness + set(MATCH_URL FALSE) + if(EXISTS "${STAMP_PATH}") + file(READ "${STAMP_PATH}" SAVED_URL) + if("${SAVED_URL}" STREQUAL "${URL}") + set(MATCH_URL TRUE) + endif("${SAVED_URL}" STREQUAL "${URL}") + endif(EXISTS "${STAMP_PATH}") + + if(NOT EXISTS "${STAMP_PATH}" OR NOT EXISTS "${OUT_DIR}" OR NOT MATCH_URL) file(REMOVE_RECURSE "${OUT_DIR}") file(REMOVE_RECURSE "${TMP_DIR}") diff --git a/cmake/option/option_armv7l-linux.cmake b/cmake/option/option_armv7l-linux.cmake index 42988bc9d..b295b4a82 100644 --- a/cmake/option/option_armv7l-linux.cmake +++ b/cmake/option/option_armv7l-linux.cmake @@ -18,6 +18,7 @@ set(FLAGS_COMMON ${FLAGS_COMMON} "-mfpu=neon-vfpv4" "-funsafe-math-optimizations" "-ftree-vectorize" + "-fPIC" ) # remove warning from arm cl diff --git a/cmake/packages/ARMCompute/CMakeLists.txt b/cmake/packages/ARMCompute/CMakeLists.txt deleted file mode 100644 index 88a79ef4a..000000000 --- a/cmake/packages/ARMCompute/CMakeLists.txt +++ /dev/null @@ -1,153 +0,0 @@ -### -### ARM Compute Library -### -set(ACL_BASE ${CMAKE_SOURCE_DIR}/externals/acl) -set(ACL_GENERATED ${CMAKE_CURRENT_BINARY_DIR}/acl_generated) -set(ACL_VERSION_TAG "${ACL_GENERATED}/arm_compute_version.embed") - -# Create 'arm_compute_version.embed' -add_custom_command(OUTPUT ${ACL_VERSION_TAG} - COMMAND mkdir -p "${ACL_GENERATED}" - COMMAND echo '"unknown"' > "${ACL_VERSION_TAG}") - -file(GLOB_RECURSE ACL_UTIL_SRCS "${ACL_BASE}/src/core/utils/*.cpp") - -### ARM Compute Library - Foundation library (such as I/O and logging) -if(BUILD_ACL_STATIC_LIB) - add_library(acl_foundation ${ACL_UTIL_SRCS}) - target_include_directories(acl_foundation PUBLIC "${ACL_BASE}") - target_include_directories(acl_foundation PUBLIC "${ACL_BASE}/include") - target_link_libraries(acl_foundation dl ${LIB_PTHREAD}) -endif(BUILD_ACL_STATIC_LIB) - -### -### ARM Compute Library Common (Core & Runtime) -### -file(GLOB ACL_CORE_COMMON_SRCS "${ACL_BASE}/src/core/*.cpp") -list(APPEND ACL_CORE_COMMON_SRCS ${ACL_VERSION_TAG}) -# Both CL & NEON runtime funtions use these CPP kernels -list(APPEND ACL_CORE_COMMON_SRCS "${ACL_BASE}/src/core/CPP/kernels/CPPCornerCandidatesKernel.cpp") -list(APPEND ACL_CORE_COMMON_SRCS "${ACL_BASE}/src/core/CPP/kernels/CPPDetectionWindowNonMaximaSuppressionKernel.cpp") -list(APPEND ACL_CORE_COMMON_SRCS "${ACL_BASE}/src/core/CPP/kernels/CPPSortEuclideanDistanceKernel.cpp") - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_core_common ${ACL_CORE_COMMON_SRCS}) - target_include_directories(acl_core_common PUBLIC "${ACL_GENERATED}") - target_link_libraries(acl_core_common acl_foundation) -endif(BUILD_ACL_STATIC_LIB) - -file(GLOB ACL_RUNTIME_COMMON_SRCS "${ACL_BASE}/src/runtime/*.cpp") -# src/runtime/Scheduler.cpp depends on this scheduler -list(APPEND ACL_RUNTIME_COMMON_SRCS "${ACL_BASE}/src/runtime/CPP/SingleThreadScheduler.cpp") - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_core_opencl ${ACL_CORE_OPENCL_SRCS}) - target_link_libraries(acl_core_opencl acl_core_common OpenCL) -endif(BUILD_ACL_STATIC_LIB) - -### -### ARM Compute Library Open CL (Core & Runtime & Example) -### -file(GLOB ACL_CORE_OPENCL_SRCS "${ACL_BASE}/src/core/CL/*.cpp") -file(GLOB ACL_CORE_OPENCL_KERNEL_SRCS "${ACL_BASE}/src/core/CL/kernels/*.cpp") -list(APPEND ACL_CORE_OPENCL_SRCS ${ACL_CORE_OPENCL_KERNEL_SRCS}) - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_runtime_opencl ${ACL_RUNTIME_OPENCL_SRCS}) - target_link_libraries(acl_runtime_opencl acl_runtime_common acl_core_opencl) -endif(BUILD_ACL_STATIC_LIB) - -file(GLOB_RECURSE ACL_RUNTIME_OPENCL_SRCS "${ACL_BASE}/src/runtime/CL/*.cpp") - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_core_neon ${ACL_CORE_NEON_SRCS}) - target_include_directories(acl_core_neon PUBLIC "${ACL_BASE}/arm_compute/core/NEON/kernels/assembly") - target_link_libraries(acl_core_neon acl_core_common) -endif(BUILD_ACL_STATIC_LIB) - -### -### ARM Compute Library NEON (Core & Runtime & Example) -### -file(GLOB ACL_CORE_NEON_SRCS "${ACL_BASE}/src/core/NEON/kernels/*.cpp" "${ACL_BASE}/src/core/NEON/kernels/arm32/*.cpp") -file(GLOB_RECURSE ACL_CORE_NEON_CONVOLUTION_SRCS "${ACL_BASE}/src/core/NEON/kernels/convolution/winograd/*.cpp" "${ACL_BASE}/src/core/NEON/kernels/convolution/depthwise/*.cpp") -list(APPEND ACL_CORE_NEON_SRCS ${ACL_CORE_NEON_CONVOLUTION_SRCS}) -list(APPEND ACL_CORE_NEON_SRCS "${ACL_BASE}/src/core/CPP/ICPPSimpleKernel.cpp") -list(APPEND ACL_CORE_NEON_SRCS "${ACL_BASE}/src/core/CPP/kernels/CPPPermuteKernel.cpp") - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_runtime_neon ${ACL_RUNTIME_NEON_SRCS}) - target_link_libraries(acl_runtime_neon acl_runtime_common acl_core_neon) -endif(BUILD_ACL_STATIC_LIB) - -file(GLOB_RECURSE ACL_RUNTIME_NEON_SRCS "${ACL_BASE}/src/runtime/NEON/*.cpp") -# runtime/NEON/functions/NEWinogradLayer.h use this implementation -list(APPEND ACL_RUNTIME_NEON_SRCS "${ACL_BASE}/src/runtime/CPP/ICPPSimpleFunction.cpp") -list(APPEND ACL_RUNTIME_NEON_SRCS "${ACL_BASE}/src/runtime/CPP/functions/CPPPermute.cpp") - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_graph ${ACL_GRAPH_SRCS}) - target_link_libraries(acl_graph acl_runtime_opencl acl_runtime_neon) -endif(BUILD_ACL_STATIC_LIB) - -# TODO Support Open MP core(?) -# TODO Support Open GLES core(?) - -### -### ARM Compute Library (Graph & Example) -### -file(GLOB ACL_GRAPH_COMMON_SRCS "${ACL_BASE}/src/graph/*.cpp" "${ACL_BASE}/src/graph/nodes/*.cpp") -file(GLOB ACL_GRAPH_OPENCL_SRCS "${ACL_BASE}/src/graph/CL/*.cpp" "${ACL_BASE}/src/graph/operations/CL*.cpp") -file(GLOB ACL_GRAPH_NEON_SRCS "${ACL_BASE}/src/graph/NE/*.cpp" "${ACL_BASE}/src/graph/operations/NE*.cpp") - -list(APPEND ACL_GRAPH_SRCS ${ACL_GRAPH_COMMON_SRCS}) -list(APPEND ACL_GRAPH_SRCS ${ACL_GRAPH_OPENCL_SRCS}) -list(APPEND ACL_GRAPH_SRCS ${ACL_GRAPH_NEON_SRCS}) - -if(BUILD_ACL_STATIC_LIB) - add_library(acl_graph ${ACL_GRAPH_SRCS}) - target_link_libraries(acl_graph acl_runtime_opencl acl_runtime_neon) -endif(BUILD_ACL_STATIC_LIB) - -### -### ARM Compute Shared Libraries -### -list(APPEND ACL_CORE_SRCS ${ACL_UTIL_SRCS}) -list(APPEND ACL_CORE_SRCS ${ACL_CORE_COMMON_SRCS}) -list(APPEND ACL_CORE_SRCS ${ACL_CORE_OPENCL_SRCS}) -list(APPEND ACL_CORE_SRCS ${ACL_CORE_NEON_SRCS}) - -add_library(arm_compute_core SHARED ${ACL_CORE_SRCS}) -target_include_directories(arm_compute_core PUBLIC "${ACL_GENERATED}") -target_include_directories(arm_compute_core PUBLIC "${ACL_BASE}") -target_include_directories(arm_compute_core PUBLIC "${ACL_BASE}/include") -target_include_directories(arm_compute_core PUBLIC "${ACL_BASE}/arm_compute/core/NEON/kernels/assembly") -target_link_libraries(arm_compute_core dl ${LIB_PTHREAD}) -install(TARGETS arm_compute_core DESTINATION lib) - -list(APPEND ACL_RUNTIME_SRCS ${ACL_RUNTIME_COMMON_SRCS}) -list(APPEND ACL_RUNTIME_SRCS ${ACL_RUNTIME_OPENCL_SRCS}) -list(APPEND ACL_RUNTIME_SRCS ${ACL_RUNTIME_NEON_SRCS}) - -add_library(arm_compute SHARED ${ACL_RUNTIME_SRCS}) -target_link_libraries(arm_compute arm_compute_core OpenCL) -install(TARGETS arm_compute DESTINATION lib) - -add_library(arm_compute_graph SHARED ${ACL_GRAPH_SRCS}) -target_link_libraries(arm_compute_graph arm_compute) -install(TARGETS arm_compute_graph DESTINATION lib) - -add_library(arm_compute_test SHARED "${ACL_BASE}/utils/Utils.cpp") -target_link_libraries(arm_compute_test arm_compute) - -add_library(arm_compute_graph_test SHARED "${ACL_BASE}/utils/GraphUtils.cpp") -target_link_libraries(arm_compute_graph_test arm_compute_graph arm_compute_test) - -add_executable(cl_convolution "${ACL_BASE}/examples/cl_convolution.cpp") -target_compile_definitions(cl_convolution PRIVATE ARM_COMPUTE_CL) -target_link_libraries(cl_convolution arm_compute_test) - -add_executable(neon_convolution "${ACL_BASE}/examples/neon_convolution.cpp") -target_link_libraries(neon_convolution arm_compute_test) - -add_executable(graph_lenet "${ACL_BASE}/examples/graph_lenet.cpp") -target_link_libraries(graph_lenet arm_compute_graph_test) diff --git a/cmake/packages/ARMComputeConfig.cmake b/cmake/packages/ARMComputeConfig.cmake index 7d3753094..656163a8e 100644 --- a/cmake/packages/ARMComputeConfig.cmake +++ b/cmake/packages/ARMComputeConfig.cmake @@ -1,14 +1,11 @@ -function(_ARMCompute_Build) - if(TARGET arm_compute_core) - set(ARMCompute_FOUND TRUE PARENT_SCOPE) - return() - endif(TARGET arm_compute_core) +function(_ARMCompute_Import) + nnfw_find_package(ARMComputeSource QUIET) - add_subdirectory("${CMAKE_CURRENT_LIST_DIR}/ARMCompute" "${CMAKE_BINARY_DIR}/externals/ARMCompute") - set(ARMCompute_FOUND TRUE PARENT_SCOPE) -endfunction(_ARMCompute_Build) + if(NOT ARMComputeSource_FOUND) + set(ARMCompute_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ARMComputeSource_FOUND) -function(_ARMCompute_Import) include(FindPackageHandleStandardArgs) list(APPEND ARMCompute_INCLUDE_SEARCH_PATHS /usr/include) @@ -81,8 +78,4 @@ function(_ARMCompute_Import) set(ARMCompute_FOUND TRUE PARENT_SCOPE) endfunction(_ARMCompute_Import) -if(BUILD_ACL) - _ARMCompute_Build() -else(BUILD_ACL) - _ARMCompute_Import() -endif(BUILD_ACL) +_ARMCompute_Import() diff --git a/cmake/packages/ARMComputeSourceConfig.cmake b/cmake/packages/ARMComputeSourceConfig.cmake new file mode 100644 index 000000000..c1a31d047 --- /dev/null +++ b/cmake/packages/ARMComputeSourceConfig.cmake @@ -0,0 +1,18 @@ +function(_ARMComputeSource_import) + if(NOT DOWNLOAD_ARMCOMPUTE) + set(ARMComputeSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_ARMCOMPUTE) + + nnfw_include(ExternalSourceTools) + nnfw_include(OptionTools) + + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(ARMCOMPUTE_URL ${EXTERNAL_DOWNLOAD_SERVER}/ARM-software/ComputeLibrary/archive/v18.11.tar.gz) + ExternalSource_Download("acl" ${ARMCOMPUTE_URL}) + + set(ARMComputeSource_DIR ${acl_SOURCE_DIR} PARENT_SCOPE) + set(ARMComputeSource_FOUND TRUE PARENT_SCOPE) +endfunction(_ARMComputeSource_import) + +_ARMComputeSource_import() diff --git a/cmake/packages/AbslSourceConfig.cmake b/cmake/packages/AbslSourceConfig.cmake new file mode 100644 index 000000000..9075b7397 --- /dev/null +++ b/cmake/packages/AbslSourceConfig.cmake @@ -0,0 +1,19 @@ +function(_AbslSource_import) + if(NOT DOWNLOAD_ABSL) + set(AbslSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_ABSL) + + nnfw_include(ExternalSourceTools) + nnfw_include(OptionTools) + + # NOTE The following URL comes from TensorFlow 1.12 + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(ABSL_URL ${EXTERNAL_DOWNLOAD_SERVER}/abseil/abseil-cpp/archive/48cd2c3f351ff188bc85684b84a91b6e6d17d896.tar.gz) + ExternalSource_Download("absl" ${ABSL_URL}) + + set(AbslSource_DIR ${absl_SOURCE_DIR} PARENT_SCOPE) + set(AbslSource_FOUND TRUE PARENT_SCOPE) +endfunction(_AbslSource_import) + +_AbslSource_import() diff --git a/cmake/packages/EigenSourceConfig.cmake b/cmake/packages/EigenSourceConfig.cmake index 0b4360953..dd94e069e 100644 --- a/cmake/packages/EigenSourceConfig.cmake +++ b/cmake/packages/EigenSourceConfig.cmake @@ -7,9 +7,9 @@ function(_EigenSource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - # NOTE The following URL comes from TensorFlow 1.9 - envoption(EIGEN_URL https://bitbucket.org/eigen/eigen/get/fd6845384b86.zip) - + # NOTE TensorFlow 1.12 downloads Eign from the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://bitbucket.org") + set(EIGEN_URL ${EXTERNAL_DOWNLOAD_SERVER}/eigen/eigen/get/fd6845384b86.tar.gz) ExternalSource_Download("eigen" ${EIGEN_URL}) set(EigenSource_DIR ${eigen_SOURCE_DIR} PARENT_SCOPE) diff --git a/cmake/packages/FarmhashSourceConfig.cmake b/cmake/packages/FarmhashSourceConfig.cmake index fe22c03a3..802367968 100644 --- a/cmake/packages/FarmhashSourceConfig.cmake +++ b/cmake/packages/FarmhashSourceConfig.cmake @@ -7,9 +7,9 @@ function(_FarmhashSource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - # NOTE TensorFlow 1.9 downloads farmhash from the following URL - envoption(FARMHASH_URL https://github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.zip) - + # NOTE TensorFlow 1.12 downloads farmhash from the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(FARMHASH_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz) ExternalSource_Download("farmhash" ${FARMHASH_URL}) set(FarmhashSource_DIR ${farmhash_SOURCE_DIR} PARENT_SCOPE) diff --git a/cmake/packages/FlatBuffersConfig.cmake b/cmake/packages/FlatBuffersConfig.cmake index fab08fe39..064673158 100644 --- a/cmake/packages/FlatBuffersConfig.cmake +++ b/cmake/packages/FlatBuffersConfig.cmake @@ -15,18 +15,22 @@ function(_FlatBuffers_import) # From FlatBuffers's CMakeLists.txt list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_cpp.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_dart.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_fbs.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_general.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_go.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_grpc.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_js.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_json_schema.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_lobster.cpp") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_lua.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_php.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_python.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_fbs.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_grpc.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_json_schema.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/flatc.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/flatc_main.cpp") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/cpp_generator.cc") list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/go_generator.cc") + list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/java_generator.cc") if(NOT TARGET flatbuffers) add_library(flatbuffers ${FlatBuffers_Library_SRCS}) diff --git a/cmake/packages/FlatBuffersSourceConfig.cmake b/cmake/packages/FlatBuffersSourceConfig.cmake index e136a266c..5f142bff7 100644 --- a/cmake/packages/FlatBuffersSourceConfig.cmake +++ b/cmake/packages/FlatBuffersSourceConfig.cmake @@ -7,9 +7,9 @@ function(_FlatBuffersSource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - # NOTE TensorFlow 1.9 downloads FlatBuffers from the following URL - envoption(FLATBUFFERS_URL https://github.com/google/flatbuffers/archive/971a68110e4fc1bace10fcb6deeb189e7e1a34ce.zip) - + # NOTE TensorFlow 1.12 downloads FlatBuffers from the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(FLATBUFFERS_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/flatbuffers/archive/1f5eae5d6a135ff6811724f6c57f911d1f46bb15.tar.gz) ExternalSource_Download("flatbuffers" ${FLATBUFFERS_URL}) set(FlatBuffersSource_DIR ${flatbuffers_SOURCE_DIR} PARENT_SCOPE) diff --git a/cmake/packages/GEMMLowpSourceConfig.cmake b/cmake/packages/GEMMLowpSourceConfig.cmake index b8a2c3ed4..613ff29b5 100644 --- a/cmake/packages/GEMMLowpSourceConfig.cmake +++ b/cmake/packages/GEMMLowpSourceConfig.cmake @@ -7,9 +7,9 @@ function(_GEMMLowpSource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - # NOTE TensorFlow 1.9 uses the following URL - envoption(GEMMLOWP_URL https://github.com/google/gemmlowp/archive/38ebac7b059e84692f53e5938f97a9943c120d98.zip) - + # NOTE TensorFlow 1.12 uses the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(GEMMLOWP_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/gemmlowp/archive/38ebac7b059e84692f53e5938f97a9943c120d98.tar.gz) ExternalSource_Download("gemmlowp" ${GEMMLOWP_URL}) set(GEMMLowpSource_DIR ${gemmlowp_SOURCE_DIR} PARENT_SCOPE) diff --git a/cmake/packages/GTestConfig.cmake b/cmake/packages/GTestConfig.cmake index a96a64ac2..62d2e8967 100644 --- a/cmake/packages/GTestConfig.cmake +++ b/cmake/packages/GTestConfig.cmake @@ -11,8 +11,8 @@ if(${BUILD_GTEST}) nnfw_include(ExternalProjectTools) nnfw_include(OptionTools) - envoption(GTEST_URL https://github.com/google/googletest/archive/release-1.8.0.zip) - + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(GTEST_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/googletest/archive/release-1.8.0.tar.gz) ExternalSource_Download("gtest" ${GTEST_URL}) # gtest_SOURCE_DIR is used in gtest subdirectorty's cmake @@ -20,7 +20,7 @@ if(${BUILD_GTEST}) unset(gtest_SOURCE_DIR) if(NOT TARGET gtest_main) - add_extdirectory(${sourcedir_gtest} gtest) + add_extdirectory(${sourcedir_gtest} gtest EXCLUDE_FROM_ALL) endif(NOT TARGET gtest_main) set(GTest_FOUND TRUE) diff --git a/cmake/packages/NEON2SSESourceConfig.cmake b/cmake/packages/NEON2SSESourceConfig.cmake index 9b703ec16..b656f5700 100644 --- a/cmake/packages/NEON2SSESourceConfig.cmake +++ b/cmake/packages/NEON2SSESourceConfig.cmake @@ -7,9 +7,9 @@ function(_NEON2SSESource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - # NOTE TensorFlow 1.9 downloads NEON2SSE from the following URL - envoption(NEON2SSE_URL https://github.com/intel/ARM_NEON_2_x86_SSE/archive/0f77d9d182265259b135dad949230ecbf1a2633d.zip) - + # NOTE TensorFlow 1.12 downloads NEON2SSE from the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(NEON2SSE_URL ${EXTERNAL_DOWNLOAD_SERVER}/intel/ARM_NEON_2_x86_SSE/archive/0f77d9d182265259b135dad949230ecbf1a2633d.tar.gz) ExternalSource_Download("neon_2_sse" ${NEON2SSE_URL}) set(NEON2SSESource_DIR ${neon_2_sse_SOURCE_DIR} PARENT_SCOPE) diff --git a/cmake/packages/NoniusConfig.cmake b/cmake/packages/NoniusConfig.cmake new file mode 100644 index 000000000..35f373a8c --- /dev/null +++ b/cmake/packages/NoniusConfig.cmake @@ -0,0 +1,18 @@ +function(_Nonius_import) + nnfw_find_package(NoniusSource QUIET) + + if(NOT NoniusSource_FOUND) + set(Nonius_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT NoniusSource_FOUND) + + if(NOT TARGET nonius) + message(STATUS "Found nonius: TRUE") + add_library(nonius INTERFACE) + target_include_directories(nonius INTERFACE "${NoniusSource_DIR}/include") + endif(NOT TARGET nonius) + + set(Nonius_FOUND TRUE PARENT_SCOPE) +endfunction(_Nonius_import) + +_Nonius_import() diff --git a/cmake/packages/NoniusSourceConfig.cmake b/cmake/packages/NoniusSourceConfig.cmake new file mode 100644 index 000000000..7349946db --- /dev/null +++ b/cmake/packages/NoniusSourceConfig.cmake @@ -0,0 +1,18 @@ +function(_NoniusSource_import) + if(NOT DOWNLOAD_NONIUS) + set(NoniusSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_NONIUS) + + nnfw_include(ExternalSourceTools) + nnfw_include(OptionTools) + + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(NONIUS_URL ${EXTERNAL_DOWNLOAD_SERVER}/libnonius/nonius/archive/v1.2.0-beta.1.tar.gz) + ExternalSource_Download("nonius" ${NONIUS_URL}) + + set(NoniusSource_DIR ${nonius_SOURCE_DIR} PARENT_SCOPE) + set(NoniusSource_FOUND TRUE PARENT_SCOPE) +endfunction(_NoniusSource_import) + +_NoniusSource_import() diff --git a/cmake/packages/TensorFlowSourceConfig.cmake b/cmake/packages/TensorFlowSourceConfig.cmake index b9c34b0a7..5828334c7 100644 --- a/cmake/packages/TensorFlowSourceConfig.cmake +++ b/cmake/packages/TensorFlowSourceConfig.cmake @@ -7,8 +7,8 @@ function(_TensorFlowSource_import) nnfw_include(ExternalSourceTools) nnfw_include(OptionTools) - envoption(TENSORFLOW_URL https://github.com/tensorflow/tensorflow/archive/v1.9.0.zip) - + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(TENSORFLOW_URL ${EXTERNAL_DOWNLOAD_SERVER}/tensorflow/tensorflow/archive/v1.12.0.tar.gz) ExternalSource_Download("tensorflow" ${TENSORFLOW_URL}) set(TensorFlowSource_DIR ${tensorflow_SOURCE_DIR} PARENT_SCOPE) diff --git a/contrib/benchmark_acl/CMakeLists.txt b/contrib/benchmark_acl/CMakeLists.txt new file mode 100644 index 000000000..b5fa3e529 --- /dev/null +++ b/contrib/benchmark_acl/CMakeLists.txt @@ -0,0 +1,24 @@ +if(NOT BUILD_BENCHMARK_ACL) + return() +endif(NOT BUILD_BENCHMARK_ACL) + +nnfw_find_package(ARMCompute REQUIRED) + +add_library(arm_compute_benchmark SHARED "src/Benchmark.cpp") +target_include_directories(arm_compute_benchmark PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) +target_link_libraries(arm_compute_benchmark arm_compute_graph) +install(TARGETS arm_compute_benchmark DESTINATION lib) + +# GoogLeNet benchmark +add_executable(benchmark_googlenet "src/benchmark_googlenet.cpp") +target_link_libraries(benchmark_googlenet arm_compute_benchmark) + +# GoogLeNet benchmark +add_executable(benchmark_inception_v3 "src/benchmark_inception_v3.cpp") +target_link_libraries(benchmark_inception_v3 arm_compute_benchmark) + +# MobileNet benchmark +add_executable(benchmark_mobilenet "src/benchmark_mobilenet.cpp") +target_link_libraries(benchmark_mobilenet arm_compute_benchmark) + +install(TARGETS benchmark_googlenet benchmark_inception_v3 benchmark_mobilenet DESTINATION bin) diff --git a/benchmark/acl/Benchmark.cpp b/contrib/benchmark_acl/src/Benchmark.cpp index ba6001232..ba6001232 100644 --- a/benchmark/acl/Benchmark.cpp +++ b/contrib/benchmark_acl/src/Benchmark.cpp diff --git a/benchmark/acl/Benchmark.h b/contrib/benchmark_acl/src/Benchmark.h index 200f40952..200f40952 100644 --- a/benchmark/acl/Benchmark.h +++ b/contrib/benchmark_acl/src/Benchmark.h diff --git a/contrib/benchmark_acl/src/benchmark_googlenet.cpp b/contrib/benchmark_acl/src/benchmark_googlenet.cpp new file mode 100644 index 000000000..8b0fbfdac --- /dev/null +++ b/contrib/benchmark_acl/src/benchmark_googlenet.cpp @@ -0,0 +1,242 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph.h" + +#include "Benchmark.h" + +#include <cstdlib> +#include <tuple> + +using namespace arm_compute::graph::frontend; + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) +{ + return get_accessor<OutputAccessor>(); +} + +/** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) + */ +class GraphGooglenetExample +{ +public: + void do_setup(int argc, char **argv) + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::Disabled; + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else if(argc == 5) + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled; + } + + graph << target_hint + << fast_math_hint + << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), + get_input_accessor()) + << ConvolutionLayer( + 7U, 7U, 64U, + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"), + PadStrideInfo(2, 2, 3, 3)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) + << ConvolutionLayer( + 1U, 1U, 64U, + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, 192U, + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); + graph << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U); + graph << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); + graph << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U); + graph << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U); + graph << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); + graph << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U); + graph << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) + << FullyConnectedLayer( + 1000U, + get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy")) + << SoftmaxLayer() + << OutputLayer(get_output_accessor()); + + // Finalize graph + GraphConfig config; + config.use_tuner = (target == 2); + graph.finalize(target_hint, config); + } + void do_run() + { + run_benchmark(graph); + } + +private: + Stream graph{ 0, "GoogleNet" }; + + ConcatLayer get_inception_node(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple<unsigned int, unsigned int> b_filters, + std::tuple<unsigned int, unsigned int> c_filters, + unsigned int d_filt) + { + std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_"; + SubStream i_a(graph); + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "1x1_w.npy"), + get_weights_accessor(data_path, total_path + "1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "3x3_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubStream i_c(graph); + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 5U, 5U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "5x5_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_b.npy"), + PadStrideInfo(1, 1, 2, 2)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "pool_proj_w.npy"), + get_weights_accessor(data_path, total_path + "pool_proj_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } +}; + +/** Main program for Googlenet + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) + */ +int main(int argc, char **argv) +{ + GraphGooglenetExample example; + + example.do_setup(argc, argv); + example.do_run(); + + return 0; +} diff --git a/contrib/benchmark_acl/src/benchmark_inception_v3.cpp b/contrib/benchmark_acl/src/benchmark_inception_v3.cpp new file mode 100644 index 000000000..382851f50 --- /dev/null +++ b/contrib/benchmark_acl/src/benchmark_inception_v3.cpp @@ -0,0 +1,891 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017-2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph.h" + +#include "Benchmark.h" + +#include <cstdlib> +#include <tuple> + +using namespace arm_compute::graph::frontend; + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) +{ + return get_accessor<OutputAccessor>(); +} + +/** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class InceptionV3Example +{ +public: + void do_setup(int argc, char **argv) + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::Disabled; + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else if(argc == 5) + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled; + } + + graph << target_hint + << fast_math_hint + << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), + get_input_accessor()) + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) + .set_name("Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_2a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") + + << ConvolutionLayer(3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("Conv2d_2b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") + + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") + + << ConvolutionLayer(1U, 1U, 80U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_3b_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") + + << ConvolutionLayer(3U, 3U, 192U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_4a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") + + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); + + graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 32U) + .set_name("Mixed_5b/concat"); + graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 64U, true) + .set_name("Mixed_5c/concat"); + graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 64U) + .set_name("Mixed_5d/concat"); + + graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); + + graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U), + std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) + .set_name("Mixed_6b/concat"); + graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U), + std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) + .set_name("Mixed_6c/concat"); + graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U), + std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) + .set_name("Mixed_6d/concat"); + graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U), + std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) + .set_name("Mixed_6e/concat"); + + graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U), + std::make_tuple(192U, 192U, 192U, 192U)) + .set_name("Mixed_7a/concat"); + + graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U), + std::make_tuple(448U, 384U, 384U, 384U), 192U) + .set_name("Mixed_7b/concat"); + graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U), + std::make_tuple(448U, 384U, 384U, 384U), 192U, true) + .set_name("Mixed_7c/concat"); + + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool") + << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Logits/Conv2d_1c_1x1/convolution") + << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") + << SoftmaxLayer().set_name("Predictions/Softmax") + << OutputLayer(get_output_accessor()); + + // Finalize graph + GraphConfig config; + config.use_tuner = (target == 2); + graph.finalize(target_hint, config); + } + + void do_run() + { + run_benchmark(graph); + } + +private: + Stream graph{ 0, "InceptionV3" }; + +private: + ConcatLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple<unsigned int, unsigned int> b_filters, + std::tuple<unsigned int, unsigned int, unsigned int> c_filters, + unsigned int d_filt, + bool is_name_different = false) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + + // This is due to a naming issue in the tf model + std::string conv_id0 = "_0a_"; + std::string conv_id1 = "2d_0b_"; + if(is_name_different) + { + conv_id0 = "_0b_"; + conv_id1 = "_1_0c_"; + } + + SubStream i_a(graph); + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu") + << ConvolutionLayer( + 5U, 5U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 2, 2)) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu"); + + SubStream i_c(graph); + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); + + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } + + ConcatLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple<unsigned int, unsigned int, unsigned int> b_filters) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubStream i_a(graph); + i_a << ConvolutionLayer( + 3U, 3U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu"); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu"); + + SubStream i_c(graph); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); + } + + ConcatLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple<unsigned int, unsigned int, unsigned int> b_filters, + std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters, + unsigned int d_filt) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubStream i_a(graph); + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 7U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 3, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") + << ConvolutionLayer( + 1U, 7U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 3)) + .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu"); + + SubStream i_c(graph); + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 1U, 7U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 3)) + .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu") + << ConvolutionLayer( + 7U, 1U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 3, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu") + << ConvolutionLayer( + 1U, 7U, std::get<3>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 3)) + .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu") + << ConvolutionLayer( + 7U, 1U, std::get<4>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 3, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); + + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } + + ConcatLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, + std::tuple<unsigned int, unsigned int> a_filters, + std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubStream i_a(graph); + i_a << ConvolutionLayer( + 1U, 1U, std::get<0>(a_filters), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<1>(a_filters), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu"); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 7U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 3, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") + << ConvolutionLayer( + 1U, 7U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 3)) + .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<3>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu"); + + SubStream i_c(graph); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); + } + + ConcatLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple<unsigned int, unsigned int, unsigned int> b_filters, + std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters, + unsigned int d_filt, + bool is_name_different = false) + { + // This is due to a naming issue in the tf model + std::string conv_id = "_0b_"; + if(is_name_different) + { + conv_id = "_0c_"; + } + + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubStream i_a(graph); + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); + + SubStream i_b(graph); + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu"); + + SubStream i_b1(static_cast<IStream &>(i_b)); + i_b1 << ConvolutionLayer( + 3U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 0)) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu"); + + SubStream i_b2(static_cast<IStream &>(i_b)); + i_b2 << ConvolutionLayer( + 1U, 3U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 1)) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu"); + + // Merge b1 and b2 + i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat"); + + SubStream i_c(graph); + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer( + 3U, 3U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu"); + + SubStream i_c1(static_cast<IStream &>(i_c)); + i_c1 << ConvolutionLayer( + 3U, 1U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 1, 0)) + .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu"); + + SubStream i_c2(static_cast<IStream &>(i_c)); + i_c2 << ConvolutionLayer( + 1U, 3U, std::get<3>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 1)) + .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu"); + + // Merge i_c1 and i_c2 + i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); + + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f) + .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); + + return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } +}; + +/** Main program for Inception V3 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) + */ +int main(int argc, char **argv) +{ + InceptionV3Example example; + + example.do_setup(argc, argv); + example.do_run(); + + return 0; +} diff --git a/contrib/benchmark_acl/src/benchmark_mobilenet.cpp b/contrib/benchmark_acl/src/benchmark_mobilenet.cpp new file mode 100644 index 000000000..085be184e --- /dev/null +++ b/contrib/benchmark_acl/src/benchmark_mobilenet.cpp @@ -0,0 +1,265 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph.h" + +#include "Benchmark.h" + +#include <cstdlib> + +using namespace arm_compute::graph::frontend; + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) +{ + return get_accessor<InputAccessor>(); +} + +inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) +{ + return get_accessor<OutputAccessor>(); +} + +/** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class GraphMobilenetExample +{ +public: + void do_setup(int argc, char **argv) + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + ConvolutionMethod convolution_hint = ConvolutionMethod::GEMM; + DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::Optimized3x3; + FastMathHint fast_math_hint = FastMathHint::Disabled; + + // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160) + int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0; + ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)"); + int layout_id = (argc > 3) ? std::strtol(argv[3], nullptr, 10) : 0; + ARM_COMPUTE_ERROR_ON_MSG(layout_id > 1, "Invalid layout ID. Layout must be 0 (NCHW) or 1 (NHWC)"); + + float depth_scale = (model_id == 0) ? 1.f : 0.75; + unsigned int spatial_size = (model_id == 0) ? 224 : 160; + std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; + TensorDescriptor input_descriptor_nchw = TensorDescriptor(TensorShape(spatial_size, spatial_size, 3U, 1U), DataType::F32); + TensorDescriptor input_descriptor_nhwc = TensorDescriptor(TensorShape(3U, spatial_size, spatial_size, 1U), DataType::F32).set_layout(DataLayout::NHWC); + TensorDescriptor input_descriptor = (layout_id == 0) ? input_descriptor_nchw : input_descriptor_nhwc; + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; + std::cout << "No data layout provided: using NCHW\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; + std::cout << "No data layout provided: using NCHW\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data layout provided: using NCHW\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 4) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 5) + { + data_path = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n"; + std::cout << "No image provided: using random values\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else if(argc == 6) + { + data_path = argv[4]; + image = argv[5]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else if(argc == 7) + { + data_path = argv[4]; + image = argv[5]; + label = argv[6]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[4]; + image = argv[5]; + label = argv[6]; + fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled; + } + + // Add model path to data path + if(!data_path.empty()) + { + data_path += model_path; + } + + graph << target_hint + << convolution_hint + << depthwise_convolution_hint + << fast_math_hint + << InputLayer(input_descriptor, + get_input_accessor()) + << ConvolutionLayer( + 3U, 3U, 32U * depth_scale, + get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) + .set_name("Conv2d_0") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), + 0.001f) + .set_name("Conv2d_0/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); + graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") + << ConvolutionLayer( + 1U, 1U, 1001U, + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Logits/Conv2d_1c_1x1") + << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") + << SoftmaxLayer().set_name("Softmax") + << OutputLayer(get_output_accessor()); + + // Finalize graph + GraphConfig config; + config.use_tuner = (target == 2); + graph.finalize(target_hint, config); + } + void do_run() + { + run_benchmark(graph); + } + +private: + Stream graph{ 0, "MobileNetV1" }; + + ConcatLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, + unsigned int conv_filt, + PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) + { + std::string total_path = param_path + "_"; + SubStream sg(graph); + sg << DepthwiseConvolutionLayer( + 3U, 3U, + get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + dwc_pad_stride_info) + .set_name(total_path + "depthwise/depthwise") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), + 0.001f) + .set_name(total_path + "depthwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") + << ConvolutionLayer( + 1U, 1U, conv_filt, + get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + conv_pad_stride_info) + .set_name(total_path + "pointwise/Conv2D") + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), + 0.001f) + .set_name(total_path + "pointwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); + + return ConcatLayer(std::move(sg)); + } +}; + +/** Main program for MobileNetV1 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), + * [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160), + * [optional] Path to the weights folder, + * [optional] image, + * [optional] labels, + * [optional] data layout, + * [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) + */ +int main(int argc, char **argv) +{ + GraphMobilenetExample example; + + example.do_setup(argc, argv); + example.do_run(); + + return 0; +} diff --git a/contrib/bindacl/CMakeLists.txt b/contrib/bindacl/CMakeLists.txt deleted file mode 100644 index 75e77a53e..000000000 --- a/contrib/bindacl/CMakeLists.txt +++ /dev/null @@ -1,20 +0,0 @@ -if(NOT BUILD_LABS) - return() -endif(NOT BUILD_LABS) - -if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - return() -endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - -nnfw_find_package(ARMCompute REQUIRED) - -file(GLOB_RECURSE NNAPI_BINDACL_SRCS "src/*.cc") - -link_directories(${CMAKE_INSTALL_PREFIX}/lib) - -add_library(exp_bindacl SHARED ${NNAPI_BINDACL_SRCS}) -target_include_directories(exp_bindacl PUBLIC ${NNFW_INCLUDE_DIR}) -target_link_libraries(exp_bindacl nnfw_util arm_compute_graph) - -# we need the library name to be 'neuralnetworks' and this will do the trick -set_target_properties(exp_bindacl PROPERTIES OUTPUT_NAME neuralnetworks) diff --git a/contrib/bindacl/README.md b/contrib/bindacl/README.md deleted file mode 100644 index 2c9f03670..000000000 --- a/contrib/bindacl/README.md +++ /dev/null @@ -1,13 +0,0 @@ -Build -``` -CROSS_BUILD=1 TARGET_ARCH=armv7l make -CROSS_BUILD=1 TARGET_ARCH=armv7l make install -``` - -Test -``` -USE_NNAPI=1 \ -LD_LIBRARY_PATH="$(pwd)/Product/out/lib:$(pwd)/Product/obj/contrib/bindacl" \ -Product/out/bin/tflite_run \ -[T/F Lite Flatbuffer Model Path] -``` diff --git a/contrib/bindacl/src/nnapi_acl.cc b/contrib/bindacl/src/nnapi_acl.cc deleted file mode 100644 index d9432465a..000000000 --- a/contrib/bindacl/src/nnapi_acl.cc +++ /dev/null @@ -1,264 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include <NeuralNetworks.h> -#include <stdexcept> -#include <iostream> -#include <string> -#include <map> -#include <cassert> -#include <memory> -#include <boost/format.hpp> -// ACL Headers -#include <arm_compute/graph.h> -#include <arm_compute/core/TensorInfo.h> - -#include "util/environment.h" - -// -// Asynchronous Event -// -struct ANeuralNetworksEvent -{ -}; - -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) -{ - delete event; -} - -// -// Memory -// -struct ANeuralNetworksMemory -{ - // 1st approach - Store all the data inside ANeuralNetworksMemory object - // 2nd approach - Store metadata only, and defer data loading as much as possible -}; - -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory** memory) -{ - *memory = new ANeuralNetworksMemory; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) -{ - delete memory; -} - -// -// Model -// -struct ANeuralNetworksModel -{ - // ANeuralNetworksModel should be a factory for Graph IR (a.k.a ISA Frontend) - // TODO Record # of operands - uint32_t numOperands; - - ANeuralNetworksModel() : numOperands(0) - { - // DO NOTHING - } -}; - -int ANeuralNetworksModel_create(ANeuralNetworksModel** model) -{ - *model = new ANeuralNetworksModel; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksModel_free(ANeuralNetworksModel* model) -{ - delete model; -} - -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, const ANeuralNetworksOperandType *type) -{ - model->numOperands += 1; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, const ANeuralNetworksMemory* memory, size_t offset, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Compilation -// -struct ANeuralNetworksCompilation -{ - // ANeuralNetworksCompilation should hold a compiled IR -}; - -int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, ANeuralNetworksCompilation** compilation) -{ - *compilation = new ANeuralNetworksCompilation; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Execution -// -struct ANeuralNetworksExecution -{ - // ANeuralNetworksExecution corresponds to NPU::Interp::Session - - arm_compute::graph::frontend::Stream graph{0, "BIND_ACL"}; -}; - -class DummyInputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - DummyInputAccessor() = default; - DummyInputAccessor(DummyInputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -bool DummyInputAccessor::access_tensor(arm_compute::ITensor&) -{ - return true; -} - -class DummyOutputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - DummyOutputAccessor() = default; - DummyOutputAccessor(DummyOutputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -bool DummyOutputAccessor::access_tensor(arm_compute::ITensor&) -{ - return false; -} - -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, ANeuralNetworksExecution** execution) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *execution = new ANeuralNetworksExecution; - - using arm_compute::DataType; - using arm_compute::graph::Target; - using arm_compute::graph::TensorDescriptor; - using arm_compute::TensorShape; - using arm_compute::graph::frontend::InputLayer; - using arm_compute::graph::frontend::OutputLayer; - - ANeuralNetworksExecution* execlocal = *execution; - arm_compute::graph::frontend::Stream& graph = execlocal->graph; - - Target target_hint = nnfw::util::get_env_int("NNFW_ACL_USENEON") - ? Target::NEON : Target::CL; - - graph << target_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), - std::unique_ptr<DummyInputAccessor>(new DummyInputAccessor())) - << arm_compute::graph::frontend::SoftmaxLayer() - << OutputLayer((std::unique_ptr<DummyOutputAccessor>(new DummyOutputAccessor()))); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -// ANeuralNetworksExecution_setInput and ANeuralNetworksExecution_setOutput specify HOST buffer for input/output -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *event = new ANeuralNetworksEvent; - - // graph.run() fails with segment fail when only target_hint is added. - // after fix adding 'Tensor' we may call graph.run() - arm_compute::graph::frontend::Stream& graph = execution->graph; - graph.run(); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) -{ - delete execution; -} diff --git a/contrib/convacl/CMakeLists.txt b/contrib/convacl/CMakeLists.txt deleted file mode 100644 index ca6411211..000000000 --- a/contrib/convacl/CMakeLists.txt +++ /dev/null @@ -1,20 +0,0 @@ -if(NOT BUILD_LABS) - return() -endif(NOT BUILD_LABS) - -if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - return() -endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - -nnfw_find_package(ARMCompute REQUIRED) - -file(GLOB_RECURSE NNAPI_CONVACL_SRCS "src/*.cc") - -link_directories(${CMAKE_INSTALL_PREFIX}/lib) - -add_library(exp_convacl SHARED ${NNAPI_CONVACL_SRCS}) -target_include_directories(exp_convacl PUBLIC ${NNFW_INCLUDE_DIR}) -target_link_libraries(exp_convacl nnfw_util arm_compute_graph) - -# we need the library name to be 'neuralnetworks' and this will do the trick -set_target_properties(exp_convacl PROPERTIES OUTPUT_NAME neuralnetworks) diff --git a/contrib/convacl/src/io_accessor.cc b/contrib/convacl/src/io_accessor.cc deleted file mode 100644 index b7fdee721..000000000 --- a/contrib/convacl/src/io_accessor.cc +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "io_accessor.h" -#include <arm_compute/core/Helpers.h> -#include <ostream> - -bool InputAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_input; - _test_input += _inc ? 1.0 : 0.0; - - std::cout << "Input " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} - -bool OutputAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - std::cout << "Output " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return false; // end the network -} - -bool WeightAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_weight; - _test_weight += _inc ? 1.0 : 0.0; - - std::cout << "Weight " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} - -bool BiasAccessor::access_tensor(arm_compute::ITensor &tensor) -{ - // Subtract the mean value from each channel - arm_compute::Window window; - window.use_tensor_dimensions(tensor.info()->tensor_shape()); - - execute_window_loop(window, [&](const arm_compute::Coordinates& id) - { - *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = 0.0; - - std::cout << "Bias " << id.y() << "," << id.x() << " = "; - std::cout << *reinterpret_cast<float *>(tensor.ptr_to_element(id)); - std::cout << std::endl; - }); - return true; -} diff --git a/contrib/convacl/src/nnapi_acl_conv.cc b/contrib/convacl/src/nnapi_acl_conv.cc deleted file mode 100644 index 091d19497..000000000 --- a/contrib/convacl/src/nnapi_acl_conv.cc +++ /dev/null @@ -1,239 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include <NeuralNetworks.h> -#include <stdexcept> -#include <iostream> -#include <string> -#include <map> -#include <cassert> -#include <memory> -#include <boost/format.hpp> -// ACL Headers -#include <arm_compute/graph.h> - -#include "util/environment.h" -#include "io_accessor.h" - -// -// Asynchronous Event -// -struct ANeuralNetworksEvent -{ -}; - -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksEvent_free(ANeuralNetworksEvent* event) -{ - delete event; -} - -// -// Memory -// -struct ANeuralNetworksMemory -{ - // 1st approach - Store all the data inside ANeuralNetworksMemory object - // 2nd approach - Store metadata only, and defer data loading as much as possible -}; - -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory** memory) -{ - *memory = new ANeuralNetworksMemory; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory) -{ - delete memory; -} - -// -// Model -// -struct ANeuralNetworksModel -{ - // ANeuralNetworksModel should be a factory for Graph IR (a.k.a ISA Frontend) - // TODO Record # of operands - uint32_t numOperands; - - ANeuralNetworksModel() : numOperands(0) - { - // DO NOTHING - } -}; - -int ANeuralNetworksModel_create(ANeuralNetworksModel** model) -{ - *model = new ANeuralNetworksModel; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksModel_free(ANeuralNetworksModel* model) -{ - delete model; -} - -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, const ANeuralNetworksOperandType *type) -{ - model->numOperands += 1; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, const ANeuralNetworksMemory* memory, size_t offset, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_finish(ANeuralNetworksModel* model) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Compilation -// -struct ANeuralNetworksCompilation -{ - // ANeuralNetworksCompilation should hold a compiled IR -}; - -int ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, ANeuralNetworksCompilation** compilation) -{ - *compilation = new ANeuralNetworksCompilation; - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -// -// Execution -// -struct ANeuralNetworksExecution -{ - // ANeuralNetworksExecution corresponds to NPU::Interp::Session - - arm_compute::graph::frontend::Stream graph{0, "ACL_CONV"}; -}; - -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, ANeuralNetworksExecution** execution) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *execution = new ANeuralNetworksExecution; - - using arm_compute::DataType; - using arm_compute::graph::Target; - using arm_compute::graph::TensorDescriptor; - using arm_compute::TensorShape; - using arm_compute::graph::frontend::InputLayer; - using arm_compute::graph::frontend::OutputLayer; - - ANeuralNetworksExecution* execlocal = *execution; - arm_compute::graph::frontend::Stream& graph = execlocal->graph; - - Target target_hint = nnfw::util::get_env_int("NNFW_ACL_USENEON") - ? Target::NEON : Target::CL; - bool autoinc = nnfw::util::get_env_bool("NNFW_TEST_AUTOINC"); - - graph << target_hint - << InputLayer(TensorDescriptor(TensorShape(3U, 3U, 1U, 1U), DataType::F32), - std::unique_ptr<InputAccessor>(new InputAccessor(autoinc))) - << arm_compute::graph::frontend::ConvolutionLayer( - 3U, 3U, 1U, - std::unique_ptr<WeightAccessor>(new WeightAccessor(autoinc)), - std::unique_ptr<BiasAccessor>(new BiasAccessor()), - arm_compute::PadStrideInfo(1, 1, 0, 0)) - << OutputLayer( - std::unique_ptr<OutputAccessor>(new OutputAccessor())); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -// ANeuralNetworksExecution_setInput and ANeuralNetworksExecution_setOutput specify HOST buffer for input/output -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event) -{ - std::cout << __FUNCTION__ << " +++" << std::endl; - *event = new ANeuralNetworksEvent; - - // graph.run() fails with segment fail when only target_hint is added. - // after fix adding 'Tensor' we may call graph.run() - arm_compute::graph::frontend::Stream& graph = execution->graph; - graph.run(); - - std::cout << __FUNCTION__ << " ---" << std::endl; - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution) -{ - delete execution; -} diff --git a/contrib/detection/CMakeLists.txt b/contrib/detection/CMakeLists.txt index 23a529c1e..37d91b527 100644 --- a/contrib/detection/CMakeLists.txt +++ b/contrib/detection/CMakeLists.txt @@ -7,5 +7,5 @@ nnfw_find_package(Tensorflow REQUIRED) list(APPEND SOURCES detection.cpp) add_executable(detection ${SOURCES}) -target_link_libraries(detection nnfw_util) +target_link_libraries(detection nnfw_lib_misc) target_link_libraries(detection tensorflow-core) diff --git a/contrib/detection/detection.cpp b/contrib/detection/detection.cpp index 58beaad25..8a988ccf5 100644 --- a/contrib/detection/detection.cpp +++ b/contrib/detection/detection.cpp @@ -1,3 +1,19 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + #include <tensorflow/core/public/session.h> #include <iostream> @@ -6,7 +22,7 @@ #include <cassert> #include <cstring> -#include "util/benchmark.h" +#include "misc/benchmark.h" #define CHECK_TF(e) { \ if(!(e).ok()) \ @@ -46,7 +62,7 @@ int main(int argc, char **argv) { std::chrono::milliseconds elapsed(0); - nnfw::util::benchmark::measure(elapsed) << [&] (void) { + nnfw::misc::benchmark::measure(elapsed) << [&] (void) { CHECK_TF(sess->Run({{"input_node", input}}, output_nodes, {}, &outputs)); }; diff --git a/contrib/example/CMakeLists.txt b/contrib/example/CMakeLists.txt deleted file mode 100644 index 2d487d53f..000000000 --- a/contrib/example/CMakeLists.txt +++ /dev/null @@ -1 +0,0 @@ -add_executable(example example.cpp) diff --git a/contrib/example/example.cpp b/contrib/example/example.cpp deleted file mode 100644 index f627e6bdf..000000000 --- a/contrib/example/example.cpp +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <iostream> - -int main(int argc, char **argv) -{ - std::cout << "Hello, World!" << std::endl; - return 0; -} diff --git a/contrib/jniacl/CMakeLists.txt b/contrib/jniacl/CMakeLists.txt deleted file mode 100644 index 20c469c9d..000000000 --- a/contrib/jniacl/CMakeLists.txt +++ /dev/null @@ -1,22 +0,0 @@ -# -# Simple Android JNI execution test of ACL -# - -if(NOT BUILD_LABS) - return() -endif(NOT BUILD_LABS) - -if(NOT "${TARGET_OS}" STREQUAL "android") - return() -endif(NOT "${TARGET_OS}" STREQUAL "android") - -nnfw_find_package(ARMCompute REQUIRED) - -link_directories(${CMAKE_INSTALL_PREFIX}/lib) - -set(JNIACL_SRCS src/jniacl_main.cc - src/io_accessor.cc) - -add_library(jniacl_jni SHARED ${JNIACL_SRCS}) -target_include_directories(jniacl_jni PUBLIC ${TFLITE_JNI_INCLUDES} src) -target_link_libraries(jniacl_jni arm_compute_graph log) diff --git a/contrib/jniacl/src/io_accessor.h b/contrib/jniacl/src/io_accessor.h deleted file mode 100644 index 4033020e0..000000000 --- a/contrib/jniacl/src/io_accessor.h +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef __IO_ACCESSOR_H__ -#define __IO_ACCESSOR_H__ - -#include <arm_compute/graph/ITensorAccessor.h> - -class InputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - InputAccessor(bool inc) : _inc(inc) { _test_input = 1.0; } - InputAccessor(InputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; - -private: - bool _inc; - float _test_input; -}; - -class OutputAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - OutputAccessor() = default; - OutputAccessor(OutputAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -class WeightAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - WeightAccessor(bool inc) : _inc(inc) { _test_weight = 1.0; } - WeightAccessor(WeightAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; - -private: - bool _inc; - float _test_weight; -}; - -class BiasAccessor : public arm_compute::graph::ITensorAccessor -{ -public: - BiasAccessor() = default; - BiasAccessor(BiasAccessor&&) = default; - - // Inherited methods overriden: - bool access_tensor(arm_compute::ITensor& tensor) override; -}; - -#endif // __IO_ACCESSOR_H__ diff --git a/contrib/kerneltesting/CMakeLists.txt b/contrib/kerneltesting/CMakeLists.txt deleted file mode 100644 index ba0d808ce..000000000 --- a/contrib/kerneltesting/CMakeLists.txt +++ /dev/null @@ -1,23 +0,0 @@ -if(NOT BUILD_LABS) - return() -endif(NOT BUILD_LABS) - -if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - return() -endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - -nnfw_find_package(ARMCompute REQUIRED) - -function(add_kerneltesting TESTNAME SRC_FILES) - link_directories(${CMAKE_INSTALL_PREFIX}/lib) - add_executable(${TESTNAME} ${SRC_FILES}) - target_include_directories(${TESTNAME} PUBLIC - ${NNFW_INCLUDE_DIR}) - target_link_libraries(${TESTNAME} nnfw_util arm_compute_graph) - install(TARGETS ${TESTNAME} DESTINATION bin) -endfunction() - -# TODO: Enable conv2d on Tizen -if (NOT ${TARGET_OS} STREQUAL "tizen") - add_subdirectory(conv2d) -endif() diff --git a/contrib/kerneltesting/conv2d/nnfw_conv2d_test.cpp b/contrib/kerneltesting/conv2d/nnfw_conv2d_test.cpp deleted file mode 100644 index 04b217595..000000000 --- a/contrib/kerneltesting/conv2d/nnfw_conv2d_test.cpp +++ /dev/null @@ -1,607 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include <iostream> -#include <vector> -#include <cassert> - -#include <Eigen/Core> -#include <gemmlowp.h> - -#include "types.h" -#include "common.h" -#include "optimized_ops.h" -#include "OperationUtils.h" - -#include <arm_compute/graph.h> - -#include <arm_compute/runtime/CL/CLFunctions.h> -#include <arm_compute/runtime/CL/functions/CLConvolution.h> - -#include "io_accessor.h" -#include "util/environment.h" - -static constexpr int kStaticBufferSize = 1605632; -static char static_scratch_buffer[kStaticBufferSize]; - -#define ANDROID_NN_CONV_PARAMETERS(Type) \ - uint32_t height = getSizeOfDimension(inputShape, 1); \ - uint32_t width = getSizeOfDimension(inputShape, 2); \ - uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ - uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ - uint32_t outHeight = getSizeOfDimension(outputShape, 1); \ - uint32_t outWidth = getSizeOfDimension(outputShape, 2); \ - uint32_t inDepth = getSizeOfDimension(inputShape, 3); \ - \ - uint32_t paddingHeight = (uint32_t)padding_top; \ - uint32_t paddingWidth = (uint32_t)padding_left; \ - \ - Dims<4> im2colDim; \ - im2colDim.sizes[3] = (int)getSizeOfDimension(outputShape, 0); \ - im2colDim.sizes[2] = (int)getSizeOfDimension(outputShape, 1); \ - im2colDim.sizes[1] = (int)getSizeOfDimension(outputShape, 2); \ - im2colDim.sizes[0] = (int)inDepth * filterHeight * filterWidth; \ - \ - im2colDim.strides[0] = 1; \ - for (int i=1; i<4; i++) { \ - im2colDim.strides[i] = im2colDim.strides[i-1] * im2colDim.sizes[i-1]; \ - } \ - \ - Type* im2colData = nullptr; \ - int im2colByteSize = sizeof(Type); \ - for (int i=0; i<4; i++) { \ - im2colByteSize *= im2colDim.sizes[i]; \ - } \ - if (im2colByteSize <= kStaticBufferSize) { \ - im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ - } else { \ - im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ - } - - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const Shape& outputShape) { - - ANDROID_NN_CONV_PARAMETERS(float) - - #define ANDROID_NN_CONV(activation) \ - Conv<FusedActivationFunctionType::activation>( \ - inputData, convertShapeToDims(inputShape), \ - filterData, convertShapeToDims(filterShape), \ - biasData, convertShapeToDims(biasShape), \ - stride_width, stride_height, paddingWidth, paddingHeight, \ - outputData, convertShapeToDims(outputShape), \ - im2colData, im2colDim) - - ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_CONV) - - #undef ANDROID_NN_CONV - - if (im2colByteSize > kStaticBufferSize) { - delete[] im2colData; - } - return true; -} - -//----------------------------------------------------------------------------- - -using arm_compute::DataType; -using arm_compute::graph::Target; -using arm_compute::graph::TensorDescriptor; -using arm_compute::TensorShape; -using arm_compute::graph::frontend::InputLayer; -using arm_compute::graph::frontend::OutputLayer; - -namespace acl_graph { - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const Shape& outputShape) -{ - // Try with simple build-run with ACL Layer - arm_compute::graph::frontend::Stream graph{0, "ACL_CONV2D_TEST"}; - - Target target_hint = nnfw::util::get_env_int("NNFW_ACL_USENEON") - ? Target::NEON : Target::CL; - - // Not sure about which index is which value - uint32_t tsi_c = getSizeOfDimension(inputShape, 0); - uint32_t tsi_h = getSizeOfDimension(inputShape, 1); - uint32_t tsi_w = getSizeOfDimension(inputShape, 2); - uint32_t tsi_n = getSizeOfDimension(inputShape, 3); - - uint32_t tsk_h = getSizeOfDimension(filterShape, 1); - uint32_t tsk_w = getSizeOfDimension(filterShape, 2); - uint32_t tsk_n = getSizeOfDimension(filterShape, 3); - - graph << target_hint - << InputLayer(TensorDescriptor(TensorShape(tsi_w, tsi_h, tsi_c, tsi_n), DataType::F32), - std::unique_ptr<InputAccessor>(new InputAccessor(inputData, inputShape))) - << arm_compute::graph::frontend::ConvolutionLayer( - tsk_w, tsk_h, tsk_n, - std::unique_ptr<WeightAccessor>(new WeightAccessor(filterData, filterShape)), - std::unique_ptr<BiasAccessor>(new BiasAccessor(biasData, biasShape)), - arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom)) - ; - if (activation != static_cast<int32_t>(FusedActivationFunc::NONE)) { - arm_compute::ActivationLayerInfo::ActivationFunction actFunc = - arm_compute::ActivationLayerInfo::ActivationFunction::RELU; - - graph << arm_compute::graph::frontend::ActivationLayer(arm_compute::ActivationLayerInfo(actFunc)); - // Activation does not provide output Tensor and makes next layer fail to add to graph - // when it's the last(output) layer. To solve this, need to add a dummy layer. - uint32_t tso_c = getSizeOfDimension(outputShape, 0); - uint32_t tso_h = getSizeOfDimension(outputShape, 1); - uint32_t tso_w = getSizeOfDimension(outputShape, 2); - uint32_t tso_n = getSizeOfDimension(outputShape, 3); - graph << arm_compute::graph::frontend::ReshapeLayer(TensorShape(tso_w, tso_h, tso_c, tso_n)); - } - graph << OutputLayer(std::unique_ptr<OutputAccessor>(new OutputAccessor(outputData, outputShape))) - ; - - graph.run(); - - return true; -} - -} // namespace acl_graph - -//----------------------------------------------------------------------------- - -using arm_compute::TensorInfo; - -namespace acl_runtime { - -TensorShape calculate_convolution_layer_output_shape( - const arm_compute::TensorShape &input_shape, - const arm_compute::TensorShape &weights_shape, - const arm_compute::PadStrideInfo &conv_info) -{ - unsigned int output_width = 0; - unsigned int output_height = 0; - - // Get output width and height - std::tie(output_width, output_height) = - arm_compute::scaled_dimensions( - input_shape.x(), input_shape.y(), - weights_shape.x(), weights_shape.y(), - conv_info); - - // Create output shape - TensorShape output_shape = input_shape; - output_shape.set(0, output_width); - output_shape.set(1, output_height); - output_shape.set(2, weights_shape[3]); - - return output_shape; -} - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const Shape& outputShape) -{ - arm_compute::CLScheduler::get().default_init(); - - uint32_t tsi_c = getSizeOfDimension(inputShape, 0); - uint32_t tsi_h = getSizeOfDimension(inputShape, 1); - uint32_t tsi_w = getSizeOfDimension(inputShape, 2); - uint32_t tsi_n = getSizeOfDimension(inputShape, 3); - - uint32_t tsk_h = getSizeOfDimension(filterShape, 1); - uint32_t tsk_w = getSizeOfDimension(filterShape, 2); - uint32_t tsk_n = getSizeOfDimension(filterShape, 3); - - TensorShape input_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); - TensorShape filter_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); - arm_compute::PadStrideInfo conv_info = - arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom); - - TensorShape output_shape = calculate_convolution_layer_output_shape( - input_shape, filter_shape, conv_info); - - uint32_t tso_c = output_shape[0]; - uint32_t tso_w = output_shape[1]; - uint32_t tso_h = output_shape[2]; - uint32_t tso_n = output_shape[3]; - - arm_compute::CLTensor input, output, bias, filter; - - input.allocator()->init(TensorInfo(tsi_w, tsi_h, arm_compute::Format::F32)); - output.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); - bias.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); - filter.allocator()->init(TensorInfo(tsk_w, tsk_h, arm_compute::Format::F32)); - - input.allocator()->allocate(); - output.allocator()->allocate(); - bias.allocator()->allocate(); - filter.allocator()->allocate(); - - input.map(); - InputAccessor ia(inputData, inputShape); - ia.access_tensor(input); - input.unmap(); - - bias.map(); - BiasAccessor ba(biasData, biasShape); - ba.access_tensor(bias); - bias.unmap(); - - filter.map(); - WeightAccessor fa(filterData, filterShape); - fa.access_tensor(filter); - filter.unmap(); - - arm_compute::CLConvolutionLayer conv_f; - conv_f.configure(&input, &filter, &bias, &output, conv_info); - - arm_compute::CLScheduler::get().sync(); - - conv_f.run(); - - output.map(); - OutputAccessor oa(outputData, outputShape); - oa.access_tensor(output); - output.unmap(); - - return true; -} - -} // namespace acl_runtime - -//----------------------------------------------------------------------------- - -enum COMPUTE_TYPE { - COMPUTE_DEFAULT = 0, - COMPUTE_ACLGRAPH, - COMPUTE_ACLRT -}; - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const Shape& outputShape, - COMPUTE_TYPE compType) { - - switch (compType) - { - case COMPUTE_DEFAULT : - return convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - - case COMPUTE_ACLGRAPH : - return acl_graph::convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - - case COMPUTE_ACLRT : - return acl_runtime::convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - } - return false; -} - -//----------------------------------------------------------------------------- - -void dumpData(const char* name, const float* data, const Shape& shape) -{ - uint32_t height = getSizeOfDimension(shape, 1); - uint32_t width = getSizeOfDimension(shape, 2); - - std::cout << "---" << name << "---" << std::endl; - for (int h = 0; h < height; h++) { - std::cout << "H=" << h << " | "; - for (int w = 0; w < width; w++) { - std::cout << data[h * width + w] << ","; - } - std::cout << std::endl; - } -} - -void initData(float* outputData, int num, float value) -{ - for (int i = 0; i < num; i++) { - *(outputData + i) = value; - } -} - -void initDataSeq(float* outputData, int num, float value) -{ - for (int i = 0; i < num; i++) { - *(outputData + i) = value; - value += 1.0; - } -} - -// compareData -// return true if result == expected with the shape info, -// otherwise false -bool compareData(const float* result, const float* expected, const Shape& shape) -{ - NN_CHECK_EQ(shape.dimensions.size(), 4); - - uint32_t height = getSizeOfDimension(shape, 1); - uint32_t width = getSizeOfDimension(shape, 2); - uint32_t numitems = height * width; - for (int item = 0; item < numitems; item++) { - if (*(result + item) != *(expected + item)) { - LOG(ERROR) << "compareData failed: result " << *(result + item) - << ", expected " << *(expected + item) << std::endl; - return false; - } - } - return true; -} - -int test_3x3_1x1_one(COMPUTE_TYPE comptype) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 0; - int32_t padding_right = 0; - int32_t padding_top = 0; - int32_t padding_bottom = 0; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - comptype); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int test_3x3_3x3_one(void) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 1; - int32_t padding_right = 1; - int32_t padding_top = 1; - int32_t padding_bottom = 1; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - COMPUTE_ACLGRAPH); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int test_3x3_3x3_seq(void) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 1; - int32_t padding_right = 1; - int32_t padding_top = 1; - int32_t padding_bottom = 1; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initDataSeq(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initDataSeq(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initDataSeq(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - COMPUTE_ACLGRAPH); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int main(int argc, char* argv[]) -{ - int result; - - // input 3x3, output 1x1, all data 1.0 - result = test_3x3_1x1_one(COMPUTE_ACLGRAPH); - if (result) return result; - result = test_3x3_1x1_one(COMPUTE_ACLRT); - if (result) return result; - - // input 3x3, output 3x3, all data 1.0 - result = test_3x3_3x3_one(); - if (result) return result; - - result = test_3x3_3x3_seq(); - if (result) return result; - - return result; -} diff --git a/contrib/labs/CMakeLists.txt b/contrib/labs/CMakeLists.txt new file mode 100644 index 000000000..57e28c11a --- /dev/null +++ b/contrib/labs/CMakeLists.txt @@ -0,0 +1,5 @@ +if(NOT BUILD_LABS) + return() +endif(NOT BUILD_LABS) + +add_subdirectories() diff --git a/contrib/labs/jniacl/CMakeLists.txt b/contrib/labs/jniacl/CMakeLists.txt new file mode 100644 index 000000000..f66127b84 --- /dev/null +++ b/contrib/labs/jniacl/CMakeLists.txt @@ -0,0 +1,18 @@ +# +# Simple Android JNI execution test of ACL +# + +if(NOT "${TARGET_OS}" STREQUAL "android") + return() +endif(NOT "${TARGET_OS}" STREQUAL "android") + +nnfw_find_package(ARMCompute REQUIRED) + +link_directories(${CMAKE_INSTALL_PREFIX}/lib) + +set(JNIACL_SRCS src/jniacl_main.cc + src/io_accessor.cc) + +add_library(jniacl_jni SHARED ${JNIACL_SRCS}) +target_include_directories(jniacl_jni PUBLIC ${TFLITE_JNI_INCLUDES} src) +target_link_libraries(jniacl_jni arm_compute_graph log) diff --git a/contrib/jniacl/src/io_accessor.cc b/contrib/labs/jniacl/src/io_accessor.cc index 103660716..103660716 100644 --- a/contrib/jniacl/src/io_accessor.cc +++ b/contrib/labs/jniacl/src/io_accessor.cc diff --git a/contrib/convacl/src/io_accessor.h b/contrib/labs/jniacl/src/io_accessor.h index 4033020e0..4033020e0 100644 --- a/contrib/convacl/src/io_accessor.h +++ b/contrib/labs/jniacl/src/io_accessor.h diff --git a/contrib/jniacl/src/jniacl_main.cc b/contrib/labs/jniacl/src/jniacl_main.cc index 515f28732..515f28732 100644 --- a/contrib/jniacl/src/jniacl_main.cc +++ b/contrib/labs/jniacl/src/jniacl_main.cc diff --git a/contrib/labs/kerneltesting/CMakeLists.txt b/contrib/labs/kerneltesting/CMakeLists.txt new file mode 100644 index 000000000..5792d0fe8 --- /dev/null +++ b/contrib/labs/kerneltesting/CMakeLists.txt @@ -0,0 +1,19 @@ +if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") + return() +endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") + +nnfw_find_package(ARMCompute REQUIRED) + +function(add_kerneltesting TESTNAME SRC_FILES) + link_directories(${CMAKE_INSTALL_PREFIX}/lib) + add_executable(${TESTNAME} ${SRC_FILES}) + target_include_directories(${TESTNAME} PUBLIC + ${NNFW_INCLUDE_DIR}) + target_link_libraries(${TESTNAME} nnfw_lib_misc arm_compute_graph) + install(TARGETS ${TESTNAME} DESTINATION bin) +endfunction() + +# TODO: Enable conv2d on Tizen +if (NOT ${TARGET_OS} STREQUAL "tizen") + add_subdirectory(conv2d) +endif() diff --git a/contrib/kerneltesting/conv2d/CMakeLists.txt b/contrib/labs/kerneltesting/conv2d/CMakeLists.txt index 25e01f584..25e01f584 100644 --- a/contrib/kerneltesting/conv2d/CMakeLists.txt +++ b/contrib/labs/kerneltesting/conv2d/CMakeLists.txt diff --git a/contrib/kerneltesting/conv2d/OperationUtils.h b/contrib/labs/kerneltesting/conv2d/OperationUtils.h index 0beac80a4..0beac80a4 100644 --- a/contrib/kerneltesting/conv2d/OperationUtils.h +++ b/contrib/labs/kerneltesting/conv2d/OperationUtils.h diff --git a/contrib/kerneltesting/conv2d/common.h b/contrib/labs/kerneltesting/conv2d/common.h index 8e675e664..8e675e664 100644 --- a/contrib/kerneltesting/conv2d/common.h +++ b/contrib/labs/kerneltesting/conv2d/common.h diff --git a/contrib/kerneltesting/conv2d/compatibility.h b/contrib/labs/kerneltesting/conv2d/compatibility.h index db8ba04bc..db8ba04bc 100644 --- a/contrib/kerneltesting/conv2d/compatibility.h +++ b/contrib/labs/kerneltesting/conv2d/compatibility.h diff --git a/contrib/kerneltesting/conv2d/io_accessor.cpp b/contrib/labs/kerneltesting/conv2d/io_accessor.cpp index 6d3cd9d04..6d3cd9d04 100644 --- a/contrib/kerneltesting/conv2d/io_accessor.cpp +++ b/contrib/labs/kerneltesting/conv2d/io_accessor.cpp diff --git a/contrib/kerneltesting/conv2d/io_accessor.h b/contrib/labs/kerneltesting/conv2d/io_accessor.h index 0201f7242..0201f7242 100644 --- a/contrib/kerneltesting/conv2d/io_accessor.h +++ b/contrib/labs/kerneltesting/conv2d/io_accessor.h diff --git a/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp b/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp new file mode 100644 index 000000000..190be016e --- /dev/null +++ b/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp @@ -0,0 +1,607 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +#include <iostream> +#include <vector> +#include <cassert> + +#include <Eigen/Core> +#include <gemmlowp.h> + +#include "types.h" +#include "common.h" +#include "optimized_ops.h" +#include "OperationUtils.h" + +#include <arm_compute/graph.h> + +#include <arm_compute/runtime/CL/CLFunctions.h> +#include <arm_compute/runtime/CL/functions/CLConvolution.h> + +#include "io_accessor.h" +#include "misc/environment.h" + +static constexpr int kStaticBufferSize = 1605632; +static char static_scratch_buffer[kStaticBufferSize]; + +#define ANDROID_NN_CONV_PARAMETERS(Type) \ + uint32_t height = getSizeOfDimension(inputShape, 1); \ + uint32_t width = getSizeOfDimension(inputShape, 2); \ + uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ + uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ + uint32_t outHeight = getSizeOfDimension(outputShape, 1); \ + uint32_t outWidth = getSizeOfDimension(outputShape, 2); \ + uint32_t inDepth = getSizeOfDimension(inputShape, 3); \ + \ + uint32_t paddingHeight = (uint32_t)padding_top; \ + uint32_t paddingWidth = (uint32_t)padding_left; \ + \ + Dims<4> im2colDim; \ + im2colDim.sizes[3] = (int)getSizeOfDimension(outputShape, 0); \ + im2colDim.sizes[2] = (int)getSizeOfDimension(outputShape, 1); \ + im2colDim.sizes[1] = (int)getSizeOfDimension(outputShape, 2); \ + im2colDim.sizes[0] = (int)inDepth * filterHeight * filterWidth; \ + \ + im2colDim.strides[0] = 1; \ + for (int i=1; i<4; i++) { \ + im2colDim.strides[i] = im2colDim.strides[i-1] * im2colDim.sizes[i-1]; \ + } \ + \ + Type* im2colData = nullptr; \ + int im2colByteSize = sizeof(Type); \ + for (int i=0; i<4; i++) { \ + im2colByteSize *= im2colDim.sizes[i]; \ + } \ + if (im2colByteSize <= kStaticBufferSize) { \ + im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ + } else { \ + im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ + } + + +bool convFloat32(const float* inputData, const Shape& inputShape, + const float* filterData, const Shape& filterShape, + const float* biasData, const Shape& biasShape, + int32_t padding_left, int32_t padding_right, + int32_t padding_top, int32_t padding_bottom, + int32_t stride_width, int32_t stride_height, + int32_t activation, + float* outputData, const Shape& outputShape) { + + ANDROID_NN_CONV_PARAMETERS(float) + + #define ANDROID_NN_CONV(activation) \ + Conv<FusedActivationFunctionType::activation>( \ + inputData, convertShapeToDims(inputShape), \ + filterData, convertShapeToDims(filterShape), \ + biasData, convertShapeToDims(biasShape), \ + stride_width, stride_height, paddingWidth, paddingHeight, \ + outputData, convertShapeToDims(outputShape), \ + im2colData, im2colDim) + + ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_CONV) + + #undef ANDROID_NN_CONV + + if (im2colByteSize > kStaticBufferSize) { + delete[] im2colData; + } + return true; +} + +//----------------------------------------------------------------------------- + +using arm_compute::DataType; +using arm_compute::graph::Target; +using arm_compute::graph::TensorDescriptor; +using arm_compute::TensorShape; +using arm_compute::graph::frontend::InputLayer; +using arm_compute::graph::frontend::OutputLayer; + +namespace acl_graph { + +bool convFloat32(const float* inputData, const Shape& inputShape, + const float* filterData, const Shape& filterShape, + const float* biasData, const Shape& biasShape, + int32_t padding_left, int32_t padding_right, + int32_t padding_top, int32_t padding_bottom, + int32_t stride_width, int32_t stride_height, + int32_t activation, + float* outputData, const Shape& outputShape) +{ + // Try with simple build-run with ACL Layer + arm_compute::graph::frontend::Stream graph{0, "ACL_CONV2D_TEST"}; + + Target target_hint = nnfw::misc::get_env_int("NNFW_ACL_USENEON") + ? Target::NEON : Target::CL; + + // Not sure about which index is which value + uint32_t tsi_c = getSizeOfDimension(inputShape, 0); + uint32_t tsi_h = getSizeOfDimension(inputShape, 1); + uint32_t tsi_w = getSizeOfDimension(inputShape, 2); + uint32_t tsi_n = getSizeOfDimension(inputShape, 3); + + uint32_t tsk_h = getSizeOfDimension(filterShape, 1); + uint32_t tsk_w = getSizeOfDimension(filterShape, 2); + uint32_t tsk_n = getSizeOfDimension(filterShape, 3); + + graph << target_hint + << InputLayer(TensorDescriptor(TensorShape(tsi_w, tsi_h, tsi_c, tsi_n), DataType::F32), + std::unique_ptr<InputAccessor>(new InputAccessor(inputData, inputShape))) + << arm_compute::graph::frontend::ConvolutionLayer( + tsk_w, tsk_h, tsk_n, + std::unique_ptr<WeightAccessor>(new WeightAccessor(filterData, filterShape)), + std::unique_ptr<BiasAccessor>(new BiasAccessor(biasData, biasShape)), + arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom)) + ; + if (activation != static_cast<int32_t>(FusedActivationFunc::NONE)) { + arm_compute::ActivationLayerInfo::ActivationFunction actFunc = + arm_compute::ActivationLayerInfo::ActivationFunction::RELU; + + graph << arm_compute::graph::frontend::ActivationLayer(arm_compute::ActivationLayerInfo(actFunc)); + // Activation does not provide output Tensor and makes next layer fail to add to graph + // when it's the last(output) layer. To solve this, need to add a dummy layer. + uint32_t tso_c = getSizeOfDimension(outputShape, 0); + uint32_t tso_h = getSizeOfDimension(outputShape, 1); + uint32_t tso_w = getSizeOfDimension(outputShape, 2); + uint32_t tso_n = getSizeOfDimension(outputShape, 3); + graph << arm_compute::graph::frontend::ReshapeLayer(TensorShape(tso_w, tso_h, tso_c, tso_n)); + } + graph << OutputLayer(std::unique_ptr<OutputAccessor>(new OutputAccessor(outputData, outputShape))) + ; + + graph.run(); + + return true; +} + +} // namespace acl_graph + +//----------------------------------------------------------------------------- + +using arm_compute::TensorInfo; + +namespace acl_runtime { + +TensorShape calculate_convolution_layer_output_shape( + const arm_compute::TensorShape &input_shape, + const arm_compute::TensorShape &weights_shape, + const arm_compute::PadStrideInfo &conv_info) +{ + unsigned int output_width = 0; + unsigned int output_height = 0; + + // Get output width and height + std::tie(output_width, output_height) = + arm_compute::scaled_dimensions( + input_shape.x(), input_shape.y(), + weights_shape.x(), weights_shape.y(), + conv_info); + + // Create output shape + TensorShape output_shape = input_shape; + output_shape.set(0, output_width); + output_shape.set(1, output_height); + output_shape.set(2, weights_shape[3]); + + return output_shape; +} + +bool convFloat32(const float* inputData, const Shape& inputShape, + const float* filterData, const Shape& filterShape, + const float* biasData, const Shape& biasShape, + int32_t padding_left, int32_t padding_right, + int32_t padding_top, int32_t padding_bottom, + int32_t stride_width, int32_t stride_height, + int32_t activation, + float* outputData, const Shape& outputShape) +{ + arm_compute::CLScheduler::get().default_init(); + + uint32_t tsi_c = getSizeOfDimension(inputShape, 0); + uint32_t tsi_h = getSizeOfDimension(inputShape, 1); + uint32_t tsi_w = getSizeOfDimension(inputShape, 2); + uint32_t tsi_n = getSizeOfDimension(inputShape, 3); + + uint32_t tsk_h = getSizeOfDimension(filterShape, 1); + uint32_t tsk_w = getSizeOfDimension(filterShape, 2); + uint32_t tsk_n = getSizeOfDimension(filterShape, 3); + + TensorShape input_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); + TensorShape filter_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); + arm_compute::PadStrideInfo conv_info = + arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom); + + TensorShape output_shape = calculate_convolution_layer_output_shape( + input_shape, filter_shape, conv_info); + + uint32_t tso_c = output_shape[0]; + uint32_t tso_w = output_shape[1]; + uint32_t tso_h = output_shape[2]; + uint32_t tso_n = output_shape[3]; + + arm_compute::CLTensor input, output, bias, filter; + + input.allocator()->init(TensorInfo(tsi_w, tsi_h, arm_compute::Format::F32)); + output.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); + bias.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); + filter.allocator()->init(TensorInfo(tsk_w, tsk_h, arm_compute::Format::F32)); + + input.allocator()->allocate(); + output.allocator()->allocate(); + bias.allocator()->allocate(); + filter.allocator()->allocate(); + + input.map(); + InputAccessor ia(inputData, inputShape); + ia.access_tensor(input); + input.unmap(); + + bias.map(); + BiasAccessor ba(biasData, biasShape); + ba.access_tensor(bias); + bias.unmap(); + + filter.map(); + WeightAccessor fa(filterData, filterShape); + fa.access_tensor(filter); + filter.unmap(); + + arm_compute::CLConvolutionLayer conv_f; + conv_f.configure(&input, &filter, &bias, &output, conv_info); + + arm_compute::CLScheduler::get().sync(); + + conv_f.run(); + + output.map(); + OutputAccessor oa(outputData, outputShape); + oa.access_tensor(output); + output.unmap(); + + return true; +} + +} // namespace acl_runtime + +//----------------------------------------------------------------------------- + +enum COMPUTE_TYPE { + COMPUTE_DEFAULT = 0, + COMPUTE_ACLGRAPH, + COMPUTE_ACLRT +}; + +bool convFloat32(const float* inputData, const Shape& inputShape, + const float* filterData, const Shape& filterShape, + const float* biasData, const Shape& biasShape, + int32_t padding_left, int32_t padding_right, + int32_t padding_top, int32_t padding_bottom, + int32_t stride_width, int32_t stride_height, + int32_t activation, + float* outputData, const Shape& outputShape, + COMPUTE_TYPE compType) { + + switch (compType) + { + case COMPUTE_DEFAULT : + return convFloat32(inputData, inputShape, filterData, filterShape, + biasData, biasShape, padding_left, padding_right, + padding_top, padding_bottom, stride_width, stride_height, + activation, outputData, outputShape); + + case COMPUTE_ACLGRAPH : + return acl_graph::convFloat32(inputData, inputShape, filterData, filterShape, + biasData, biasShape, padding_left, padding_right, + padding_top, padding_bottom, stride_width, stride_height, + activation, outputData, outputShape); + + case COMPUTE_ACLRT : + return acl_runtime::convFloat32(inputData, inputShape, filterData, filterShape, + biasData, biasShape, padding_left, padding_right, + padding_top, padding_bottom, stride_width, stride_height, + activation, outputData, outputShape); + } + return false; +} + +//----------------------------------------------------------------------------- + +void dumpData(const char* name, const float* data, const Shape& shape) +{ + uint32_t height = getSizeOfDimension(shape, 1); + uint32_t width = getSizeOfDimension(shape, 2); + + std::cout << "---" << name << "---" << std::endl; + for (int h = 0; h < height; h++) { + std::cout << "H=" << h << " | "; + for (int w = 0; w < width; w++) { + std::cout << data[h * width + w] << ","; + } + std::cout << std::endl; + } +} + +void initData(float* outputData, int num, float value) +{ + for (int i = 0; i < num; i++) { + *(outputData + i) = value; + } +} + +void initDataSeq(float* outputData, int num, float value) +{ + for (int i = 0; i < num; i++) { + *(outputData + i) = value; + value += 1.0; + } +} + +// compareData +// return true if result == expected with the shape info, +// otherwise false +bool compareData(const float* result, const float* expected, const Shape& shape) +{ + NN_CHECK_EQ(shape.dimensions.size(), 4); + + uint32_t height = getSizeOfDimension(shape, 1); + uint32_t width = getSizeOfDimension(shape, 2); + uint32_t numitems = height * width; + for (int item = 0; item < numitems; item++) { + if (*(result + item) != *(expected + item)) { + LOG(ERROR) << "compareData failed: result " << *(result + item) + << ", expected " << *(expected + item) << std::endl; + return false; + } + } + return true; +} + +int test_3x3_1x1_one(COMPUTE_TYPE comptype) +{ + float inputData[9]; + const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float filterData[9]; + const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float biasData[1] = { 1.0 }; + const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; + int32_t padding_left = 0; + int32_t padding_right = 0; + int32_t padding_top = 0; + int32_t padding_bottom = 0; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); + float* outputData = new float[9]; + const Shape outputShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; + float* expectData = new float[9]; + bool bret; + + initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); + initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); + initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); + initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + expectData, outputShape, + COMPUTE_DEFAULT); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + outputData, outputShape, + comptype); + + dumpData("Input ", inputData, inputShape); + dumpData("Filter ", filterData, filterShape); + dumpData("Bias ", biasData, biasShape); + dumpData("Output ", outputData, outputShape); + std::cout << std::endl; + + bret = compareData(outputData, expectData, outputShape); + + delete outputData; + delete expectData; + + if (!bret) + { + LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; + return -1; + } + return 0; +} + +int test_3x3_3x3_one(void) +{ + float inputData[9]; + const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float filterData[9]; + const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float biasData[1] = { 1.0 }; + const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; + int32_t padding_left = 1; + int32_t padding_right = 1; + int32_t padding_top = 1; + int32_t padding_bottom = 1; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); + float* outputData = new float[9]; + const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float* expectData = new float[9]; + bool bret; + + initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); + initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); + initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); + initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + expectData, outputShape, + COMPUTE_DEFAULT); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + outputData, outputShape, + COMPUTE_ACLGRAPH); + + dumpData("Input ", inputData, inputShape); + dumpData("Filter ", filterData, filterShape); + dumpData("Bias ", biasData, biasShape); + dumpData("Output ", outputData, outputShape); + std::cout << std::endl; + + bret = compareData(outputData, expectData, outputShape); + + delete outputData; + delete expectData; + + if (!bret) + { + LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; + return -1; + } + return 0; +} + +int test_3x3_3x3_seq(void) +{ + float inputData[9]; + const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float filterData[9]; + const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float biasData[1] = { 1.0 }; + const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; + int32_t padding_left = 1; + int32_t padding_right = 1; + int32_t padding_top = 1; + int32_t padding_bottom = 1; + int32_t stride_width = 1; + int32_t stride_height = 1; + int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); + float* outputData = new float[9]; + const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; + float* expectData = new float[9]; + bool bret; + + initDataSeq(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); + initDataSeq(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); + initDataSeq(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); + initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + expectData, outputShape, + COMPUTE_DEFAULT); + + bret = convFloat32(inputData, inputShape, + filterData, filterShape, + biasData, biasShape, + padding_left, padding_right, + padding_top, padding_bottom, + stride_width, stride_height, + activation, + outputData, outputShape, + COMPUTE_ACLGRAPH); + + dumpData("Input ", inputData, inputShape); + dumpData("Filter ", filterData, filterShape); + dumpData("Bias ", biasData, biasShape); + dumpData("Output ", outputData, outputShape); + std::cout << std::endl; + + bret = compareData(outputData, expectData, outputShape); + + delete outputData; + delete expectData; + + if (!bret) + { + LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; + return -1; + } + return 0; +} + +int main(int argc, char* argv[]) +{ + int result; + + // input 3x3, output 1x1, all data 1.0 + result = test_3x3_1x1_one(COMPUTE_ACLGRAPH); + if (result) return result; + result = test_3x3_1x1_one(COMPUTE_ACLRT); + if (result) return result; + + // input 3x3, output 3x3, all data 1.0 + result = test_3x3_3x3_one(); + if (result) return result; + + result = test_3x3_3x3_seq(); + if (result) return result; + + return result; +} diff --git a/contrib/kerneltesting/conv2d/optimized_ops.h b/contrib/labs/kerneltesting/conv2d/optimized_ops.h index 1d8c4ff28..1d8c4ff28 100644 --- a/contrib/kerneltesting/conv2d/optimized_ops.h +++ b/contrib/labs/kerneltesting/conv2d/optimized_ops.h diff --git a/contrib/kerneltesting/conv2d/types.h b/contrib/labs/kerneltesting/conv2d/types.h index 3d09457c7..3d09457c7 100644 --- a/contrib/kerneltesting/conv2d/types.h +++ b/contrib/labs/kerneltesting/conv2d/types.h diff --git a/contrib/labs/opencl_test/CMakeLists.txt b/contrib/labs/opencl_test/CMakeLists.txt new file mode 100644 index 000000000..5e99fa051 --- /dev/null +++ b/contrib/labs/opencl_test/CMakeLists.txt @@ -0,0 +1,12 @@ +if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") + return() +endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") + +list(APPEND OPENCL_INFO_SOURCE "src/opencl_test.cc") + +add_executable(opencl_test ${OPENCL_INFO_SOURCE}) +target_include_directories(opencl_test PUBLIC ${CMAKE_SOURCE_DIR}/externals/acl) +target_include_directories(opencl_test PUBLIC ${CMAKE_SOURCE_DIR}/externals/acl/include) +target_include_directories(opencl_test PUBLIC ${CMAKE_SOURCE_DIR}/libs/ARMComputeEx) +target_link_libraries(opencl_test arm_compute) +target_link_libraries(opencl_test arm_compute_ex) diff --git a/contrib/opencl_test/README.md b/contrib/labs/opencl_test/README.md index 950528f81..950528f81 100644 --- a/contrib/opencl_test/README.md +++ b/contrib/labs/opencl_test/README.md diff --git a/contrib/labs/opencl_test/src/opencl_test.cc b/contrib/labs/opencl_test/src/opencl_test.cc new file mode 100644 index 000000000..93994ae43 --- /dev/null +++ b/contrib/labs/opencl_test/src/opencl_test.cc @@ -0,0 +1,397 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/******************************************************************************* + * Copyright (c) 2008-2015 The Khronos Group Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a + * copy of this software and/or associated documentation files (the + * "Materials"), to deal in the Materials without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, + * distribute, sublicense, and/or sell copies of the Materials, and to + * permit persons to whom the Materials are furnished to do so, subject to + * the following conditions: + * + * The above copyright notice and this permission notice shall be included + * in all copies or substantial portions of the Materials. + * + * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. + * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, + * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE + * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. + ******************************************************************************/ + +#include "arm_compute/core/CL/OpenCL.h" +#include "arm_compute/core/CL/OpenCLEx.h" + +#include <iostream> +#include <vector> + +void printDeviceInfo(int n, cl::Device &device, cl::Device &default_device) +{ + bool is_default = (device() == default_device()); + std::cout << "\t\t\t#" << n << " Device: (id: " << device() << ") " + << (is_default ? " -> default" : "") << "\n"; + + const auto name = device.getInfo<CL_DEVICE_NAME>(); + std::cout << "\t\t\t\tName: " << name << "\n"; + + const auto compute_unit = device.getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>(); + std::cout << "\t\t\t\tMax Compute Unit: " << compute_unit << "\n"; + + const auto max_work_item_size = device.getInfo<CL_DEVICE_MAX_WORK_ITEM_SIZES>(); + std::cout << "\t\t\t\tMax Work Item Size: ["; + for (auto size : max_work_item_size) + std::cout << size << ","; + std::cout << "]\n"; + + const auto max_work_group_size = device.getInfo<CL_DEVICE_MAX_WORK_GROUP_SIZE>(); + std::cout << "\t\t\t\tMax Work Grpup Size: " << max_work_group_size << "\n"; + + const auto max_clock_frequency = device.getInfo<CL_DEVICE_MAX_CLOCK_FREQUENCY>(); + std::cout << "\t\t\t\tMax Clock Frequency: " << max_clock_frequency << "\n"; + + std::cout << "\n"; +} + + +class OpenCLGpu +{ + public: + cl::Platform platform_; + cl::Context context_; + cl::vector<cl::Device> devices_; + std::vector<cl::CommandQueue*> q_; + cl::Program program_; + + OpenCLGpu() + { + cl_int cl_error; + + platform_ = cl::Platform::getDefault(); + + try + { + cl_context_properties properties[3] = { + CL_CONTEXT_PLATFORM, (cl_context_properties)platform_(), 0 + }; + + context_ = cl::Context(CL_DEVICE_TYPE_GPU, properties, NULL, NULL, &cl_error); + } + catch (cl::Error &err) // thrown when there is no Context for this platform + { + std::cout << "\t\t No Context Found\n"; + return; + } + + devices_ = context_.getInfo<CL_CONTEXT_DEVICES>(); + + for (int dev_id = 0; dev_id < devices_.size(); dev_id++) + { + cl::CommandQueue* que = new cl::CommandQueue(context_, devices_[dev_id]); + q_.emplace_back(que); + } + } + + ~OpenCLGpu() + { + for (auto each_q : q_) + delete each_q; + } + + void buildProgram(std::string& kernel_source_code) + { + std::vector<std::string> programStrings {kernel_source_code}; + + program_ = cl::Program(context_, programStrings); + + try + { + program_.build("-cl-std=CL1.2"); + } + catch (cl::Error &err) + { + cl_int buildErr = CL_SUCCESS; + auto buildInfo = program_.getBuildInfo<CL_PROGRAM_BUILD_LOG>(&buildErr); + for (auto &pair : buildInfo) { + std::cerr << pair.second << std::endl << std::endl; + } + } + } +}; + + +void checkContextMem() +{ + cl_int cl_error; + + // get context, devices + // + std::cout << "\nChecking if devices in GPU shares the same memory address:\n\n"; + + OpenCLGpu gpu; + + std::cout << "\nDevices in GPU:\n\n"; + + auto &devices = gpu.devices_; + auto default_device = cl::Device::getDefault(); + + int d = 0; + for (auto device : devices) + printDeviceInfo(++d, device, default_device); + + if (d < 2) + { + std::cout << "\t\t This options works when there are n (>= 2) devices.\n"; + return; + } + + // allocate and map memory + + typedef cl_int T; + const int items_per_device = 128; + const int length = items_per_device * devices.size(); + + std::vector<T> input(length); + std::vector<T> output(length, 0); + + for (int i = 0; i < length; i++) + input[i] = i; + + cl::Buffer input_buf(gpu.context_, (cl_mem_flags)CL_MEM_USE_HOST_PTR, length*sizeof(T), input.data(), &cl_error); + cl::Buffer output_buf(gpu.context_, (cl_mem_flags)CL_MEM_USE_HOST_PTR, length*sizeof(T), output.data(), &cl_error); + + // compile test cl code + + std::string kernel_source { + "typedef int T; \n" \ + "kernel void memory_test( \n" \ + " const int dev_id, \n" \ + " global T* input, \n" \ + " global T* output, \n" \ + " const int start_idx, \n" \ + " const int count) \n" \ + "{ \n" \ + " int input_idx = get_global_id(0); \n" \ + " if(input_idx < count) \n" \ + " { \n" \ + " int output_idx = start_idx + input_idx; \n" \ + " output[output_idx] = input[input_idx] + dev_id; \n" \ + " } \n" \ + "} \n" + }; + + gpu.buildProgram(kernel_source); + + try + { + auto kernel_functor = cl::KernelFunctor<cl_int, cl::Buffer, cl::Buffer, cl_int, cl_int> + (gpu.program_, "memory_test"); // name should be same as cl function name + + // create a queue per device and queue a kernel job + + for (int dev_id = 0; dev_id < devices.size(); dev_id++) + { + kernel_functor( + cl::EnqueueArgs( + *(gpu.q_[dev_id]), + cl::NDRange(items_per_device)), + (cl_int)dev_id, // dev id + input_buf, + output_buf, + (cl_int)(items_per_device * dev_id), // start index + (cl_int)(items_per_device), // count + cl_error + ); + } + + // sync + + for (d = 0; d < devices.size(); d++) + (gpu.q_[d])->finish(); + + // check if memory state changed by all devices + + cl::copy(*(gpu.q_[0]), output_buf, begin(output), end(output)); + + bool use_same_memory = true; + + for (int dev_id = 0; dev_id < devices.size(); dev_id++) + { + for (int i = 0; i < items_per_device; ++i) + { + int output_idx = items_per_device * dev_id + i; + if (output[output_idx] != input[i] + dev_id) + { + std::cout << "Output[" << output_idx << "] : " + << "expected = " << input[i] + dev_id + << "; actual = " << output[output_idx] << "\n"; + use_same_memory = false; + break; + } + } + } + + if (use_same_memory) + std::cout << "\n=> Mapped memory addresses used by devices in GPU are same.\n\n"; + else + std::cout << "\n=> Mapped memory addresses used by devices in GPU are different.\n\n"; + } + catch (cl::Error &err) + { + std::cerr << "error: code: " << err.err() << ", what: " << err.what() << std::endl; + } +} + +void printHelp() +{ + std::cout << "opencl information: \n\n"; + std::cout << "\t -h : help\n"; + std::cout << "\t -g : print if memory map is shared among devices in GPU (in default platform)\n\n"; + std::cout << "\t -s : test for synchronized work by two devices in a GPU\n\n"; +} + +#include <mutex> +#include <chrono> +#include <thread> +#include <condition_variable> + +#define MAX_DEVICE_NUM 8 // just for testing + +int kernel_idx[MAX_DEVICE_NUM]; +unsigned char kernel_completed = 0x00; // bit 0 = 1 means kernel by device[0] was completed. +unsigned char kernel_completed_flag; // if comparing kernel_completed with this var, all kernels are completed +int device_num; +std::mutex kernel_complete_handler_mutex; + +std::condition_variable wakeup_main; +std::mutex wakeup_main_mutex; + +void notifyKernelFinished(cl_event ev, cl_int ev_info, void * device_idx) +{ + std::cout << "callback from device[" << *((int*)device_idx) << "] : ==> completed.\n"; + + std::unique_lock<std::mutex> lock(kernel_complete_handler_mutex); + + kernel_completed |= 0x01 << *((int*)device_idx); + if (kernel_completed == kernel_completed_flag) + wakeup_main.notify_one(); +} + +void testSync() +{ + OpenCLGpu gpu; + + cl_int cl_error; + typedef cl_int T; + const int items_per_device = 1024*768; + const int length = items_per_device * gpu.devices_.size(); + + std::vector<T> output(length, 0); + + cl::Buffer output_buf(gpu.context_, (cl_mem_flags)CL_MEM_USE_HOST_PTR, length*sizeof(T), output.data(), &cl_error); + + std::string kernel_source { + "kernel void test(global float* output, const int count) \n" \ + "{ \n" \ + " int idx = get_global_id(0); \n" \ + " if(idx < count) \n" \ + " { \n" \ + " float x = hypot(idx/1.111, idx*1.111); \n" \ + " for (int y = 0; y < 200; y++) \n" \ + " x = rootn(log(pown(rootn(log(pown(x, 20)), 5), 20)), 5); \n" \ + " output[idx] = x; \n" \ + " } \n" \ + "} \n" + }; + + gpu.buildProgram(kernel_source); + + try + { + auto kernel_functor = cl::KernelFunctor<cl::Buffer, cl_int> + (gpu.program_, "test"); // name should be same as cl function name + + // variable init + cl::Event ev[MAX_DEVICE_NUM]; + + device_num = gpu.devices_.size(); + + kernel_completed = 0; + kernel_completed_flag = 0; + for (int i = 0; i < device_num; i++) + { + kernel_idx[i] = i; + kernel_completed_flag |= 0x01 << i; + } + + // create a queue per device and queue a kernel job + // queueing with callback function + for (int dev_id = 0; dev_id < gpu.devices_.size(); dev_id++) + { + ev[dev_id] = kernel_functor( + cl::EnqueueArgs( + *(gpu.q_[dev_id]), + cl::NDRange(items_per_device)), + output_buf, + (cl_int)(items_per_device), // count + cl_error + ); + ev[dev_id].setCallback(CL_COMPLETE, notifyKernelFinished, (void*)(kernel_idx+dev_id)); + + // how to check kernel execution status + // + // auto status = ev[dev_id].getInfo<CL_EVENT_COMMAND_EXECUTION_STATUS>(); + // std::cout << "Event status = " << (status == CL_QUEUED ? "CL_QUEUED" : status == CL_SUBMITTED ? "CL_SUBMITTED" : status == CL_COMPLETE ? "CL_COMPLETE" : "unknown") + // << std::endl; + // std::cout << "Event status code = " << status << std::endl; + } + + // long wait until kernels are over + { + std::unique_lock<std::mutex> lk(wakeup_main_mutex); + wakeup_main.wait(lk, []{ return (kernel_completed == kernel_completed_flag); }); + + std::cout << "all devices were completed.\n"; + } + } + catch (cl::Error &err) + { + std::cerr << "error: code: " << err.err() << ", what: " << err.what() << std::endl; + } + +} + +int main(const int argc, char **argv) +{ + if (argc < 2) + printHelp(); + else + { + std::string option = argv[1]; + + if (option == "-h") // help + printHelp(); + else if (option == "-g") // check if devices in GPU uses same memory address + checkContextMem(); + else if (option == "-s") // check synchronization between devices in GPU + testSync(); + } + return 0; +} diff --git a/contrib/labs/tflite_examples/CMakeLists.txt b/contrib/labs/tflite_examples/CMakeLists.txt new file mode 100644 index 000000000..463bc5531 --- /dev/null +++ b/contrib/labs/tflite_examples/CMakeLists.txt @@ -0,0 +1,2 @@ +add_executable(tflite_conv_example "src/conv.cpp") +target_link_libraries(tflite_conv_example tensorflow-lite ${LIB_PTHREAD} dl nnfw_lib_tflite) diff --git a/contrib/labs/tflite_examples/src/conv.cpp b/contrib/labs/tflite_examples/src/conv.cpp new file mode 100644 index 000000000..e517da9f3 --- /dev/null +++ b/contrib/labs/tflite_examples/src/conv.cpp @@ -0,0 +1,330 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/contrib/lite/builtin_op_data.h" + +#include <iostream> + +using namespace tflite; +using namespace nnfw::tflite; + +namespace vector +{ + +template <typename T> struct View +{ + virtual ~View() = default; + + virtual int32_t size(void) const = 0; + virtual T at(uint32_t off) const = 0; +}; +} + +namespace feature +{ + +struct Shape +{ + int32_t C; + int32_t H; + int32_t W; +}; + +template <typename T> struct View +{ + virtual ~View() = default; + + virtual const Shape &shape(void) const = 0; + virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; +}; +} + +namespace kernel +{ + +struct Shape +{ + int32_t N; + int32_t C; + int32_t H; + int32_t W; +}; + +template <typename T> struct View +{ + virtual ~View() = default; + + virtual const Shape &shape(void) const = 0; + virtual T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const = 0; +}; +} + +const int32_t N = 1; +const int32_t C = 2; + +class SampleBiasObject final : public vector::View<float> +{ +public: + SampleBiasObject() : _size(N) + { + // DO NOTHING + } + +public: + int32_t size(void) const override { return _size; } + + float at(uint32_t off) const override { return 0.0f; } + +private: + int32_t _size; +}; + +class SampleFeatureObject final : public feature::View<float> +{ +public: + SampleFeatureObject() + { + _shape.C = C; + _shape.H = 3; + _shape.W = 4; + + const uint32_t size = _shape.C * _shape.H * _shape.W; + + for (uint32_t off = 0; off < size; ++off) + { + _value.emplace_back(off); + } + + assert(_value.size() == size); + } + +public: + const feature::Shape &shape(void) const override { return _shape; }; + + float at(uint32_t ch, uint32_t row, uint32_t col) const override + { + return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); + } + +public: + float &at(uint32_t ch, uint32_t row, uint32_t col) + { + return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); + } + +private: + feature::Shape _shape; + std::vector<float> _value; +}; + +class SampleKernelObject final : public kernel::View<float> +{ +public: + SampleKernelObject() + { + _shape.N = N; + _shape.C = C; + _shape.H = 3; + _shape.W = 4; + + const uint32_t size = _shape.N * _shape.C * _shape.H * _shape.W; + + for (uint32_t off = 0; off < size; ++off) + { + _value.emplace_back(off); + } + + assert(_value.size() == size); + } + +public: + const kernel::Shape &shape(void) const override { return _shape; }; + + float at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override + { + return _value.at(nth * _shape.C * _shape.H * _shape.W + ch * _shape.H * _shape.W + + row * _shape.W + col); + } + +private: + kernel::Shape _shape; + std::vector<float> _value; +}; + +int main(int argc, char **argv) +{ + const SampleFeatureObject ifm; + const SampleKernelObject kernel; + const SampleBiasObject bias; + + const int32_t IFM_C = ifm.shape().C; + const int32_t IFM_H = ifm.shape().H; + const int32_t IFM_W = ifm.shape().W; + + const int32_t KER_N = kernel.shape().N; + const int32_t KER_C = kernel.shape().C; + const int32_t KER_H = kernel.shape().H; + const int32_t KER_W = kernel.shape().W; + + const int32_t OFM_C = kernel.shape().N; + const int32_t OFM_H = (IFM_H - KER_H) + 1; + const int32_t OFM_W = (IFM_W - KER_W) + 1; + + // Assumption on this example + assert(IFM_C == KER_C); + assert(KER_N == bias.size()); + + // Comment from 'context.h' + // + // Parameters for asymmetric quantization. Quantized values can be converted + // back to float using: + // real_value = scale * (quantized_value - zero_point); + // + // Q: Is this necessary? + TfLiteQuantizationParams quantization; + + quantization.scale = 1; + quantization.zero_point = 0; + + Interpreter interp; + + // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) + interp.AddTensors(5); + + // Configure OFM + interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */, + {1 /*N*/, OFM_H, OFM_W, OFM_C} /* dims */, quantization); + + // Configure IFM + interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */, + {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization); + + // Configure Filter + const uint32_t kernel_size = KER_N * KER_C * KER_H * KER_W; + float kernel_data[kernel_size] = { + 0.0f, + }; + + // Fill kernel data in NHWC order + { + uint32_t off = 0; + + for (uint32_t nth = 0; nth < KER_N; ++nth) + { + for (uint32_t row = 0; row < KER_H; ++row) + { + for (uint32_t col = 0; col < KER_W; ++col) + { + for (uint32_t ch = 0; ch < KER_C; ++ch) + { + const auto value = kernel.at(nth, ch, row, col); + kernel_data[off++] = value; + } + } + } + } + + assert(kernel_size == off); + } + + interp.SetTensorParametersReadOnly( + 2, kTfLiteFloat32 /* type */, "filter" /* name */, {KER_N, KER_H, KER_W, KER_C} /* dims */, + quantization, reinterpret_cast<const char *>(kernel_data), sizeof(kernel_data)); + + // Configure Bias + const uint32_t bias_size = bias.size(); + float bias_data[bias_size] = { + 0.0f, + }; + + // Fill bias data + for (uint32_t off = 0; off < bias.size(); ++off) + { + bias_data[off] = bias.at(off); + } + + interp.SetTensorParametersReadOnly(3, kTfLiteFloat32 /* type */, "bias" /* name */, + {bias.size()} /* dims */, quantization, + reinterpret_cast<const char *>(bias_data), sizeof(bias_data)); + + // Add Convolution Node + // + // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free + // So, param should be allocated with malloc + TfLiteConvParams *param = reinterpret_cast<TfLiteConvParams *>(malloc(sizeof(TfLiteConvParams))); + + param->padding = kTfLitePaddingValid; + param->stride_width = 1; + param->stride_height = 1; + param->activation = kTfLiteActRelu; + + // Run Convolution and store its result into Tensor #0 + // - Read IFM from Tensor #1 + // - Read Filter from Tensor #2, + // - Read Bias from Tensor #3 + interp.AddNodeWithParameters({1, 2, 3}, {0}, nullptr, 0, reinterpret_cast<void *>(param), + BuiltinOpResolver().FindOp(BuiltinOperator_CONV_2D, 1)); + + // Set Tensor #1 as Input #0, and Tensor #0 as Output #0 + interp.SetInputs({1}); + interp.SetOutputs({0}); + + // Let's use NNAPI (if possible) + interp.UseNNAPI(true); + + // Allocate Tensor + interp.AllocateTensors(); + + // Fill IFM data in HWC order + { + uint32_t off = 0; + + for (uint32_t row = 0; row < ifm.shape().H; ++row) + { + for (uint32_t col = 0; col < ifm.shape().W; ++col) + { + for (uint32_t ch = 0; ch < ifm.shape().C; ++ch) + { + const auto value = ifm.at(ch, row, col); + interp.typed_input_tensor<float>(0)[off++] = value; + } + } + } + } + + // Let's Rock-n-Roll! + interp.Invoke(); + + // Print OFM + { + uint32_t off = 0; + + for (uint32_t row = 0; row < OFM_H; ++row) + { + for (uint32_t col = 0; col < OFM_W; ++col) + { + for (uint32_t ch = 0; ch < kernel.shape().N; ++ch) + { + std::cout << interp.typed_output_tensor<float>(0)[off++] << std::endl; + } + } + } + } + + return 0; +} diff --git a/contrib/opencl_test/CMakeLists.txt b/contrib/opencl_test/CMakeLists.txt deleted file mode 100644 index 45b07e181..000000000 --- a/contrib/opencl_test/CMakeLists.txt +++ /dev/null @@ -1,11 +0,0 @@ -if(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - return() -endif(NOT ${TARGET_ARCH_BASE} STREQUAL "arm") - -list(APPEND OPENCL_INFO_SOURCE "src/opencl_test.cc") - -add_executable(opencl_test ${OPENCL_INFO_SOURCE}) -target_include_directories(opencl_test PUBLIC ${CMAKE_SOURCE_DIR}/externals/acl) -target_include_directories(opencl_test PUBLIC ${CMAKE_SOURCE_DIR}/externals/acl/include) -target_link_libraries(opencl_test arm_compute) - diff --git a/contrib/opencl_test/src/opencl_test.cc b/contrib/opencl_test/src/opencl_test.cc deleted file mode 100644 index 5364e55d8..000000000 --- a/contrib/opencl_test/src/opencl_test.cc +++ /dev/null @@ -1,252 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/******************************************************************************* - * Copyright (c) 2008-2015 The Khronos Group Inc. - * - * Permission is hereby granted, free of charge, to any person obtaining a - * copy of this software and/or associated documentation files (the - * "Materials"), to deal in the Materials without restriction, including - * without limitation the rights to use, copy, modify, merge, publish, - * distribute, sublicense, and/or sell copies of the Materials, and to - * permit persons to whom the Materials are furnished to do so, subject to - * the following conditions: - * - * The above copyright notice and this permission notice shall be included - * in all copies or substantial portions of the Materials. - * - * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, - * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF - * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. - * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY - * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, - * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE - * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. - ******************************************************************************/ - -#include "arm_compute/core/CL/OpenCL.h" - -#include <iostream> -#include <vector> - -void printDeviceInfo(int n, cl::Device &device, cl::Device &default_device) -{ - bool is_default = (device() == default_device()); - std::cout << "\t\t\t#" << n << " Device: (id: " << device() << ") " - << (is_default ? " -> default" : "") << "\n"; - - const auto name = device.getInfo<CL_DEVICE_NAME>(); - std::cout << "\t\t\t\tName: " << name << "\n"; - - const auto compute_unit = device.getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>(); - std::cout << "\t\t\t\tMax Compute Unit: " << compute_unit << "\n"; - - const auto max_work_item_size = device.getInfo<CL_DEVICE_MAX_WORK_ITEM_SIZES>(); - std::cout << "\t\t\t\tMax Work Item Size: ["; - for (auto size : max_work_item_size) - std::cout << size << ","; - std::cout << "]\n"; - - const auto max_work_group_size = device.getInfo<CL_DEVICE_MAX_WORK_GROUP_SIZE>(); - std::cout << "\t\t\t\tMax Work Grpup Size: " << max_work_group_size << "\n"; - - const auto max_clock_frequency = device.getInfo<CL_DEVICE_MAX_CLOCK_FREQUENCY>(); - std::cout << "\t\t\t\tMax Clock Frequency: " << max_clock_frequency << "\n"; - - std::cout << "\n"; -} - -void checkContextMem() -{ - // get context, devices - // - std::cout << "\nChecking if devices in GPU shares the same memory address:\n\n"; - - cl_int cl_error; - - cl::Platform platform = cl::Platform::getDefault(); - cl::Context context; - try - { - cl_context_properties properties[3] = { - CL_CONTEXT_PLATFORM, (cl_context_properties)platform(), 0 - }; - - context = cl::Context(CL_DEVICE_TYPE_GPU, properties, NULL, NULL, &cl_error); - } - catch (cl::Error &err) // thrown when there is no Context for this platform - { - std::cout << "\t\t No Context Found\n"; - return; - } - - std::cout << "\nDevices in GPU:\n\n"; - - auto devices = context.getInfo<CL_CONTEXT_DEVICES>(); - auto default_device = cl::Device::getDefault(); - - int d = 0; - for (auto device : devices) - printDeviceInfo(++d, device, default_device); - - if (d < 2) - { - std::cout << "\t\t This options works when there are n (>= 2) devices.\n"; - return; - } - - // allocate and map memory - - typedef cl_int T; - const int items_per_device = 128; - const int length = items_per_device * devices.size(); - - std::vector<T> input(length); - std::vector<T> output(length, 0); - - for (int i = 0; i < length; i++) - input[i] = i; - - cl::Buffer input_buf(context, (cl_mem_flags)CL_MEM_USE_HOST_PTR, length*sizeof(T), input.data(), &cl_error); - cl::Buffer output_buf(context, (cl_mem_flags)CL_MEM_USE_HOST_PTR, length*sizeof(T), input.data(), &cl_error); - - // compile test cl code - - std::string kernel_source { - "typedef int T; \n" \ - "kernel void memory_test( \n" \ - " const int dev_id, \n" \ - " global T* input, \n" \ - " global T* output, \n" \ - " const int start_idx, \n" \ - " const int count) \n" \ - "{ \n" \ - " int input_idx = get_global_id(0); \n" \ - " if(input_idx < count) \n" \ - " { \n" \ - " int output_idx = start_idx + input_idx; \n" \ - " output[output_idx] = input[input_idx] + dev_id; \n" \ - " } \n" \ - "} \n" - }; - - std::vector<std::string> programStrings {kernel_source}; - - cl::Program program(context, programStrings); - - try - { - program.build("-cl-std=CL1.2"); - } - catch (cl::Error &err) - { - cl_int buildErr = CL_SUCCESS; - auto buildInfo = program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(&buildErr); - for (auto &pair : buildInfo) { - std::cerr << pair.second << std::endl << std::endl; - } - } - - try - { - auto kernel_functor = cl::KernelFunctor<cl_int, cl::Buffer, cl::Buffer, cl_int, cl_int> - (program, "memory_test"); // name should be same as cl function name - - // create a queue per device and queue a kernel job - - std::vector<cl::CommandQueue*> queues; - - for (int dev_id = 0; dev_id < devices.size(); dev_id++) - { - cl::CommandQueue* que = new cl::CommandQueue(context, devices[dev_id]); - queues.emplace_back(que); - - kernel_functor( - cl::EnqueueArgs( - *que, - cl::NDRange(items_per_device)), - (cl_int)dev_id, // dev id - input_buf, - output_buf, - (cl_int)(items_per_device * dev_id), // start index - (cl_int)(items_per_device), // count - cl_error - ); - } - - // sync - - for (d = 0; d < devices.size(); d++) - (queues.at(d))->finish(); - - // check if memory state changed by all devices - - cl::copy(*(queues.at(0)), output_buf, begin(output), end(output)); - - bool use_same_memory = true; - - for (int dev_id = 0; dev_id < devices.size(); dev_id++) - { - for (int i = 0; i < items_per_device; ++i) - { - int output_idx = items_per_device * dev_id + i; - if (output[output_idx] != input[i] + dev_id) - { - std::cout << "Output[" << output_idx << "] : " - << "expected = " << input[i] + dev_id - << "; actual = " << output[output_idx] << "\n"; - use_same_memory = false; - break; - } - } - } - - if (use_same_memory) - std::cout << "\n=> Mapped memory addresses used by devices in GPU are same.\n\n"; - else - std::cout << "\n=> Mapped memory addresses used by devices in GPU are different.\n\n"; - - for (auto q : queues) - delete q; - } - catch (cl::Error &err) - { - std::cerr << "error: code: " << err.err() << ", what: " << err.what() << std::endl; - } -} - -void printHelp() -{ - std::cout << "opencl information: \n\n"; - std::cout << "\t -h : help\n"; - std::cout << "\t -g : print if memory map is shared among devices in GPU (in default platform)\n\n"; -} - -int main(const int argc, char **argv) -{ - if (argc < 2) - printHelp(); - else - { - std::string option = argv[1]; - - if (option == "-h") - printHelp(); - else if (option == "-g") - checkContextMem(); - } - return 0; -} diff --git a/contrib/tf_test/CMakeLists.txt b/contrib/tf_test/CMakeLists.txt deleted file mode 100644 index 91616cc21..000000000 --- a/contrib/tf_test/CMakeLists.txt +++ /dev/null @@ -1,12 +0,0 @@ -nnfw_find_package(Tensorflow QUIET) - -if(NOT Tensorflow_FOUND) - return() -endif(NOT Tensorflow_FOUND) - -list(APPEND SOURCES tf_test.cpp) - -add_executable(tf_test ${SOURCES}) -target_link_libraries(tf_test PRIVATE nnfw_support_tflite) -target_link_libraries(tf_test PRIVATE tensorflow-lite) -target_link_libraries(tf_test PRIVATE tensorflow-core) diff --git a/contrib/tf_test/tf_test.cpp b/contrib/tf_test/tf_test.cpp deleted file mode 100644 index 7597a9aea..000000000 --- a/contrib/tf_test/tf_test.cpp +++ /dev/null @@ -1,236 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <tensorflow/contrib/lite/model.h> -#include <tensorflow/core/public/session.h> - -#include "support/tflite/Assert.h" -#include "support/tflite/Session.h" -#include "support/tflite/InterpreterSession.h" -#include "support/tflite/NNAPISession.h" -#include "support/tflite/kernels/register.h" - -#include "util/fp32.h" - -#include <iostream> - -#include <string> -#include <vector> - -#define TF_ENSURE(e) { \ - if(!(e).ok()) \ - { \ - throw std::runtime_error{"'" #e "' FAILED"}; \ - } \ -} - -using namespace tflite; -using namespace tflite::ops::builtin; - -std::unique_ptr<FlatBufferModel> BuildModelFromFile(const std::string &path) -{ - static StderrReporter reporter; - return FlatBufferModel::BuildFromFile(path.c_str(), &reporter); -} - -std::unique_ptr<Interpreter> BuildInterpFromModel(const std::unique_ptr<FlatBufferModel> &model) -{ - std::unique_ptr<Interpreter> interp; - - BuiltinOpResolver resolver; - InterpreterBuilder builder(*model, resolver); - - TFLITE_ENSURE(builder(&interp)); - - return std::move(interp); -} - -tensorflow::TensorShape asTensorflowShape(const TfLiteTensor *tensor) -{ - tensorflow::TensorShape shape; - - const int rank = tensor->dims->size; - - for (int axis = 0; axis < rank; ++axis) - { - shape.AddDim(tensor->dims->data[axis]); - } - - return shape; -} - -uint32_t count_elements(const TfLiteTensor *tensor) -{ - const int rank = tensor->dims->size; - - if (rank == 0) - { - return 0; - } - - uint32_t res = 1; - - for (int axis = 0; axis < rank; ++axis) - { - res *= tensor->dims->data[axis]; - } - - return res; -} - -int main(int argc, char **argv) -{ - bool use_nnapi = false; - - if (std::getenv("USE_NNAPI") != nullptr) - { - use_nnapi = true; - } - - if (argc < 3) - { - std::cerr << "USAGE: " << argv[0] << " [T/F lite model] [T/F model]" << std::endl; - return 255; - } - - // - // Prepare Tensorflow Lite session - // - const std::string lite_model_path{argv[1]}; - - auto lite_model = BuildModelFromFile(lite_model_path); - auto lite_interp = BuildInterpFromModel(lite_model); - - std::shared_ptr<nnfw::support::tflite::Session> lite_sess; - - if (use_nnapi) - { - lite_sess = std::make_shared<nnfw::support::tflite::NNAPISession>(lite_interp.get()); - } - else - { - lite_sess = std::make_shared<nnfw::support::tflite::InterpreterSession>(lite_interp.get()); - } - - // - // Prepare Tensorflow session - // - const std::string full_model_path{argv[2]}; - - tensorflow::Session* full_sess; - tensorflow::GraphDef full_model; - - TF_ENSURE(tensorflow::NewSession(tensorflow::SessionOptions(), &full_sess)); - TF_ENSURE(ReadBinaryProto(tensorflow::Env::Default(), full_model_path, &full_model)); - TF_ENSURE(full_sess->Create(full_model)); - - // - // - // - std::vector<tensorflow::Tensor> input_nodes; - std::vector<std::string> input_names; - - for (uint32_t n = 0; n < lite_interp->inputs().size(); ++n) - { - const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->inputs().at(n)); - - input_nodes.emplace_back(tensorflow::DT_FLOAT, asTensorflowShape(tensor)); - input_names.emplace_back(tensor->name); - } - - assert(input_nodes.size() == input_names.size()); - assert(input_nodes.size() == lite_interp->inputs().size()); - - std::vector<std::string> output_names; - std::vector<tensorflow::Tensor> output_nodes; - - for (uint32_t n = 0; n < lite_interp->outputs().size(); ++n) - { - const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->outputs().at(n)); - - output_names.emplace_back(tensor->name); - } - - assert(output_names.size() == lite_interp->outputs().size()); - // output_nodes will be initialized after Tensorflow Session run - assert(output_nodes.size() == 0); - - // - // Prepare inference - // - lite_sess->prepare(); - - // TODO Feed Inputs (for both Tensorflow and Tensorflow Lite) - std::vector<std::pair<std::string, tensorflow::Tensor>> inputs; - - for (uint32_t n = 0; n < input_nodes.size(); ++n) - { - inputs.emplace_back(input_names.at(0), input_nodes.at(0)); - } - - // - // Run inference - // - TF_ENSURE(full_sess->Run(inputs, output_names, {}, &output_nodes)); - - lite_sess->run(); - - // - // Compare Output - // - auto equals = [] (float lhs, float rhs) { - // TODO Allow users to set tolerance - if (nnfw::util::fp32::absolute_epsilon_equal(lhs, rhs)) - { - return true; - } - - return nnfw::util::fp32::epsilon_equal(lhs, rhs); - }; - - const uint32_t output_count = output_names.size(); - - bool matched = true; - - for (uint32_t n = 0; n < output_count; ++n) - { - const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->outputs().at(n)); - - // TODO Compare shape - - const auto element_count = count_elements(tensor); - - std::cout << "Compare output #" << n << "(" << tensor->name << ", " << element_count << " elements)" << std::endl; - for (uint32_t index = 0; index < element_count; ++index) - { - const auto full_value = output_nodes.at(n).flat<float>().data()[index]; - const auto lite_value = lite_sess->interp()->typed_output_tensor<float>(n)[index]; - - if (!equals(full_value, lite_value)) - { - std::cerr << full_value << " is expected, but " << lite_value << " is obtaeind (at " << n << ":" << index << ")" << std::endl; - matched = false; - } - } - } - - // - // Cleanup - // - lite_sess->teardown(); - - return matched ? 0 : 255; -} diff --git a/tools/tflite_benchmark_model/.FORMATDENY b/contrib/tflite_classify/.FORMATCHECKED index e69de29bb..e69de29bb 100644 --- a/tools/tflite_benchmark_model/.FORMATDENY +++ b/contrib/tflite_classify/.FORMATCHECKED diff --git a/contrib/tflite_classify/CMakeLists.txt b/contrib/tflite_classify/CMakeLists.txt new file mode 100644 index 000000000..3ae4adcc7 --- /dev/null +++ b/contrib/tflite_classify/CMakeLists.txt @@ -0,0 +1,22 @@ +if(NOT BUILD_TFLITE_CLASSIFY_APP) + return() +endif(NOT BUILD_TFLITE_CLASSIFY_APP) + +list(APPEND SOURCES "src/tflite_classify.cc") +list(APPEND SOURCES "src/ImageClassifier.cc") +list(APPEND SOURCES "src/InferenceInterface.cc") + +## Required package +find_package(OpenCV REQUIRED) +find_package(Boost REQUIRED COMPONENTS system filesystem) + +# Without this line, this appliation couldn't search the opencv library that were already installed in ${ROOTFS_ARM}/usr/lib/arm-linux-gnueabihf directory +set(CMAKE_EXE_LINKER_FLAGS "-Wl,--as-needed -Wl,--rpath=${ROOTFS_ARM}/usr/lib/arm-linux-gnueabihf -Wl,--rpath=${ROOTFS_ARM}/lib/arm-linux-gnueabihf") + +add_executable(tflite_classify ${SOURCES}) +target_include_directories(tflite_classify PRIVATE src) +target_link_libraries(tflite_classify tensorflow-lite ${LIB_PTHREAD} dl nnfw_lib_tflite) +target_link_libraries(tflite_classify ${Boost_LIBRARIES}) +target_link_libraries(tflite_classify ${OpenCV_LIBRARIES}) + +install(TARGETS tflite_classify DESTINATION bin) diff --git a/contrib/tflite_classify/src/ImageClassifier.cc b/contrib/tflite_classify/src/ImageClassifier.cc new file mode 100644 index 000000000..fae4f066c --- /dev/null +++ b/contrib/tflite_classify/src/ImageClassifier.cc @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ImageClassifier.h" + +#include <fstream> +#include <queue> +#include <algorithm> + +ImageClassifier::ImageClassifier(const std::string &model_file, const std::string &label_file, + const int input_size, const int image_mean, const int image_std, + const std::string &input_name, const std::string &output_name, + const bool use_nnapi) + : _inference(new InferenceInterface(model_file, use_nnapi)), _input_size(input_size), + _image_mean(image_mean), _image_std(image_std), _input_name(input_name), + _output_name(output_name) +{ + // Load label + std::ifstream label_stream(label_file.c_str()); + assert(label_stream); + + std::string line; + while (std::getline(label_stream, line)) + { + _labels.push_back(line); + } + _num_classes = _inference->getTensorSize(_output_name); + std::cout << "Output tensor size is " << _num_classes << ", label size is " << _labels.size() + << std::endl; + + // Pre-allocate buffers + _fdata.reserve(_input_size * _input_size * 3); + _outputs.reserve(_num_classes); +} + +std::vector<Recognition> ImageClassifier::recognizeImage(const cv::Mat &image) +{ + // Resize image + cv::Mat cropped; + cv::resize(image, cropped, cv::Size(_input_size, _input_size), 0, 0, cv::INTER_AREA); + + // Preprocess the image data from 0~255 int to normalized float based + // on the provided parameters + _fdata.clear(); + for (int y = 0; y < cropped.rows; ++y) + { + for (int x = 0; x < cropped.cols; ++x) + { + cv::Vec3b color = cropped.at<cv::Vec3b>(y, x); + color[0] = color[0] - (float)_image_mean / _image_std; + color[1] = color[1] - (float)_image_mean / _image_std; + color[2] = color[2] - (float)_image_mean / _image_std; + + _fdata.push_back(color[0]); + _fdata.push_back(color[1]); + _fdata.push_back(color[2]); + + cropped.at<cv::Vec3b>(y, x) = color; + } + } + + // Copy the input data into model + _inference->feed(_input_name, _fdata, 1, _input_size, _input_size, 3); + + // Run the inference call + _inference->run(_output_name); + + // Copy the output tensor back into the output array + _inference->fetch(_output_name, _outputs); + + // Find the best classifications + auto compare = [](const Recognition &lhs, const Recognition &rhs) { + return lhs.confidence < rhs.confidence; + }; + + std::priority_queue<Recognition, std::vector<Recognition>, decltype(compare)> pq(compare); + for (int i = 0; i < _num_classes; ++i) + { + if (_outputs[i] > _threshold) + { + pq.push(Recognition(_outputs[i], _labels[i])); + } + } + + std::vector<Recognition> results; + int min = std::min(pq.size(), _max_results); + for (int i = 0; i < min; ++i) + { + results.push_back(pq.top()); + pq.pop(); + } + + return results; +} diff --git a/contrib/tflite_classify/src/ImageClassifier.h b/contrib/tflite_classify/src/ImageClassifier.h new file mode 100644 index 000000000..1ba19afb0 --- /dev/null +++ b/contrib/tflite_classify/src/ImageClassifier.h @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file ImageClassifier.h + * @brief This file contains ImageClassifier class and Recognition structure + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __TFLITE_CLASSIFY_IMAGE_CLASSIFIER_H__ +#define __TFLITE_CLASSIFY_IMAGE_CLASSIFIER_H__ + +#include "InferenceInterface.h" + +#include <iostream> +#include <string> +#include <vector> + +#include <opencv2/opencv.hpp> + +/** + * @brief struct to define an immutable result returned by a Classifier + */ +struct Recognition +{ +public: + /** + * @brief Construct a new Recognition object with confidence and title + * @param[in] _confidence A sortable score for how good the recognition is relative to others. + * Higher should be better. + * @param[in] _title Display name for the recognition + */ + Recognition(float _confidence, std::string _title) : confidence(_confidence), title(_title) {} + + float confidence; /** A sortable score for how good the recognition is relative to others. Higher + should be better. */ + std::string title; /** Display name for the recognition */ +}; + +/** + * @brief Class to define a classifier specialized to label images + */ +class ImageClassifier +{ +public: + /** + * @brief Construct a new ImageClassifier object with parameters + * @param[in] model_file The filepath of the model FlatBuffer protocol buffer + * @param[in] label_file The filepath of label file for classes + * @param[in] input_size The input size. A square image of input_size x input_size is assumed + * @param[in] image_mean The assumed mean of the image values + * @param[in] image_std The assumed std of the image values + * @param[in] input_name The label of the image input node + * @param[in] output_name The label of the output node + * @param[in] use_nnapi The flag to distinguish between TfLite interpreter and NNFW runtime + */ + ImageClassifier(const std::string &model_file, const std::string &label_file, + const int input_size, const int image_mean, const int image_std, + const std::string &input_name, const std::string &output_name, + const bool use_nnapi); + + /** + * @brief Recognize the given image data + * @param[in] image The image data to recognize + * @return An immutable result vector array + */ + std::vector<Recognition> recognizeImage(const cv::Mat &image); + +private: + const float _threshold = 0.1f; + const unsigned int _max_results = 3; + + std::unique_ptr<InferenceInterface> _inference; + int _input_size; + int _image_mean; + int _image_std; + std::string _input_name; + std::string _output_name; + + std::vector<std::string> _labels; + std::vector<float> _fdata; + std::vector<float> _outputs; + int _num_classes; +}; + +#endif // __TFLITE_CLASSIFY_IMAGE_CLASSIFIER_H__ diff --git a/contrib/tflite_classify/src/InferenceInterface.cc b/contrib/tflite_classify/src/InferenceInterface.cc new file mode 100644 index 000000000..160943477 --- /dev/null +++ b/contrib/tflite_classify/src/InferenceInterface.cc @@ -0,0 +1,114 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "InferenceInterface.h" + +using namespace tflite; +using namespace tflite::ops::builtin; + +InferenceInterface::InferenceInterface(const std::string &model_file, const bool use_nnapi) + : _interpreter(nullptr), _model(nullptr), _sess(nullptr) +{ + // Load model + StderrReporter error_reporter; + _model = FlatBufferModel::BuildFromFile(model_file.c_str(), &error_reporter); + BuiltinOpResolver resolver; + InterpreterBuilder builder(*_model, resolver); + builder(&_interpreter); + + if (use_nnapi) + { + _sess = std::make_shared<nnfw::tflite::NNAPISession>(_interpreter.get()); + } + else + { + _sess = std::make_shared<nnfw::tflite::InterpreterSession>(_interpreter.get()); + } + + _sess->prepare(); +} + +InferenceInterface::~InferenceInterface() { _sess->teardown(); } + +void InferenceInterface::feed(const std::string &input_name, const std::vector<float> &data, + const int batch, const int height, const int width, const int channel) +{ + // Set input tensor + for (const auto &id : _interpreter->inputs()) + { + if (_interpreter->tensor(id)->name == input_name) + { + assert(_interpreter->tensor(id)->type == kTfLiteFloat32); + float *p = _interpreter->tensor(id)->data.f; + + // TODO consider batch + for (int y = 0; y < height; ++y) + { + for (int x = 0; x < width; ++x) + { + for (int c = 0; c < channel; ++c) + { + *p++ = data[y * width * channel + x * channel + c]; + } + } + } + } + } +} + +void InferenceInterface::run(const std::string &output_name) +{ + // Run model + _sess->run(); +} + +void InferenceInterface::fetch(const std::string &output_name, std::vector<float> &outputs) +{ + // Get output tensor + for (const auto &id : _interpreter->outputs()) + { + if (_interpreter->tensor(id)->name == output_name) + { + assert(_interpreter->tensor(id)->type == kTfLiteFloat32); + assert(getTensorSize(output_name) == outputs.capacity()); + float *p = _interpreter->tensor(id)->data.f; + + outputs.clear(); + for (int i = 0; i < outputs.capacity(); ++i) + { + outputs.push_back(p[i]); + } + } + } +} + +int InferenceInterface::getTensorSize(const std::string &name) +{ + for (const auto &id : _interpreter->outputs()) + { + if (_interpreter->tensor(id)->name == name) + { + TfLiteTensor *t = _interpreter->tensor(id); + int v = 1; + for (int i = 0; i < t->dims->size; ++i) + { + v *= t->dims->data[i]; + } + return v; + } + } + return -1; +} diff --git a/contrib/tflite_classify/src/InferenceInterface.h b/contrib/tflite_classify/src/InferenceInterface.h new file mode 100644 index 000000000..06ebb3dad --- /dev/null +++ b/contrib/tflite_classify/src/InferenceInterface.h @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file InferenceInterface.h + * @brief This file contains class for running the actual inference model + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __TFLITE_CLASSIFY_INFERENCE_INTERFACE_H__ +#define __TFLITE_CLASSIFY_INFERENCE_INTERFACE_H__ + +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#include "tflite/InterpreterSession.h" +#include "tflite/NNAPISession.h" + +#include <iostream> +#include <string> + +/** + * @brief Class to define a inference interface for recognizing data + */ +class InferenceInterface +{ +public: + /** + * @brief Construct a new InferenceInterface object with parameters + * @param[in] model_file The filepath of the model FlatBuffer protocol buffer + * @param[in] use_nnapi The flag to distinguish between TfLite interpreter and NNFW runtime + */ + InferenceInterface(const std::string &model_file, const bool use_nnapi); + + /** + * @brief Destructor an InferenceInterface object + */ + ~InferenceInterface(); + + /** + * @brief Copy the input data into model + * @param[in] input_name The label of the image input node + * @param[in] data The actual data to be copied into input tensor + * @param[in] batch The number of batch size + * @param[in] height The number of height size + * @param[in] width The number of width size + * @param[in] channel The number of channel size + * @return N/A + */ + void feed(const std::string &input_name, const std::vector<float> &data, const int batch, + const int height, const int width, const int channel); + /** + * @brief Run the inference call + * @param[in] output_name The label of the output node + * @return N/A + */ + void run(const std::string &output_name); + + /** + * @brief Copy the output tensor back into the output array + * @param[in] output_node The label of the output node + * @param[in] outputs The output data array + * @return N/A + */ + void fetch(const std::string &output_name, std::vector<float> &outputs); + + /** + * @brief Get tensor size + * @param[in] name The label of the node + * @result The size of tensor + */ + int getTensorSize(const std::string &name); + +private: + std::unique_ptr<tflite::Interpreter> _interpreter; + std::unique_ptr<tflite::FlatBufferModel> _model; + std::shared_ptr<nnfw::tflite::Session> _sess; +}; + +#endif // __TFLITE_CLASSIFY_INFERENCE_INTERFACE_H__ diff --git a/contrib/tflite_classify/src/tflite_classify.cc b/contrib/tflite_classify/src/tflite_classify.cc new file mode 100644 index 000000000..40c15f331 --- /dev/null +++ b/contrib/tflite_classify/src/tflite_classify.cc @@ -0,0 +1,132 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ImageClassifier.h" + +#include <iostream> + +#include <boost/filesystem.hpp> +#include <opencv2/opencv.hpp> + +namespace fs = boost::filesystem; + +int main(const int argc, char **argv) +{ + const std::string MODEL_FILE = "tensorflow_inception_graph.tflite"; + const std::string LABEL_FILE = "imagenet_comp_graph_label_strings.txt"; + + const std::string INPUT_NAME = "input"; + const std::string OUTPUT_NAME = "output"; + const int INPUT_SIZE = 224; + const int IMAGE_MEAN = 117; + const int IMAGE_STD = 1; + const int OUTPUT_SIZE = 1008; + + const int FRAME_WIDTH = 640; + const int FRAME_HEIGHT = 480; + + bool use_nnapi = false; + bool debug_mode = false; + + if (std::getenv("USE_NNAPI") != nullptr) + { + use_nnapi = true; + } + + if (std::getenv("DEBUG_MODE") != nullptr) + { + debug_mode = true; + } + + std::cout << "USE_NNAPI : " << use_nnapi << std::endl; + std::cout << "DEBUG_MODE : " << debug_mode << std::endl; + + std::cout << "Model : " << MODEL_FILE << std::endl; + std::cout << "Label : " << LABEL_FILE << std::endl; + + if (!fs::exists(MODEL_FILE)) + { + std::cerr << "model file not found: " << MODEL_FILE << std::endl; + exit(1); + } + + if (!fs::exists(LABEL_FILE)) + { + std::cerr << "label file not found: " << LABEL_FILE << std::endl; + exit(1); + } + + // Create ImageClassifier + std::unique_ptr<ImageClassifier> classifier( + new ImageClassifier(MODEL_FILE, LABEL_FILE, INPUT_SIZE, IMAGE_MEAN, IMAGE_STD, INPUT_NAME, + OUTPUT_NAME, use_nnapi)); + + // Cam setting + cv::VideoCapture cap(0); + cv::Mat frame; + + // Initialize camera + cap.set(CV_CAP_PROP_FRAME_WIDTH, FRAME_WIDTH); + cap.set(CV_CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT); + cap.set(CV_CAP_PROP_FPS, 5); + + std::vector<Recognition> results; + clock_t begin, end; + while (cap.isOpened()) + { + // Get image data + if (!cap.read(frame)) + { + std::cout << "Frame is null..." << std::endl; + break; + } + + if (debug_mode) + { + begin = clock(); + } + // Recognize image + results = classifier->recognizeImage(frame); + if (debug_mode) + { + end = clock(); + } + + // Show result data + std::cout << std::endl; + if (results.size() > 0) + { + for (int i = 0; i < results.size(); ++i) + { + std::cout << results[i].title << ": " << results[i].confidence << std::endl; + } + } + else + { + std::cout << "." << std::endl; + } + if (debug_mode) + { + std::cout << "Frame: " << FRAME_WIDTH << "x" << FRAME_HEIGHT << std::endl; + std::cout << "Crop: " << INPUT_SIZE << "x" << INPUT_SIZE << std::endl; + std::cout << "Inference time(ms): " << ((end - begin) / (CLOCKS_PER_SEC / 1000)) << std::endl; + } + } + + cap.release(); + + return 0; +} diff --git a/contrib/tflite_test/CMakeLists.txt b/contrib/tflite_test/CMakeLists.txt new file mode 100644 index 000000000..760952a84 --- /dev/null +++ b/contrib/tflite_test/CMakeLists.txt @@ -0,0 +1,16 @@ +if(NOT BUILD_TFLITE_TEST) + return() +endif(NOT BUILD_TFLITE_TEST) + +nnfw_find_package(Tensorflow QUIET) + +if(NOT Tensorflow_FOUND) + return() +endif(NOT Tensorflow_FOUND) + +list(APPEND SOURCES tflite_test.cpp) + +add_executable(tflite_test ${SOURCES}) +target_link_libraries(tflite_test PRIVATE nnfw_lib_tflite) +target_link_libraries(tflite_test PRIVATE tensorflow-lite) +target_link_libraries(tflite_test PRIVATE tensorflow-core) diff --git a/contrib/tflite_test/tflite_test.cpp b/contrib/tflite_test/tflite_test.cpp new file mode 100644 index 000000000..4ee9e5a8d --- /dev/null +++ b/contrib/tflite_test/tflite_test.cpp @@ -0,0 +1,236 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <tensorflow/contrib/lite/model.h> +#include <tensorflow/core/public/session.h> + +#include "tflite/Assert.h" +#include "tflite/Session.h" +#include "tflite/InterpreterSession.h" +#include "tflite/NNAPISession.h" +#include "tflite/ext/kernels/register.h" + +#include "misc/fp32.h" + +#include <iostream> + +#include <string> +#include <vector> + +#define TF_ENSURE(e) { \ + if(!(e).ok()) \ + { \ + throw std::runtime_error{"'" #e "' FAILED"}; \ + } \ +} + +using namespace tflite; +using namespace tflite::ops::builtin; + +std::unique_ptr<FlatBufferModel> BuildModelFromFile(const std::string &path) +{ + static StderrReporter reporter; + return FlatBufferModel::BuildFromFile(path.c_str(), &reporter); +} + +std::unique_ptr<Interpreter> BuildInterpFromModel(const std::unique_ptr<FlatBufferModel> &model) +{ + std::unique_ptr<Interpreter> interp; + + BuiltinOpResolver resolver; + InterpreterBuilder builder(*model, resolver); + + TFLITE_ENSURE(builder(&interp)); + + return std::move(interp); +} + +tensorflow::TensorShape asTensorflowShape(const TfLiteTensor *tensor) +{ + tensorflow::TensorShape shape; + + const int rank = tensor->dims->size; + + for (int axis = 0; axis < rank; ++axis) + { + shape.AddDim(tensor->dims->data[axis]); + } + + return shape; +} + +uint32_t count_elements(const TfLiteTensor *tensor) +{ + const int rank = tensor->dims->size; + + if (rank == 0) + { + return 0; + } + + uint32_t res = 1; + + for (int axis = 0; axis < rank; ++axis) + { + res *= tensor->dims->data[axis]; + } + + return res; +} + +int main(int argc, char **argv) +{ + bool use_nnapi = false; + + if (std::getenv("USE_NNAPI") != nullptr) + { + use_nnapi = true; + } + + if (argc < 3) + { + std::cerr << "USAGE: " << argv[0] << " [T/F lite model] [T/F model]" << std::endl; + return 255; + } + + // + // Prepare Tensorflow Lite session + // + const std::string lite_model_path{argv[1]}; + + auto lite_model = BuildModelFromFile(lite_model_path); + auto lite_interp = BuildInterpFromModel(lite_model); + + std::shared_ptr<nnfw::tflite::Session> lite_sess; + + if (use_nnapi) + { + lite_sess = std::make_shared<nnfw::tflite::NNAPISession>(lite_interp.get()); + } + else + { + lite_sess = std::make_shared<nnfw::tflite::InterpreterSession>(lite_interp.get()); + } + + // + // Prepare Tensorflow session + // + const std::string full_model_path{argv[2]}; + + tensorflow::Session* full_sess; + tensorflow::GraphDef full_model; + + TF_ENSURE(tensorflow::NewSession(tensorflow::SessionOptions(), &full_sess)); + TF_ENSURE(ReadBinaryProto(tensorflow::Env::Default(), full_model_path, &full_model)); + TF_ENSURE(full_sess->Create(full_model)); + + // + // + // + std::vector<tensorflow::Tensor> input_nodes; + std::vector<std::string> input_names; + + for (uint32_t n = 0; n < lite_interp->inputs().size(); ++n) + { + const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->inputs().at(n)); + + input_nodes.emplace_back(tensorflow::DT_FLOAT, asTensorflowShape(tensor)); + input_names.emplace_back(tensor->name); + } + + assert(input_nodes.size() == input_names.size()); + assert(input_nodes.size() == lite_interp->inputs().size()); + + std::vector<std::string> output_names; + std::vector<tensorflow::Tensor> output_nodes; + + for (uint32_t n = 0; n < lite_interp->outputs().size(); ++n) + { + const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->outputs().at(n)); + + output_names.emplace_back(tensor->name); + } + + assert(output_names.size() == lite_interp->outputs().size()); + // output_nodes will be initialized after Tensorflow Session run + assert(output_nodes.size() == 0); + + // + // Prepare inference + // + lite_sess->prepare(); + + // TODO Feed Inputs (for both Tensorflow and Tensorflow Lite) + std::vector<std::pair<std::string, tensorflow::Tensor>> inputs; + + for (uint32_t n = 0; n < input_nodes.size(); ++n) + { + inputs.emplace_back(input_names.at(0), input_nodes.at(0)); + } + + // + // Run inference + // + TF_ENSURE(full_sess->Run(inputs, output_names, {}, &output_nodes)); + + lite_sess->run(); + + // + // Compare Output + // + auto equals = [] (float lhs, float rhs) { + // TODO Allow users to set tolerance + if (nnfw::misc::fp32::absolute_epsilon_equal(lhs, rhs)) + { + return true; + } + + return nnfw::misc::fp32::epsilon_equal(lhs, rhs); + }; + + const uint32_t output_count = output_names.size(); + + bool matched = true; + + for (uint32_t n = 0; n < output_count; ++n) + { + const TfLiteTensor *tensor = lite_interp->tensor(lite_interp->outputs().at(n)); + + // TODO Compare shape + + const auto element_count = count_elements(tensor); + + std::cout << "Compare output #" << n << "(" << tensor->name << ", " << element_count << " elements)" << std::endl; + for (uint32_t index = 0; index < element_count; ++index) + { + const auto full_value = output_nodes.at(n).flat<float>().data()[index]; + const auto lite_value = lite_sess->interp()->typed_output_tensor<float>(n)[index]; + + if (!equals(full_value, lite_value)) + { + std::cerr << full_value << " is expected, but " << lite_value << " is obtaeind (at " << n << ":" << index << ")" << std::endl; + matched = false; + } + } + } + + // + // Cleanup + // + lite_sess->teardown(); + + return matched ? 0 : 255; +} diff --git a/docs/howto/CrossBuildForAarch64.md b/docs/howto/CrossBuildForAarch64.md index e400a9597..f3dc55236 100644 --- a/docs/howto/CrossBuildForAarch64.md +++ b/docs/howto/CrossBuildForAarch64.md @@ -60,7 +60,7 @@ sudo apt-get install gcc-aarch64-linux-gnu g++-aarch64-linux-gnu Build and install ARM Compute Library ``` -TARGET_ARCH=aarch64 make acl +CROSS_BUILD=1 TARGET_ARCH=aarch64 make acl ``` Mostly you only need once of ACL build. This will build and install to `Product/(target_arch-os)/out/bin` folder. diff --git a/docs/howto/CrossBuildForArm.md b/docs/howto/CrossBuildForArm.md index 38e544bb9..e307596d0 100644 --- a/docs/howto/CrossBuildForArm.md +++ b/docs/howto/CrossBuildForArm.md @@ -79,7 +79,7 @@ Then, copy `libstdc++.so.6.0.24` into `/usr/lib/arm-linux-gnueabihf`, and update ## Build and install ARM Compute Library ``` -TARGET_ARCH=armv7l make acl +CROSS_BUILD=1 TARGET_ARCH=armv7l make acl ``` Mostly you only need once of ACL build. This will build and install to `Product/(target_arch-os)/out/bin` folder. - this is required for ARM on Ubuntu @@ -103,5 +103,5 @@ CROSS_BUILD=1 TARGET_ARCH=armv7l make all install ## Run test ``` - ./tools/test_driver/test_driver.sh --artifactpath=. + ./tests/scripts/test_driver.sh --artifactpath=. ``` diff --git a/docs/howto/HowToUseDockerImage.md b/docs/howto/HowToUseDockerImage.md index f9a59451f..a28502cf0 100644 --- a/docs/howto/HowToUseDockerImage.md +++ b/docs/howto/HowToUseDockerImage.md @@ -102,6 +102,39 @@ $ docker pull docker.sec.samsung.net:5000/star/nnfw/nnfw_docker_tizen:1.2 $ docker tag docker.sec.samsung.net:5000/star/nnfw/nnfw_docker_tizen:1.2 nnfw_docker_tizen:latest ``` +## Build docker image instead of pull + +You can build docker image in your environment instead of pull docker image from server. + +``` +$ cd nnfw +$ ./run build-docker +``` + +Default docker image name is `nnfw_docker`. If you want to change image name and/or tag, use `-t` or `--tag` option + +``` +$ cd nnfw +$ ./run build-docker -t nnfw_docker_test +``` + +You can use options supported by `docker build` command (ex. `--network` option) + +``` +$ cd nnfw +$ ./run build-docker --network=host --no-cache +``` + +If you want to build docker image for tizen build, use `--tizen` option + +``` +``` +$ cd nnfw +$ ./run build-docker --tizen +``` +``` + + ## Use docker image to build `nnfw` Three different targets for `nnfw` can be built using docker image. diff --git a/externals/CMakeLists.txt b/externals/CMakeLists.txt index 5bfc4ad4e..1c8a14a7d 100644 --- a/externals/CMakeLists.txt +++ b/externals/CMakeLists.txt @@ -2,6 +2,7 @@ set(TENSORFLOW_BASE ${CMAKE_CURRENT_SOURCE_DIR}/tensorflow) set(TENSORFLOW_LITE_BASE ${TENSORFLOW_BASE}/tensorflow/contrib/lite) # Required source & package +nnfw_find_package(AbslSource REQUIRED) nnfw_find_package(Eigen REQUIRED) nnfw_find_package(FarmhashSource REQUIRED) nnfw_find_package(FlatBuffersSource REQUIRED) @@ -19,23 +20,32 @@ file(GLOB_RECURSE TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/*.cc") file(GLOB_RECURSE TFLITE_KERNEL_TESTS "${TENSORFLOW_LITE_BASE}/kernels/*test*.cc") list(REMOVE_ITEM TFLITE_KERNEL_SRCS ${TFLITE_KERNEL_TESTS}) +file(GLOB TFLITE_LIB_SRCS "${TENSORFLOW_LITE_BASE}/c/*.c" "${TENSORFLOW_LITE_BASE}/c/*.cc") +file(GLOB TFLITE_LIB_TESTS "${TENSORFLOW_LITE_BASE}/c/*test*.cc") +list(REMOVE_ITEM TFLITE_LIB_SRCS ${TFLITE_LIB_TESTS}) + +file(GLOB TFLITE_API_SRCS "${TENSORFLOW_LITE_BASE}/core/api/*.c" "${TENSORFLOW_LITE_BASE}/core/api/*.cc") +file(GLOB TFLITE_API_TESTS "${TENSORFLOW_LITE_BASE}/core/api/*test*.cc") +list(REMOVE_ITEM TFLITE_API_SRCS ${TFLITE_API_TESTS}) + +file(GLOB TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/*.cc") +file(GLOB TFLITE_PROFILING_TESTS "${TENSORFLOW_LITE_BASE}/profiling/*test*.cc") +list(REMOVE_ITEM TFLITE_PROFILING_SRCS ${TFLITE_PROFILING_TESTS}) + # We will use our own BuiltinOpResolver list(REMOVE_ITEM TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/register.cc") +# We will use our own summarizer +list(REMOVE_ITEM TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/profile_summarizer.cc") list(APPEND TFLITE_SRCS ${TFLITE_CORE_SRCS}) list(APPEND TFLITE_SRCS ${TFLITE_KERNEL_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_LIB_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_API_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) list(APPEND TFLITE_SRCS "${TFLITE_DEPEND_DIR}/farmhash/src/farmhash.cc") -# Profiling -if(BUILD_TFLITE_BENCHMARK_MODEL) - file(GLOB TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/*.cc") - file(GLOB TFLITE_PROFILING_TESTS "${TENSORFLOW_LITE_BASE}/profiling/*test*.cc") - list(REMOVE_ITEM TFLITE_PROFILING_SRCS ${TFLITE_PROFILING_TESTS}) - list(APPEND TFLITE_PROFILING_SRCS "${TENSORFLOW_BASE}/tensorflow/core/util/stats_calculator.cc") - list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) -endif() - list(APPEND TFLITE_INCLUDES "${CMAKE_CURRENT_SOURCE_DIR}/tensorflow") +list(APPEND TFLITE_INCLUDES "${TFLITE_DEPEND_DIR}/absl") list(APPEND TFLITE_INCLUDES "${TFLITE_DEPEND_DIR}/gemmlowp") list(APPEND TFLITE_INCLUDES "${TFLITE_DEPEND_DIR}/farmhash/src") list(APPEND TFLITE_INCLUDES "${TFLITE_DEPEND_DIR}/flatbuffers/include") diff --git a/externals/lcov-to-cobertura-xml/README.md b/externals/lcov-to-cobertura-xml/README.md new file mode 100644 index 000000000..2fc4cff42 --- /dev/null +++ b/externals/lcov-to-cobertura-xml/README.md @@ -0,0 +1,5 @@ +# lcov to cobertura XML converter + +lcov to cobertura XML converter is imported from + +https://github.com/eriwen/lcov-to-cobertura-xml diff --git a/scripts/command/lcov-to-covertura-xml.sh b/externals/lcov-to-cobertura-xml/lcov_cobertura.py index 7aae6d115..7aae6d115 100755 --- a/scripts/command/lcov-to-covertura-xml.sh +++ b/externals/lcov-to-cobertura-xml/lcov_cobertura.py diff --git a/include/.FORMATCHECKED b/include/.FORMATCHECKED new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/include/.FORMATCHECKED diff --git a/include/NeuralNetworksEx.h b/include/NeuralNetworksEx.h index 284f3af84..727ca9484 100644 --- a/include/NeuralNetworksEx.h +++ b/include/NeuralNetworksEx.h @@ -15,6 +15,11 @@ * limitations under the License. */ +/** + * @file NeuralNetworksEx.h + * @brief This file contains ANeuralNetworksModel_addOperationEx function definition + * @ingroup COM_AI_RUNTIME + */ #ifndef NN_RUNTIME_NEURAL_NETWORKS_EX_H #define NN_RUNTIME_NEURAL_NETWORKS_EX_H @@ -22,44 +27,644 @@ __BEGIN_DECLS +/** + * @brief Extended operation types + */ typedef enum { - /** extends operation. */ - ANEURALNETWORKS_CAST_EX = 50000, - ANEURALNETWORKS_GATHER_EX = 50001, - ANEURALNETWORKS_TOPK_V2_EX = 50002, - ANEURALNETWORKS_TENSORFLOW_MAX_EX = 50003, - ANEURALNETWORKS_SPLIT_EX = 50004, - ANEURALNETWORKS_RSQRT_EX = 50005, - ANEURALNETWORKS_SQUARED_DIFFERENCE_EX = 50006, -} OperationCodeEx; // extends OperationCode + /** extends operation. */ + + /** + * Casts a tensor/tensor-values to a new type + * + * The output value is calucated as: + * + * output = new_type(input) + * + * Ex: + * X = {1.8,2.2}, dtype of X = float32 + * Y = Cast(X), dtype of Y = int32 + * then Y = {1,2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_CAST_EX = 50000, + + /** + * Gathers values along an axis. + * + * Produces an output tensor with shape + * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] + * where: + * # Vector indices (output is rank(input0)). + * output[a_0, ..., a_n, i, b_0, ..., b_n] = + * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n] + * + * # Higher rank indices (output is rank(input0) + rank(indices) - 1). + * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = + * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor from which to gather values. + * * 1: A k-D tensor {@link ANEURALNETWORKS_TENSOR_INT32} of indices. + * The values must be in the bounds of the corresponding dimensions + * of input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis. + * Negative index is used to specify axis from the end + * (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: An (n + k - 1)-D tensor with the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_GATHER_EX = 50001, /**< Gather slices according to indexes and axis */ + + /** + * Finds values and indices of the k largest entries for the last dimension. + * + * Resulting values in each dimensions are sorted in descending order. If + * two values are equal, the one with larger index appears first. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: k, an {@link ANEURALNETWORKS_INT32} scalar, specifying the number of + * top elements to look for along the last dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input, containing the k + * largest elements along each last dimensional slice. + * * 1: An n-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32} + * containing the indices of values within the last dimension of input. + */ + ANEURALNETWORKS_TOPK_V2_EX = 50002, + + /** + * Computes the maximum of elements across dimensions of a tensor. + * + * Reduces the input tensor along the given dimensions to reduce. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_TENSORFLOW_MAX_EX = 50003, + + /** + * Splits a tensor along a given axis into num_splits subtensors. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to split. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar specifying the axis along + * which to split. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar indicating the number of + * splits along given axis. Must evenly divide axis size. + * + * Outputs: + * * 0 ~ (num_splits - 1): Resulting subtensors. + */ + ANEURALNETWORKS_SPLIT_EX = 50004, /**< Splits a tensor into sub tensors */ + + /** + * Computes element-wise reciprocal of square root of the input tensor. + * + * The output is calculated using this formula: + * + * output = 1/sqrt(input) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_RSQRT_EX = 50005, + + /** + * Computes element-wise squared difference on the input tensors. + * + * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. + * The output is the result of squaring of difference given by subtracting the second input tensor + * from the first one. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_SQUARED_DIFFERENCE_EX = 50006, + + /** + * Computes numerical negative value element-wise on the input tensor. + * + * Given an input tensor of {@link OperandCode}, + * The output is the numerical negative value element-wise on the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_NEG_EX = 50007, + + /** + * Computes exponential value element-wise on the input tensor. + * + * Given an input tensor of {@link OperandCode}, + * The output is the exponential value element-wise on the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_EXP_EX = 50008, + + /** + * Computes the sum of elements across dimensions of a tensor. + * + * Reduces the input tensor along the given dimensions to reduce. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_REDUCE_SUM_EX = 50009, + + /** + * A transposed convolutional layer carries out a regular convolution + * but reverts its spatial transformation. + * Transpose convolution basically performs convolution with transposed weights. + * + * Supported tensor {@link OperandCode}: + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: only 4 + * + * Inputs: + * 0: An {@link ANEURALNETWORKS_INT32} 1-D four element tensor, specifying the output shape. + * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], + * specifying the filter. + * 2: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding type. + * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the height dimension. + * + * Outputs: + * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. + */ + ANEURALNETWORKS_TRANSPOSE_CONV_EX = 50010, + + /** + * Computes element-wise truth value by comparing the two input tensors for equality. + * + * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. + * The output is the result of comparison of two input tensors. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * + * Outputs: + * * 0: A boolean tensor indicating the truth value of (x == y) + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + */ + ANEURALNETWORKS_EQUAL_EX = 50011, + + /** + * Computes element-wise absolute value of the input tensor. + * + * The output is calculated using this formula: + * + * output = fabs(input) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_ABS_EX = 50012, + /** + * Packs a list of rank-R tensors into one rank- (R+1) tensor along the axis dimension. + * + * The input tensors must have identical {@link OperandCode} and the same + * dimensions. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 3 + * + * Inputs: + * * 0 ~ n-1: The list of n input tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all input tensors + * must have the same scale and zeroPoint. + * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * number of input tensors. + * * n+1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * pack axis. + * + * Outputs: + * * 0: The output, a tensor of the same {@link OperandCode} as the input + * tensors. The output shape is [D0, D1, ..., N at Daxis(i), ..., Dm+1] + * where N is the number of tensors to be packed. + */ + ANEURALNETWORKS_PACK_EX = 50013, + /** + * Unpacks a given rank-R tensors into num_splits rank- (R-1) tensors along the axis dimention. + * num_splits has to respect integral divisibility of dimention value along axis dimention + * of the input. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: The input shape is [D0, D1, ..., N at Daxis(i), ..., Dm+1]. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * number of splits along unpack axis. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * unpack axis. + * + * Outputs: + * * 0 ~ n-1: The list of n output tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. The output tensors are of the same + * {@link OperandCode} as the input tensor 0. + */ + ANEURALNETWORKS_UNPACK_EX = 50014, + + /** + * Find index with the largest value across axes of a input tensor. + * + * Reduces the input tensor along the given dimensions to reduce. The reduced + * dimensions are retained with length 1. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Inputs: + * * 0: An n-D tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)). + * + * Outputs: + * * 0: A output tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. + * The rank of output tensor should be same rank of input0. + */ + ANEURALNETWORKS_ARGMAX_EX = 50015, + + /** + * Element-wise square root computation of the input tensor. + * + * The output is calculated using this formula: + * + * output = sqrt(input) + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandCode} and shape as input0. + */ + ANEURALNETWORKS_SQRT_EX = 50016, + + /** + * Computes element-wise truth value by comparing the input tensors for non-equality. + * + * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. + * The output is the result of comparison of two input tensors. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * + * Outputs: + * * 0: A boolean tensor indicating the truth value of non-equality of input tensors + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + */ + ANEURALNETWORKS_NOT_EQUAL_EX = 50017, + + /** + * Computes element-wise truth value of the input tensor negation. + * + * Takes one input tensor. + * The output is the negation, which is logical complement, of the input tensor. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D boolean tensor, specifying the input. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True. A zero indicates otherwise. + * + * Outputs: + * * 0: A boolean tensor of the same size as input indicating the truth value of (NOT x) + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True. A zero indicates otherwise. + */ + ANEURALNETWORKS_LOGICAL_NOT_EX = 50018, + + /** + * Computes element-wise truth value of two input tensors for LOGICAL AND. + * + * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. + * The output is the result of comparison of two input tensors. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D boolean tensor, specifying the first input. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * + * Outputs: + * * 0: A boolean tensor indicating the truth value of two input tensors for LOGICAL AND. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + */ + ANEURALNETWORKS_LOGICAL_AND_EX = 50019, + + /** + * Computes element-wise truth value of two input tensors for LOGICAL OR. + * + * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. + * The output is the result of comparison of two input tensors. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D boolean tensor, specifying the first input. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * + * Outputs: + * * 0: A boolean tensor indicating the truth value of two input tensors for LOGICAL OR. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + */ + ANEURALNETWORKS_LOGICAL_OR_EX = 50020, + + /** + * Computes the minimum of elements across dimensions of a tensor. + * + * Reduces the input tensor along the given dimensions to reduce. + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_INT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_REDUCE_MIN_EX = 500021, + + /** + * Parametric Rectified Linear Unit. + * + * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha + * is a learned array with the same {@link OperandCode} and compatible + * dimensions as input x. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input.dimension = {4, 1, 2} + * alpha.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandCode}: + * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0, specifying the alpha. + * + * Outputs: + * * 0: A tensor of the same {@link OperandCode} as input0. + */ + ANEURALNETWORKS_PRELU_EX = 500022, +} OperationCodeEx; // extends OperationCode typedef OperationCodeEx ANeuralNetworksOperationTypeEx; /** - * Add an extended operation to a model. - * - * @param model The model to be modified. - * @param type The type of extended operation. - * @param inputCount The number of entries in the inputs array. - * @param inputs An array of indexes identifying each operand. - * @param outputCount The number of entries in the outputs array. - * @param outputs An array of indexes identifying each operand. - * - * The operands specified by inputs and outputs must have been - * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * @brief Add an extended operation to a model. * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been - * called will return an error. + * @param[in] model The model to be modified. + * @param[in] type The type of extended operation. + * @param[in] inputCount The number of entries in the inputs array. + * @param[in] inputs An array of indexes identifying each operand. + * @param[in] outputCount The number of entries in the outputs array. + * @param[in] outputs An array of indexes identifying each operand. * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * @note The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}.\n + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has been + * called will return an error.\n + * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ -int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel* model, +int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, ANeuralNetworksOperationTypeEx type, uint32_t inputCount, - const uint32_t* inputs, uint32_t outputCount, - const uint32_t* outputs); + const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs); __END_DECLS -#endif // NN_RUNTIME_NEURAL_NETWORKS_EX_H +#endif // NN_RUNTIME_NEURAL_NETWORKS_EX_H diff --git a/include/NeuralNetworksExShim.h b/include/NeuralNetworksExShim.h index 0fee60a8d..f684dab90 100644 --- a/include/NeuralNetworksExShim.h +++ b/include/NeuralNetworksExShim.h @@ -1,18 +1,24 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (C) 2017 The Android Open Source Project - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/** + * @file NeuralNetworksExShim.h + * @brief This file contains an actual implementation of + * ANeuralNetworksModel_addOperationEx function + * @ingroup COM_AI_RUNTIME */ #ifndef NN_API_EX_SHIM_H @@ -21,40 +27,38 @@ #include "NeuralNetworksEx.h" #include "NeuralNetworksLoadHelpers.h" -typedef int (*ANeuralNetworksModel_addOperationEx_fn)( - ANeuralNetworksModel *model, ANeuralNetworksOperationTypeEx type, - uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs); +typedef int (*ANeuralNetworksModel_addOperationEx_fn)(ANeuralNetworksModel *model, + ANeuralNetworksOperationTypeEx type, + uint32_t inputCount, const uint32_t *inputs, + uint32_t outputCount, + const uint32_t *outputs); /** - * Add an extended operation to a model. - * - * @param model The model to be modified. - * @param type The type of extended operation. - * @param inputCount The number of entries in the inputs array. - * @param inputs An array of indexes identifying each operand. - * @param outputCount The number of entries in the outputs array. - * @param outputs An array of indexes identifying each operand. - * - * The operands specified by inputs and outputs must have been - * previously added by calls to {@link ANeuralNetworksModel_addOperand}. + * @brief Add an extended operation to a model. * - * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has - * been - * called will return an error. + * @param[in] model The model to be modified. + * @param[in] type The type of extended operation. + * @param[in] inputCount The number of entries in the inputs array. + * @param[in] inputs An array of indexes identifying each operand. + * @param[in] outputCount The number of entries in the outputs array. + * @param[in] outputs An array of indexes identifying each operand. * - * See {@link ANeuralNetworksModel} for information on multithreaded usage. + * @note The operands specified by inputs and outputs must have been + * previously added by calls to {@link ANeuralNetworksModel_addOperand}.\n + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} + * has been called will return an error.\n + * See {@link ANeuralNetworksModel} for information on multithreaded usage. * * @return ANEURALNETWORKS_NO_ERROR if successful. */ -inline int ANeuralNetworksModel_addOperationEx( - ANeuralNetworksModel *model, ANeuralNetworksOperationTypeEx type, - uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) { +inline int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, + ANeuralNetworksOperationTypeEx type, + uint32_t inputCount, const uint32_t *inputs, + uint32_t outputCount, const uint32_t *outputs) +{ LOAD_FUNCTION(ANeuralNetworksModel_addOperationEx); - EXECUTE_FUNCTION_RETURN(model, type, inputCount, inputs, outputCount, - outputs); + EXECUTE_FUNCTION_RETURN(model, type, inputCount, inputs, outputCount, outputs); } #endif // NN_API_EX_SHIM_H diff --git a/include/NeuralNetworksLoadHelpers.h b/include/NeuralNetworksLoadHelpers.h index 93dd967bf..eeef2b5ec 100644 --- a/include/NeuralNetworksLoadHelpers.h +++ b/include/NeuralNetworksLoadHelpers.h @@ -1,18 +1,29 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (C) 2017 The Android Open Source Project - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from part of the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h' + +/** + * @file NeuralNetworksLoadHelpers.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains functions to load NN API runtime library */ #ifndef __NEURAL_NETWORKS_LOAD_HELPER_H__ @@ -23,42 +34,81 @@ #include <stdio.h> #include <stdlib.h> +/** + * @brief Print log data + * @param[in] format Format string of @c printf + * @param[in] args Argument after format string. (Same with @c printf) + */ #define NNAPI_LOG(format, ...) printf(format "\n", __VA_ARGS__); -#define LOAD_FUNCTION(name) \ - static name##_fn fn = reinterpret_cast<name##_fn>(loadNNAPIFunction(#name)); -#define EXECUTE_FUNCTION(...) \ - if (fn != nullptr) { \ - fn(__VA_ARGS__); \ + +/** + * @brief Create a function pointer named @c fn after loading NN API library + * @param[in] name Name of a function + */ +#define LOAD_FUNCTION(name) \ + static name##_fn fn = reinterpret_cast<name##_fn>(nnfw::loadFunction(#name)); + +/** + * @brief Run @c fn function. @c fn is created by @ref LOAD_FUNCTION + * @param[in] args List of arguments for the function @c fn + */ +#define EXECUTE_FUNCTION(...) \ + if (fn != nullptr) { \ + fn(__VA_ARGS__); \ } + +/** + * @brief Run @c fn function. @c fn is created by @ref LOAD_FUNCTION + * @param[in] args List of arguments for the function @c fn + * @return the return value of @c fn + */ #define EXECUTE_FUNCTION_RETURN(...) return fn != nullptr ? fn(__VA_ARGS__) : 0; -inline void* loadNNAPILibrary(const char* name) { +namespace nnfw +{ + +/** + * @brief Load NN API library + * @param[in] name path of NN API library + * @return a symbol table handle of NN API library + */ +inline void* loadLibrary(const char* name) { // TODO: change RTLD_LOCAL? Assumes there can be multiple instances of nn // api RT - void* handle = dlopen(name, RTLD_LAZY | RTLD_LOCAL); + void* handle = nullptr; +#if 1 //#ifdef __ANDROID__ + handle = dlopen(name, RTLD_LAZY | RTLD_LOCAL); if (handle == nullptr) { NNAPI_LOG("nnapi error: unable to open library %s", name); } +#endif return handle; } -inline void* getNNAPILibraryHandle() { - static void* handle = loadNNAPILibrary("libneuralnetworks.so"); +/** + * @brief Load libneuralnetworks.so and return handle of library + * @return a symbol table handle of NN API library + */ +inline void* getLibraryHandle() { + static void* handle = loadLibrary("libneuralnetworks.so"); return handle; } -inline void* loadNNAPIFunction(const char* name) { +/** + * @brief Return function ptr in libneuralnetworks.so + * @param[in] name Name of function + * @return function pointer + */ +inline void* loadFunction(const char* name) { void* fn = nullptr; - if (getNNAPILibraryHandle() != nullptr) { - fn = dlsym(getNNAPILibraryHandle(), name); + if (getLibraryHandle() != nullptr) { + fn = dlsym(getLibraryHandle(), name); } - if (fn == nullptr) - { + if (fn == nullptr) { NNAPI_LOG("nnapi error: unable to open function %s", name); abort(); } - else - { + else { #ifdef _GNU_SOURCE Dl_info info; dladdr(fn, &info); @@ -68,9 +118,15 @@ inline void* loadNNAPIFunction(const char* name) { return fn; } +/** + * @brief Check if libneuralnetworks.so can be loaded + * @return @c true if loading is successful, otherwise @c false. + */ inline bool NNAPIExists() { - static bool nnapi_is_available = getNNAPILibraryHandle(); + static bool nnapi_is_available = getLibraryHandle(); return nnapi_is_available; } +} // namespace nnfw + #endif // __NEURAL_NETWORKS_LOAD_HELPER_H__ diff --git a/include/NeuralNetworksShim.h b/include/NeuralNetworksShim.h index a7bd745fb..60b16f766 100644 --- a/include/NeuralNetworksShim.h +++ b/include/NeuralNetworksShim.h @@ -14,7 +14,10 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// NOTE This header is derived from the following file (in TensorFlow) +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from part of the following file (in TensorFlow v1.12) // 'externals/tensorflow/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h' #ifndef __NEURAL_NETWORKS_SHIM__ #define __NEURAL_NETWORKS_SHIM__ @@ -68,6 +71,9 @@ typedef int (*ANeuralNetworksModel_identifyInputsAndOutputs_fn)( ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs); +typedef int (*ANeuralNetworksModel_relaxComputationFloat32toFloat16_fn)( + ANeuralNetworksModel* model, bool allow); + typedef int (*ANeuralNetworksExecution_create_fn)( ANeuralNetworksCompilation* compilation, ANeuralNetworksExecution** execution); @@ -360,6 +366,34 @@ inline int ANeuralNetworksModel_identifyInputsAndOutputs( } /** + * Specifies whether {@link ANEURALNETWORKS_TENSOR_FLOAT32} is allowed to be + * calculated with range and/or precision as low as that of the IEEE 754 16-bit + * floating-point format. By default, {@link ANEURALNETWORKS_TENSOR_FLOAT32} + * must be calculated using at least the range and precision of the IEEE 754 + * 32-bit floating-point format. + * + * @param model The model to be modified. + * @param allow 'true' indicates {@link ANEURALNETWORKS_TENSOR_FLOAT32} may be + * calculated with range and/or precision as low as that of the + * IEEE 754 16-bit floating point format. 'false' indicates + * {@link ANEURALNETWORKS_TENSOR_FLOAT32} must be calculated using + * at least the range and precision of the IEEE 754 32-bit floating + * point format. + * + * Attempting to modify a model once {@link ANeuralNetworksModel_finish} has + * been called will return an error. + * + * Available since API level 28. + * + * See {@link ANeuralNetworksModel} for information on multithreaded usage. + */ +inline int ANeuralNetworksModel_relaxComputationFloat32toFloat16( + ANeuralNetworksModel* model, bool allow) { + LOAD_FUNCTION(ANeuralNetworksModel_relaxComputationFloat32toFloat16); + EXECUTE_FUNCTION_RETURN(model, allow); +} + +/** * Create a {@link ANeuralNetworksCompilation} to compile the given model. * This only creates the object. Compilation is only performed once * {@link ANeuralNetworksCompilation_start} is invoked. diff --git a/include/kernel/acl/Add.h b/include/kernel/acl/Add.h deleted file mode 100644 index 73a7d752a..000000000 --- a/include/kernel/acl/Add.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_ADD_H__ -#define __NNFW_KERNEL_ACL_ADD_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool addFloat32(const float* inputData1, const nnfw::rt::Shape& inputShape1, - const float* inputData2, const nnfw::rt::Shape& inputShape2, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool addFloat32(const float* inputData1, const nnfw::rt::Shape& inputShape1, - const float* inputData2, const nnfw::rt::Shape& inputShape2, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernal -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_ADD_H__ diff --git a/include/kernel/acl/Concatenation.h b/include/kernel/acl/Concatenation.h deleted file mode 100644 index 3e5185102..000000000 --- a/include/kernel/acl/Concatenation.h +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_CONCATENATION_H__ -#define __NNFW_KERNEL_ACL_CONCATENATION_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool concatenationFloat32(const std::vector<const float*>& inputDataPtrs, - const std::vector<nnfw::rt::Shape>& inputShapes, int32_t axis, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool concatenationFloat32(const std::vector<const float*>& inputDataPtrs, - const std::vector<nnfw::rt::Shape>& inputShapes, int32_t axis, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernal -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_CONCATENATION_H__ diff --git a/include/kernel/acl/Conv2D.h b/include/kernel/acl/Conv2D.h deleted file mode 100644 index 5f2cd4d06..000000000 --- a/include/kernel/acl/Conv2D.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_CONV2D_H__ -#define __NNFW_KERNEL_ACL_CONV2D_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool convFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* filterData, const nnfw::rt::Shape& filterShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool convFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* filterData, const nnfw::rt::Shape& filterShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernal -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_CONV2D_H__ diff --git a/include/kernel/acl/DepthwiseConv2D.h b/include/kernel/acl/DepthwiseConv2D.h deleted file mode 100644 index b6576d206..000000000 --- a/include/kernel/acl/DepthwiseConv2D.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_DEPTHWISECONV2D_H__ -#define __NNFW_KERNEL_ACL_DEPTHWISECONV2D_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool depthwiseConvFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* filterData, const nnfw::rt::Shape& filterShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t depth_multiplier, int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool depthwiseConvFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* filterData, const nnfw::rt::Shape& filterShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t depth_multiplier, int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_DEPTHWISECONV2D_H__ diff --git a/include/kernel/acl/FullyConnected.h b/include/kernel/acl/FullyConnected.h deleted file mode 100644 index 9695d638a..000000000 --- a/include/kernel/acl/FullyConnected.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_FULLYCONNECTED_H__ -#define __NNFW_KERNEL_ACL_FULLYCONNECTED_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool fullyConnectedFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* weightsData, const nnfw::rt::Shape& weightsShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool fullyConnectedFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float* weightsData, const nnfw::rt::Shape& weightsShape, - const float* biasData, const nnfw::rt::Shape& biasShape, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_FULLYCONNECTED_H__ diff --git a/include/kernel/acl/Mul.h b/include/kernel/acl/Mul.h deleted file mode 100644 index 7e0c15c6c..000000000 --- a/include/kernel/acl/Mul.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_MUL_H__ -#define __NNFW_KERNEL_ACL_MUL_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool mulFloat32(const float* inputData1, const nnfw::rt::Shape& inputShape1, - const float* inputData2, const nnfw::rt::Shape& inputShape2, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool mulFloat32(const float* inputData1, const nnfw::rt::Shape& inputShape1, - const float* inputData2, const nnfw::rt::Shape& inputShape2, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon -} // namespace acl -} // namespace kernal -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_MUL_H__ diff --git a/include/kernel/acl/Pooling.h b/include/kernel/acl/Pooling.h deleted file mode 100644 index 0205f9989..000000000 --- a/include/kernel/acl/Pooling.h +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_POOLING_H__ -#define __NNFW_KERNEL_ACL_POOLING_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool maxPoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t filter_width, int32_t filter_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -bool averagePoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t filter_width, int32_t filter_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool maxPoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t filter_width, int32_t filter_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -bool averagePoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - int32_t padding_left, int32_t padding_right, - int32_t padding_top, int32_t padding_bottom, - int32_t stride_width, int32_t stride_height, - int32_t filter_width, int32_t filter_height, - int32_t activation, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_POOLING_H__ diff --git a/include/kernel/acl/ReLU.h b/include/kernel/acl/ReLU.h deleted file mode 100644 index 20278bf98..000000000 --- a/include/kernel/acl/ReLU.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_RELU_H__ -#define __NNFW_KERNEL_ACL_RELU_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool reluFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - - bool reluFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_RELU_H__ diff --git a/include/kernel/acl/ReLU6.h b/include/kernel/acl/ReLU6.h deleted file mode 100644 index d68c792e4..000000000 --- a/include/kernel/acl/ReLU6.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_RELU6H__ -#define __NNFW_KERNEL_ACL_RELU6H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool relu6Float32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); - -bool relu6LUFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); -namespace neon { - - bool relu6Float32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); - - bool relu6LUFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_RELU6H__ diff --git a/include/kernel/acl/Reshape.h b/include/kernel/acl/Reshape.h deleted file mode 100644 index 418db7374..000000000 --- a/include/kernel/acl/Reshape.h +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_RESHAPE_H__ -#define __NNFW_KERNEL_ACL_RESHAPE_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool reshapeGeneric(const void* inputData, const nnfw::rt::Shape& inputShape, - void* outputData, const nnfw::rt::Shape& outputShape); -namespace neon { -bool reshapeGeneric(const void* inputData, const nnfw::rt::Shape& inputShape, - void* outputData, const nnfw::rt::Shape& outputShape); -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_RESHAPE_H__ diff --git a/include/kernel/acl/Softmax.h b/include/kernel/acl/Softmax.h deleted file mode 100644 index d1f7dafb3..000000000 --- a/include/kernel/acl/Softmax.h +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_SOFTMAX_H__ -#define __NNFW_KERNEL_ACL_SOFTMAX_H__ - -#include <OperationsUtils.h> - -namespace nnfw { -namespace kernel { -namespace acl { - -bool softmaxFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float beta, - float* outputData, const nnfw::rt::Shape& outputShape); - -namespace neon { - -bool softmaxFloat32(const float* inputData, const nnfw::rt::Shape& inputShape, - const float beta, - float* outputData, const nnfw::rt::Shape& outputShape); - -} // namespace neon - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_SOFTMAX_H__ diff --git a/include/kernel/acl/nnfw_kernel_acl.h b/include/kernel/acl/nnfw_kernel_acl.h deleted file mode 100644 index 71975029e..000000000 --- a/include/kernel/acl/nnfw_kernel_acl.h +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_KERNEL_ACL_H__ -#define __NNFW_KERNEL_ACL_H__ - -namespace nnfw { -namespace kernel { -namespace acl { - -void Initialize(void); - -} // namespace acl -} // namespace kernel -} // namespace nnfw - -#endif // __NNFW_KERNEL_ACL_H__ diff --git a/include/nnfw/std/memory.h b/include/nnfw/std/memory.h deleted file mode 100644 index dd0236958..000000000 --- a/include/nnfw/std/memory.h +++ /dev/null @@ -1,17 +0,0 @@ -#ifndef __NNFW_STD_MEMORY_H__ -#define __NNFW_STD_MEMORY_H__ - -#include <memory> - -namespace nnfw -{ - -template <typename T, typename... Args> std::unique_ptr<T> make_unique(Args &&... args) -{ - // NOTE std::make_unique is missing in C++11 standard - return std::unique_ptr<T>(new T(std::forward<Args>(args)...)); -} - -} // namespace nnfw - -#endif // __NNFW_STD_MEMORY_H__ diff --git a/include/support/nnapi/Utils.h b/include/support/nnapi/Utils.h deleted file mode 100644 index d34d2f64b..000000000 --- a/include/support/nnapi/Utils.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_NNAPI_UTILS_H__ -#define __NNFW_SUPPORT_NNAPI_UTILS_H__ - -#include "NeuralNetworks.h" - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ - -const char *to_string(const PaddingCode &code); - -} // namespace nnapi -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_NNAPI_UTILS_H__ diff --git a/include/support/nnapi/feature/Reader.h b/include/support/nnapi/feature/Reader.h deleted file mode 100644 index b6ab0a1e2..000000000 --- a/include/support/nnapi/feature/Reader.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_NNAPI_FEATURE_READER_H__ -#define __NNFW_SUPPORT_NNAPI_FEATURE_READER_H__ - -#include "support/nnapi/feature/Utils.h" - -#include "util/feature/Shape.h" -#include "util/feature/Reader.h" - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ -namespace feature -{ - -template<typename T> class Reader : public nnfw::util::feature::Reader<T> -{ -public: - Reader(const nnfw::util::feature::Shape &shape, const T *base) - : _shape{shape}, _base{base} - { - // DO NOTHING - } - -public: - T at(uint32_t ch, uint32_t row, uint32_t col) const override - { - return *(_base + indexOf(_shape, ch, row, col)); - } - -private: - nnfw::util::feature::Shape _shape; - const T *_base; -}; - -} // namespace feature -} // namespace nnapi -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_NNAPI_FEATURE_READER_H__ diff --git a/include/support/nnapi/feature/Utils.h b/include/support/nnapi/feature/Utils.h deleted file mode 100644 index e666f54b3..000000000 --- a/include/support/nnapi/feature/Utils.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_NNAPI_FEATURE_UTILS_H__ -#define __NNFW_SUPPORT_NNAPI_FEATURE_UTILS_H__ - -#include "util/feature/Shape.h" - -#include <cstdint> - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ -namespace feature -{ - -uint32_t indexOf(const nnfw::util::feature::Shape &shape, uint32_t ch, uint32_t row, uint32_t col); - -} // namespace feature -} // namespace nnapi -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_NNAPI_FEATURE_UTILS_H__ diff --git a/include/support/tflite/Assert.h b/include/support/tflite/Assert.h deleted file mode 100644 index f5c6bf219..000000000 --- a/include/support/tflite/Assert.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_ASSERT_H__ -#define __NNFW_SUPPORT_TFLITE_ASSERT_H__ - -#include "tensorflow/contrib/lite/context.h" - -#include <sstream> - -#define STR_DETAIL(value) #value -#define STR(value) STR_DETAIL(value) - -#define TFLITE_ENSURE(exp) { \ - const TfLiteStatus status = (exp); \ - \ - if (status != kTfLiteOk) \ - { \ - std::ostringstream ss; \ - ss << #exp << " failed (" << __FILE__ << ":" << __LINE__ << ")"; \ - throw std::runtime_error{ss.str()}; \ - } \ -} - -#endif // __NNFW_SUPPORT_TFLITE_ASSERT_H__ diff --git a/include/support/tflite/Diff.h b/include/support/tflite/Diff.h deleted file mode 100644 index f4f3f6fe8..000000000 --- a/include/support/tflite/Diff.h +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_COMPARE_H__ -#define __NNFW_SUPPORT_TFLITE_COMPARE_H__ - -#include "tensorflow/contrib/lite/interpreter.h" - -#include "util/tensor/Index.h" -#include "util/tensor/Diff.h" -#include "util/tensor/Shape.h" -#include "util/tensor/Comparator.h" - -#include "support/tflite/TensorView.h" - -#include <functional> -#include <vector> - -class TfLiteInterpMatchApp -{ -public: - TfLiteInterpMatchApp(const nnfw::util::tensor::Comparator &comparator) - : _verbose{false}, _comparator(comparator) - { - // DO NOTHING - } - -public: - int &verbose(void) { return _verbose; } - -private: - int _verbose; - -public: - bool run(::tflite::Interpreter &pure, ::tflite::Interpreter &nnapi) const; - template <typename T> - bool compareSingleTensorView(const nnfw::support::tflite::TensorView<T> &expected, - const nnfw::support::tflite::TensorView<T> &obtained, - int id) const; - -private: - const nnfw::util::tensor::Comparator &_comparator; -}; - -#include "support/tflite/interp/Builder.h" -#include "support/tflite/Quantization.h" - -#include <random> - -class RandomGenerator -{ -public: - RandomGenerator(int seed, float mean, float stddev, - const TfLiteQuantizationParams quantization = make_default_quantization()) - : _rand{seed}, _dist{mean, stddev}, _quantization{quantization} - { - // DO NOTHING - } - -public: - template <typename T> - T generate(const ::nnfw::util::tensor::Shape &, const ::nnfw::util::tensor::Index &) - { - return generate<T>(); - } - - template <typename T> T generate(void) - { - return _dist(_rand); - } - -private: - std::minstd_rand _rand; - std::normal_distribution<float> _dist; - const TfLiteQuantizationParams _quantization; -}; - -template <> -uint8_t RandomGenerator::generate<uint8_t>(void); - -// For NNAPI testing -struct RandomTestParam -{ - int verbose; - int tolerance; - int tensor_logging = 0; - std::string log_path = ""; // meaningful only when tensor_logging is 1 -}; - -class RandomTestRunner -{ -public: - RandomTestRunner(int seed, const RandomTestParam ¶m, - const TfLiteQuantizationParams quantization = make_default_quantization()) - : _randgen{seed, 0.0f, 2.0f, quantization}, _param{param} - { - // DO NOTHING - } - -public: - // NOTE this method updates '_rand' - // Return 0 if test succeeds - int run(const nnfw::support::tflite::interp::Builder &builder); - -public: - RandomGenerator &generator() { return _randgen; }; - -private: - RandomGenerator _randgen; - const RandomTestParam _param; - -public: - static RandomTestRunner make(int seed); -}; - -#endif // __NNFW_SUPPORT_TFLITE_COMPARE_H__ diff --git a/include/support/tflite/FeatureView.h b/include/support/tflite/FeatureView.h deleted file mode 100644 index 3a7d75ec4..000000000 --- a/include/support/tflite/FeatureView.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_FEATURE_VIEW_H__ -#define __NNFW_SUPPORT_TFLITE_FEATURE_VIEW_H__ - -#include "tensorflow/contrib/lite/interpreter.h" - -#include "support/tflite/InputIndex.h" -#include "support/tflite/OutputIndex.h" - -#include "util/feature/Shape.h" -#include "util/feature/Reader.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -template<typename T> class FeatureView; - -template<> class FeatureView<float> : public nnfw::util::feature::Reader<float> -{ -public: - FeatureView(::tflite::Interpreter &interp, const InputIndex &index); - FeatureView(::tflite::Interpreter &interp, const OutputIndex &index); - -public: - float at(uint32_t ch, uint32_t row, uint32_t col) const; - float &at(uint32_t ch, uint32_t row, uint32_t col); - -private: - uint32_t getElementOffset(uint32_t ch, uint32_t row, uint32_t col) const - { - uint32_t res = 0; - - // TensorFlow Lite assumes that NHWC ordering for tessor - res += row * _shape.W * _shape.C; - res += col * _shape.C; - res += ch; - - return res; - } - -private: - nnfw::util::feature::Shape _shape; - float *_base; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_FEATURE_VIEW_H__ diff --git a/include/support/tflite/InputIndex.h b/include/support/tflite/InputIndex.h deleted file mode 100644 index c3ed891fe..000000000 --- a/include/support/tflite/InputIndex.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_INPUT_INDEX_H__ -#define __NNFW_SUPPORT_TFLITE_INPUT_INDEX_H__ - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -class InputIndex -{ -public: - InputIndex(int index) : _index(index) - { - // DO NOTHING - } - -public: - int asInt(void) const { return _index; } - -private: - int _index; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INPUT_INDEX_H__ diff --git a/include/support/tflite/InterpreterSession.h b/include/support/tflite/InterpreterSession.h deleted file mode 100644 index 662de1d17..000000000 --- a/include/support/tflite/InterpreterSession.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_INTERPRETER_SESSION_H__ -#define __NNFW_SUPPORT_TFLITE_INTERPRETER_SESSION_H__ - -#include "Session.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -class InterpreterSession final : public Session -{ -public: - InterpreterSession(::tflite::Interpreter *interp) : _interp{interp} - { - // DO NOTHING - } - -public: - ::tflite::Interpreter *interp(void) override { return _interp; } - -public: - bool prepare(void) override - { - _interp->UseNNAPI(false); - - if (kTfLiteOk != _interp->AllocateTensors()) - { - return false; - } - - return true; - } - - bool run(void) override - { - // Return true if Invoke returns kTfLiteOk - return kTfLiteOk == _interp->Invoke(); - } - - bool teardown(void) override - { - // Do NOTHING currently - return true; - } - -private: - ::tflite::Interpreter * const _interp; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INTERPRETER_SESSION_H__ diff --git a/include/support/tflite/NNAPISession.h b/include/support/tflite/NNAPISession.h deleted file mode 100644 index 4a8a2162b..000000000 --- a/include/support/tflite/NNAPISession.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_NNAPI_SESSION_H__ -#define __NNFW_SUPPORT_TFLITE_NNAPI_SESSION_H__ - -#include "Session.h" -#include "support/tflite/nnapi_delegate.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -class NNAPISession final : public Session -{ -public: - NNAPISession(::tflite::Interpreter *interp) : _interp{interp} - { - // Construct Graph from Interpreter - _delegate.BuildGraph(_interp); - } - -public: - ::tflite::Interpreter *interp(void) override { return _interp; } - -public: - bool prepare(void) override - { - // Explicitly turn off T/F lite internal NNAPI delegation in order to use locally defined - // NNAPI delegation. - _interp->UseNNAPI(false); - - if (kTfLiteOk != _interp->AllocateTensors()) - { - return false; - } - - return true; - } - - bool run(void) override - { - return kTfLiteOk == _delegate.Invoke(_interp); - } - - bool teardown(void) override - { - // DO NOTHING - return true; - } - -private: - ::tflite::Interpreter * const _interp; - nnfw::NNAPIDelegate _delegate; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_NNAPI_SESSION_H__ diff --git a/include/support/tflite/OutputIndex.h b/include/support/tflite/OutputIndex.h deleted file mode 100644 index be6556ce7..000000000 --- a/include/support/tflite/OutputIndex.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_OUTPUT_INDEX_H__ -#define __NNFW_SUPPORT_TFLITE_OUTPUT_INDEX_H__ - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -class OutputIndex -{ -public: - OutputIndex(int index) : _index(index) - { - // DO NOTHING - } - -public: - int asInt(void) const { return _index; } - -private: - int _index; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_OUTPUT_INDEX_H__ diff --git a/include/support/tflite/Quantization.h b/include/support/tflite/Quantization.h deleted file mode 100644 index a9027278a..000000000 --- a/include/support/tflite/Quantization.h +++ /dev/null @@ -1,31 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_QUANTIZATION_H__ -#define __NNFW_SUPPORT_TFLITE_QUANTIZATION_H__ - -union BitwiseIntToFloat { - int i; - float f; -}; - -static const float FLOAT_NEAREST_TO_1 = BitwiseIntToFloat{0x3f7fffff}.f; - -#include "tensorflow/contrib/lite/context.h" - -TfLiteQuantizationParams make_default_quantization(void); - -#endif // __NNFW_SUPPORT_TFLITE_QUANTIZATION_H__ diff --git a/include/support/tflite/Session.h b/include/support/tflite/Session.h deleted file mode 100644 index 2ee2abe23..000000000 --- a/include/support/tflite/Session.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_SESSION_H__ -#define __NNFW_SUPPORT_TFLITE_SESSION_H__ - -#include <tensorflow/contrib/lite/interpreter.h> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -struct Session -{ - virtual ~Session() = default; - - virtual ::tflite::Interpreter *interp(void) = 0; - - virtual bool prepare(void) = 0; - virtual bool run(void) = 0; - virtual bool teardown(void) = 0; -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INTERP_SESSION_H__ diff --git a/include/support/tflite/TensorLogger.h b/include/support/tflite/TensorLogger.h deleted file mode 100644 index 97a0a49d7..000000000 --- a/include/support/tflite/TensorLogger.h +++ /dev/null @@ -1,166 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_TENSOR_LOGGER_H__ -#define __NNFW_SUPPORT_TFLITE_TENSOR_LOGGER_H__ - -#include "util/tensor/IndexIterator.h" -#include "support/tflite/TensorView.h" - -#include <tensorflow/contrib/lite/interpreter.h> -#include <tensorflow/contrib/lite/context.h> -#include <fstream> -#include <iomanip> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -/* -This is a utility to write input and output value / shape into a file in python form. -any python app can load this value by running the python code below: - - exec(open(filename).read()) - -generated python code looks like the following: - -# ------------- test name ------------- -tensor_shape_gen = [] -tensor_value_gen = [] - -tensor_shape_gen.append("{2, 1, 2}") -tensor_value_gen.append([1, 2, 3, 4]) - -tensor_shape_gen.append("{2}") -tensor_value_gen.append([1, 2]) - -tensor_shape_gen.append("{2, 1, 2}") -tensor_value_gen.append([1, 4, 3, 8]) -# ----------------------------------------- -*/ - -class TensorLogger -{ - private: - std::ofstream _outfile; - - public: - - static TensorLogger &instance() - { - static TensorLogger instance; - return instance; - } - - void save(const std::string &path, ::tflite::Interpreter &interp) - { - open(path); - - int log_index = 0; - for (const auto id : interp.inputs()) - { - _outfile << "# input tensors" << std::endl; - printTensor(interp, id, log_index++); - } - for (const auto id : interp.outputs()) - { - _outfile << "# output tensors" << std::endl; - printTensor(interp, id, log_index++); - } - close(); - } - - private: - - void open(const std::string &path) - { - if (! _outfile.is_open()) - _outfile.open(path, std::ios_base::out); - - _outfile << "# ------ file: " << path << " ------" << std::endl - << "tensor_shape_gen = []" << std::endl - << "tensor_value_gen = []" << std::endl << std::endl; - } - - void printTensor(::tflite::Interpreter &interp, const int id, const int log_index) - { - const TfLiteTensor* tensor = interp.tensor(id); - - _outfile << "# tensor name: " << tensor->name << std::endl; - _outfile << "# tflite::interpreter.tensor("<< id <<") -> " - "tensor_value_gen["<< log_index << "]" << std::endl; - - if (tensor->type == kTfLiteInt32) - { - printTensorShape(tensor); - printTensorValue<int32_t>(tensor, tensor->data.i32); - } - else if (interp.tensor(id)->type == kTfLiteUInt8) - { - printTensorShape(tensor); - printTensorValue<uint8_t>(tensor, tensor->data.uint8); - } - else if (tensor->type == kTfLiteFloat32) - { - printTensorShape(tensor); - printTensorValue<float>(tensor, tensor->data.f); - } - } - - void printTensorShape(const TfLiteTensor* tensor) - { - _outfile << "tensor_shape_gen.append('{"; - - size_t r = 0; - for (; r < tensor->dims->size - 1; r++) - { - _outfile << tensor->dims->data[r] - << ", "; - } - _outfile << tensor->dims->data[r]; - - _outfile << "}')" << std::endl; - } - - template <typename T> - void printTensorValue(const TfLiteTensor* tensor, T* tensor_data_ptr) - { - _outfile << "tensor_value_gen.append(["; - - _outfile << std::fixed << std::setprecision(10); - - const T *end = reinterpret_cast<const T *>(tensor->data.raw_const + tensor->bytes); - for (T *ptr = tensor_data_ptr; ptr < end; ptr++) - _outfile << *ptr << ", "; - - _outfile << "])" << std::endl << std::endl; - } - - void close() - { - _outfile << "# --------- tensor shape and value defined above ---------" << std::endl; - _outfile.close(); - } -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_TENSOR_LOGGER_H__ diff --git a/include/support/tflite/TensorShapeUtils.h b/include/support/tflite/TensorShapeUtils.h deleted file mode 100644 index 711128b48..000000000 --- a/include/support/tflite/TensorShapeUtils.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_TENSOR_SHAPE_UTILS_H__ -#define __NNFW_SUPPORT_TFLITE_TENSOR_SHAPE_UTILS_H__ - -#include "util/tensor/Shape.h" - -#include <vector> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -// Converts tensor::Shape into a vector -static inline std::vector<int32_t> as_dims(const nnfw::util::tensor::Shape &shape) -{ - std::vector<int32_t> dims; - - for (uint32_t axis = 0; axis < shape.rank(); ++axis) - { - dims.emplace_back(shape.dim(axis)); - } - - return dims; -} - -nnfw::util::tensor::Shape broadcast(const nnfw::util::tensor::Shape &lhs_shape, - const nnfw::util::tensor::Shape &rhs_shape); - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_TENSOR_SHAPE_UTILS_H__ diff --git a/include/support/tflite/TensorUtils.h b/include/support/tflite/TensorUtils.h deleted file mode 100644 index 815cfcd29..000000000 --- a/include/support/tflite/TensorUtils.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_TENSOR_UTILS_H__ -#define __NNFW_SUPPORT_TFLITE_TENSOR_UTILS_H__ - -#include <tensorflow/contrib/lite/context.h> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -inline bool isFloatTensor(const TfLiteTensor *tensor) -{ - return tensor->type == kTfLiteFloat32; -} - -inline bool isFeatureTensor(const TfLiteTensor *tensor) -{ - return (tensor->dims->size == 4) && (tensor->dims->data[0] == 1); -} - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_TENSOR_UTILS_H__ diff --git a/include/support/tflite/TensorView.h b/include/support/tflite/TensorView.h deleted file mode 100644 index 0475a4b45..000000000 --- a/include/support/tflite/TensorView.h +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_TENSOR_VIEW_H__ -#define __NNFW_SUPPORT_TFLITE_TENSOR_VIEW_H__ - -#include "tensorflow/contrib/lite/interpreter.h" - -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" -#include "util/tensor/Reader.h" -#include "util/tensor/NonIncreasingStride.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -template<typename T> class TensorView final : public nnfw::util::tensor::Reader<T> -{ -public: - TensorView(const nnfw::util::tensor::Shape &shape, T *base) - : _shape{shape}, _base{base} - { - // Set 'stride' - _stride.init(_shape); - } - -public: - const nnfw::util::tensor::Shape &shape(void) const { return _shape; } - -public: - T at(const nnfw::util::tensor::Index &index) const override - { - const auto offset = _stride.offset(index); - return *(_base + offset); - } - -public: - T &at(const nnfw::util::tensor::Index &index) - { - const auto offset = _stride.offset(index); - return *(_base + offset); - } - -private: - nnfw::util::tensor::Shape _shape; - -public: - T *_base; - nnfw::util::tensor::NonIncreasingStride _stride; - -public: - // TODO Introduce Operand ID class - static TensorView<T> make(::tflite::Interpreter &interp, int tensor_index) - { - auto tensor_ptr = interp.tensor(tensor_index); - - // Set 'shape' - nnfw::util::tensor::Shape shape(tensor_ptr->dims->size); - - for (uint32_t axis = 0; axis < shape.rank(); ++axis) - { - shape.dim(axis) = tensor_ptr->dims->data[axis]; - } - - return TensorView<T>(shape, interp.typed_tensor<T>(tensor_index)); - } -}; - -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_TENSOR_VIEW_H__ diff --git a/include/support/tflite/interp/Builder.h b/include/support/tflite/interp/Builder.h deleted file mode 100644 index 4a5a2f26f..000000000 --- a/include/support/tflite/interp/Builder.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_INTERP_BUILDER_H__ -#define __NNFW_SUPPORT_TFLITE_INTERP_BUILDER_H__ - -#include <tensorflow/contrib/lite/interpreter.h> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -struct Builder -{ - virtual ~Builder() = default; - - virtual std::unique_ptr<::tflite::Interpreter> build(void) const = 0; -}; - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INTERP_BUILDER_H__ diff --git a/include/support/tflite/interp/FlatBufferBuilder.h b/include/support/tflite/interp/FlatBufferBuilder.h deleted file mode 100644 index dab151dcf..000000000 --- a/include/support/tflite/interp/FlatBufferBuilder.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ -#define __NNFW_SUPPORT_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ - -#include <tensorflow/contrib/lite/model.h> - -#include "support/tflite/interp/Builder.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -class FlatBufferBuilder final : public Builder -{ -public: - FlatBufferBuilder(const ::tflite::FlatBufferModel &model) : _model{model} - { - // DO NOTHING - } - -public: - std::unique_ptr<::tflite::Interpreter> build(void) const override; - -private: - const ::tflite::FlatBufferModel &_model; -}; - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ diff --git a/include/support/tflite/interp/FunctionBuilder.h b/include/support/tflite/interp/FunctionBuilder.h deleted file mode 100644 index 1ac5918e8..000000000 --- a/include/support/tflite/interp/FunctionBuilder.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_INTERP_FUNCTION_BUILDER_H__ -#define __NNFW_SUPPORT_TFLITE_INTERP_FUNCTION_BUILDER_H__ - -#include <tensorflow/contrib/lite/model.h> - -#include "support/tflite/interp/Builder.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -class FunctionBuilder final : public Builder -{ -public: - using SetupFunc = std::function<void (::tflite::Interpreter &)>; - -public: - FunctionBuilder(const SetupFunc &fn) : _fn{fn} - { - // DO NOTHING - } - -public: - std::unique_ptr<::tflite::Interpreter> build(void) const override; - -private: - SetupFunc _fn; -}; - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw - -#endif // __NNFW_SUPPORT_TFLITE_INTERP_FUNCTION_BUILDER_H__ diff --git a/include/support/tflite/kernels/CustomOps.h b/include/support/tflite/kernels/CustomOps.h deleted file mode 100644 index 36c1c972c..000000000 --- a/include/support/tflite/kernels/CustomOps.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_KERNELS_CUSTOM_OP_H__ -#define __NNFW_SUPPORT_TFLITE_KERNELS_CUSTOM_OP_H__ - -#include "tensorflow/contrib/lite/context.h" -#include "support/tflite/kernels/TensorFlowMax.h" -#include "support/tflite/kernels/RSQRT.h" -#include "support/tflite/kernels/SquaredDifference.h" - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ - -#define REGISTER_FUNCTION(Name) \ - TfLiteRegistration *Register_##Name(void) \ - { \ - static TfLiteRegistration r = { Name::Init##Name , Name::Free##Name , Name::Prepare##Name , \ - Name::Eval##Name , 0, #Name}; \ - return &r; \ - } - -REGISTER_FUNCTION(TensorFlowMax) -REGISTER_FUNCTION(RSQRT) -REGISTER_FUNCTION(SquaredDifference) -#undef REGISTER_FUNCTION - -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite - -#endif // __NNFW_SUPPORT_TFLITE_KERNELS_CUSTOM_OP_H__ diff --git a/include/support/tflite/kernels/RSQRT.h b/include/support/tflite/kernels/RSQRT.h deleted file mode 100644 index d52442861..000000000 --- a/include/support/tflite/kernels/RSQRT.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_KERNELS_RSQRT_H__ -#define __NNFW_SUPPORT_TFLITE_KERNELS_RSQRT_H__ - -#include "tensorflow/contrib/lite/context.h" - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace RSQRT -{ - - void *InitRSQRT(TfLiteContext *context, const char *buffer, size_t length); - void FreeRSQRT(TfLiteContext *context, void *buffer); - TfLiteStatus PrepareRSQRT(TfLiteContext *context, TfLiteNode *node); - TfLiteStatus EvalRSQRT(TfLiteContext *context, TfLiteNode *node); - -} // namespace RSQRT -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite - -#endif diff --git a/include/support/tflite/kernels/SquaredDifference.h b/include/support/tflite/kernels/SquaredDifference.h deleted file mode 100644 index d0b7d3e7b..000000000 --- a/include/support/tflite/kernels/SquaredDifference.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_KERNELS_SQUARED_DIFFERENCE_H__ -#define __NNFW_SUPPORT_TFLITE_KERNELS_SQUARED_DIFFERENCE_H__ - -#include "tensorflow/contrib/lite/context.h" - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace SquaredDifference -{ - - void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length); - void FreeSquaredDifference(TfLiteContext *context, void *buffer); - TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node); - TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node); - -} // namespace SquaredDifference -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite - -#endif diff --git a/include/support/tflite/kernels/TensorFlowMax.h b/include/support/tflite/kernels/TensorFlowMax.h deleted file mode 100644 index 99c9fdc68..000000000 --- a/include/support/tflite/kernels/TensorFlowMax.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_TFLITE_KERNELS_TENSORFLOW_MAX_H__ -#define __NNFW_SUPPORT_TFLITE_KERNELS_TENSORFLOW_MAX_H__ - -#include "tensorflow/contrib/lite/context.h" - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace TensorFlowMax -{ - - void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length); - void FreeTensorFlowMax(TfLiteContext *context, void *buffer); - TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node); - TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node); - -} // namespace TensorFlowMax -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite - -#endif diff --git a/include/support/tflite/kernels/register.h b/include/support/tflite/kernels/register.h deleted file mode 100644 index 43a4c1a23..000000000 --- a/include/support/tflite/kernels/register.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE This header is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.h' -#ifndef __NNFW_SUPPORT_TFLITE_KERNELS_REGISTER_H__ -#define __NNFW_SUPPORT_TFLITE_KERNELS_REGISTER_H__ - -#include <unordered_map> -#include "tensorflow/contrib/lite/context.h" -#include "tensorflow/contrib/lite/model.h" - -// TODO Use namespace nnfw -namespace tflite { -namespace ops { -namespace builtin { - -class BuiltinOpResolver : public MutableOpResolver { - public: - BuiltinOpResolver(); -}; - -} // namespace builtin -} // namespace ops -} // namespace tflite - -#endif // __NNFW_SUPPORT_TFLITE_KERNELS_REGISTER_H__ diff --git a/include/support/tflite/nnapi_delegate.h b/include/support/tflite/nnapi_delegate.h deleted file mode 100644 index a5da8ac39..000000000 --- a/include/support/tflite/nnapi_delegate.h +++ /dev/null @@ -1,84 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This header is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.h' -#ifndef __NNFW_SUPPORT_TFLITE_NNAPI_DELEGATE_H__ -#define __NNFW_SUPPORT_TFLITE_NNAPI_DELEGATE_H__ - -#include "tensorflow/contrib/lite/allocation.h" -#include "tensorflow/contrib/lite/context.h" -#include "tensorflow/contrib/lite/error_reporter.h" -#include "tensorflow/contrib/lite/interpreter.h" -#include "NeuralNetworksShim.h" - -class ANeuralNetworksModel; -class ANeuralNetworksCompilation; - -namespace nnfw { - -class NNAPIAllocation : public tflite::MMAPAllocation { - public: - NNAPIAllocation(const char* filename, ::tflite::ErrorReporter* error_reporter); - ~NNAPIAllocation(); - - size_t offset(const void* ptr) const { - auto signed_offset = reinterpret_cast<const uint8_t*>(ptr) - - reinterpret_cast<const uint8_t*>(mmapped_buffer_); - - return static_cast<size_t>(signed_offset); - } - - ANeuralNetworksMemory* memory() const { return handle_; } - bool valid() const override { return handle_ != nullptr; } - - private: - mutable ANeuralNetworksMemory* handle_ = nullptr; -}; - -class NNAPIDelegate { - public: - ~NNAPIDelegate(); - - // Convert a tflite graph to NNAPI - TfLiteStatus BuildGraph(::tflite::Interpreter* interpreter); - - // Run - TfLiteStatus Invoke(::tflite::Interpreter* interpreter); - - private: - // The NN API model handle - ANeuralNetworksModel* nn_model_ = nullptr; - // The NN API compilation handle - ANeuralNetworksCompilation* nn_compiled_model_ = nullptr; - - // List of state tensors for LSTM, RNN, SVDF. - // NN API does not allow ops to maintain states across multiple - // invocations. We need to manually create state input tensors from - // corresponding state output tensors of TFLite operations, and map them - // correctly. - std::vector<int> model_states_inputs_; // holds NNAPI operand ids - std::vector<int> model_states_outputs_; // holds TFLite tensor ids -}; - -} // namespace nnfw - -#endif // TENSORFLOW_CONTRIB_LITE_NNAPI_DELEGATE_H_ - -// clang-format on diff --git a/include/util/EnvVar.h b/include/util/EnvVar.h deleted file mode 100644 index 5512fedbd..000000000 --- a/include/util/EnvVar.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_ENV_VAR__ -#define __NNFW_UTIL_ENV_VAR__ - -#include <algorithm> -#include <array> -#include <cstdlib> -#include <string> - - -namespace nnfw -{ -namespace util -{ - -class EnvVar -{ -public: - EnvVar(const std::string &key) - { - const char *value = std::getenv(key.c_str()); - if (value == nullptr) - { - // An empty string is considered as an empty value - _value = ""; - } - else - { - _value = value; - } - } - - std::string asString(const std::string &def) const - { - if (_value.empty()) - return def; - return _value; - } - - bool asBool(bool def) const - { - if (_value.empty()) - return def; - static const std::array<std::string, 5> false_list{"0", "OFF", "FALSE", "N", "NO"}; - return std::find(false_list.begin(), false_list.end(), _value); - } - - int asInt(int def) const - { - if (_value.empty()) - return def; - return std::stoi(_value); - } - -private: - std::string _value; -}; - -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_ENV_VAR__ diff --git a/include/util/benchmark.h b/include/util/benchmark.h deleted file mode 100644 index b07656c88..000000000 --- a/include/util/benchmark.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_BENCHMARK_H__ -#define __NNFW_UTIL_BENCHMARK_H__ - -#include <chrono> - -namespace nnfw -{ -namespace util -{ -// Benckmark support -namespace benchmark -{ - -template <typename T> class Accumulator -{ -public: - Accumulator(T &ref) : _ref(ref) - { - // DO NOTHING - } - -public: - T &operator()(void) { return _ref; } - -private: - T &_ref; -}; - -template <typename T, typename Callable> -Accumulator<T> &operator<<(Accumulator<T> &&acc, Callable cb) -{ - auto begin = std::chrono::steady_clock::now(); - cb(); - auto end = std::chrono::steady_clock::now(); - - acc() += std::chrono::duration_cast<T>(end - begin); - - return acc; -} - -template <typename T> Accumulator<T> measure(T &out) { return Accumulator<T>(out); } - -} // namespace benchmark -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_BENCHMARK_H__ diff --git a/include/util/environment.h b/include/util/environment.h deleted file mode 100644 index 84df65f95..000000000 --- a/include/util/environment.h +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __UTIL_ENVIRONMENT_H__ -#define __UTIL_ENVIRONMENT_H__ - -namespace nnfw -{ -namespace util -{ - -int get_env_int(const char *name, int defaultValue = 0); -bool get_env_bool(const char *name, bool defaultValue = false); -} -} - -#include <string> - -namespace nnfw -{ -namespace util -{ -namespace env -{ - -template <typename T> struct Accessor -{ - virtual ~Accessor() = default; - - virtual bool access(T &out) const = 0; -}; - -class IntAccessor : public Accessor<int> -{ -public: - IntAccessor(const std::string &tag); - -public: - bool access(int &out) const override; - -private: - std::string _tag; -}; - -class FloatAccessor : public Accessor<float> -{ -public: - FloatAccessor(const std::string &tag); - -public: - bool access(float &out) const override; - -private: - std::string _tag; -}; - -} // namespace env -} // namespace util -} // namespace nnfw - -#endif // __UTIL_ENVIRONMENT_H__ diff --git a/include/util/feature/Index.h b/include/util/feature/Index.h deleted file mode 100644 index 83b51f8a6..000000000 --- a/include/util/feature/Index.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_INDEX_H__ -#define __NNFW_UTIL_FEATURE_INDEX_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -class Index -{ -public: - Index() = default; - -public: - Index(int32_t ch, int32_t row, int32_t col) : _batch{1}, _ch{ch}, _row{row}, _col{col} - { - // DO NOTHING - } - Index(int32_t batch, int32_t ch, int32_t row, int32_t col) : _batch{batch}, _ch{ch}, _row{row}, _col{col} - { - // DO NOTHING - } - -public: - int32_t batch(void) const { return _batch; } - int32_t ch(void) const { return _ch; } - int32_t row(void) const { return _row; } - int32_t col(void) const { return _col; } - -public: - int32_t &batch(void) { return _batch; } - int32_t &ch(void) { return _ch; } - int32_t &row(void) { return _row; } - int32_t &col(void) { return _col; } - -private: - int32_t _batch; - int32_t _ch; - int32_t _row; - int32_t _col; -}; - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_INDEX_H__ diff --git a/include/util/feature/IndexIterator.h b/include/util/feature/IndexIterator.h deleted file mode 100644 index e2a7196e8..000000000 --- a/include/util/feature/IndexIterator.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_INDEX_ITERATOR_H__ -#define __NNFW_UTIL_FEATURE_INDEX_ITERATOR_H__ - -#include "util/feature/Shape.h" - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -class IndexIterator -{ -public: - IndexIterator(const Shape &shape) : _shape{shape} - { - // DO NOTHING - } - -public: - template <typename Callable> IndexIterator &iter(Callable cb) - { - for (int32_t batch = 0; batch < _shape.N; ++batch) - { - for (int32_t ch = 0; ch < _shape.C; ++ch) - { - for (int32_t row = 0; row < _shape.H; ++row) - { - for (int32_t col = 0; col < _shape.W; ++col) - { - cb(batch, ch, row, col); - } - } - } - } - - return (*this); - } - -private: - const Shape _shape; -}; - -static inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } - -template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) -{ - return it.iter(cb); -} - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_INDEX_ITERATOR_H__ diff --git a/include/util/feature/Object.h b/include/util/feature/Object.h deleted file mode 100644 index ce66c2437..000000000 --- a/include/util/feature/Object.h +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_OBJECT_H__ -#define __NNFW_UTIL_FEATURE_OBJECT_H__ - -#include "util/feature/Shape.h" -#include "util/feature/Index.h" -#include "util/feature/Reader.h" - -#include <vector> - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -template <typename T> class Object final : public Reader<T> -{ -public: - using Generator = std::function<T(const Shape &shape, const Index &index)>; - -public: - Object(const Shape &shape, const Generator &fn) : _shape{shape} - { - _value.resize(_shape.C * _shape.H * _shape.W); - - for (int32_t ch = 0; ch < _shape.C; ++ch) - { - for (int32_t row = 0; row < _shape.H; ++row) - { - for (int32_t col = 0; col < _shape.W; ++col) - { - _value.at(offsetOf(ch, row, col)) = fn(_shape, Index{ch, row, col}); - } - } - } - } - -public: - const Shape &shape(void) const { return _shape; } - -public: - T at(uint32_t ch, uint32_t row, uint32_t col) const override - { - return _value.at(offsetOf(ch, row, col)); - } - -private: - uint32_t offsetOf(uint32_t ch, uint32_t row, uint32_t col) const - { - return ch * _shape.H * _shape.W + row * _shape.W + col; - } - -private: - Shape _shape; - std::vector<T> _value; -}; - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_OBJECT_H__ diff --git a/include/util/feature/Reader.h b/include/util/feature/Reader.h deleted file mode 100644 index f870f7d11..000000000 --- a/include/util/feature/Reader.h +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_READER_H__ -#define __NNFW_UTIL_FEATURE_READER_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -template <typename T> struct Reader -{ - virtual ~Reader() = default; - - virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; - virtual T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_READER_H__ diff --git a/include/util/feature/Shape.h b/include/util/feature/Shape.h deleted file mode 100644 index 2a2c59eed..000000000 --- a/include/util/feature/Shape.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_SHAPE_H__ -#define __NNFW_UTIL_FEATURE_SHAPE_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -struct Shape -{ - int32_t N; // Batch - int32_t C; // Depth - int32_t H; // Height - int32_t W; // Width - - Shape() = default; - Shape(int32_t depth, int32_t height, int32_t width) : N{1}, C{depth}, H{height}, W{width} - { - // DO NOTHING - } - Shape(int32_t batch, int32_t depth, int32_t height, int32_t width) : N{batch}, C{depth}, H{height}, W{width} - { - // DO NOTHING - } -}; - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_H__ diff --git a/include/util/feature/TextFormatter.h b/include/util/feature/TextFormatter.h deleted file mode 100644 index de5c4c13a..000000000 --- a/include/util/feature/TextFormatter.h +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FEATURE_TEXT_FORMATTER_H__ -#define __NNFW_UTIL_FEATURE_TEXT_FORMATTER_H__ - -#include "util/feature/Shape.h" -#include "util/feature/Reader.h" - -#include <ostream> -#include <iomanip> -#include <limits> - -namespace nnfw -{ -namespace util -{ -namespace feature -{ - -template <typename T> class TextFormatter -{ -public: - TextFormatter(const Shape &shape, const Reader<T> &data) : _shape(shape), _data(data) - { - // DO NOTHING - } - -public: - const Shape &shape(void) const { return _shape; } - const Reader<T> &data(void) const { return _data; } - -private: - const Shape &_shape; - const Reader<T> &_data; -}; - -template <typename T> std::ostream &operator<<(std::ostream &os, const TextFormatter<T> &fmt) -{ - const auto &shape = fmt.shape(); - - for (uint32_t ch = 0; ch < shape.C; ++ch) - { - os << " Channel " << ch << ":" << std::endl; - for (uint32_t row = 0; row < shape.H; ++row) - { - os << " "; - for (uint32_t col = 0; col < shape.W; ++col) - { - const auto value = fmt.data().at(ch, row, col); - os << std::right; - os << std::fixed; - os << std::setw(std::numeric_limits<T>::digits10 + 2); - os << std::setprecision(5); - os << value; - os << " "; - } - os << std::endl; - } - } - - return os; -} - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_TEXT_FORMATTER_H__ diff --git a/include/util/fp32.h b/include/util/fp32.h deleted file mode 100644 index 604435470..000000000 --- a/include/util/fp32.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_FP32_H__ -#define __NNFW_UTIL_FP32_H__ - -#include <cmath> -#include <cfloat> -#include <algorithm> -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace fp32 -{ - -inline float relative_diff(float lhs, float rhs) -{ - const auto diff = std::fabs(lhs - rhs); - const auto base = std::max(std::fabs(lhs), std::fabs(rhs)); - - return diff / base; -} - -inline bool epsilon_equal(float expected, float obtained, uint32_t tolerance = 1) -{ - if (std::isnan(expected) && std::isnan(obtained)) - { - return true; - } - - // Let's use relative epsilon comparision - const auto diff = std::fabs(expected - obtained); - const auto max = std::max(std::fabs(expected), std::fabs(obtained)); - - return diff <= (max * FLT_EPSILON * tolerance); -} - -inline bool absolute_epsilon_equal(float expected, float obtained, float tolerance = 0.001) -{ - if (std::isnan(expected) && std::isnan(obtained)) - { - return true; - } - - // Let's use absolute epsilon comparision - const auto diff = std::fabs(expected - obtained); - - return diff <= tolerance; -} - -} // namespace fp32 -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FP32_H__ diff --git a/include/util/kernel/IndexIterator.h b/include/util/kernel/IndexIterator.h deleted file mode 100644 index d01656447..000000000 --- a/include/util/kernel/IndexIterator.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_KERNEL_INDEX_ITERATOR_H__ -#define __NNFW_UTIL_KERNEL_INDEX_ITERATOR_H__ - -#include "util/kernel/Shape.h" - -namespace nnfw -{ -namespace util -{ -namespace kernel -{ - -class IndexIterator -{ -public: - IndexIterator(const Shape &shape) : _shape{shape} - { - // DO NOTHING - } - -public: - template <typename Callable> IndexIterator &iter(Callable cb) - { - for (int32_t nth = 0; nth < _shape.N; ++nth) - { - for (int32_t ch = 0; ch < _shape.C; ++ch) - { - for (int32_t row = 0; row < _shape.H; ++row) - { - for (int32_t col = 0; col < _shape.W; ++col) - { - cb(nth, ch, row, col); - } - } - } - } - - return (*this); - } - -private: - const Shape _shape; -}; - -inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } - -template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) -{ - return it.iter(cb); -} - -} // namespace kernel -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_INDEX_ITERATOR_H__ diff --git a/include/util/kernel/RandomObject.h b/include/util/kernel/RandomObject.h deleted file mode 100644 index d6fca9a66..000000000 --- a/include/util/kernel/RandomObject.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_KERNEL_RANDOM_OBJECT_H__ -#define __NNFW_UTIL_KERNEL_RANDOM_OBJECT_H__ - -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" - -#include <vector> - -namespace nnfw -{ -namespace util -{ -namespace kernel -{ - -template <typename T> class RandomObject final : public Reader<T> -{ -public: - RandomObject(const Shape &shape) : _shape{shape} - { - const uint32_t size = _shape.N * _shape.C * _shape.H * _shape.W; - - // TODO Use random number - for (uint32_t off = 0; off < size; ++off) - { - _value.emplace_back(static_cast<float>(off)); - } - } - -public: - const Shape &shape(void) const { return _shape; } - -public: - T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override - { - uint32_t index = 0; - - index += nth * _shape.C * _shape.H * _shape.W; - index += ch * _shape.H * _shape.W; - index += row * _shape.W; - index += col; - - return _value.at(index); - } - -private: - const Shape _shape; - std::vector<T> _value; -}; - -} // namespace kernel -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_KERNEL_RANDOM_OBJECT_H__ diff --git a/include/util/kernel/Reader.h b/include/util/kernel/Reader.h deleted file mode 100644 index 9d8f33ad6..000000000 --- a/include/util/kernel/Reader.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_KERNEL_READER_H__ -#define __NNFW_UTIL_KERNEL_READER_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace kernel -{ - -template <typename T> struct Reader -{ - virtual ~Reader() = default; - - virtual T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; - -} // namespace kernel -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_KERNEL_READER_H__ diff --git a/include/util/kernel/Shape.h b/include/util/kernel/Shape.h deleted file mode 100644 index bd2332989..000000000 --- a/include/util/kernel/Shape.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_KERNEL_SHAPE_H__ -#define __NNFW_UTIL_KERNEL_SHAPE_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace kernel -{ - -struct Shape -{ - int32_t N; - int32_t C; - int32_t H; - int32_t W; - - Shape() = default; - Shape(int32_t count, int32_t depth, int32_t height, int32_t width) - : N{count}, C{depth}, H{height}, W{width} - { - // DO NOTHING - } -}; - -} // namespace kernel -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_KERNEL_SHAPE_H__ diff --git a/include/util/matrix/IndexIterator.h b/include/util/matrix/IndexIterator.h deleted file mode 100644 index b6fccff45..000000000 --- a/include/util/matrix/IndexIterator.h +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_MATRIX_INDEX_ITERATOR_H__ -#define __NNFW_UTIL_MATRIX_INDEX_ITERATOR_H__ - -#include "util/matrix/Shape.h" - -namespace nnfw -{ -namespace util -{ -namespace matrix -{ - -class IndexIterator -{ -public: - IndexIterator(const Shape &shape) : _shape{shape} - { - // DO NOTHING - } - -public: - template <typename Callable> IndexIterator &iter(Callable cb) - { - for (uint32_t row = 0; row < _shape.H; ++row) - { - for (uint32_t col = 0; col < _shape.W; ++col) - { - cb(row, col); - } - } - - return (*this); - } - -private: - const Shape _shape; -}; - -inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } - -template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) -{ - return it.iter(cb); -} - -} // namespace matrix -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_MATRIX_INDEX_ITERATOR_H__ diff --git a/include/util/matrix/Reader.h b/include/util/matrix/Reader.h deleted file mode 100644 index 526eaf5cd..000000000 --- a/include/util/matrix/Reader.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_MATRIX_READER_H__ -#define __NNFW_UTIL_MATRIX_READER_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace matrix -{ - -template <typename T> struct Reader -{ - virtual ~Reader() = default; - - virtual T at(uint32_t row, uint32_t col) const = 0; -}; - -} // namespace matrix -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_MATRIX_READER_H__ diff --git a/include/util/matrix/Shape.h b/include/util/matrix/Shape.h deleted file mode 100644 index e2c20b4ca..000000000 --- a/include/util/matrix/Shape.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// for 2D tensor -#ifndef __NNFW_UTIL_MATRIX_SHAPE_H__ -#define __NNFW_UTIL_MATRIX_SHAPE_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace matrix -{ - -struct Shape -{ - int32_t H; // Height - int32_t W; // Width - - Shape() = default; - Shape(int32_t height, int32_t width) : H{height}, W{width} - { - // DO NOTHING - } -}; - -} // namespace feature -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_MATRIX_SHAPE_H__ diff --git a/include/util/profiling/profile_buffer.h b/include/util/profiling/profile_buffer.h deleted file mode 100644 index 8785a40d3..000000000 --- a/include/util/profiling/profile_buffer.h +++ /dev/null @@ -1,162 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ -#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ - -#include <cstddef> -#include <cstdint> - -#include "util/profiling/time.h" - -namespace tflite { -namespace profiling { - -// A profiling event. -struct ProfileEvent { - // Describes the type of event. - // The event_metadata field may contain additional data for interpreting - // the event. - enum class EventType { - // Default event type, the metadata field has no special significance. - DEFAULT = 0, - // The event is an operator invocation and the event_metadata field is the - // index of operator node. - OPERATOR_INVOKE_EVENT = 1 - }; - - // Label of the event. This usually describes the event. - const char* tag; - // Timestamp in microseconds when the event began. - uint64_t begin_timestamp_us; - // Timestamp in microseconds when the event ended. - uint64_t end_timestamp_us; - // The field containing the type of event. This must be one of the event types - // in EventType. - EventType event_type; - // Extra data describing the details of the event. - uint32_t event_metadata; -}; -} // namespace profiling -} // namespace tflite - -#ifdef TFLITE_PROFILING_ENABLED - -#include <sys/time.h> -#include <vector> - -namespace tflite { -namespace profiling { -constexpr uint32_t kInvalidEventHandle = static_cast<uint32_t>(~0) - 1; - -// A ring buffer of profile events. -// This class is not thread safe. -class ProfileBuffer { - public: - ProfileBuffer(uint32_t max_num_entries, bool enabled) - : enabled_(enabled), current_index_(0), event_buffer_(max_num_entries) {} - - // Adds an event to the buffer with begin timestamp set to the current - // timestamp. Returns a handle to event that can be used to call EndEvent. If - // buffer is disabled this has no affect. - // The tag of the event should remain valid till the buffer is valid. - uint32_t BeginEvent(const char* tag, ProfileEvent::EventType event_type, - uint32_t event_metadata) { - if (!enabled_) { - return kInvalidEventHandle; - } - uint64_t timestamp = time::NowMicros(); - int index = current_index_ % event_buffer_.size(); - event_buffer_[index].tag = tag; - event_buffer_[index].event_type = event_type; - event_buffer_[index].event_metadata = event_metadata; - event_buffer_[index].begin_timestamp_us = timestamp; - event_buffer_[index].end_timestamp_us = 0; - current_index_++; - return index; - } - - // Sets the enabled state of buffer to |enabled| - void SetEnabled(bool enabled) { enabled_ = enabled; } - - // Sets the end timestamp for event for the handle to current time. - // If the buffer is disabled or previous event has been overwritten this - // operation has not effect. - void EndEvent(uint32_t event_handle) { - if (!enabled_ || event_handle == kInvalidEventHandle || - event_handle > current_index_) { - return; - } - const uint32_t max_size = event_buffer_.size(); - if (current_index_ > (max_size + event_handle)) { - // Ignore, buffer has already overflowed. - return; - } - - int event_index = event_handle % max_size; - event_buffer_[event_index].end_timestamp_us = time::NowMicros(); - } - - // Returns the size of the buffer. - size_t Size() const { - return (current_index_ >= event_buffer_.size()) ? event_buffer_.size() - : current_index_; - } - - // Resets the buffer. - void Reset() { - enabled_ = false; - current_index_ = 0; - } - - // Returns the profile event at the given index. If the index is invalid a - // nullptr is returned. The return event may get overwritten if more events - // are added to buffer. - const struct ProfileEvent* const At(int index) const { - size_t size = Size(); - if (index >= size) { - return nullptr; - } - const uint32_t max_size = event_buffer_.size(); - uint32_t start = - (current_index_ > max_size) ? current_index_ % max_size : max_size; - index = (index + start) % max_size; - return &event_buffer_[index]; - } - - private: - bool enabled_; - uint32_t current_index_; - std::vector<ProfileEvent> event_buffer_; -}; -} // namespace profiling -} // namespace tflite -#endif // TFLITE_PROFILING_ENABLED -#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ diff --git a/include/util/profiling/profiler.h b/include/util/profiling/profiler.h deleted file mode 100644 index 0d68b8f4b..000000000 --- a/include/util/profiling/profiler.h +++ /dev/null @@ -1,195 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ -#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ - -#include <vector> - -#include "util/profiling/profile_buffer.h" - -#ifdef TFLITE_PROFILING_ENABLED - -namespace tflite { -namespace profiling { -class ScopedProfile; -class ScopedOperatorProfile; - -// Controls whether profiling is enabled or disabled and collects profiles. -// TFLite is used on platforms that don't have posix threads, so the profiler is -// kept as simple as possible. It is designed to be used only on a single -// thread. -// -// Profiles are collected using Scoped*Profile objects that begin and end a -// profile event. -// An example usage is shown in the example below: -// -// Say Worker class has a DoWork method and we are interested in profiling -// the overall execution time for DoWork and time spent in Task1 and Task2 -// functions. -// -// class Worker { -// public: -// void DoWork() { -// ScopedProfile(&controller, "DoWork"); -// Task1(); -// Task2(); -// ..... -// } -// -// void Task1() { -// ScopedProfile(&controller, "Task1"); -// .... -// } -// -// void Task2() { -// ScopedProfile(&controller, "Task2"); -// } -// -// Profiler profiler; -// } -// -// We instrument the functions that need to be profiled. -// -// Profile can be collected by enable profiling and then getting profile -// events. -// -// void ProfileWorker() { -// Worker worker; -// worker.profiler.EnableProfiling(); -// worker.DoWork(); -// worker.profiler.DisableProfiling(); -// // Profiling is complete, extract profiles. -// auto profile_events = worker.profiler.GetProfiles(); -// } -// -// -class Profiler { - public: - Profiler() : buffer_(1024, false) {} - - void StartProfiling() { buffer_.SetEnabled(true); } - void StopProfiling() { buffer_.SetEnabled(false); } - void Reset() { buffer_.Reset(); } - std::vector<const ProfileEvent*> GetProfileEvents() { - std::vector<const ProfileEvent*> profile_events; - profile_events.reserve(buffer_.Size()); - for (size_t i = 0; i < buffer_.Size(); i++) { - profile_events.push_back(buffer_.At(i)); - } - return profile_events; - } - - private: - friend class ScopedProfile; - friend class ScopedOperatorProfile; - ProfileBuffer* GetProfileBuffer() { return &buffer_; } - ProfileBuffer buffer_; -}; - -class ScopedProfile { - public: - // Adds a profile event to profile that begins with the construction - // of object and ends when the object goes out of scope. - // The lifetime of tag should be at least the lifetime of profiler. - - ScopedProfile(Profiler* profiler, const char* tag) - : buffer_(nullptr), event_handle_(0) { - if (profiler) { - buffer_ = profiler->GetProfileBuffer(); - event_handle_ = - buffer_->BeginEvent(tag, ProfileEvent::EventType::DEFAULT, 0); - } - } - ~ScopedProfile() { - if (buffer_) { - buffer_->EndEvent(event_handle_); - } - } - - private: - ProfileBuffer* buffer_; - int32_t event_handle_; -}; - -class ScopedOperatorProfile { - public: - // Adds a profile event to profile that begins with the construction - // of object and ends when the object goes out of scope. - // The lifetime of tag should be at least the lifetime of profiler. - ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index) - : buffer_(nullptr), event_handle_(0) { - if (profiler) { - buffer_ = profiler->GetProfileBuffer(); - event_handle_ = buffer_->BeginEvent( - tag, ProfileEvent::EventType::OPERATOR_INVOKE_EVENT, node_index); - } - } - - ~ScopedOperatorProfile() { - if (buffer_) { - buffer_->EndEvent(event_handle_); - } - } - - private: - ProfileBuffer* buffer_; - int32_t event_handle_; -}; - -} // namespace profiling -} // namespace tflite - -#define VARNAME_UNIQ(name, ctr) name##ctr - -#define SCOPED_OPERATOR_PROFILE(profiler, node_index) \ - tflite::profiling::ScopedOperatorProfile VARNAME_UNIQ( \ - _profile_, __COUNTER__)((profiler), "OpInvoke", (node_index)) -#else - -namespace tflite { -namespace profiling { -// A noop version of profiler when profiling is disabled. -class Profiler { - public: - Profiler() {} - void StartProfiling() {} - void StopProfiling() {} - void Reset() {} - std::vector<const ProfileEvent*> GetProfileEvents() { return {}; } -}; -} // namespace profiling -} // namespace tflite - -#define SCOPED_OPERATOR_PROFILE(profiler, node_index) - -#endif // TFLITE_PROFILING_ENABLED - -#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ diff --git a/include/util/profiling/profiling.h b/include/util/profiling/profiling.h deleted file mode 100644 index 3365dc8d6..000000000 --- a/include/util/profiling/profiling.h +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_PROFILING_H__ -#define __NNFW_UTIL_PROFILING_H__ - -#include <iostream> - -namespace tflite { -namespace profiling { -class Profiler; // forward declaration -} -} - -namespace profiling -{ - -class Sync -{ -public: - Sync() : _enabled{false} - { - auto env = std::getenv("PURE_ARM_COMPUTE_SYNC_ENABLE"); - - if (env && std::atoi(env) != 0) - { - _enabled = true; - } - } - -public: - bool enabled(void) const { return _enabled; } - -private: - bool _enabled; -}; - -} // namespace profiling - -namespace profiling -{ - -class Context -{ -public: - Context() : _sync(), _profiler(nullptr) {} - -public: - const Sync &sync(void) const { return _sync; } - tflite::profiling::Profiler* getProfiler() { return _profiler; } - void setProfiler(tflite::profiling::Profiler* p) { _profiler = p; } - -private: - Sync _sync; - tflite::profiling::Profiler* _profiler; - -public: - static Context &get(void) - { - static Context ctx{}; - return ctx; - } -}; - -} // namespace profiling -#endif // __NNFW_UTIL_PROFILING_H__ diff --git a/include/util/profiling/time.h b/include/util/profiling/time.h deleted file mode 100644 index da3289359..000000000 --- a/include/util/profiling/time.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ -#define TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ - -#include <cstdint> - -namespace tflite { -namespace profiling { -namespace time { -uint64_t NowMicros(); -} // namespace time -} // namespace profiling -} // namespace tflite -#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ diff --git a/include/util/tensor/Comparator.h b/include/util/tensor/Comparator.h deleted file mode 100644 index f0ab4ab20..000000000 --- a/include/util/tensor/Comparator.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_COMPARATOR_H__ -#define __NNFW_UTIL_TENSOR_COMPARATOR_H__ - -#include "util/tensor/Index.h" -#include "util/tensor/Shape.h" -#include "util/tensor/Reader.h" -#include "util/tensor/Diff.h" - -#include <functional> - -#include <vector> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -class Comparator -{ -public: - Comparator(const std::function<bool (float lhs, float rhs)> &fn) : _compare_fn{fn} - { - // DO NOTHING - } - -public: - struct Observer - { - virtual void notify(const Index &index, float expected, float obtained) = 0; - }; - -public: - // NOTE Observer should live longer than comparator - std::vector<Diff<float>> compare(const Shape &shape, - const Reader<float> &expected, - const Reader<float> &obtained, - Observer *observer = nullptr) const; - -private: - std::function<bool (float lhs, float rhs)> _compare_fn; -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_COMPARATOR_H__ diff --git a/include/util/tensor/Diff.h b/include/util/tensor/Diff.h deleted file mode 100644 index 25a9bafcf..000000000 --- a/include/util/tensor/Diff.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_DIFF_H__ -#define __NNFW_UTIL_TENSOR_DIFF_H__ - -#include "util/tensor/Index.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -template<typename T> struct Diff -{ - Index index; - - T expected; - T obtained; - - Diff(const Index &i) : index(i) - { - // DO NOTHING - } - - Diff(const Index &i, const T &e, const T &o) : index(i), expected{e}, obtained{o} - { - // DO NOTHING - } -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_DIFF_H__ diff --git a/include/util/tensor/Index.h b/include/util/tensor/Index.h deleted file mode 100644 index bc41d3c8e..000000000 --- a/include/util/tensor/Index.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_INDEX_H__ -#define __NNFW_UTIL_TENSOR_INDEX_H__ - -#include <cstdint> -#include <cstddef> - -#include <vector> -#include <initializer_list> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -struct Index -{ -public: - Index(size_t rank) { _offsets.resize(rank); } - -public: - Index(std::initializer_list<int32_t> offsets) : _offsets{offsets} - { - // DO NOTHING - } - -public: - size_t rank(void) const { return _offsets.size(); } - -public: - int32_t at(size_t n) const { return _offsets.at(n); } - int32_t &at(size_t n) { return _offsets.at(n); } - -private: - std::vector<int32_t> _offsets; -}; - -// This is used to convert NNAPI tensor index to ARM tensor index or vice versa -inline static Index copy_reverse(const Index &origin) -{ - size_t rank = origin.rank(); - Index target(rank); - for (int i = 0; i < rank; i++) - target.at(i) = origin.at(rank-1 -i); - return target; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_INDEX_H__ diff --git a/include/util/tensor/IndexEnumerator.h b/include/util/tensor/IndexEnumerator.h deleted file mode 100644 index 30325cbfa..000000000 --- a/include/util/tensor/IndexEnumerator.h +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_INDEX_ENUMERATOR_H__ -#define __NNFW_UTIL_TENSOR_INDEX_ENUMERATOR_H__ - -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -class IndexEnumerator -{ -public: - explicit IndexEnumerator(const Shape &shape) : _shape(shape), _index(shape.rank()), _cursor(0) - { - const size_t rank = _shape.rank(); - - for (size_t axis = 0; axis < rank; ++axis) - { - _index.at(axis) = 0; - } - - for (_cursor = 0; _cursor < rank; ++_cursor) - { - if (_index.at(_cursor) < _shape.dim(_cursor)) - { - break; - } - } - } - -public: - IndexEnumerator(IndexEnumerator &&) = delete; - IndexEnumerator(const IndexEnumerator &) = delete; - -public: - bool valid(void) const { return _cursor < _shape.rank(); } - -public: - const Index &curr(void) const { return _index; } - -public: - void advance(void) - { - const size_t rank = _shape.rank(); - - // Find axis to be updated - while((_cursor < rank) && !(_index.at(_cursor) + 1 < _shape.dim(_cursor))) - { - ++_cursor; - } - - if(_cursor == rank) - { - return; - } - - // Update index - _index.at(_cursor) += 1; - - for (size_t axis = 0; axis < _cursor; ++axis) - { - _index.at(axis) = 0; - } - - // Update cursor - _cursor = 0; - } - -public: - const Shape _shape; - -private: - size_t _cursor; - Index _index; -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_INDEX_ENUMERATOR_H__ diff --git a/include/util/tensor/IndexFormatter.h b/include/util/tensor/IndexFormatter.h deleted file mode 100644 index 8014a42b6..000000000 --- a/include/util/tensor/IndexFormatter.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_INDEX_FORMATTER_H__ -#define __NNFW_UTIL_TENSOR_INDEX_FORMATTER_H__ - -#include "util/tensor/Index.h" - -#include <ostream> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -class IndexFormatter -{ -public: - IndexFormatter(const nnfw::util::tensor::Index &index) : _index(index) - { - // DO NOTHING - } - -public: - const nnfw::util::tensor::Index &index(void) const { return _index; } - -private: - const nnfw::util::tensor::Index &_index; -}; - -std::ostream &operator<<(std::ostream &os, const IndexFormatter &fmt); - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_INDEX_FORMATTER_H__ diff --git a/include/util/tensor/IndexIterator.h b/include/util/tensor/IndexIterator.h deleted file mode 100644 index cbe895166..000000000 --- a/include/util/tensor/IndexIterator.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_INDEX_ITERATOR_H__ -#define __NNFW_UTIL_TENSOR_INDEX_ITERATOR_H__ - -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" -#include "util/tensor/IndexEnumerator.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -class IndexIterator -{ -public: - IndexIterator(const Shape &shape) : _shape(shape) - { - // DO NOTHING - } - -public: - // Allow move, but disallow copy - IndexIterator(IndexIterator &&) = default; - IndexIterator(const IndexIterator &) = delete; - -public: - template <typename Callable> IndexIterator &iter(Callable fn) - { - for (IndexEnumerator e{_shape}; e.valid(); e.advance()) - { - fn(e.curr()); - } - - return (*this); - } - -private: - const Shape &_shape; -}; - -inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } - -template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) -{ - return it.iter(cb); -} - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_INDEX_ITERATOR_H__ diff --git a/include/util/tensor/NonIncreasingStride.h b/include/util/tensor/NonIncreasingStride.h deleted file mode 100644 index ff013ffa2..000000000 --- a/include/util/tensor/NonIncreasingStride.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_NON_INCREASING_STRIDE_H__ -#define __NNFW_UTIL_TENSOR_NON_INCREASING_STRIDE_H__ - -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" - -#include <vector> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -// As its name suggests, stride[N-1] >= stride[N] holds for all N < rank in NonIncreasingStride. -class NonIncreasingStride -{ -public: - void init(const Shape &shape) - { - _stride.resize(shape.rank()); - _stride.at(shape.rank() - 1) = 1; - - for (uint32_t axis = shape.rank() - 1; axis > 0; --axis) - { - _stride.at(axis - 1) = _stride.at(axis) * shape.dim(axis); - } - } - -public: - uint32_t at(uint32_t axis) const { return _stride.at(axis); } - -public: - uint32_t offset(const Index &index) const; - -private: - std::vector<uint32_t> _stride; -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_NON_INCREASING_STRIDE_H__ diff --git a/include/util/tensor/Object.h b/include/util/tensor/Object.h deleted file mode 100644 index 7afd089ea..000000000 --- a/include/util/tensor/Object.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_OBJECT_H__ -#define __NNFW_UTIL_TENSOR_OBJECT_H__ - -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" -#include "util/tensor/IndexIterator.h" -#include "util/tensor/NonIncreasingStride.h" -#include "util/tensor/Reader.h" - -#include <vector> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -template <typename T> class Object final : public Reader<T> -{ -public: - using Generator = std::function<T(const Shape &shape, const Index &index)>; - -public: - Object(const Shape &shape, const Generator &fn) : _shape{shape} - { - // Set 'stride' - _stride.init(shape); - - // Pre-allocate buffer - _values.resize(_shape.dim(0) * _stride.at(0)); - - // Set 'value' - iterate(_shape) << - [this, &fn](const Index &index) { _values.at(_stride.offset(index)) = fn(_shape, index); }; - } - -public: - const Shape &shape(void) const { return _shape; } - -public: - T at(const Index &index) const override { return _values.at(_stride.offset(index)); } - -private: - Shape _shape; - NonIncreasingStride _stride; - -private: - std::vector<T> _values; -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_FEATURE_OBJECT_H__ diff --git a/include/util/tensor/Reader.h b/include/util/tensor/Reader.h deleted file mode 100644 index 654214880..000000000 --- a/include/util/tensor/Reader.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_READER_H__ -#define __NNFW_UTIL_TENSOR_READER_H__ - -#include "util/tensor/Index.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -template <typename T> struct Reader -{ - virtual ~Reader() = default; - - virtual T at(const Index &index) const = 0; -}; - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_READER_H__ diff --git a/include/util/tensor/Shape.h b/include/util/tensor/Shape.h deleted file mode 100644 index a4401c1ed..000000000 --- a/include/util/tensor/Shape.h +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_SHAPE_H__ -#define __NNFW_UTIL_TENSOR_SHAPE_H__ - -#include <cstdint> -#include <cstddef> -#include <deque> -#include <initializer_list> -#include <ostream> -#include <string> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -class Shape -{ -public: - Shape(size_t rank) { _dimensions.resize(rank); } - -public: - Shape(const std::initializer_list<int32_t> &dimensions) : _dimensions{dimensions} - { - // DO NOTHING - } - - Shape(const Shape &origin) = default; - -public: - void prepend(int32_t d) { _dimensions.emplace_front(d); } - void append(int32_t d) { _dimensions.emplace_back(d); } - -public: - size_t rank(void) const { return _dimensions.size(); } - -public: - int32_t dim(size_t n) const { return _dimensions.at(n); } - int32_t &dim(size_t n) { return _dimensions.at(n); } - -public: - size_t element_nums() const - { - size_t nums = 1; - for (auto d : _dimensions) - { - nums *= d; - } - return nums; - } - -private: - std::deque<int32_t> _dimensions; - -public: - static Shape from(const std::string &s); -}; - -bool operator==(const Shape &, const Shape &); - -std::ostream &operator<<(std::ostream &os, const Shape &shape); - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_SHAPE_H__ diff --git a/include/util/tensor/Zipper.h b/include/util/tensor/Zipper.h deleted file mode 100644 index 5d40736f3..000000000 --- a/include/util/tensor/Zipper.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_TENSOR_ZIPPER_H__ -#define __NNFW_UTIL_TENSOR_ZIPPER_H__ - -#include "util/tensor/Index.h" -#include "util/tensor/IndexIterator.h" -#include "util/tensor/Reader.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -template <typename T> class Zipper -{ -public: - Zipper(const Shape &shape, const Reader<T> &lhs, const Reader<T> &rhs) - : _shape{shape}, _lhs{lhs}, _rhs{rhs} - { - // DO NOTHING - } - -public: - template <typename Callable> void zip(Callable cb) const - { - iterate(_shape) << - [this, &cb](const Index &index) { cb(index, _lhs.at(index), _rhs.at(index)); }; - } - -private: - const Shape &_shape; - const Reader<T> &_lhs; - const Reader<T> &_rhs; -}; - -template <typename T, typename Callable> -const Zipper<T> &operator<<(const Zipper<T> &zipper, Callable cb) -{ - zipper.zip(cb); - return zipper; -} - -template <typename T> Zipper<T> zip(const Shape &shape, const Reader<T> &lhs, const Reader<T> &rhs) -{ - return Zipper<T>{shape, lhs, rhs}; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_TENSOR_ZIPPER_H__ diff --git a/include/util/vector.h b/include/util/vector.h deleted file mode 100644 index 02f78257c..000000000 --- a/include/util/vector.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_VECTOR_H__ -#define __NNFW_UTIL_VECTOR_H__ - -#include <vector> - -template <typename T> bool operator==(const std::vector<T> &lhs, const std::vector<T> &rhs) -{ - if (lhs.size() != rhs.size()) - { - return false; - } - - for (size_t ind = 0; ind < lhs.size(); ++ind) - { - if (lhs.at(ind) != rhs.at(ind)) - { - return false; - } - } - - return true; -} - -#endif // __NNFW_UTIL_VECTOR_H__ diff --git a/include/util/vector/Object.h b/include/util/vector/Object.h deleted file mode 100644 index 407c06dc6..000000000 --- a/include/util/vector/Object.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_VECTOR_OBJECT_H__ -#define __NNFW_UTIL_VECTOR_OBJECT_H__ - -#include "util/vector/Reader.h" - -#include <vector> -#include <functional> - -namespace nnfw -{ -namespace util -{ -namespace vector -{ - -template <typename T> class Object final : public Reader<T> -{ -public: - using Generator = std::function<T(int32_t size, int32_t offset)>; - -public: - Object(int32_t size, const Generator &gen) : _size{size} - { - _value.resize(_size); - - for (int32_t offset = 0; offset < size; ++offset) - { - _value.at(offset) = gen(size, offset); - } - } - -public: - int32_t size(void) const { return _size; } - -public: - T at(uint32_t nth) const override { return _value.at(nth); } - -private: - const int32_t _size; - std::vector<T> _value; -}; - -} // namespace vector -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_VECTOR_OBJECT_H__ diff --git a/include/util/vector/Reader.h b/include/util/vector/Reader.h deleted file mode 100644 index a3c5cb359..000000000 --- a/include/util/vector/Reader.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_UTIL_VECTOR_READER_H__ -#define __NNFW_UTIL_VECTOR_READER_H__ - -#include <cstdint> - -namespace nnfw -{ -namespace util -{ -namespace vector -{ - -template <typename T> struct Reader -{ - virtual ~Reader() = default; - - virtual T at(uint32_t nth) const = 0; -}; - -} // namespace vector -} // namespace util -} // namespace nnfw - -#endif // __NNFW_UTIL_VECTOR_READER_H__ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/CLKernelLibraryEx.h b/libs/ARMComputeEx/arm_compute/core/CL/CLKernelLibraryEx.h index 026487077..e4e752ef9 100644 --- a/libs/ARMComputeEx/arm_compute/core/CL/CLKernelLibraryEx.h +++ b/libs/ARMComputeEx/arm_compute/core/CL/CLKernelLibraryEx.h @@ -14,6 +14,14 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLKernelLibraryEx.h + * @ingroup COM_AI_RUNTIME + * @brief This file is a cloned version of CLKernelLibrary.h in ACL. This file defines + * an interface for CLKernelLibrary.cpp which adds more OpenCL kernels on top of ACL. + */ + #ifndef __ARM_COMPUTE_CLKERNELLIBRARY_EX_H__ #define __ARM_COMPUTE_CLKERNELLIBRARY_EX_H__ @@ -27,58 +35,76 @@ namespace arm_compute { -/** CLKernelLibrary class */ +/** + * @brief Class to build OpenCL kernels added from nnfw + * */ class CLKernelLibraryEx { using StringSet = std::set<std::string>; private: - /** Default Constructor. */ + /** + * @brief Construct a new CLKernelLibraryEx object + */ CLKernelLibraryEx(); public: - /** Prevent instances of this class from being copied */ + /** + * @brief Prevent instances of this class from being copied. + */ CLKernelLibraryEx(const CLKernelLibraryEx &) = delete; - /** Prevent instances of this class from being copied */ + + /** + * @brief Prevent instances of this class from being copied. + */ const CLKernelLibraryEx &operator=(const CLKernelLibraryEx &) = delete; - /** Access the KernelLibrary singleton. - * @return The KernelLibrary instance. + + /** + * @brief Get the KernelLibrary singleton. + * @return The KernelLibrary instance */ static CLKernelLibraryEx &get(); - /** Initialises the kernel library. - * - * @param[in] kernel_path (Optional) Path of the directory from which kernel sources are loaded. - * @param[in] context (Optional) CL context used to create programs. - * @param[in] device (Optional) CL device for which the programs are created. - */ - void init(std::string kernel_path = ".", cl::Context context = cl::Context::getDefault(), - cl::Device device = cl::Device::getDefault()) + + /** + * @brief Initialise the kernel library. + * @param[in] kernel_path Path of the directory from which kernel sources are loaded. + * @param[in] context CL context used to create programs. + * @param[in] device CL device for which the programs are created. + * @return N/A + */ + void init(std::string kernel_path, cl::Context context, cl::Device device) { _kernel_path = std::move(kernel_path); _context = std::move(context); _device = std::move(device); } - /** Sets the path that the kernels reside in. - * - * @param[in] kernel_path Path of the kernel. + + /** + * @brief Set the path that the kernels reside in. + * @param[in] kernel_path Path of the directory from which kernel sources are loaded. + * @return N/A */ void set_kernel_path(const std::string &kernel_path) { _kernel_path = kernel_path; }; - /** Gets the path that the kernels reside in. + + /** + * @brief Get the path that the kernels reside in. + * @return the path of kernel files */ std::string get_kernel_path() { return _kernel_path; }; - /** Gets the source of the selected program. - * + + /** + * @brief Get the source of the selected program. * @param[in] program_name Program name. - * * @return Source of the selected program. */ std::string get_program_source(const std::string &program_name); - /** Sets the CL context used to create programs. - * + + /** + * @brief Set the CL context used to create programs. * @note Setting the context also resets the device to the * first one available in the new context. - * * @param[in] context A CL context. + * @return N/A */ void set_context(cl::Context context) { @@ -102,42 +128,56 @@ public: } } - /** Accessor for the associated CL context. - * + /** + * @brief Return associated CL context. * @return A CL context. */ cl::Context &context() { return _context; } - /** Sets the CL device for which the programs are created. - * + /** + * @brief Set the CL device for which the programs are created. * @param[in] device A CL device. + * @return N/A */ void set_device(cl::Device device) { _device = std::move(device); } - /** Return the device version - * + /** + * @brief Gets the CL device for which the programs are created. + * @return A CL device. + */ + cl::Device &get_device() { return _device; } + + /** + * @brief Return the device version * @return The content of CL_DEVICE_VERSION */ std::string get_device_version(); - /** Creates a kernel from the kernel library. - * + + /** + * @brief Create a kernel from the kernel library. * @param[in] kernel_name Kernel name. * @param[in] build_options_set Kernel build options as a set. - * * @return The created kernel. */ Kernel create_kernel(const std::string &kernel_name, const StringSet &build_options_set = {}) const; - /** Find the maximum number of local work items in a workgroup can be supported for the kernel. - * + + /** + * @brief Find the maximum number of local work items in a workgroup can be supported for the + * kernel. + * @param[in] kernel kernel object */ + size_t max_local_workgroup_size(const cl::Kernel &kernel) const; - /** Return the default NDRange for the device. - * + /** + * @brief Return the default NDRange for the device. + * @return default NDRangeof the device */ cl::NDRange default_ndrange() const; - /** Clear the library's cache of binary programs + /** + * @brief Clear the library's cache of binary programs + * @return N/A */ void clear_programs_cache() { @@ -145,29 +185,45 @@ public: _built_programs_map.clear(); } - /** Access the cache of built OpenCL programs */ + /** + * @brief Access the cache of built OpenCL programs + * @return program map data structure of which key is name of kernel and value is + * kerel source name. (*.cl) + */ const std::map<std::string, cl::Program> &get_built_programs() const { return _built_programs_map; } - /** Add a new built program to the cache - * + /** + * @brief Add a new built program to the cache * @param[in] built_program_name Name of the program * @param[in] program Built program to add to the cache + * @return N/A */ void add_built_program(const std::string &built_program_name, cl::Program program); + /** + * @brief Returns true if FP16 is supported by the CL device + * @return true if the CL device supports FP16 + */ + bool fp16_supported() const; + + /** + * @brief Returns true if int64_base_atomics extension is supported by the CL device + * @return true if the CL device supports int64_base_atomics extension + */ + bool int64_base_atomics_supported() const; + private: - /** Load program and its dependencies. - * + /** + * @brief Load program and its dependencies. * @param[in] program_name Name of the program to load. */ const Program &load_program(const std::string &program_name) const; - /** Concatenates contents of a set into a single string. - * + /** + * @brief Concatenates contents of a set into a single string. * @param[in] s Input set to concatenate. - * * @return Concatenated string. */ std::string stringify_set(const StringSet &s) const; diff --git a/libs/ARMComputeEx/arm_compute/core/CL/OpenCLEx.h b/libs/ARMComputeEx/arm_compute/core/CL/OpenCLEx.h new file mode 100644 index 000000000..dbda354d6 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/OpenCLEx.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef __ARM_COMPUTE_OPENCLEX_H__ +#define __ARM_COMPUTE_OPENCLEX_H__ + +#include <string> +#include <utility> + +/* Configure the Khronos C++ wrapper to target OpenCL 1.2: */ +#ifndef ARM_COMPUTE_NO_EXCEPTIONS +#define CL_HPP_ENABLE_EXCEPTIONS +#endif // ARM_COMPUTE_NO_EXCEPTIONS +#define CL_HPP_CL_1_2_DEFAULT_BUILD +#define CL_HPP_TARGET_OPENCL_VERSION 110 +#define CL_HPP_MINIMUM_OPENCL_VERSION 110 +#include <CL/cl2.hpp> + +namespace arm_compute +{ +/** Class for loading OpenCL symbols. */ +class CLSymbolsEx final +{ +private: + CLSymbolsEx() = default; + void load_symbols(void *handle); + +public: + /** Get the static instance of CLSymbols. + * + * @return The static instance of CLSymbols. + */ + static CLSymbolsEx &get(); + /** Load symbols from the given OpenCL library path. + * + * @param[in] library Path to the OpenCL library. + * + * @return True if loading the library is successful. + */ + bool load(const std::string &library); + /** Load symbols from any of the default OpenCL library names. + * + * @return True if loading any library is successful. + */ + bool load_default(); + +#define DECLARE_FUNCTION_PTR(func_name) std::function<decltype(func_name)> func_name##_ptr = nullptr + + DECLARE_FUNCTION_PTR(clGetEventInfo); + DECLARE_FUNCTION_PTR(clSetEventCallback); + +#undef DECLARE_FUNCTION_PTR + +private: + std::pair<bool, bool> _loaded{false, false}; +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_OPENCLEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLActivationLayerExKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLActivationLayerExKernel.h new file mode 100644 index 000000000..080cc47ef --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLActivationLayerExKernel.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLACTIVATIONLAYEREXKERNEL_H__ +#define __ARM_COMPUTE_CLACTIVATIONLAYEREXKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Interface for the activation layer kernel. */ +class CLActivationLayerExKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLActivationLayerExKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLActivationLayerExKernel(const CLActivationLayerExKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLActivationLayerExKernel &operator=(const CLActivationLayerExKernel &) = delete; + /** Allow instances of this class to be moved */ + CLActivationLayerExKernel(CLActivationLayerExKernel &&) = default; + /** Allow instances of this class to be moved */ + CLActivationLayerExKernel &operator=(CLActivationLayerExKernel &&) = default; + /** Default destructor */ + ~CLActivationLayerExKernel() = default; + /** Set the input and output tensor. + * + * @note If the output tensor is a nullptr, the activation function will be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will + * store the result + * of the activation function. Data types supported: + * QASYMM8/F16/F32. + * @param[out] output Destination tensor. Data type supported: same as @p input + * @param[in] act_info Activation layer information. + */ + void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfoEx act_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLActivationLayerKernel + * + * @param[in] input Source tensor info. In case of @p output tensor info = nullptr, this tensor + * will store the result + * of the activation function. Data types supported: QASYMM8/F16/F32. + * @param[in] output Destination tensor info. Data type supported: same as @p input + * @param[in] act_info Activation layer information. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const ActivationLayerInfoEx &act_info); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + ICLTensor *_input; + ICLTensor *_output; + bool _run_in_place; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLACTIVATIONLAYEREXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArgMinMaxKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArgMinMaxKernel.h new file mode 100644 index 000000000..b91a26159 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArgMinMaxKernel.h @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLArgMinMaxKernel.h + * @brief This file defines CLArgMinMaxKernel + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __ARM_COMPUTE_CLARG_MIN_MAX_KERNEL_H__ +#define __ARM_COMPUTE_CLARG_MIN_MAX_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to define interface for the argminmax max kernel. + */ +class CLArgMinMaxKernel : public ICLKernel +{ +public: + /** + * @brief Default constructor. + */ + CLArgMinMaxKernel(); + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLArgMinMaxKernel to be copied + */ + CLArgMinMaxKernel(const CLArgMinMaxKernel &) = delete; + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLArgMinMaxKernel to be copied + * @return Reference of this instance + */ + CLArgMinMaxKernel &operator=(const CLArgMinMaxKernel &) = delete; + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLArgMinMaxKernel to be moved + */ + CLArgMinMaxKernel(CLArgMinMaxKernel &&) = default; + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLArgMinMaxKernel to be moved + * @return Reference of this instance + */ + CLArgMinMaxKernel &operator=(CLArgMinMaxKernel &&) = default; + /** + * @brief Initialise the kernel's input, output and border mode. + * @param[in] input An input tensor. Data types supported: U8/QASYMM8/S32/F32. + * @param[out] output The output tensor, Data types supported: same as @p input. + * @param[in] argminmax_axis Axis to argminmax + * return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, const uint32_t argminmax_axis, + ArgOperation op); + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLArgMinMaxKernel + * @param[in] input An input tensor info. Data types supported: U8/QASYMM8/S32/F32. + * @param[in] output The output tensor info, Data types supported: same as @p input1. + * @param[in] argminmax_axis Axis to argminmax + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const uint32_t argminmax_axis, ArgOperation op); + + /* + * @brief Run CLArgMinMaxKernel op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ + void run(const Window &window, cl::CommandQueue &queue) override; + /* + * @brief Run CLArgMinMaxKernel op on CPU + * @param[in] queue cl::CommandQueue + * @return N/A + */ + void run_on_cpu(cl::CommandQueue &queue); + +private: + const ICLTensor *_input; + ICLTensor *_output; + uint32_t _argminmax_axis; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLargminmaxMAXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArithmeticSubtractionExKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArithmeticSubtractionExKernel.h new file mode 100644 index 000000000..9a765f310 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLArithmeticSubtractionExKernel.h @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEXKERNEL_H__ +#define __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEXKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Interface for the arithmetic subtraction kernel (support broadcasting) + * + * Arithmetic subtraction is computed by: + * @f[ output(x,y) = input1(x,y) - input2(x,y) @f] + */ +class CLArithmeticSubtractionExKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLArithmeticSubtractionExKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLArithmeticSubtractionExKernel(const CLArithmeticSubtractionExKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLArithmeticSubtractionExKernel &operator=(const CLArithmeticSubtractionExKernel &) = delete; + /** Allow instances of this class to be moved */ + CLArithmeticSubtractionExKernel(CLArithmeticSubtractionExKernel &&) = default; + /** Allow instances of this class to be moved */ + CLArithmeticSubtractionExKernel &operator=(CLArithmeticSubtractionExKernel &&) = default; + /** Default destructor */ + ~CLArithmeticSubtractionExKernel() = default; + + /** Initialise the kernel's inputs, output and convertion policy. + * + * @param[in] input1 First tensor input. Data types supported: U8/S16/F16/F32. + * @param[in] input2 Second tensor input. Data types supported: U8/S16/F16/F32. + * @param[out] output Output tensor. Data types supported: U8 (Only if both inputs are U8), + * S16/F16/F32. + * @param[in] policy Policy to use to handle overflow. + */ + void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, + ConvertPolicy policy); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLArithmeticSubtractionExKernel + * + * @param[in] input1 First tensor input info. Data types supported: U8/S16/F16/F32. + * @param[in] input2 Second tensor input info. Data types supported: U8/S16/F16/F32. + * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), + * S16/F16/F32. + * @param[in] policy Policy to use to handle overflow. + * + * @return a status + */ + static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, ConvertPolicy policy); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + BorderSize border_size() const override; + +private: + const ICLTensor *_input1; /**< Source tensor 1 */ + const ICLTensor *_input2; /**< Source tensor 2 */ + ICLTensor *_output; /**< Destination tensor */ +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBatchToSpaceNDKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBatchToSpaceNDKernel.h new file mode 100644 index 000000000..1387897c9 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBatchToSpaceNDKernel.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLBATCH_TO_SPACE_ND_KERNEL_H__ +#define __ARM_COMPUTE_CLBATCH_TO_SPACE_ND_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform BATCH_TO_SPACE_ND operation */ +class CLBatchToSpaceNDKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLBatchToSpaceNDKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLBatchToSpaceNDKernel(const CLBatchToSpaceNDKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLBatchToSpaceNDKernel &operator=(const CLBatchToSpaceNDKernel &) = delete; + /** Allow instances of this class to be moved */ + CLBatchToSpaceNDKernel(CLBatchToSpaceNDKernel &&) = default; + /** Allow instances of this class to be moved */ + CLBatchToSpaceNDKernel &operator=(CLBatchToSpaceNDKernel &&) = default; + /** Default destructor */ + ~CLBatchToSpaceNDKernel() = default; + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + */ + void configure(const ICLTensor *input, ICLTensor *output, const int32_t *block_size); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /**< Source tensor */ + ICLTensor *_output; /**< Destination tensor */ +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h new file mode 100644 index 000000000..ab33d9d3a --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLBINARYLOGICALOPKERNEL_H__ +#define __ARM_COMPUTE_CLBINARYLOGICALOPKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to return truth values of two input tensors for Binary Logical Op*/ +class CLBinaryLogicalOpKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLBinaryLogicalOpKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLBinaryLogicalOpKernel(const CLBinaryLogicalOpKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLBinaryLogicalOpKernel &operator=(const CLBinaryLogicalOpKernel &) = delete; + /** Allow instances of this class to be moved */ + CLBinaryLogicalOpKernel(CLBinaryLogicalOpKernel &&) = default; + /** Allow instances of this class to be moved */ + CLBinaryLogicalOpKernel &operator=(CLBinaryLogicalOpKernel &&) = default; + /** Initialize the kernel's input, output. + * + * @param[in] input1 Source tensor1. + * @param[in] input2 Source tensor2. + * @param[out] output Output tensor. + */ + void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, + BinaryLogicalOperation op); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + + BorderSize border_size() const override; + +private: + const ICLTensor *_input1; + const ICLTensor *_input2; + ICLTensor *_output; +}; + +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLBINARYLOGICALOPKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLCastKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLCastKernel.h index 6bd33bf8f..4c2feb903 100644 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLCastKernel.h +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLCastKernel.h @@ -14,6 +14,13 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLCastKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLCastKernel class + */ + #ifndef __ARM_COMPUTE_CLCASTKERNEL_H__ #define __ARM_COMPUTE_CLCASTKERNEL_H__ @@ -23,30 +30,62 @@ namespace arm_compute { class ICLTensor; -/** OpenCL kernel to perform a cast operation */ +/** + * @brief Class to define OpenCL kernel for cast operation + */ class CLCastKernel : public ICLKernel { public: - /** Default constructor */ + /** + * @brief Construct CLCastKernel object + */ CLCastKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ CLCastKernel(const CLCastKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ CLCastKernel &operator=(const CLCastKernel &) = delete; - /** Allow instances of this class to be moved */ + + /** + * @brief Construct CLCastKernel object using default move constructor + * @param[in] CLCastKernel object to move + */ CLCastKernel(CLCastKernel &&) = default; - /** Allow instances of this class to be moved */ + + /** + * @brief Allow instances of this class to be moved + * @param[in] CLCastKernel object to move + */ CLCastKernel &operator=(CLCastKernel &&) = default; - /** Default destructor */ + + /** + * @brief Destruct this CLCastKernel object + */ ~CLCastKernel() = default; - /** Initialise the kernel's input and output. - * + + /** + * @brief Initialise the kernel's input and output. * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @return N/A */ void configure(const ICLTensor *input, ICLTensor *output); - // Inherited methods overridden: + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLComparisonOpKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLComparisonOpKernel.h new file mode 100644 index 000000000..f5f455993 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLComparisonOpKernel.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLCOMPARISON_OP_KERNEL_H__ +#define __ARM_COMPUTE_CLCOMPARISON_OP_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to check if values in both tensors are equal*/ +class CLComparisonOpKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLComparisonOpKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLComparisonOpKernel(const CLComparisonOpKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLComparisonOpKernel &operator=(const CLComparisonOpKernel &) = delete; + /** Allow instances of this class to be moved */ + CLComparisonOpKernel(CLComparisonOpKernel &&) = default; + /** Allow instances of this class to be moved */ + CLComparisonOpKernel &operator=(CLComparisonOpKernel &&) = default; + /** Initialize the kernel's input, output. + * + * @param[in] input1 Source tensor1. + * @param[in] input2 Source tensor2. + * @param[out] output Output tensor. + */ + void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, + const ComparisonOperation &op); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + + BorderSize border_size() const override; + +private: + const ICLTensor *_input1; + const ICLTensor *_input2; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLCOMPARISON_OP_KERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h new file mode 100644 index 000000000..60ec7a82a --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__ +#define __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform depthTospace operation */ +class CLDepthToSpaceKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLDepthToSpaceKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLDepthToSpaceKernel(const CLDepthToSpaceKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLDepthToSpaceKernel &operator=(const CLDepthToSpaceKernel &) = delete; + /** Allow instances of this class to be moved */ + CLDepthToSpaceKernel(CLDepthToSpaceKernel &&) = default; + /** Allow instances of this class to be moved */ + CLDepthToSpaceKernel &operator=(CLDepthToSpaceKernel &&) = default; + /** Default destructor */ + ~CLDepthToSpaceKernel() = default; + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + */ + void configure(const ICLTensor *input, ICLTensor *output, const int32_t block_size); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /**< Source tensor */ + ICLTensor *_output; /**< Destination tensor */ +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLDEPTHTOSPACEKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLEmbeddingLookupKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLEmbeddingLookupKernel.h new file mode 100644 index 000000000..da075db69 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLEmbeddingLookupKernel.h @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLEmbeddingLookupKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLEmbeddingLookupKernel class + */ + +#ifndef __ARM_COMPUTE_CLEMBEDDINGLOOKUPKERNEL_H__ +#define __ARM_COMPUTE_CLEMBEDDINGLOOKUPKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** +* @brief Class to perform EmbeddingLookup operation with opencl kernel +*/ +class CLEmbeddingLookupKernel : public ICLKernel +{ +public: + /** + * @brief Construct a CLEmbeddingLookupKernel object + * */ + CLEmbeddingLookupKernel(); + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLEmbeddingLookupKernel(const CLEmbeddingLookupKernel &) = delete; + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLEmbeddingLookupKernel &operator=(const CLEmbeddingLookupKernel &) = delete; + + /** + * @brief Construct a CLEmbeddingLookupKernel object by using default move constructor + * @param[in] CLEmbeddingLookupKernel object to move + * */ + CLEmbeddingLookupKernel(CLEmbeddingLookupKernel &&) = default; + + /** + * @brief Move assignment operator + * @param[in] CLEmbeddingLookupKernel object to move + * */ + CLEmbeddingLookupKernel &operator=(CLEmbeddingLookupKernel &&) = default; + + /** + * @brief Destruct this object + * */ + ~CLEmbeddingLookupKernel() = default; + + /** + * @brief Set the input and output of the kernel + * @param[in] input Source tensor. + * Data type supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Destination tensor. Data type supported: Same as @p input + * @param[in] lookups Lookups are 1D tensor that values are indices into the first + * dimension of input. + * Data types supported: S32. + * @return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *lookups); + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLEmbeddingLookupKernel + * @param[in] input The input tensor info. + * Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[in] output The output tensor info, Data types supported: same as @p input1. + * @param[in] lookups Lookups info. Data types supported: S32. + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *lookups); + + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /** Source tensor */ + ICLTensor *_output; /** Destination tensor */ + const ICLTensor *_lookups; /** Lookups tensor */ +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLEMBEDDINGLOOKUPKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLExpKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLExpKernel.h new file mode 100644 index 000000000..a6ea539f8 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLExpKernel.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLEXPKERNEL_H__ +#define __ARM_COMPUTE_CLEXPKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform an exponential operation */ +class CLExpKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLExpKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLExpKernel(const CLExpKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLExpKernel &operator=(const CLExpKernel &) = delete; + /** Allow instances of this class to be moved */ + CLExpKernel(CLExpKernel &&) = default; + /** Allow instances of this class to be moved */ + CLExpKernel &operator=(CLExpKernel &&) = default; + /** Default destructor */ + ~CLExpKernel() = default; + /** Set the source, destination of the kernel + * + * @param[in] input Source tensor. Data type supported: F32. + * @param[out] output Destination tensor. Data type supported: F32. + */ + void configure(const ICLTensor *input, ICLTensor *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLEXPKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLGatherKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLGatherKernel.h index a51441aca..7e35a80b0 100644 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLGatherKernel.h +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLGatherKernel.h @@ -14,52 +14,85 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLGatherKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLGatherKernel class + */ + #ifndef __ARM_COMPUTE_CLGATHERKERNEL_H__ #define __ARM_COMPUTE_CLGATHERKERNEL_H__ #include "arm_compute/core/CL/ICLKernel.h" -#include "arm_compute/core/Types.h" namespace arm_compute { class ICLTensor; -/** Interface for the gather kernel. - * +/** + * @brief Class to define an interface for the gather kernel. */ class CLGatherKernel : public ICLKernel { public: - /** Default constructor.*/ + /** + * @brief Construct CLGatherKernel object + * */ CLGatherKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers). */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + */ CLGatherKernel(const CLGatherKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers). */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + */ CLGatherKernel &operator=(const CLGatherKernel &) = delete; - /** Allow instances of this class to be moved */ + + /** + * @brief Construct CLGatherKernel object by using default move constructor + * @param[in] CLGatherKernel object to move + */ CLGatherKernel(CLGatherKernel &&) = default; - /** Allow instances of this class to be moved */ + + /** + * @brief Move assignment operator + * @param[in] CLGatherKernel object to move + */ CLGatherKernel &operator=(CLGatherKernel &&) = default; - /** Initialise the kernel's input, output and border mode. - * + + /** + * @brief Initialise the kernel's input, output and border mode. * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. * @param[in] input2 An input tensor. Data types supported: S32. * @param[out] output The output tensor, Data types supported: same as @p input1. + * @return N/A */ void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref * CLGatherKernel - * * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. * @param[in] input2 An input tensor. Data types supported: S32. * @param[out] output The output tensor, Data types supported: same as @p input1. - * * @return a status */ static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output); - // Inherited methods overridden: + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLHashtableLookupKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLHashtableLookupKernel.h new file mode 100644 index 000000000..c3fc15637 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLHashtableLookupKernel.h @@ -0,0 +1,129 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLHashtableLookupKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLHashtableLookupKernel class + */ + +#ifndef __ARM_COMPUTE_CLHASHTABLELOOKUPKERNEL_H__ +#define __ARM_COMPUTE_CLHASHTABLELOOKUPKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/runtime/CL/CLTensor.h" + +namespace arm_compute +{ +class ICLTensor; + +/** +* @brief Class to perform HashtableLookup operation with opencl kernel +*/ +class CLHashtableLookupKernel : public ICLKernel +{ +public: + /** + * @brief Construct a CLHashtableLookupKernel object + * */ + CLHashtableLookupKernel(); + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLHashtableLookupKernel(const CLHashtableLookupKernel &) = delete; + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLHashtableLookupKernel &operator=(const CLHashtableLookupKernel &) = delete; + + /** + * @brief Construct a CLHashtableLookupKernel object by using default move constructor + * @param[in] CLHashtableLookupKernel object to move + * */ + CLHashtableLookupKernel(CLHashtableLookupKernel &&) = default; + + /** + * @brief Move assignment operator + * @param[in] CLHashtableLookupKernel object to move + * */ + CLHashtableLookupKernel &operator=(CLHashtableLookupKernel &&) = default; + + /** + * @brief Destruct this object + * */ + ~CLHashtableLookupKernel() = default; + + /** + * @brief Set the input and output of the kernel + * @param[in] lookups Lookups 1D tensor that values are indices into the first dimension of + * input. + * @param[in] keys Keys 1D tensor. keys and input pair represent a map. + * Data types supported: S32 + * @param[in] input Source tensor. + * Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p + * input. + * @param[out] hits Hits 1D tensor. A boolean tensor that indicates whether the lookup hits + * (True) or not (False). Data types supported: U8/QASYMM8 + * @return N/A + */ + void configure(const ICLTensor *lookups, const ICLTensor *keys, const ICLTensor *input, + ICLTensor *output, ICLTensor *hits); + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLHashtableLookupKernel + * @param[in] lookups The lookups tensor info. Data types supported: S32. + * @param[in] keys The keys tensor info. keys and input pair represent a map. + * Data types supported: S32 + * @param[in] input The input tensor info. + * Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output The output tensor. Data types and data layouts supported: Same as @p + * input. + * @param[out] hits The hits tensor info. A boolean tensor that indicates whether the lookup + * hits + * (True) or not (False). Data types supported: U8/QASYMM8 + * @return a status + */ + static Status validate(const ITensorInfo *lookups, const ITensorInfo *keys, + const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *hits); + + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_lookups; /** Lookups tensor */ + const ICLTensor *_keys; /** Keys tensor */ + const ICLTensor *_input; /** Source tensor */ + ICLTensor *_output; /** Destination tensor */ + ICLTensor *_hits; /** Hits tensor */ + std::unique_ptr<CLTensor> _lookup_indices{nullptr}; /** Lookup indices tensor */ +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLHASHTABLELOOKUPKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNegKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNegKernel.h new file mode 100644 index 000000000..ccbea147e --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNegKernel.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLNEGKERNEL_H__ +#define __ARM_COMPUTE_CLNEGKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform a negation operation on tensor*/ +class CLNegKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLNegKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLNegKernel(const CLNegKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLNegKernel &operator=(const CLNegKernel &) = delete; + /** Allow instances of this class to be moved */ + CLNegKernel(CLNegKernel &&) = default; + /** Allow instances of this class to be moved */ + CLNegKernel &operator=(CLNegKernel &&) = default; + /** Initialize the kernel's input, output. + * + * @param[in] input Source tensor. + * @param[out] output Destination tensor. + */ + void configure(const ICLTensor *input, ICLTensor *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLNEGKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNormalizationLayerExKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNormalizationLayerExKernel.h new file mode 100644 index 000000000..181a6226a --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLNormalizationLayerExKernel.h @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLNORMALIZATIONLAYEREXKERNEL_H__ +#define __ARM_COMPUTE_CLNORMALIZATIONLAYEREXKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Interface for the normalization layer kernel. + */ +class CLNormalizationLayerExKernel : public ICLKernel +{ +public: + /** Constructor */ + CLNormalizationLayerExKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLNormalizationLayerExKernel(const CLNormalizationLayerExKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLNormalizationLayerExKernel &operator=(const CLNormalizationLayerExKernel &) = delete; + /** Default Move Constructor. */ + CLNormalizationLayerExKernel(CLNormalizationLayerExKernel &&) = default; + /** Default move assignment operator */ + CLNormalizationLayerExKernel &operator=(CLNormalizationLayerExKernel &&) = default; + /** Set the input and output tensors. + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types supported: + * F16/F32. + * @param[out] output Destination tensor. Output will have the same number of dimensions as + * input. Data types supported: same as @p input. + * @param[in] norm_info Normalization layer information like the normalization type, + * normalization size and other parameters. + */ + void configure(const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLNormalizationLayerKernel + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types supported: + * F16/F32. + * @param[in] output Destination tensor. Output will have the same number of dimensions as + * input. Data types supported: same as @p input. + * @param[in] norm_info Normalization layer information like the normalization type, normalization + * size and other parameters. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + NormalizationLayerInfo norm_info); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + BorderSize border_size() const override; + +private: + const ICLTensor *_input; + ICLTensor *_output; + BorderSize _border_size; + bool _is_in_map; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLNORMALIZATIONLAYEREXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPReLUKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPReLUKernel.h new file mode 100644 index 000000000..eff1b8bd5 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPReLUKernel.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLPRELU_KERNEL_H__ +#define __ARM_COMPUTE_CLPRELU_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to calculate PReLU*/ +class CLPReLUKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLPReLUKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLPReLUKernel(const CLPReLUKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLPReLUKernel &operator=(const CLPReLUKernel &) = delete; + /** Allow instances of this class to be moved */ + CLPReLUKernel(CLPReLUKernel &&) = default; + /** Allow instances of this class to be moved */ + CLPReLUKernel &operator=(CLPReLUKernel &&) = default; + /** Initialize the kernel's input, output. + * + * @param[in] input Source tensor1. + * @param[in] alpha Source tensor2. + * @param[out] output Output tensor. + */ + void configure(const ICLTensor *input, const ICLTensor *alpha, ICLTensor *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + + BorderSize border_size() const override; + +private: + const ICLTensor *_input; + const ICLTensor *_alpha; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLPRELU_KERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPadLayerKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPadLayerKernel.h new file mode 100644 index 000000000..cbaa2adee --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPadLayerKernel.h @@ -0,0 +1,60 @@ +/* +* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +* Copyright (c) 2016-2018 ARM Limited. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ +#ifndef __ARM_COMPUTE_CLPADLAYERKERNEL_H__ +#define __ARM_COMPUTE_CLPADLAYERKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform PAD operation */ +class CLPadLayerKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLPadLayerKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLPadLayerKernel(const CLPadLayerKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLPadLayerKernel &operator=(const CLPadLayerKernel &) = delete; + /** Allow instances of this class to be moved */ + CLPadLayerKernel(CLPadLayerKernel &&) = default; + /** Allow instances of this class to be moved */ + CLPadLayerKernel &operator=(CLPadLayerKernel &&) = default; + /** Default destructor */ + ~CLPadLayerKernel() = default; + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] pad_size Padding Size tensor. Data types supported : S32 + */ + void configure(const ICLTensor *input, ICLTensor *output, ICLTensor *pad_size); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /**< Source tensor */ + ICLTensor *_output; /**< Destination tensor */ + ICLTensor *_pad_size; /**< Padding Size tensor */ +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLPADLAYERKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPermuteExKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPermuteExKernel.h new file mode 100644 index 000000000..3434deee8 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPermuteExKernel.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLPERMUTEEXKERNEL_H__ +#define __ARM_COMPUTE_CLPERMUTEEXKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform tensor permutation. + * + * Permutes given a permutation vector + */ +class CLPermuteExKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLPermuteExKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLPermuteExKernel(const CLPermuteExKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLPermuteExKernel &operator=(const CLPermuteExKernel &) = delete; + /** Allow instances of this class to be moved */ + CLPermuteExKernel(CLPermuteExKernel &&) = default; + /** Allow instances of this class to be moved */ + CLPermuteExKernel &operator=(CLPermuteExKernel &&) = default; + /** Set the input and output of the kernel. + * + * @param[in] input The input tensor to permute. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] output The output tensor. Data types supported: Same as @p input + * @param[in] perm Permutation vector + */ + void configure(const ICLTensor *input, ICLTensor *output, const PermutationVector &perm); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLPermuteKernel + * + * @param[in] input First tensor input info. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[in] output Output tensor info. Data types supported: same as @p input. + * @param[in] perm Permutation vector + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const PermutationVector &perm); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + ICLTensor *_output; + PermutationVector _perm; +}; +} // arm_compute +#endif /*__ARM_COMPUTE_CLPERMUTEEXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h index cd2b255bc..d579f5d8f 100644 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h @@ -14,68 +14,106 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLPixelWiseDivisionKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLPixelWiseDivisionKernel class + */ + #ifndef __ARM_COMPUTE_CLPIXELWISEDIVISIONKERNEL_H__ #define __ARM_COMPUTE_CLPIXELWISEDIVISIONKERNEL_H__ #include "arm_compute/core/CL/ICLKernel.h" -#include "arm_compute/core/Types.h" namespace arm_compute { class ICLTensor; -/** Interface for the pixelwise division kernel. - * +/** + * @brief Interface for the pixelwise division kernel. */ class CLPixelWiseDivisionKernel : public ICLKernel { public: - /** Default constructor.*/ + /** + * @brief Construct a CLPixelWiseDivisionKernel object + */ CLPixelWiseDivisionKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers). */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + */ CLPixelWiseDivisionKernel(const CLPixelWiseDivisionKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers). */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + */ CLPixelWiseDivisionKernel &operator=(const CLPixelWiseDivisionKernel &) = delete; - /** Allow instances of this class to be moved */ + + /** + * @brief Construct a CLPixelWiseDivisionKernel object by using move constructor + * @param[in] CLPixelWiseDivisionKernel object to move + */ CLPixelWiseDivisionKernel(CLPixelWiseDivisionKernel &&) = default; - /** Allow instances of this class to be moved */ + + /** + * @brief Allow instances of this class to be moved + * @param[in] CLPixelWiseDivisionKernel object to move + */ CLPixelWiseDivisionKernel &operator=(CLPixelWiseDivisionKernel &&) = default; - /** Initialise the kernel's input, output and border mode. - * - * @param[in] input1 An input tensor. Data types supported: U8/QS8/QS16/S16/F16/F32. + + /** + * @brief Initialise the kernel's input, output and border mode. + * @param[in] input1 An input tensor. Data types supported: U8/S16/F16/F32. * @param[in] input2 An input tensor. Data types supported: same as @p input1. * @param[out] output The output tensor, Data types supported: same as @p input1. Note: - * U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). + * U8 requires both inputs to be U8. * @param[in] scale Scale to apply after division. * Scale must be positive and its value must be either 1/255 or 1/2^n - * where n is between 0 and 15. For QS8 and QS16 scale must be 1. + * where n is between 0 and 15. * @param[in] overflow_policy Overflow policy. Supported overflow policies: Wrap, Saturate * @param[in] rounding_policy Rounding policy. Supported rounding modes: to zero, to nearest * even. + * @return N/A */ void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy); - /** Static function to check if given info will lead to a valid configuration of @ref + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref * CLPixelWiseDivisionKernel - * - * @param[in] input1 An input tensor info. Data types supported: U8/QS8/QS16/S16/F16/F32. + * @param[in] input1 An input tensor info. Data types supported: U8/S16/F16/F32. * @param[in] input2 An input tensor info. Data types supported: same as @p input1. * @param[in] output The output tensor info, Data types supported: same as @p input1. - * Note: U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). + * Note: U8 requires both inputs to be U8. * @param[in] scale Scale to apply after division. * Scale must be positive and its value must be either 1/255 or 1/2^n - * where n is between 0 and 15. For QS8 and QS16 scale must be 1. + * where n is between 0 and 15. * @param[in] overflow_policy Overflow policy. Supported overflow policies: Wrap, Saturate * @param[in] rounding_policy Rounding policy. Supported rounding modes: to zero, to nearest even. - * * @return a status */ static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy); - // Inherited methods overridden: + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; + + /** + * @brief The size of the border for that kernel + * @return The width in number of elements of the border. + */ BorderSize border_size() const override; private: diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceMaxKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceMaxKernel.h deleted file mode 100644 index a7d96cc5c..000000000 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceMaxKernel.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ARM_COMPUTE_CLREDUCEMAXKERNEL_H__ -#define __ARM_COMPUTE_CLREDUCEMAXKERNEL_H__ - -#include "arm_compute/core/CL/ICLKernel.h" -#include "arm_compute/core/Types.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the pixelwise division kernel. - * - */ -class CLReduceMaxKernel : public ICLKernel -{ -public: - /** Default constructor.*/ - CLReduceMaxKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers). */ - CLReduceMaxKernel(const CLReduceMaxKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers). */ - CLReduceMaxKernel &operator=(const CLReduceMaxKernel &) = delete; - /** Allow instances of this class to be moved */ - CLReduceMaxKernel(CLReduceMaxKernel &&) = default; - /** Allow instances of this class to be moved */ - CLReduceMaxKernel &operator=(CLReduceMaxKernel &&) = default; - /** Initialise the kernel's input, output and border mode. - * - * @param[in] input An input tensor. Data types supported: U8/QS8/QS16/S16/F16/F32. - * @param[in] axis Axis to reduce - * @param[out] output The output tensor, Data types supported: same as @p input1. Note: - * U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). - */ - void configure(const ICLTensor *input, int32_t axis, ICLTensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref - * CLReduceMaxKernel - * - * @param[in] input An input tensor info. Data types supported: U8/QS8/QS16/S16/F16/F32. - * @param[in] axis Axis to reduce - * @param[in] output The output tensor info, Data types supported: same as @p input1. - * Note: U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). - * - * @return a status - */ - static Status validate(const ITensorInfo *input, int32_t axis, const ITensorInfo *output); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - void run_on_cpu(cl::CommandQueue &queue); - -private: - const ICLTensor *_input; - ICLTensor *_output; - int32_t _axis; -}; -} // namespace arm_compute -#endif /*__ARM_COMPUTE_CLREDUCEMAXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceOperationKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceOperationKernel.h new file mode 100644 index 000000000..a26a4a7fc --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReduceOperationKernel.h @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLReduceOperationKernel.h + * @brief This file defines CLReduceOperationKernel class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __ARM_COMPUTE_CLREDUCEOPERATIONKERNEL_H__ +#define __ARM_COMPUTE_CLREDUCEOPERATIONKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to define interface for the reduce operation kernel + */ +class CLReduceOperationKernel : public ICLKernel +{ +public: + /** + * @brief Default constructor + */ + CLReduceOperationKernel(); + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ + CLReduceOperationKernel(const CLReduceOperationKernel &) = delete; + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ + CLReduceOperationKernel &operator=(const CLReduceOperationKernel &) = delete; + /** + * @brief Allow instances of this class to be moved + */ + CLReduceOperationKernel(CLReduceOperationKernel &&) = default; + /** + * @brief Allow instances of this class to be moved + */ + CLReduceOperationKernel &operator=(CLReduceOperationKernel &&) = default; + /** + * @brief Default destructor + */ + ~CLReduceOperationKernel() = default; + + /** + * @brief Set the input and output tensors. + * @param[in] input Source tensor. Data types supported: U8/S32/F32. + * @param[out] output Destination tensor. Data types supported: Same as @p input. + * Output will have the same number of dimensions as input. + * @param[in] axis Axis along which to reduce. + * @param[in] op Reduce operation to perform. + * @return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, const uint32_t axis, + ReduceOperation op); + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLReduceOperationKernel. + * @param[in] input Source tensor info. Data types supported: U8/S32/F32. + * @param[in] output Destination tensor info. Data types supported: Same as @p input. + * Output will have the same number of dimensions as input. + * @param[in] axis Axis along which to reduce. + * @param[in] op Reduce operation to perform. + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const uint32_t axis, + ReduceOperation op); + + /* + * @brief Run CLReduceOperationKernel op + * @param[in] window Window to be used for in_slice + * @param[in] queue CLQueue + * @return N/A + */ + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + ICLTensor *_output; + uint32_t _axis; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLREDUCEOPERATIONKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReductionMeanKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReductionMeanKernel.h deleted file mode 100644 index de9df3381..000000000 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLReductionMeanKernel.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ARM_COMPUTE_CLREDUCTIONMEANKERNEL_H__ -#define __ARM_COMPUTE_CLREDUCTIONMEANKERNEL_H__ - -#include "arm_compute/core/CL/ICLKernel.h" -#include "arm_compute/core/Types.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the reduction operation kernel */ -class CLReductionMeanKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLReductionMeanKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLReductionMeanKernel(const CLReductionMeanKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLReductionMeanKernel &operator=(const CLReductionMeanKernel &) = delete; - /** Allow instances of this class to be moved */ - CLReductionMeanKernel(CLReductionMeanKernel &&) = default; - /** Allow instances of this class to be moved */ - CLReductionMeanKernel &operator=(CLReductionMeanKernel &&) = default; - /** Default destructor */ - ~CLReductionMeanKernel() = default; - - /** Set the input and output tensors. - * - * @param[in] input Source tensor. Data types supported: F32. Data layouts supported: NCHW. - * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p input. - * Output will have the same number of dimensions as input. - * @param[in] axis Axis along which to reduce. Supported reduction axis : 0, 1 - */ - void configure(const ICLTensor *input, ICLTensor *output, std::vector<uint32_t> axis); - - /** Static function to check if given info will lead to a valid configuration of @ref - * CLReductionMeanKernel. - * - * @param[in] input Source tensor info. Data types supported: F32. Data layouts supported: NCHW. - * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p - * input. - * Output will have the same number of dimensions as input. - * @param[in] axis Axis along which to reduce. Supported reduction axis : 0, 1 - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, - std::vector<uint32_t> axis); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - BorderSize border_size() const override; - -private: - const ICLTensor *_input; - ICLTensor *_output; - std::vector<uint32_t> _reduction_axis; - BorderSize _border_size; -}; -} // namespace arm_compute -#endif /*__ARM_COMPUTE_CLREDUCTIONMEANKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h new file mode 100644 index 000000000..68534f1ab --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ +#define __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform SPACE_TO_BATCH_ND operation */ +class CLSpaceToBatchNDKernel final : public ICLKernel +{ +public: + /** Default constructor */ + CLSpaceToBatchNDKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLSpaceToBatchNDKernel(const CLSpaceToBatchNDKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLSpaceToBatchNDKernel &operator=(const CLSpaceToBatchNDKernel &) = delete; + /** Allow instances of this class to be moved */ + CLSpaceToBatchNDKernel(CLSpaceToBatchNDKernel &&) = default; + /** Allow instances of this class to be moved */ + CLSpaceToBatchNDKernel &operator=(CLSpaceToBatchNDKernel &&) = default; + /** Default destructor */ + ~CLSpaceToBatchNDKernel() = default; + /** Initialise the kernel's input and output. + * + * @note The data layout of input and output must be the same. + * @note The number of dimensions of input and output must be 4, and `spatial` dimensions + * are height and width. + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. + * Data layout supported: NCHW/NHWC + * @param[in] block_size Block size tensor. Data types supported: S32. + * @param[in] padding_size Padding size tensor. Data types supported: S32. + * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. + * Data layout supported: NCHW/NHWC + */ + void configure(const ICLTensor *input, const ICLTensor *block_size, const ICLTensor *padding_size, + ICLTensor *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /**< Source tensor */ + const ICLTensor *_block_size; /**< Block size tensor */ + const ICLTensor *_padding_size; /**< Padding size tensor */ + ICLTensor *_output; /**< Destination tensor */ +}; + +} // namespace arm_compute + +#endif /* __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToDepthKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToDepthKernel.h new file mode 100644 index 000000000..be845a549 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToDepthKernel.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSPACETODEPTHKERNEL_H__ +#define __ARM_COMPUTE_CLSPACETODEPTHKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to perform spaceTodepth operation */ +class CLSpaceToDepthKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLSpaceToDepthKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLSpaceToDepthKernel(const CLSpaceToDepthKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLSpaceToDepthKernel &operator=(const CLSpaceToDepthKernel &) = delete; + /** Allow instances of this class to be moved */ + CLSpaceToDepthKernel(CLSpaceToDepthKernel &&) = default; + /** Allow instances of this class to be moved */ + CLSpaceToDepthKernel &operator=(CLSpaceToDepthKernel &&) = default; + /** Default destructor */ + ~CLSpaceToDepthKernel() = default; + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + */ + void configure(const ICLTensor *input, ICLTensor *output, const int32_t block_size); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /**< Source tensor */ + ICLTensor *_output; /**< Destination tensor */ +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLSPACETODEPTHKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h new file mode 100644 index 000000000..a4c44e35d --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__ +#define __ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** OpenCL kernel to return squared difference value of two tensors (x-y)^2*/ +class CLSquaredDifferenceKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLSquaredDifferenceKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLSquaredDifferenceKernel(const CLSquaredDifferenceKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers). */ + CLSquaredDifferenceKernel &operator=(const CLSquaredDifferenceKernel &) = delete; + /** Allow instances of this class to be moved */ + CLSquaredDifferenceKernel(CLSquaredDifferenceKernel &&) = default; + /** Allow instances of this class to be moved */ + CLSquaredDifferenceKernel &operator=(CLSquaredDifferenceKernel &&) = default; + /** Initialize the kernel's input, output. + * + * @param[in] input1 Source tensor1. + * @param[in] input2 Source tensor2. + * @param[out] output Output tensor. + */ + void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + + BorderSize border_size() const override; + +private: + const ICLTensor *_input1; + const ICLTensor *_input2; + ICLTensor *_output; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLSQUARED_DIFFERENCE_KERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceExKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceExKernel.h new file mode 100644 index 000000000..6368c380e --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceExKernel.h @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLStridedSliceExKernel.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines CLStridedSliceExKernel class + */ + +#ifndef __ARM_COMPUTE_CLSTRIDEDSLICEEXKERNEL_H__ +#define __ARM_COMPUTE_CLSTRIDEDSLICEEXKERNEL_H__ + +#include "arm_compute/core/CL/ICLKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** +* @brief Class to define an interface for the kernel to extract a strided slice of a tensor +*/ +class CLStridedSliceExKernel : public ICLKernel +{ +public: + /** + * @brief Construct a CLStridedSliceExKernel object + * */ + CLStridedSliceExKernel(); + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLStridedSliceExKernel(const CLStridedSliceExKernel &) = delete; + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * */ + CLStridedSliceExKernel &operator=(const CLStridedSliceExKernel &) = delete; + + /** + * @brief Construct a CLStridedSliceExKernel object by using default move constructor + * @param[in] CLStridedSliceExKernel object to move + * */ + CLStridedSliceExKernel(CLStridedSliceExKernel &&) = default; + + /** + * @brief Move assignment operator + * @param[in] CLStridedSliceExKernel object to move + * */ + CLStridedSliceExKernel &operator=(CLStridedSliceExKernel &&) = default; + + /** + * @brief Destruct this object + * */ + ~CLStridedSliceExKernel() = default; + + /** + * @brief Set the input and output of the kernel + * @param[in] input Source tensor. Data type supported: + * U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Destination tensor. Data type supported: Same as @p input + * @param[in] beginData The begin tensor. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] endData The end tensor. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] strideData The stride tensor. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] beginMask Mask for begin + * @param[in] endMask Mask for end + * @param[in] shrinkAxisMask Mask for shrink axis. + * @return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, + ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, int32_t endMask, + int32_t shrinkAxisMask); + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLStridedSliceExKernel + * @param[in] input The input tensor info. Data types supported: + * U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[in] output The output tensor info, Data types supported: same as @p input1. + * @param[in] begin The begin tensor info. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] end The end tensor info. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] stride The stride tensor info. Data types supported: S32. + * The number of dimensions must be 1. + * The length must be the same as the number of dimensions of input. + * @param[in] beginMask Mask for begin + * @param[in] endMask Mask for end + * @param[in] shrinkAxisMask Mask for shrink axis. + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *begin, const ITensorInfo *end, + const ITensorInfo *stride, int32_t beginMask, int32_t endMask, + int32_t shrinkAxisMask); + + /** + * @brief Enqueue the OpenCL kernel to process the given window on the passed OpenCL command + * queue. + * @note The queue is *not* flushed by this method, and therefore the kernel will not have + * been executed by the time this method returns. + * @param[in] window Region on which to execute the kernel. (Must be a valid region of + * the window returned by window()). + * @param[in,out] queue Command queue on which to enqueue the kernel.@return N/A + * @return N/A + */ + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; /** Source tensor */ + ICLTensor *_output; /** Destination tensor */ + ICLTensor *_beginData; /** Start indices of input tensor */ + ICLTensor *_endData; /** Stop indices of input tensor */ + ICLTensor *_stridesData; /** Strides tensor */ + int32_t _beginMask; /** Begin mask */ + int32_t _endMask; /** End mask */ + int32_t _shrinkAxisMask; /** Shrink axis mask */ +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLSTRIDEDSLICEEXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceKernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceKernel.h deleted file mode 100644 index 248ae6635..000000000 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLStridedSliceKernel.h +++ /dev/null @@ -1,106 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ARM_COMPUTE_CLSTRIDEDSLICEKERNEL_H__ -#define __ARM_COMPUTE_CLSTRIDEDSLICEKERNEL_H__ - -#include "arm_compute/core/CL/ICLKernel.h" -#include "arm_compute/core/Types.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the kernel to extract a strided slice of a tensor */ -class CLStridedSliceKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLStridedSliceKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLStridedSliceKernel(const CLStridedSliceKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLStridedSliceKernel &operator=(const CLStridedSliceKernel &) = delete; - /** Allow instances of this class to be moved */ - CLStridedSliceKernel(CLStridedSliceKernel &&) = default; - /** Allow instances of this class to be moved */ - CLStridedSliceKernel &operator=(CLStridedSliceKernel &&) = default; - /** Default destructor */ - ~CLStridedSliceKernel() = default; - /** Set the input and output of the kernel - * - * @param[in] input Source tensor. Data type supported: - * U8/S8/QS8/QASYMM8/U16/S16/QS16/U32/S32/F16/F32 - * @param[out] output Destination tensor. Data type supported: Same as @p input - * @param[in] beginData The begin tensor. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] endData The end tensor. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] strideData The stride tensor. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] beginMask Mask for begin - * @param[in] endMask Mask for end - * @param[in] shrinkAxisMask Mask for shrink axis. - * - */ - void configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, - ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, int32_t endMask, - int32_t shrinkAxisMask); - - /** Static function to check if given info will lead to a valid configuration of @ref - * CLStridedSliceKernel - * - * @param[in] input The input tensor info. Data types supported: - * U8/S8/QS8/QASYMM8/U16/S16/QS16/U32/S32/F16/F32 - * @param[in] output The output tensor info, Data types supported: same as @p input1. - * @param[in] begin The begin tensor info. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] end The end tensor info. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] stride The stride tensor info. Data types supported: S32. - * The number of dimensions must be 1. - * The length must be the same as the number of dimensions of input. - * @param[in] beginMask Mask for begin - * @param[in] endMask Mask for end - * @param[in] shrinkAxisMask Mask for shrink axis. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, - const ITensorInfo *begin, const ITensorInfo *end, - const ITensorInfo *stride, int32_t beginMask, int32_t endMask, - int32_t shrinkAxisMask); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input; /** Source tensor */ - ICLTensor *_output; /** Destination tensor */ - ICLTensor *_beginData; /** Start indices of input tensor */ - ICLTensor *_endData; /** Stop indices of input tensor */ - ICLTensor *_stridesData; /** Strides tensor */ - int32_t _beginMask; /** Begin mask */ - int32_t _endMask; /** End mask */ - int32_t _shrinkAxisMask; /** Shrink axis mask */ -}; -} // namespace arm_compute -#endif /*__ARM_COMPUTE_CLSTRIDEDSLICEKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLTopKV2Kernel.h b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLTopKV2Kernel.h index 5c567f38e..eb2bad254 100644 --- a/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLTopKV2Kernel.h +++ b/libs/ARMComputeEx/arm_compute/core/CL/kernels/CLTopKV2Kernel.h @@ -14,14 +14,18 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLTopKV2Kernel.h + * @brief This file defines classes for TopKV2Kernel + * @ingroup COM_AI_RUNTIME + */ + #ifndef __ARM_COMPUTE_CLTOPKV2KERNEL_H__ #define __ARM_COMPUTE_CLTOPKV2KERNEL_H__ -#include "arm_compute/core/CL/ICLArray.h" #include "arm_compute/core/CL/ICLKernel.h" -#include <array> - // these parameters can be changed #define _ITEMS 16 // number of items in a group #define _GROUPS 4 // the number of virtual processors is _ITEMS * _GROUPS @@ -33,24 +37,59 @@ namespace arm_compute { class ICLTensor; +/** + * @brief Class to define CLTopKV2Single + */ class CLTopKV2Single : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLTopKV2Single(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Single to be copied + */ CLTopKV2Single(const CLTopKV2Single &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Single to be copied + * @return Reference of this instance + */ CLTopKV2Single &operator=(const CLTopKV2Single &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Single to be moved + */ CLTopKV2Single(CLTopKV2Single &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Single to be moved + * @return Reference of this instance + */ CLTopKV2Single &operator=(CLTopKV2Single &&) = default; + /** + * @brief Initialise kernel with params + * @param[in] input An input tensor + * @param[in] topk_values Values of the top k predictions + * @param[in] topk_indices Indices of the top k predictions + * @param[in] indices Indices + * @param[in] temp_stack Temp stack + * @param[in] k K of the top k predictions + * @param[in] n Number times to quick-sort + * return N/A + */ void configure(ICLTensor *input, ICLTensor *topk_values, ICLTensor *topk_indices, cl::Buffer *indices, cl::Buffer *temp_stack, int k, int n); - // Inherited methods overridden: + /* + * @brief Run CLTopKV2Single op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: @@ -59,52 +98,121 @@ private: ICLTensor *_topk_indices; }; +/** + * @brief Class to define CLTopKV2Init + */ class CLTopKV2Init : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLTopKV2Init(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Init to be copied + */ CLTopKV2Init(const CLTopKV2Init &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Init to be copied + * @return Reference of this instance + */ CLTopKV2Init &operator=(const CLTopKV2Init &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Init to be moved + */ CLTopKV2Init(CLTopKV2Init &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Init to be moved + * @return Reference of this instance + */ CLTopKV2Init &operator=(CLTopKV2Init &&) = default; + /** + * @brief Initialise kernel with params + * @param[in] input An input tensor + * @param[in] in_key_buf Buffer of input key + * @param[in] in_ind_buf Buffer of input index + * @param[in] n Number times to quick-sort + * return N/A + */ void configure(ICLTensor *input, cl::Buffer *in_key_buf, cl::Buffer *in_ind_buf, int n); - // Inherited methods overridden: + /* + * @brief Run CLTopKV2Init op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: ICLTensor *_input; }; +/** + * @brief Class to define CLRadixSortHistogram + */ class CLRadixSortHistogram : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLRadixSortHistogram(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortHistogram to be copied + */ CLRadixSortHistogram(const CLRadixSortHistogram &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortHistogram to be copied + * @return Reference of this instance + */ CLRadixSortHistogram &operator=(const CLRadixSortHistogram &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortHistogram to be moved + */ CLRadixSortHistogram(CLRadixSortHistogram &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortHistogram to be moved + * @return Reference of this instance + */ CLRadixSortHistogram &operator=(CLRadixSortHistogram &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] hist_buf Buffer of histogram + * @param[in] bits Number of bits to be used for radix sort + * @param[in] n Integer number size to sort + * return N/A + */ void configure(cl::Buffer *hist_buf, int bits, int n); + /** + * @brief Set pass + * @param[in] pass Passes made of in radix sort algorithm + * @param[in] in_key_buf Buffer of input key + * return N/A + */ void setPass(int pass, cl::Buffer *in_key_buf) { _pass = pass; _in_key_buf = in_key_buf; } - // Inherited methods overridden: + /* + * @brief Run CLRadixSortHistogram op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: @@ -112,82 +220,210 @@ private: cl::Buffer *_in_key_buf; }; +/** + * @brief Class to define CLRadixSortScanHistogram + */ class CLRadixSortScanHistogram : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLRadixSortScanHistogram(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortScanHistogram to be copied + */ CLRadixSortScanHistogram(const CLRadixSortScanHistogram &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortScanHistogram to be copied + * @return Reference of this instance + */ CLRadixSortScanHistogram &operator=(const CLRadixSortScanHistogram &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortScanHistogram to be moved + */ CLRadixSortScanHistogram(CLRadixSortScanHistogram &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortScanHistogram to be moved + * @return Reference of this instance + */ CLRadixSortScanHistogram &operator=(CLRadixSortScanHistogram &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] hist_buf Buffer of histogram + * @param[out] glob_sum_buf Buffer of global sum + * @param[in] bits Number of bits to be used for radix sort + * return N/A + */ void configure(cl::Buffer *hist_buf, cl::Buffer *glob_sum_buf, int bits); - // Inherited methods overridden: + /* + * @brief Run CLRadixSortScanHistogram op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; }; +/** + * @brief Class to define CLRadixSortGlobalScanHistogram + */ class CLRadixSortGlobalScanHistogram : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLRadixSortGlobalScanHistogram(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortGlobalScanHistogram to be copied + */ CLRadixSortGlobalScanHistogram(const CLRadixSortGlobalScanHistogram &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortGlobalScanHistogram to be copied + * @return Reference of this instance + */ CLRadixSortGlobalScanHistogram &operator=(const CLRadixSortGlobalScanHistogram &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortGlobalScanHistogram to be moved + */ CLRadixSortGlobalScanHistogram(CLRadixSortGlobalScanHistogram &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortGlobalScanHistogram to be moved + * @return Reference of this instance + */ CLRadixSortGlobalScanHistogram &operator=(CLRadixSortGlobalScanHistogram &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] glob_sum_buf Buffer of global sum + * @param[out] temp_buf Temp buffer to be used while RadixSortGlobalScanHistogram + * @param[in] bits Number of bits to be used for radix sort + * return N/A + */ void configure(cl::Buffer *glob_sum_buf, cl::Buffer *temp_buf, int bits); - // Inherited methods overridden: + /* + * @brief Run CLRadixSortGlobalScanHistogram op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; }; +/** + * @brief Class to define CLRadixSortPasteHistogram + */ class CLRadixSortPasteHistogram : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLRadixSortPasteHistogram(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortPasteHistogram to be copied + */ CLRadixSortPasteHistogram(const CLRadixSortPasteHistogram &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortPasteHistogram to be copied + * @return Reference of this instance + */ CLRadixSortPasteHistogram &operator=(const CLRadixSortPasteHistogram &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortPasteHistogram to be moved + */ CLRadixSortPasteHistogram(CLRadixSortPasteHistogram &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortPasteHistogram to be moved + * @return Reference of this instance + */ CLRadixSortPasteHistogram &operator=(CLRadixSortPasteHistogram &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] hist_buf Buffer of histogram + * @param[out] glob_sum_buf Buffer of global sum + * @param[in] bits Number of bits to be used for radix sort + * return N/A + */ void configure(cl::Buffer *hist_buf, cl::Buffer *glob_sum_buf, int bits); - // Inherited methods overridden: + /* + * @brief Run CLRadixSortPasteHistogram op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; }; +/** + * @brief Class to define CLRadixSortReorder + */ class CLRadixSortReorder : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLRadixSortReorder(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortReorder to be copied + */ CLRadixSortReorder(const CLRadixSortReorder &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLRadixSortReorder to be copied + * @return Reference of this instance + */ CLRadixSortReorder &operator=(const CLRadixSortReorder &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortReorder to be moved + */ CLRadixSortReorder(CLRadixSortReorder &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLRadixSortReorder to be moved + * @return Reference of this instance + */ CLRadixSortReorder &operator=(CLRadixSortReorder &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] hist_buf Buffer of histogram + * @param[in] bits Number of bits to be used for radix sort + * @param[in] n Integer number size to sort + * return N/A + */ void configure(cl::Buffer *hist_buf, int bits, int n); + /** + * @brief Set pass + * @param[in] pass Passes made of in radix sort algorithm + * @param[in] in_key_buf Buffer of input key + * @param[out] out_key_buf Buffer of output key + * @param[in] in_ind_buf Buffer of input index + * @param[out] out_ind_buf Buffer of output index + * return N/A + */ void setPass(int pass, cl::Buffer *in_key_buf, cl::Buffer *out_key_buf, cl::Buffer *in_ind_buf, cl::Buffer *out_ind_buf) { @@ -197,7 +433,12 @@ public: _in_ind_buf = in_ind_buf; _out_ind_buf = out_ind_buf; } - // Inherited methods overridden: + /* + * @brief Run CLRadixSortReorder op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: @@ -208,47 +449,115 @@ private: cl::Buffer *_out_ind_buf; }; +/** + * @brief Class to define CLTopKV2FindFirstNegative + */ class CLTopKV2FindFirstNegative : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLTopKV2FindFirstNegative(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2FindFirstNegative to be copied + */ CLTopKV2FindFirstNegative(const CLTopKV2FindFirstNegative &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2FindFirstNegative to be copied + * @return Reference of this instance + */ CLTopKV2FindFirstNegative &operator=(const CLTopKV2FindFirstNegative &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2FindFirstNegative to be moved + */ CLTopKV2FindFirstNegative(CLTopKV2FindFirstNegative &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2FindFirstNegative to be moved + * @return Reference of this instance + */ CLTopKV2FindFirstNegative &operator=(CLTopKV2FindFirstNegative &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] first_negative_idx_buf Buffer of the first negative index + * @param[in] n Number times to find + * return N/A + */ void configure(cl::Buffer *first_negative_idx_buf, int n); + /** + * @brief Set output buffer + * @param[out] out_key_buf Buffer of output key + * return N/A + */ void setOutputBuffer(cl::Buffer *out_key_buf) { _out_key_buf = out_key_buf; } - // Inherited methods overridden: + /* + * @brief Run CLTopKV2FindFirstNegative op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: cl::Buffer *_out_key_buf; }; +/** + * @brief Class to define CLTopKV2ReorderNegatives + */ class CLTopKV2ReorderNegatives : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLTopKV2ReorderNegatives(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2ReorderNegatives to be copied + */ CLTopKV2ReorderNegatives(const CLTopKV2ReorderNegatives &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2ReorderNegatives to be copied + * @return Reference of this instance + */ CLTopKV2ReorderNegatives &operator=(const CLTopKV2ReorderNegatives &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2ReorderNegatives to be moved + */ CLTopKV2ReorderNegatives(CLTopKV2ReorderNegatives &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2ReorderNegatives to be moved + * @return Reference of this instance + */ CLTopKV2ReorderNegatives &operator=(CLTopKV2ReorderNegatives &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] first_negative_idx_buf Buffer of the first negative index + * @param[in] n Number times to find + * return N/A + */ void configure(cl::Buffer *first_negative_idx_buf, int n); + /** + * @brief Set buffers + * @param[in] in_key_buf Buffer of input key + * @param[out] out_key_buf Buffer of output key + * @param[in] in_ind_buf Buffer of input index + * @param[out] out_ind_buf Buffer of output index + * return N/A + */ void setBuffers(cl::Buffer *in_key_buf, cl::Buffer *out_key_buf, cl::Buffer *in_ind_buf, cl::Buffer *out_ind_buf) { @@ -258,7 +567,12 @@ public: _out_ind_buf = out_ind_buf; } - // Inherited methods overridden: + /* + * @brief Run CLTopKV2ReorderNegatives op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: @@ -268,25 +582,63 @@ private: cl::Buffer *_out_ind_buf; }; +/** + * @brief Class to define CLTopKV2Store + */ class CLTopKV2Store : public ICLKernel { public: - /** Constructor */ + /** + * @brief Constructor + */ CLTopKV2Store(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Store to be copied + */ CLTopKV2Store(const CLTopKV2Store &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers). + * @param [in] copiedInstance Const reference of CLTopKV2Store to be copied + * @return Reference of this instance + */ CLTopKV2Store &operator=(const CLTopKV2Store &) = delete; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Store to be moved + */ CLTopKV2Store(CLTopKV2Store &&) = default; - /** Allow instances of this class to be moved */ + /** + * @brief Allow instances of this class to be moved + * @param [in] movedInstance Rvalue reference of CLTopKV2Store to be moved + * @return Reference of this instance + */ CLTopKV2Store &operator=(CLTopKV2Store &&) = default; + /** + * @brief Initialise kernel with params + * @param[out] values Values tensor to store + * @param[out] indices Indices tensor to be used for store + * @param[in] k K of the top k predictions + * @param[in] n Number times to store + * return N/A + */ void configure(ICLTensor *values, ICLTensor *indices, int k, int n); + /** + * @brief Set buffers + * @param[out] out_key_buf Buffer of output key + * @param[out] out_ind_buf Buffer of output index + * return N/A + */ void setOutputBuffers(cl::Buffer *out_key_buf, cl::Buffer *out_ind_buf); - // Inherited methods overridden: + /* + * @brief Run CLTopKV2Store op + * @param[in] window Window to be used for in_slice + * @param[in] queue cl::CommandQueue + * @return N/A + */ void run(const Window &window, cl::CommandQueue &queue) override; private: diff --git a/libs/ARMComputeEx/arm_compute/core/NEON/kernels/NENormalizationLayerExKernel.h b/libs/ARMComputeEx/arm_compute/core/NEON/kernels/NENormalizationLayerExKernel.h new file mode 100644 index 000000000..f7bf72985 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/NEON/kernels/NENormalizationLayerExKernel.h @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__ +#define __ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__ + +#include "arm_compute/core/NEON/INEKernel.h" + +namespace arm_compute +{ +class ITensor; + +/** Interface for the normalization layer kernel. + */ +class NENormalizationLayerExKernel : public INEKernel +{ +public: + const char *name() const override { return "NENormalizationLayerKernel"; } + /** Default constructor */ + NENormalizationLayerExKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NENormalizationLayerExKernel(const NENormalizationLayerExKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NENormalizationLayerExKernel &operator=(const NENormalizationLayerExKernel &) = delete; + /** Default Move Constructor. */ + NENormalizationLayerExKernel(NENormalizationLayerExKernel &&) = default; + /** Default move assignment operator */ + NENormalizationLayerExKernel &operator=(NENormalizationLayerExKernel &&) = default; + /** Default destructor */ + ~NENormalizationLayerExKernel() = default; + /** Set the input and output tensors. + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types + * supported: FP16/F32. + * @param[in] input_squared Source with each element has been squared. 3 lower dims represent a + * single input with dimensions [width, height, IFM], + * Data type supported: same as @p input + * @param[out] output Destination tensor. Output will have the same number of dimensions as + * input. Data type supported: same as @p input + * @param[in] norm_info Normalization layer information like the normalization type, + * normalization size and other parameters. + */ + void configure(const ITensor *input, const ITensor *input_squared, ITensor *output, + NormalizationLayerInfo norm_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * NENormalizationLayerKernel + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types + * supported: FP16/F32. + * @param[in] input_squared Source with each element has been squared. 3 lower dims represent a + * single input with dimensions [width, height, IFM], + * Data type supported: same as @p input + * @param[in] output Destination tensor. Output will have the same number of dimensions as + * input. Data type supported: same as @p input + * @param[in] norm_info Normalization layer information like the normalization type, + * normalization size and other parameters. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *input_squared, + const ITensorInfo *output, NormalizationLayerInfo norm_info); + + // Inherited methods overridden: + void run(const Window &window, const ThreadInfo &info) override; + BorderSize border_size() const override; + +private: + /** Function to perform normalization depending on the given template + * dimension. The second template parameter specifies whether the + * normalization has to be 1D or 2D. + * + * @note Only supported normalizations are: + * - 1D over X or Z + * - 2D over X and Y + * + * @param[in] window Region on which to execute the kernel. + */ + template <DataType dt, unsigned int dim, bool do_2D_norm> + void normalize_float(const Window &window); + + /** Common signature for all the specialised normalization functions + * + * @param[in] window Region on which to execute the kernel. + */ + using NormalizationFunctionEx = void (NENormalizationLayerExKernel::*)(const Window &window); + +private: + NormalizationFunctionEx _func; + const ITensor *_input; + const ITensor *_input_squared; + ITensor *_output; + NormalizationLayerInfo _norm_info; + BorderSize _border_size; +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/TypesEx.h b/libs/ARMComputeEx/arm_compute/core/TypesEx.h new file mode 100644 index 000000000..8381f1cc6 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/TypesEx.h @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_TYPESEX_H__ +#define __ARM_COMPUTE_TYPESEX_H__ + +#include <cmath> +#include <cstddef> +#include <cstdint> +#include <string> +#include <utility> + +namespace arm_compute +{ + +/** Available ArgIndex operations **/ +enum class ArgOperation +{ + MAX, + MIN, +}; + +/** Available reduce operations */ +enum class ReduceOperation +{ + MAX, /**< Max */ + MEAN, /**< Mean */ + SUM, /**< Sum */ + MIN, /**< Min */ +}; + +/** Available binary logical operations */ +enum class BinaryLogicalOperation +{ + AND, /**< AND */ + OR, /**< OR */ +}; + +enum class ComparisonOperation +{ + EQUAL, /**< EQUAL */ + NOT_EQUAL, /**< NOT_EQUAL */ +}; + +/** Activation Layer Information class */ +class ActivationLayerInfoEx +{ +public: + /** Available activation functions */ + enum class ActivationFunction + { + RSQRT /**< Inverse Square root ( \f$ f(x) = \rsqrt{x} \f$ )*/ + }; + + ActivationLayerInfoEx() = default; + /** Default Constructor + * + * @param[in] f The activation function to use. + * @param[in] a (Optional) The alpha parameter used by some activation functions + * (@ref ActivationFunction::BOUNDED_RELU, @ref ActivationFunction::LU_BOUNDED_RELU, + * @ref ActivationFunction::LINEAR, @ref ActivationFunction::TANH). + * @param[in] b (Optional) The beta parameter used by some activation functions (@ref + * ActivationFunction::LINEAR, @ref ActivationFunction::LU_BOUNDED_RELU, @ref + * ActivationFunction::TANH). + */ + ActivationLayerInfoEx(ActivationFunction f, float a = 0.0f, float b = 0.0f) + : _act(f), _a(a), _b(b), _enabled(true) + { + } + /** Get the type of activation function */ + ActivationFunction activation() const { return _act; } + /** Get the alpha value */ + float a() const { return _a; } + /** Get the beta value */ + float b() const { return _b; } + /** Check if initialised */ + bool enabled() const { return _enabled; } + +private: + ActivationFunction _act = {ActivationLayerInfoEx::ActivationFunction::RSQRT}; + float _a = {}; + float _b = {}; + bool _enabled = {false}; +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TYPESEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/core/UtilsEx.h b/libs/ARMComputeEx/arm_compute/core/UtilsEx.h new file mode 100644 index 000000000..8dd68a0c3 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/core/UtilsEx.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_UTILSEX_H__ +#define __ARM_COMPUTE_UTILSEX_H__ + +#include "arm_compute/core/TypesEx.h" + +#include <cstdint> +#include <cstdlib> +#include <sstream> +#include <string> + +namespace arm_compute +{ +/** Translates a given activation function to a string. + * + * @param[in] act @ref ActivationLayerInfo::ActivationFunction to be translated to string. + * + * @return The string describing the activation function. + */ +const std::string &string_from_activation_func_ex(ActivationLayerInfoEx::ActivationFunction act); +} +#endif /*__ARM_COMPUTE_UTILSEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLActivationLayerEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLActivationLayerEx.h new file mode 100644 index 000000000..7e578550f --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLActivationLayerEx.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLACTIVATIONLAYEREX_H__ +#define __ARM_COMPUTE_CLACTIVATIONLAYEREX_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLActivationLayerExKernel + * + * @note The function simulates an activation layer with the specified activation function. + */ +class CLActivationLayerEx : public ICLSimpleFunction +{ +public: + /** Set the input and output tensor. + * + * @note If the output tensor is a nullptr or is equal to the input, the activation function will + * be performed in-place + * + * @param[in, out] input Source tensor. In case of @p output tensor = nullptr, this tensor will + * store the result + * of the activation function. Data types supported: + * QASYMM8/F16/F32. + * @param[out] output Destination tensor. Data type supported: same as @p input + * @param[in] act_info Activation layer parameters. + */ + void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfoEx act_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLActivationLayer + * + * @param[in] input Source tensor info. In case of @p output tensor info = nullptr, this tensor + * will store the result + * of the activation function. Data types supported: QASYMM8/F16/F32. + * @param[in] output Destination tensor info. Data type supported: same as @p input + * @param[in] act_info Activation layer information. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const ActivationLayerInfoEx &act_info); +}; +} +#endif /* __ARM_COMPUTE_CLACTIVATIONLAYEREX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArgMinMax.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArgMinMax.h new file mode 100644 index 000000000..8044c58af --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArgMinMax.h @@ -0,0 +1,114 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLArgMinMax.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLArgMinMax class + */ + +#ifndef __ARM_COMPUTE_CLARG_MIN_MAX_H__ +#define __ARM_COMPUTE_CLARG_MIN_MAX_H__ + +#include "arm_compute/core/CL/kernels/CLArgMinMaxKernel.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/IFunction.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to execute CLArgMinMax operation + */ +class CLArgMinMax : public IFunction +{ +public: + /** + * @brief Construct a new CLArgMinMax object + */ + CLArgMinMax(); + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ + CLArgMinMax(const CLArgMinMax &) = delete; + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ + CLArgMinMax &operator=(const CLArgMinMax &) = delete; + + /** + * @brief Construct a new CLArgMinMax object by using copy constructor + * @param[in] CLArgMinMax object to move + */ + CLArgMinMax(CLArgMinMax &&) = default; + + /** + * @brief Assign a CLArgMinMax object. + * @param[in] CLArgMinMax object to assign. This object will be moved. + */ + CLArgMinMax &operator=(CLArgMinMax &&) = default; + + /** + * @brief Initialise the kernel's inputs and outputs. + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S32/F32. + * @param[out] output The result of argminmaxMax operation. Data types supported: same as @p + * input. + * @param[in] axis Axis to argminmax. It must be sorted and no duplicates. + * @param[in] is_min True for ArgMin operation. + * @param[in] is_max Ture for ArgMax operation. + * @return N/A + */ + void configure(ICLTensor *input, ICLTensor *output, std::vector<uint32_t> argminmax_axis, + ArgOperation op); + + /** + * @brief Static function to check if given info will lead to a valid configuration + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S32/F32. + * @param[in] axis Axis to argminmax + * @param[out] output The result of argminmaxMax operation. Data types supported: same as @p + * input. + * @return a status + */ + static Status validate(const ITensorInfo *input, const std::vector<uint32_t> &argminmax_axis, + const ITensorInfo *output, ArgOperation op); + + /** + * @brief Run the kernels contained in the function + * This operation works on CPU on GPU depending on the value of argminmax_MAX_RUN_ON_CPU macro + * in CLArgMinMax.cpp. + * If argminmax_MAX_RUN_ON_CPU == 1, CPU runs this operation. + * Otherwise GPU runs this operation. + * @return N/A + */ + void run() override; + +private: + ICLTensor *_input; + ICLTensor *_output; + std::vector<uint32_t> _argminmax_axis; + ArgOperation _arg_op; + + std::unique_ptr<CLTensor[]> _interm_tensors{nullptr}; + std::unique_ptr<CLArgMinMaxKernel[]> _argminmax_kernels{nullptr}; + size_t _num_of_kernels; +}; +} +#endif /*__ARM_COMPUTE_CLargminmax_MAX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArithmeticSubtractionEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArithmeticSubtractionEx.h new file mode 100644 index 000000000..34e6c6334 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLArithmeticSubtractionEx.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEX_H__ +#define __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEX_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLArithmeticSubtractionExKernel + * + * @note The tensor data type for the inputs must be U8/S16/F16/F32. + * @note The function performs an arithmetic subtraction between two tensors. + */ +class CLArithmeticSubtractionEx : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's inputs, output and convertion policy. + * + * @param[in, out] input1 An input tensor. Data types supported: U8/S16/F16/F32. + * The input tensor is [in, out] because its TensorInfo might be modified + * inside the kernel in case of broadcasting of dimension 0. + * @param[in, out] input2 An input tensor. Data types supported: same as @p input1. + * The input tensor is [in, out] because its TensorInfo might be modified + * inside the kernel in case of broadcasting of dimension 0. + * @param[out] output Output tensor. Data types supported: U8 (Only if both inputs are U8), + * S16/F16/F32. + * @param[in] policy Policy to use to handle overflow. + */ + void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLArithmeticSubtractionEx + * + * @param[in] input1 First tensor input info. Data types supported: U8/S16/F16/F32. + * @param[in] input2 Second tensor input info. Data types supported: U8/S16/F16/F32. + * @param[in] output Output tensor info. Data types supported: U8 (Only if both inputs are U8), + * S16/F16/F32. + * @param[in] policy Policy to use to handle overflow. + * + * @return a status + */ + static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, ConvertPolicy policy); +}; +} +#endif /* __ARM_COMPUTE_CLARITHMETICSUBTRACTIONEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBatchToSpaceND.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBatchToSpaceND.h new file mode 100644 index 000000000..d16a0762d --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBatchToSpaceND.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLBATCH_TO_SPACE_ND_H__ +#define __ARM_COMPUTE_CLBATCH_TO_SPACE_ND_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLBatchToSpaceNDKernel + * + * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32. + * @note The function converts the input tensor to the tensor of the output tensor's type. + */ +class CLBatchToSpaceND : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[in] block_size A pointer to an array of integer values specifying block sizes + * for spatial dimension. + */ + void configure(ICLTensor *input, ICLTensor *output, const int32_t *block_size); +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLBATCH_TO_SPACE_ND_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBinaryLogicalOp.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBinaryLogicalOp.h new file mode 100644 index 000000000..061e34f26 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLBinaryLogicalOp.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLBINARYLOGICALOP_H__ +#define __ARM_COMPUTE_CLBINARYLOGICALOP_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +class CLBinaryLogicalOp : public ICLSimpleFunction +{ +public: + /** Initialise the function's source and destination. + * + * @param[in] input1 Source tensor1. Data types supported: U8, QASYMM8. + * @param[in] input2 Source tensor2. Data types supported: U8 QASYMM8. + * @param[out] output Output tensor. Data types supported: U8, QASYMM8. + */ + void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, + BinaryLogicalOperation op); +}; + +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLBINARYLOGICALOP_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLCast.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLCast.h index 63050067d..56b8408e2 100644 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLCast.h +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLCast.h @@ -14,30 +14,35 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLCast.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLCast class + */ + #ifndef __ARM_COMPUTE_CLCAST_H__ #define __ARM_COMPUTE_CLCAST_H__ -#include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/ICLSimpleFunction.h" namespace arm_compute { class ICLTensor; -/** Basic function to run @ref CLCastKernel - * - * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32. - * @note The function converts the input tensor to the tensor of the output tensor's type. +/** + * @brief Class to run @ref CLCastKernel. + * This converts the input tensor to the tensor of the output tensor's type. */ class CLCast : public ICLSimpleFunction { public: - /** Initialise the kernel's input and output. - * - * @param[in, out] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. - * The input tensor is [in, out] because its TensorInfo might be modified - * inside the kernel. - * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + /** + * @brief Initialise the kernel's input and output + * @param[in, out] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * The input tensor is [in, out] because its TensorInfo might be + * modified inside the kernel. + * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. */ void configure(ICLTensor *input, ICLTensor *output); }; diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLComparisonOp.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLComparisonOp.h new file mode 100644 index 000000000..1b0d70e7f --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLComparisonOp.h @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLCOMPARISON_OP_H__ +#define __ARM_COMPUTE_CLCOMPARISON_OP_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" +#include "arm_compute/core/TypesEx.h" + +namespace arm_compute +{ +class ICLTensor; + +class CLComparisonOp : public ICLSimpleFunction +{ +public: + /** Initialise the function's source and destination. + * + * @param[in] input1 Source tensor1. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[in] input2 Source tensor2. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[out] output Output tensor. Data types supported: Same as @p input. + */ + void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, + const ComparisonOperation &op); +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLCOMPARISON_OP_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLDepthToSpace.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLDepthToSpace.h new file mode 100644 index 000000000..d78a6ada4 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLDepthToSpace.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLDEPTHTOSPACE_H__ +#define __ARM_COMPUTE_CLDEPTHTOSPACE_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLDepthToSpaceKernel + * + * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32. + * @note The function converts the input tensor to the tensor of the output tensor's type. + */ +class CLDepthToSpace : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[block_size] block size integer only + */ + void configure(ICLTensor *input, ICLTensor *output, const int32_t block_size); +}; +} // namesace arm_compute + +#endif /* __ARM_COMPUTE_CLDEPTHTOSPACE_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLEmbeddingLookup.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLEmbeddingLookup.h new file mode 100644 index 000000000..257772a89 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLEmbeddingLookup.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLEmbeddingLookup.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLEmbeddingLookup class + */ + +#ifndef __ARM_COMPUTE_CLEMBEDDINGLOOKUP_H__ +#define __ARM_COMPUTE_CLEMBEDDINGLOOKUP_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +#include <vector> + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to perform EmbeddingLookup operation + */ +class CLEmbeddingLookup : public ICLSimpleFunction +{ +public: + /** + * @brief Set the input and output tensors. + * @param[in] input Source tensor. + * Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p + * input. + * @param[in] lookups Lookups 1D tensor that values are indices into the first dimension of + * input. + * @return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *lookups); +}; +} +#endif /*__ARM_COMPUTE_CLEMBEDDINGLOOKUP_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLExp.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLExp.h new file mode 100644 index 000000000..2d0fc23a4 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLExp.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLEXP_H__ +#define __ARM_COMPUTE_CLEXP_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLExpKernel */ +class CLExp : public ICLSimpleFunction +{ +public: + /** Set the source, destination of the kernel + * + * @param[in] input Source tensor. Data type supported: F32. + * @param[out] output Destination tensor. Data type supported: F32. + */ + void configure(const ICLTensor *input, ICLTensor *output); +}; +} +#endif /* __ARM_COMPUTE_CLEXP_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLGather.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLGather.h index 3ae7afe14..f7fd3cda1 100644 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLGather.h +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLGather.h @@ -14,32 +14,43 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLGather.h + * @brief This file contains CLGather class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __ARM_COMPUTE_CLGATHER_H__ #define __ARM_COMPUTE_CLGATHER_H__ -#include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/ICLSimpleFunction.h" namespace arm_compute { class ICLTensor; -/** Basic function to run @ref CLGatherKernel. */ +/** + * @brief Class to to run @ref CLGatherKernel. + */ class CLGather : public ICLSimpleFunction { public: - /** Initialise the kernel's inputs, output and convertion policy. - * - * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. - * @param[in] input2 An indexes tensor. Data types supported: S32. - * @param[out] output The output tensor, Data types supported: same as @p input1. - */ + /** + * @brief Initialise the kernel's inputs, output and convertion policy. + * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. + * @param[in] input2 An indexes tensor. Data types supported: S32. + * @param[out] output The output tensor, Data types supported: same as @p input1. + * @return N/A + */ void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref CLGather - * - * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. - * @param[in] input2 An indexes tensor. Data types supported: S32. - * @param[out] output The output tensor, Data types supported: same as @p input1. + + /** + * @brief Static function to check if given info will lead to a valid configuration + * of @ref CLGather + * @param[in] input1 An input tensor. Data types supported: U8/S32/F32. + * @param[in] input2 An indexes tensor. Data types supported: S32. + * @param[out] output The output tensor, Data types supported: same as @p input1. * @return a status */ static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLHashtableLookup.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLHashtableLookup.h new file mode 100644 index 000000000..65aa6cbd5 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLHashtableLookup.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLHashtableLookup.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLHashtableLookup class + */ + +#ifndef __ARM_COMPUTE_CLHASHTABLELOOKUP_H__ +#define __ARM_COMPUTE_CLHASHTABLELOOKUP_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +#include <vector> + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to perform HashtableLookup operation + */ +class CLHashtableLookup : public ICLSimpleFunction +{ +public: + /** + * @brief Set the input and output tensors. + * @param[in] lookups Lookups 1D tensor that values are indices into the first dimension of + * input. + * @param[in] keys Keys 1D tensor. keys and input pair represent a map. + * Data types supported: S32 + * @param[in] input Source tensor. + * Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p + * input. + * @param[out] hits Hits 1D tensor. A boolean tensor that indicates whether the lookup hits + * (True) or not (False). Data types supported: U8/QASYMM8 + * @return N/A + */ + void configure(const ICLTensor *lookups, const ICLTensor *keys, const ICLTensor *intput, + ICLTensor *output, ICLTensor *hits); +}; +} +#endif /*__ARM_COMPUTE_CLHASHTABLELOOKUP_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNeg.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNeg.h new file mode 100644 index 000000000..198a0fd4e --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNeg.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLNEG_H__ +#define __ARM_COMPUTE_CLNEG_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +class CLNeg : public ICLSimpleFunction +{ +public: + /** Initialise the function's source and destination. + * + * @param[in] input Source tensor. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[out] output Output tensor. Data types supported: Same as @p input. + * + */ + void configure(ICLTensor *input, ICLTensor *output); +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLNEG_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNormalizationLayerEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNormalizationLayerEx.h new file mode 100644 index 000000000..4077245d5 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLNormalizationLayerEx.h @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLNORMALIZATIONLAYEREX_H__ +#define __ARM_COMPUTE_CLNORMALIZATIONLAYEREX_H__ + +#include "arm_compute/runtime/IFunction.h" + +#include "arm_compute/core/CL/kernels/CLFillBorderKernel.h" +#include "arm_compute/core/CL/kernels/CLNormalizationLayerExKernel.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to compute a normalization layer. This function calls the following CL kernels: + * + * -# @ref CLFillBorderKernel + * -# @ref CLNormalizationLayerKernelEx + * + */ +class CLNormalizationLayerEx : public IFunction +{ +public: + /** Default constructor */ + CLNormalizationLayerEx(); + /** Set the input and output tensors. + * + * @param[in, out] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types + * supported: F16/F32 (Written to by the border handler) + * @param[out] output Destination tensor. Dimensions, data type and number of channels must + * match the input ones. + * @param[in] norm_info Normalization layer information like the normalization type, + * normalization size and other parameters. + */ + void configure(ICLTensor *input, ICLTensor *output, const NormalizationLayerInfo &norm_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * CLNormalizationLayer + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data types supported: + * F16/F32 + * @param[in] output Destination tensor. Dimensions, data type and number of channels must + * match the input ones. + * @param[in] norm_info Normalization layer information like the normalization type, normalization + * size and other parameters. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const NormalizationLayerInfo &norm_info); + + // Inherited methods overridden: + void run() override; + +private: + CLNormalizationLayerExKernel _norm_kernel; /**< Normalization layer kernel to run */ + CLFillBorderKernel _border_handler; /**< Kernel to handle borders */ +}; +} +#endif /* __ARM_COMPUTE_CLNORMALIZATIONLAYEREX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPReLU.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPReLU.h new file mode 100644 index 000000000..622a61b5e --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPReLU.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLPRELU_H__ +#define __ARM_COMPUTE_CLPRELU_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +class CLPReLU : public ICLSimpleFunction +{ +public: + /** Initialise the function's source and destination. + * + * @param[in] input. Data types supported: + * QASYMM8/F16/F32. + * @param[in] alpha. Data types supported: + * QASYMM8/F16/F32. + * @param[out] output Output tensor. Data types supported: Same as @p input. + */ + void configure(ICLTensor *input, ICLTensor *alpha, ICLTensor *output); +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLPRELU_H__*/ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPadLayerEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPadLayerEx.h new file mode 100644 index 000000000..d6ea486d1 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPadLayerEx.h @@ -0,0 +1,47 @@ +/* +* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +* Copyright (c) 2016-2018 ARM Limited. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ +#ifndef __ARM_COMPUTE_CLPADLAYEREX_H__ +#define __ARM_COMPUTE_CLPADLAYEREX_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLPadLayerKernel + * + * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32. + * @note The function converts the input tensor to the tensor of the output tensor's type. + */ +class CLPadLayerEx : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: + * U8/QASYMM8/S16/S32/F16/F32. + * @param[out] output Output tensor. Data types supported: + * U8/QASYMM8/S16/S32/F16/F32. + * @param[in] pad_size Tensor for Padding values in NHWC format shape [n, 2], + * where n is the rank of tensor . Data types supported: S32 + */ + void configure(ICLTensor *input, ICLTensor *output, ICLTensor *pad_size); +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLPADLAYEREX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPermuteEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPermuteEx.h new file mode 100644 index 000000000..9a0cc213c --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPermuteEx.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLPERMUTEEX_H__ +#define __ARM_COMPUTE_CLPERMUTEEX_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to execute an @ref CLPermuteKernel. */ +class CLPermuteEx : public ICLSimpleFunction +{ +public: + /** Set the input and output tensors. + * + * @param[in] input The input tensor to permute. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] output The output tensor. Data types supported: Same as @p input + * @param[in] perm Permutation vector + */ + void configure(const ICLTensor *input, ICLTensor *output, const PermutationVector &perm); + /** Static function to check if given info will lead to a valid configuration of @ref CLPermute. + * + * @param[in] input First tensor input info. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[in] output Output tensor info. Data types supported: same as @p input. + * @param[in] perm Permutation vector + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const PermutationVector &perm); +}; +} +#endif /*__ARM_COMPUTE_CLPERMUTEEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPixelWiseDivision.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPixelWiseDivision.h index c1383e21f..b142d3a2e 100644 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPixelWiseDivision.h +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLPixelWiseDivision.h @@ -14,53 +14,61 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLPixelWiseDivision.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLPixelWiseDivision class + */ #ifndef __ARM_COMPUTE_CLPIXELWISEDIVISION_H__ #define __ARM_COMPUTE_CLPIXELWISEDIVISION_H__ -#include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/ICLSimpleFunction.h" namespace arm_compute { class ICLTensor; -/** Basic function to run @ref CLPixelWiseDivisionKernel. */ +/** + * @brief Class to run @ref CLPixelWiseDivisionKernel. + */ class CLPixelWiseDivision : public ICLSimpleFunction { public: - /** Initialise the kernel's inputs, output and convertion policy. - * - * @param[in, out] input1 An input tensor. Data types supported: U8/QS8/QS16/S16/F16/F32. + /** + * @brief Initialise the kernel's inputs, output and convertion policy. + * @param[in, out] input1 An input tensor. Data types supported: U8/S16/F16/F32 * The input tensor is [in, out] because its TensorInfo might be * modified inside the kernel in case of broadcasting of dimension 0. * @param[in, out] input2 An input tensor. Data types supported: same as @p input1. * The input tensor is [in, out] because its TensorInfo might be * modified inside the kernel in case of broadcasting of dimension 0. * @param[out] output The output tensor, Data types supported: same as @p input1. - * Note: U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). + * Note: U8 requires both inputs to be U8. * @param[in] scale Scale to apply after multiplication. * Scale must be positive and its value must be either 1/255 or - * 1/2^n where n is between 0 and 15. For QS8 and QS16 scale must be 1. + * 1/2^n where n is between 0 and 15. * @param[in] overflow_policy Overflow policy. Supported overflow policies: Wrap, Saturate * @param[in] rounding_policy Rounding policy. Supported rounding modes: to zero, to nearest * even. + * @return N/A */ void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale = 1.f, ConvertPolicy overflow_policy = ConvertPolicy::WRAP, RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO); - /** Static function to check if given info will lead to a valid configuration of @ref + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref * CLPixelWiseDivision - * - * @param[in] input1 An input tensor info. Data types supported: U8/QS8/QS16/S16/F16/F32. + * @param[in] input1 An input tensor info. Data types supported: U8/S16/F16/F32 * @param[in] input2 An input tensor info. Data types supported: same as @p input1. * @param[in] output The output tensor info, Data types supported: same as @p input1. - * Note: U8 (QS8, QS16) requires both inputs to be U8 (QS8, QS16). + * Note: U8 requires both inputs to be U8. * @param[in] scale Scale to apply after multiplication. * Scale must be positive and its value must be either 1/255 or 1/2^n - * where n is between 0 and 15. For QS8 and QS16 scale must be 1. + * where n is between 0 and 15. * @param[in] overflow_policy Overflow policy. Supported overflow policies: Wrap, Saturate * @param[in] rounding_policy Rounding policy. Supported rounding modes: to zero, to nearest even. - * * @return a status */ static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceMax.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceMax.h deleted file mode 100644 index 14b473f33..000000000 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceMax.h +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ARM_COMPUTE_CLREDUCE_MAX_H__ -#define __ARM_COMPUTE_CLREDUCE_MAX_H__ - -#include "arm_compute/runtime/CL/CLArray.h" -#include "arm_compute/runtime/IFunction.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Basic function to execute TopK operation. This function calls the following OpenCL kernels: - * - * -# @ref CLTopKV2Kernel - */ -class CLReduceMax : public IFunction -{ -public: - /** Constructor */ - CLReduceMax(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLReduceMax(const CLReduceMax &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLReduceMax &operator=(const CLReduceMax &) = delete; - /** Allow instances of this class to be moved */ - CLReduceMax(CLReduceMax &&) = default; - /** Allow instances of this class to be moved */ - CLReduceMax &operator=(CLReduceMax &&) = default; - /** Initialise the kernel's inputs and outputs. - * - * @note When locations of min and max occurrences are requested, the reported number of locations - * is limited to the given array size. - * - * @param[in] input Input image. Data types supported: F32 - * @param[in] axis Axis to reduce. Data type supported: S32 - * @param[out] output indices related to top k values. Data types supported: F32. - */ - void configure(ICLTensor *input, int32_t axis, ICLTensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref - * CLPixelWiseDivision - * - * @param[in] input Input image. Data types supported: F32 - * @param[in] axis Axis to reduce. Data type supported: S32 - * @param[out] output indices related to top k values. Data types supported: F32. * - * - * @return a status - */ - static Status validate(const ITensorInfo *input, int32_t axis, const ITensorInfo *output); - - // Inherited methods overridden: - void run() override; - -private: - void run_on_cpu(); - - int32_t _axis; - - ICLTensor *_input; - ICLTensor *_output; - - std::unique_ptr<ICLKernel> _kernel; -}; -} -#endif /*__ARM_COMPUTE_CLREDUCE_MAX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceOperation.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceOperation.h new file mode 100644 index 000000000..e1a6f6ab4 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReduceOperation.h @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLReduceOperation.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLReduceOperation class + */ + +#ifndef __ARM_COMPUTE_CLREDUCEOPERATION_H__ +#define __ARM_COMPUTE_CLREDUCEOPERATION_H__ + +#include "arm_compute/core/CL/kernels/CLReduceOperationKernel.h" +#include "arm_compute/core/TypesEx.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/IFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to perform ReduceOperation + */ +class CLReduceOperation : public IFunction +{ +public: + /** + * @brief Construct a new ReduceOperation object + */ + CLReduceOperation(); + + /** + * @brief Set the input and output tensors. + * @param[in] input Source tensor. Data types supported: U8/S32/F32 + * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p + * input. + * @param[in] axis Axis along which to reduce. It must be sorted and no duplicates. + * @param[in] op Reduce operation to perform. + * @return N/A + */ + void configure(ICLTensor *input, ICLTensor *output, const std::set<uint32_t> &axis, + ReduceOperation op); + + /** + * @brief Static function to check if given info will lead to a valid configuration of @ref + * CLReduceOperation. + * @param[in] input Source tensor info. Data types supported: U8/S32/F32 + * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p + * input. + * @param[in] axis Axis along which to reduce. It must be sorted and no duplicates. + * @param[in] op Reduce operation to perform. + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const std::set<uint32_t> &axis, const ReduceOperation &op); + + /** + * @brief Run the OpenCL kernel for this operation + * @return N/A + */ + void run() override; + +private: + ICLTensor *_input; + ICLTensor *_output; + std::set<uint32_t> _axis; + + std::unique_ptr<CLTensor[]> _interm_tensors{nullptr}; + std::unique_ptr<CLReduceOperationKernel[]> _reduce_kernels{nullptr}; +}; +} +#endif /*__ARM_COMPUTE_CLREDUCEOPERATION_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReductionMean.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReductionMean.h deleted file mode 100644 index 2081518c1..000000000 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLReductionMean.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ARM_COMPUTE_CLREDUCTIONMEAN_H__ -#define __ARM_COMPUTE_CLREDUCTIONMEAN_H__ - -#include "arm_compute/core/CL/kernels/CLFillBorderKernel.h" -#include "arm_compute/core/CL/kernels/CLReductionMeanKernel.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/IFunction.h" - -#include <cstdint> -#include <memory> -#include <vector> - -namespace arm_compute -{ -class ICLTensor; - -/** Perform reduction operation. - */ -class CLReductionMean : public IFunction -{ -public: - /** Default Constructor. - */ - CLReductionMean(); - - /** Set the input and output tensors. - * - * @param[in] input Source tensor. Data types supported: F32. Data layouts supported: NCHW. - * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p input. - * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1 - */ - void configure(ICLTensor *input, ICLTensor *output, std::vector<uint32_t> axis); - - /** Static function to check if given info will lead to a valid configuration of @ref - * CLReductionMean. - * - * @param[in] input Source tensor info. Data types supported: F32. Data layouts supported: NCHW. - * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p - * input. - * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1 - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, - std::vector<uint32_t> axis); - - // Inherited methods overridden: - void run() override; - -private: - CLReductionMeanKernel _reduction_mean_kernel; - CLFillBorderKernel _fill_border_kernel; -}; -} -#endif /*__ARM_COMPUTE_CLREDUCTIONMEAN_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h new file mode 100644 index 000000000..7e2df8986 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ +#define __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLSpaceToBatchNDKernel + * + * @note The tensor data type for the inputs must be U8/QASYMM8/S16/F16/S32/F32. + * @note The function divides "spatial" dimensions of the input into a grid of blocks of shape + * block_shape, and interleaves these blocks with the "batch" dimension such that in the output. + */ +class CLSpaceToBatchND : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's input and output. + * + * @note The data layout of input and output must be the same. + * @note The number of dimensions of input and output must be 4, and `spatial` dimensions + * are height and width. + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. + * Data layout supported: NCHW/NHWC + * @param[in] block_size Tensor of integer values specifying block sizes for spatial + * dimension. + * Data types supported: S32 + * @param[in] padding_size Tensor of integer values specifying padding sizes for spatial + * dimension. + * Data types supported: S32 + * @param[out] output Output tensor. Data types supported: same as @p input. + * Data layout supported: NCHW/NHWC + */ + void configure(const ICLTensor *input, const ICLTensor *block_size, const ICLTensor *padding_size, + ICLTensor *output); +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToDepth.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToDepth.h new file mode 100644 index 000000000..17f762092 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToDepth.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSPACETODEPTH_H__ +#define __ARM_COMPUTE_CLSPACETODEPTH_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Basic function to run @ref CLSpaceToDepthKernel + * + * @note The tensor data type for the inputs must be U8/QASYMM8/S16/S32/F16/F32. + * @note The function converts the input tensor to the tensor of the output tensor's type. + */ +class CLSpaceToDepth : public ICLSimpleFunction +{ +public: + /** Initialise the kernel's input and output. + * + * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/S32/F16/F32. + * @param[block_size] block size integer only + */ + void configure(ICLTensor *input, ICLTensor *output, const int32_t block_size); +}; + +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLSPACETODEPTH_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSquaredDifference.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSquaredDifference.h new file mode 100644 index 000000000..3610ba71c --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLSquaredDifference.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__ +#define __ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +class CLSquaredDifference : public ICLSimpleFunction +{ +public: + /** Initialise the function's source and destination. + * + * @param[in] input1 Source tensor1. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[in] input2 Source tensor2. Data types supported: + * U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[out] output Output tensor. Data types supported: Same as @p input. + */ + void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output); +}; +} // namespace arm_compute +#endif /*__ARM_COMPUTE_CLSQUARED_DIFFERENCE_H__*/ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSlice.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSlice.h deleted file mode 100644 index f223a79be..000000000 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSlice.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ARM_COMPUTE_CLSTRIDEDSLICE_H__ -#define __ARM_COMPUTE_CLSTRIDEDSLICE_H__ - -#include "arm_compute/runtime/IFunction.h" -#include "arm_compute/runtime/CL/ICLSimpleFunction.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Basic function to run @ref CLStridedSliceKernel */ -class CLStridedSlice : public ICLSimpleFunction -{ -public: - /** Initialise the kernel's inputs and outputs - * - * @param[in] input First tensor input. Data type supported: - * U8/S8/QS8/QASYMM8/U16/S16/QS16/U32/S32/F16/F32 - * @param[out] output Output tensor. Data type supported: Same as @p input - */ - void configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, - ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, int32_t endMask, - int32_t shrinkAxisMask); -}; - -class CLStridedSliceCPU : public IFunction -{ -public: - /** Initialise inputs and outputs - * - * @param[in] input First tensor input. - * @param[out] output Output tensor. - */ - void configure(ICLTensor *input, ICLTensor *output, ICLTensor *beginData, ICLTensor *endData, - ICLTensor *stridesData, int32_t beginMask, int32_t endMask, - int32_t shrinkAxisMask); - - void run() override; - -private: - void run_on_cpu(); - - ICLTensor *_input; - ICLTensor *_output; - ICLTensor *_beginData; - ICLTensor *_endData; - ICLTensor *_stridesData; - int32_t _beginMask; - int32_t _endMask; - int32_t _shrinkAxisMask; -}; -} -#endif /*__ARM_COMPUTE_CLSTRIDEDSLICE_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSliceEx.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSliceEx.h new file mode 100644 index 000000000..6b26a85c8 --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLStridedSliceEx.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CLStridedSlice.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLStridedSlice and arm_compute::CLStridedSliceCPU class + */ + +#ifndef __ARM_COMPUTE_CLSTRIDEDSLICEEX_H__ +#define __ARM_COMPUTE_CLSTRIDEDSLICEEX_H__ + +#include "arm_compute/runtime/CL/ICLSimpleFunction.h" + +namespace arm_compute +{ +class ICLTensor; + +/** + * @brief Class to run @ref CLStridedSliceKernel + */ +class CLStridedSliceEx : public ICLSimpleFunction +{ +public: + /** + * @brief Initialise the kernel's inputs and outputs + * @param[in] input Tensor input. Data type supported: + * U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[out] output Output tensor. Data type supported: Same as @p input + * @param[in] beginData 'begin' vector of strided slice operation + * @param[in] endData 'end' vector of strided slice operation + * @param[in] stridesData 'strides' vector of strided slice operation + * @param[in] beginMask If the ith bit is set, begin[i] is ignored + * @param[in] endMask If the ith bit is set, end[i] is ignored + * @param[in] shrinkAxisMask If the ith bit is set, the ith specification shrinks the + * dimensionality by 1, taking on the value at index begin[i] + * @return N/A + */ + void configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, + ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, int32_t endMask, + int32_t shrinkAxisMask); +}; +} +#endif /*__ARM_COMPUTE_CLSTRIDEDSLICEEX_H__ */ diff --git a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLTopKV2.h b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLTopKV2.h index 06cd1ee9b..5327e016f 100644 --- a/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLTopKV2.h +++ b/libs/ARMComputeEx/arm_compute/runtime/CL/functions/CLTopKV2.h @@ -14,51 +14,79 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +/** + * @file CLTopKV2.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains arm_compute::CLTopKV2 class + */ #ifndef __ARM_COMPUTE_CLTOPK_V2_H__ #define __ARM_COMPUTE_CLTOPK_V2_H__ #include "arm_compute/core/CL/kernels/CLTopKV2Kernel.h" -#include "arm_compute/runtime/CL/CLArray.h" #include "arm_compute/runtime/IFunction.h" namespace arm_compute { class ICLTensor; -/** Basic function to execute TopK operation. This function calls the following OpenCL kernels: - * - * -# @ref CLTopKV2Kernel +/** + * @brief Class to execute TopKV2 operation. */ class CLTopKV2 : public IFunction { public: - /** Constructor */ + /** + * @brief Construct a new CLTopKV2 object + */ CLTopKV2(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ CLTopKV2(const CLTopKV2 &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + */ CLTopKV2 &operator=(const CLTopKV2 &) = delete; - /** Allow instances of this class to be moved */ + + /** + * @brief Construct a new CLTopKV2 object by using copy constructor + * @param[in] CLTopKV2 object to move + */ CLTopKV2(CLTopKV2 &&) = default; - /** Allow instances of this class to be moved */ + + /** + * @brief Assign a CLTopKV2 object. + * @param[in] CLTopKV2 object to assign. This object will be moved. + */ CLTopKV2 &operator=(CLTopKV2 &&) = default; - /** Initialise the kernel's inputs and outputs. - * - * @note When locations of min and max occurrences are requested, the reported number of locations - * is limited to the given array size. - * + + /** + * @brief Initialise the kernel's inputs and outputs. * @param[in] input Input image. Data types supported: U8/S16/F32. * @param[in] k The value of `k`. * @param[out] values Top k values. Data types supported: S32 if input type is U8/S16, F32 if * input type is F32. - * @param[out] indices indices related to top k values. Data types supported: S32 if input type + * @param[out] indices Indices related to top k values. Data types supported: S32 if input type * is U8/S16, F32 if input type is F32. + * @return N/A */ void configure(ICLTensor *input, int k, ICLTensor *values, ICLTensor *indices, int total_bits = 32, int bits = 4); - // Inherited methods overridden: + /** + * @brief Run the kernels contained in the function + * Depending on the value of the following environment variables it works differently: + * - If the value of environment variable "ACL_TOPKV2" == "GPU_SINGLE", + * quick sort on GPU is used. + * - If the value of environment variable "ACL_TOPKV2" == ""GPU"", + * radix sort on GPU is used. + * - For other value, TopKV2 runs on CPU + * @return N/A + */ void run() override; private: diff --git a/libs/ARMComputeEx/arm_compute/runtime/NEON/functions/NENormalizationLayerEx.h b/libs/ARMComputeEx/arm_compute/runtime/NEON/functions/NENormalizationLayerEx.h new file mode 100644 index 000000000..fa7408ecd --- /dev/null +++ b/libs/ARMComputeEx/arm_compute/runtime/NEON/functions/NENormalizationLayerEx.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __ARM_COMPUTE_NENORMALIZATIONLAYEREX_H__ +#define __ARM_COMPUTE_NENORMALIZATIONLAYEREX_H__ + +#include "arm_compute/runtime/IFunction.h" + +#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h" +#include "arm_compute/core/NEON/kernels/NENormalizationLayerExKernel.h" +#include "arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h" +#include "arm_compute/runtime/MemoryGroup.h" + +namespace arm_compute +{ +class ITensor; + +/** Basic function to compute a normalization layer. This function calls the following NEON kernels: + * + * -# @ref NEPixelWiseMultiplicationKernel + * -# @ref NEFillBorderKernel + * -# @ref NENormalizationLayerKernelEx + * + */ +class NENormalizationLayerEx : public IFunction +{ +public: + /** Default constructor */ + NENormalizationLayerEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr); + /** Set the input and output tensors. + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data type supported: + * F16/F32 + * @param[out] output Destination with the same dimensions, data type and number of channels of + * @p input + * @param[in] norm_info Normalization layer information like the normalization type, + * normalization size and other parameters. + */ + void configure(const ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info); + /** Static function to check if given info will lead to a valid configuration of @ref + * NENormalizationLayer + * + * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions + * [width, height, IFM], + * and an optional 4th dimension for batch of inputs. Data type supported: + * F16/F32 + * @param[in] output Destination with the same dimensions, data type and number of channels of + * @p input + * @param[in] norm_info Normalization layer information like the normalization type, normalization + * size and other parameters. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, + const NormalizationLayerInfo &norm_info); + + // Inherited methods overridden: + void run() override; + +private: + MemoryGroup _memory_group; /**< Function memory group */ + NENormalizationLayerExKernel _norm_kernel; /**< Normalization layer kernel */ + NEPixelWiseMultiplicationKernel _multiply_kernel; /**< Pixel multiplication kernel */ + NEFillBorderKernel _border_handler; /**< Kernel to handle borders */ + Tensor _input_squared; /**< The intermediate buffer which stores results of squaring input */ +}; +} +#endif /* __ARM_COMPUTE_NENORMALIZATIONLAYEREX_H__ */ diff --git a/libs/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp b/libs/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp index d535c5da4..05ecdeb22 100644 --- a/libs/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp +++ b/libs/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp @@ -38,255 +38,37 @@ using namespace arm_compute; const std::map<std::string, std::string> CLKernelLibraryEx::_kernel_program_map = { - {"absdiff", "absdiff.cl"}, - {"accumulate", "accumulate.cl"}, - {"accumulate_squared", "accumulate.cl"}, - {"accumulate_weighted", "accumulate.cl"}, - {"activation_layer", "activation_layer.cl"}, - {"activation_layer_qa8", "activation_layer_qa8.cl"}, - {"activation_layer_logistic_qa8", "activation_layer_qa8.cl"}, - {"arithmetic_add", "arithmetic_op.cl"}, - {"arithmetic_sub", "arithmetic_op.cl"}, + // ARMComputeEx kernels + {"activation_layer_ex", "activation_layer_ex.cl"}, + {"arg_op", "arg_operation.cl"}, + {"arithmetic_sub_ex", "arithmetic_op_ex.cl"}, {"arithmetic_add_qasymm8", "arithmetic_op_quantized.cl"}, - {"batchnormalization_layer_nchw", "batchnormalization_layer.cl"}, - {"batchnormalization_layer_nhwc", "batchnormalization_layer.cl"}, - {"bitwise_or", "bitwise_op.cl"}, - {"bitwise_and", "bitwise_op.cl"}, - {"bitwise_xor", "bitwise_op.cl"}, - {"bitwise_not", "bitwise_op.cl"}, + {"batch_to_space_nd", "batch_to_space_nd.cl"}, + {"binary_logical_op", "binary_logical_op.cl"}, {"cast", "cast.cl"}, {"cast_qasymm_in", "cast.cl"}, {"cast_qasymm_out", "cast.cl"}, - {"channel_combine_NV", "channel_combine.cl"}, - {"channel_combine_RGB888", "channel_combine.cl"}, - {"channel_combine_RGBA8888", "channel_combine.cl"}, - {"channel_combine_UYVY422", "channel_combine.cl"}, - {"channel_combine_YUYV422", "channel_combine.cl"}, - {"channel_shuffle_nchw", "channel_shuffle.cl"}, - {"channel_extract_NV12", "channel_extract.cl"}, - {"channel_extract_NV21", "channel_extract.cl"}, - {"channel_extract_RGB888", "channel_extract.cl"}, - {"channel_extract_RGBA8888", "channel_extract.cl"}, - {"channel_extract_UYVY422", "channel_extract.cl"}, - {"channel_extract_YUYV422", "channel_extract.cl"}, - {"combine_gradients_L1", "canny.cl"}, - {"combine_gradients_L2", "canny.cl"}, - {"concatenate_depth", "concatenate.cl"}, - {"concatenate_width", "concatenate.cl"}, - {"convolution_rectangle", "convolution_rectangle.cl"}, - {"col2im", "col2im.cl"}, - {"convert_depth_down", "depth_convert.cl"}, - {"convert_depth_up", "depth_convert.cl"}, - {"convert_fc_weights", "convert_fc_weights.cl"}, - {"convolution3x3_static", "convolution3x3.cl"}, - {"convolution5x5_static", "convolution5x5.cl"}, - {"convolution7x7_static", "convolution7x7.cl"}, - {"convolution9x9_static", "convolution9x9.cl"}, - {"convolution_separable1x5_static", "convolution5x5.cl"}, - {"convolution_separable5x1_static", "convolution5x5.cl"}, - {"convolution_separable1x7_static", "convolution7x7.cl"}, - {"convolution_separable7x1_static", "convolution7x7.cl"}, - {"convolution_separable1x9_static", "convolution9x9.cl"}, - {"convolution_separable9x1_static", "convolution9x9.cl"}, - {"copy_tensor", "copy_tensor.cl"}, - {"copy_plane", "channel_extract.cl"}, - {"copy_planes_3p", "channel_combine.cl"}, - {"copy_to_keypoint", "fast_corners.cl"}, - {"deconvolution_upsample", "deconvolution_layer.cl"}, - {"depthwise_convolution_3x3", "depthwise_convolution.cl"}, - {"depthwise_convolution_3x3_f16", "depthwise_convolution.cl"}, - {"depthwise_convolution_3x3_quantized_nchw", "depthwise_convolution_quantized.cl"}, - {"depthwise_convolution_3x3_quantized_nhwc_stride1", "depthwise_convolution_quantized.cl"}, - {"depthwise_convolution_3x3_quantized_nhwc_stride2", "depthwise_convolution_quantized.cl"}, - {"depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16", "depthwise_convolution.cl"}, - {"depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16", "depthwise_convolution.cl"}, - {"depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32", "depthwise_convolution.cl"}, - {"depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32", "depthwise_convolution.cl"}, - {"depthwise_im2col", "depthwise_convolution.cl"}, - {"depthwise_vector_to_tensor", "depthwise_convolution.cl"}, - {"depthwise_weights_reshape", "depthwise_convolution.cl"}, - {"dequantization_layer", "dequantization_layer.cl"}, - {"derivative", "derivative.cl"}, - {"dilate", "dilate.cl"}, - {"direct_convolution1x1", "direct_convolution1x1.cl"}, - {"direct_convolution1x1_f32_bifrost", "direct_convolution1x1.cl"}, - {"direct_convolution3x3", "direct_convolution3x3.cl"}, - {"direct_convolution3x3_f32_bifrost", "direct_convolution3x3.cl"}, - {"direct_convolution5x5", "direct_convolution5x5.cl"}, - {"direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl"}, - {"direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl"}, - {"erode", "erode.cl"}, - {"fast_corners", "fast_corners.cl"}, - {"fill_image_borders_constant", "fill_border.cl"}, - {"fill_image_borders_replicate", "fill_border.cl"}, - {"finalize", "optical_flow_pyramid_lk.cl"}, - {"floor_layer", "floor.cl"}, + {"comparison_op", "comparison_op.cl"}, + {"comparison_op_qasymm8", "comparison_op_quantized.cl"}, + {"depth_to_space", "depth_to_space.cl"}, + {"embedding_lookup", "embedding_lookup.cl"}, + {"exp_layer", "exp.cl"}, {"gather", "gather.cl"}, {"gather_1d", "gather.cl"}, {"gather_1d_out", "gather.cl"}, - {"gaussian1x5_sub_x", "gaussian_pyramid.cl"}, - {"gaussian5x1_sub_y", "gaussian_pyramid.cl"}, - {"gemm_accumulate_biases", "gemm.cl"}, - {"gemm_interleave4x4", "gemm.cl"}, - {"gemm_ma_f16", "gemm.cl"}, - {"gemm_ma_f32", "gemm.cl"}, - {"gemm_ma_qs8", "gemm.cl"}, - {"gemm_ma_qs16", "gemm.cl"}, - {"gemm_mv", "gemv.cl"}, - {"gemm_mv_quantized", "gemv.cl"}, - {"gemm_mm_interleaved_transposed_f16", "gemm.cl"}, - {"gemm_mm_interleaved_transposed_f16_bifrost", "gemm.cl"}, - {"gemm_mm_interleaved_transposed_f32", "gemm.cl"}, - {"gemm_mm_interleaved_transposed_f32_bifrost", "gemm.cl"}, - {"gemm_mm_interleaved_transposed_qs8", "gemm.cl"}, - {"gemm_mm_interleaved_transposed_qs16", "gemm.cl"}, - {"gemm_mm_floating_point", "gemm.cl"}, - {"gemm_mm_floating_point_f16_bifrost", "gemm.cl"}, - {"gemm_mm_floating_point_f32_bifrost", "gemm.cl"}, - {"gemm_mm_floating_point_f32_bifrost_1000", "gemm.cl"}, - {"gemm_mm_qs8", "gemm.cl"}, - {"gemm_mm_qs16", "gemm.cl"}, - {"gemm_lc_vm_f32", "gemm.cl"}, - {"gemm_transpose1xW", "gemm.cl"}, - {"gemmlowp_matrix_a_reduction", "gemmlowp.cl"}, - {"gemmlowp_matrix_b_reduction", "gemmlowp.cl"}, - {"gemmlowp_mm_bifrost", "gemmlowp.cl"}, - {"gemmlowp_mm_midgard", "gemmlowp.cl"}, - {"gemmlowp_mm_interleaved_transposed_bifrost", "gemmlowp.cl"}, - {"gemmlowp_mm_interleaved_transposed_midgard", "gemmlowp.cl"}, - {"gemmlowp_offset_contribution", "gemmlowp.cl"}, - {"gemmlowp_output_stage_quantize_down", "gemmlowp.cl"}, - {"gemmlowp_output_stage_quantize_down_fixedpoint", "gemmlowp.cl"}, - {"harris_score_3x3", "harris_corners.cl"}, - {"harris_score_5x5", "harris_corners.cl"}, - {"harris_score_7x7", "harris_corners.cl"}, - {"hist_border_kernel", "histogram.cl"}, - {"hist_border_kernel_fixed", "histogram.cl"}, - {"hist_local_kernel", "histogram.cl"}, - {"hist_local_kernel_fixed", "histogram.cl"}, - {"hog_block_normalization", "hog.cl"}, - {"hog_detector", "hog.cl"}, - {"hog_orientation_binning", "hog.cl"}, - {"hysteresis", "canny.cl"}, - {"im2col1x1_stridex1_dchw", "im2col.cl"}, - {"im2col3x3_dchw", "im2col.cl"}, - {"im2col5x5_dchw", "im2col.cl"}, - {"im2col11x11_padx0_pady0_dchw", "im2col.cl"}, - {"im2col_generic_dchw", "im2col.cl"}, - {"im2col_generic_padx0_pady0_dchw", "im2col.cl"}, - {"im2col_reduced_dchw", "im2col.cl"}, - {"init_level", "optical_flow_pyramid_lk.cl"}, - {"init_level_max", "optical_flow_pyramid_lk.cl"}, - {"init_level_max_initial_estimate", "optical_flow_pyramid_lk.cl"}, - {"integral_horizontal", "integral_image.cl"}, - {"integral_vertical", "integral_image.cl"}, - {"IYUV_to_NV12_bt709", "color_convert.cl"}, - {"IYUV_to_RGB888_bt709", "color_convert.cl"}, - {"IYUV_to_RGBA8888_bt709", "color_convert.cl"}, - {"IYUV_to_YUV444_bt709", "color_convert.cl"}, - {"l2_normalize", "l2_normalize.cl"}, - {"lktracker_stage0", "optical_flow_pyramid_lk.cl"}, - {"lktracker_stage1", "optical_flow_pyramid_lk.cl"}, - {"magnitude_phase", "magnitude_phase.cl"}, - {"mean_stddev_accumulate", "mean_stddev.cl"}, - {"minmax", "minmaxloc.cl"}, - {"minmax_border", "minmaxloc.cl"}, - {"minmax_layer", "minmax_layer.cl"}, - {"minmaxloc", "minmaxloc.cl"}, - {"non_linear_filter_box3x3", "non_linear_filter3x3.cl"}, - {"non_linear_filter_cross3x3", "non_linear_filter3x3.cl"}, - {"non_linear_filter_disk3x3", "non_linear_filter3x3.cl"}, - {"non_linear_filter_box5x5", "non_linear_filter5x5.cl"}, - {"non_linear_filter_cross5x5", "non_linear_filter5x5.cl"}, - {"non_linear_filter_disk5x5", "non_linear_filter5x5.cl"}, - {"non_max_suppression", "nonmax.cl"}, - {"normalization_layer_cross_map", "normalization_layer.cl"}, - {"normalization_layer_in_map", "normalization_layer.cl"}, - {"NV12_to_IYUV_bt709", "color_convert.cl"}, - {"NV12_to_RGB888_bt709", "color_convert.cl"}, - {"NV12_to_RGBA8888_bt709", "color_convert.cl"}, - {"NV12_to_YUV444_bt709", "color_convert.cl"}, - {"NV21_to_IYUV_bt709", "color_convert.cl"}, - {"NV21_to_RGB888_bt709", "color_convert.cl"}, - {"NV21_to_RGBA8888_bt709", "color_convert.cl"}, - {"NV21_to_YUV444_bt709", "color_convert.cl"}, - {"output_stage_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl"}, - {"permute_201", "permute.cl"}, - {"permute_120", "permute.cl"}, - {"permute_3201", "permute.cl"}, - {"pixelwise_mul_float", "pixelwise_mul_float.cl"}, - {"pixelwise_mul_int", "pixelwise_mul_int.cl"}, + {"hashtable_lookup", "hashtable_lookup.cl"}, + {"neg_tensor", "neg_tensor.cl"}, + {"pad", "pad.cl"}, + {"permute_generic", "permute_ex.cl"}, {"pixelwise_mul_qasymm8", "pixelwise_mul_quantized.cl"}, {"pixelwise_div_float", "pixelwise_div_float.cl"}, {"pixelwise_div_int", "pixelwise_div_int.cl"}, - {"pooling_layer_2", "pooling_layer.cl"}, - {"pooling_layer_3", "pooling_layer.cl"}, - {"pooling_layer_optimized_3", "pooling_layer.cl"}, - {"pooling_layer_7", "pooling_layer.cl"}, - {"pooling_layer_MxN_nchw", "pooling_layer.cl"}, - {"pooling_layer_MxN_nhwc", "pooling_layer.cl"}, - {"pooling_layer_MxN_quantized_nhwc", "pooling_layer_quantized.cl"}, - {"pooling_layer_MxN_quantized_nchw", "pooling_layer_quantized.cl"}, - {"quantization_layer", "quantization_layer.cl"}, - {"reduce_max", "reduce_max.cl"}, - {"reduction_operation", "reduction_operation.cl"}, - {"reduction_mean", "reduction_mean.cl"}, - {"remap_nearest_neighbour", "remap.cl"}, - {"remap_bilinear", "remap.cl"}, - {"reshape_layer", "reshape_layer.cl"}, - {"reshape_to_columns", "convolution_layer.cl"}, - {"RGB888_to_IYUV_bt709", "color_convert.cl"}, - {"RGB888_to_NV12_bt709", "color_convert.cl"}, - {"RGB888_to_RGBA8888_bt709", "color_convert.cl"}, - {"RGB888_to_YUV444_bt709", "color_convert.cl"}, - {"RGBA8888_to_IYUV_bt709", "color_convert.cl"}, - {"RGBA8888_to_NV12_bt709", "color_convert.cl"}, - {"RGBA8888_to_RGB888_bt709", "color_convert.cl"}, - {"RGBA8888_to_YUV444_bt709", "color_convert.cl"}, - {"roi_pooling_layer", "roi_pooling_layer.cl"}, - {"scale_nearest_neighbour", "scale.cl"}, - {"scale_bilinear", "scale.cl"}, - {"scharr3x3", "scharr_filter.cl"}, - {"sobel3x3", "sobel_filter.cl"}, - {"sobel_separable5x1", "sobel_filter.cl"}, - {"sobel_separable1x5", "sobel_filter.cl"}, - {"sobel_separable7x1", "sobel_filter.cl"}, - {"sobel_separable1x7", "sobel_filter.cl"}, - {"softmax_layer_norm", "softmax_layer.cl"}, - {"softmax_layer_norm_quantized", "softmax_layer_quantized.cl"}, - {"softmax_layer_max_shift_exp_sum_quantized_serial", "softmax_layer_quantized.cl"}, - {"softmax_layer_max_shift_exp_sum_quantized_parallel", "softmax_layer_quantized.cl"}, - {"softmax_layer_max_shift_exp_sum_serial", "softmax_layer.cl"}, - {"softmax_layer_max_shift_exp_sum_parallel", "softmax_layer.cl"}, - {"strided_slice", "strided_slice.cl"}, - {"suppress_non_maximum", "canny.cl"}, - {"tablelookup_U8", "tablelookup.cl"}, - {"tablelookup_S16", "tablelookup.cl"}, - {"threshold_binary", "threshold.cl"}, - {"threshold_range", "threshold.cl"}, - {"transpose", "transpose.cl"}, - {"UYVY422_to_IYUV_bt709", "color_convert.cl"}, - {"UYVY422_to_NV12_bt709", "color_convert.cl"}, - {"UYVY422_to_RGB888_bt709", "color_convert.cl"}, - {"UYVY422_to_RGBA8888_bt709", "color_convert.cl"}, - {"warp_affine_nearest_neighbour", "warp_affine.cl"}, - {"warp_affine_bilinear", "warp_affine.cl"}, - {"warp_perspective_nearest_neighbour", "warp_perspective.cl"}, - {"warp_perspective_bilinear", "warp_perspective.cl"}, - {"winograd_filter_transform_2x2_3x3_nchw", "winograd.cl"}, - {"winograd_filter_transform_4x4_3x3_nchw", "winograd.cl"}, - {"winograd_filter_transform_4x4_5x5_nchw", "winograd.cl"}, - {"winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd.cl"}, - {"winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl"}, - {"winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl"}, - {"winograd_input_transform_4x4_3x3_stepz1_nchw", "winograd.cl"}, - {"winograd_output_transform_2x2_3x3_nchw", "winograd.cl"}, - {"winograd_output_transform_4x4_3x3_nchw", "winograd.cl"}, - {"winograd_output_transform_4x4_5x5_nchw", "winograd.cl"}, - {"YUYV422_to_IYUV_bt709", "color_convert.cl"}, - {"YUYV422_to_NV12_bt709", "color_convert.cl"}, - {"YUYV422_to_RGB888_bt709", "color_convert.cl"}, - {"YUYV422_to_RGBA8888_bt709", "color_convert.cl"}, + {"prelu", "prelu.cl"}, + {"prelu_qasymm8", "prelu_quantized.cl"}, + {"reduce_min_max", "reduce_operation.cl"}, + {"reduce_sum_mean", "reduce_operation.cl"}, + {"squared_difference", "squared_difference.cl"}, + {"strided_slice_ex", "strided_slice_ex.cl"}, {"topkv2_init", "topkv2.cl"}, {"topkv2_find_first_negative", "topkv2.cl"}, {"topkv2_reorder_negatives", "topkv2.cl"}, @@ -296,23 +78,62 @@ const std::map<std::string, std::string> CLKernelLibraryEx::_kernel_program_map {"radixsort_pastehistograms", "topkv2_radixsort.cl"}, {"radixsort_reorder", "topkv2_radixsort.cl"}, {"topkv2_quicksort", "topkv2_quicksort.cl"}, + {"space_to_batch_4d_nchw", "space_to_batch.cl"}, + {"space_to_batch_4d_nhwc", "space_to_batch.cl"}, + {"space_to_depth", "space_to_depth.cl"}, }; const std::map<std::string, std::string> CLKernelLibraryEx::_program_source_map = { #ifdef EMBEDDED_KERNELS { + "activation_layer_ex.cl", +#include "./cl_kernels/activation_layer_ex.clembed" + }, + { + "arg_operation.cl", +#include "./cl_kernels/arg_operation.clembed" + }, + { + "arithmetic_op_ex.cl", +#include "./cl_kernels/arithmetic_op_ex.clembed" + }, + { + "batch_to_space_nd.cl", +#include "./cl_kernels/batch_to_space_nd.clembed" + }, + { "cast.cl", #include "./cl_kernels/cast.clembed" }, { - "fixed_point.h", -#include "./cl_kernels/fixed_point.hembed" + "comparison_op.cl", +#include "./cl_kernels/comparison_op.clembed" + }, + { + "comparison_op_quantized.cl", +#include "./cl_kernels/comparison_op_quantized.clembed" + }, + { + "embedding_lookup.cl", +#include "./cl_kernels/embedding_lookup.clembed" + }, + { + "depth_to_space.cl", +#include "./cl_kernels/depth_to_space.clembed" + }, + { + "exp.cl", +#include "./cl_kernels/exp.clembed" }, { "gather.cl", #include "./cl_kernels/gather.clembed" }, { + "hashtable_lookup.cl", +#include "./cl_kernels/hashtable_lookup.clembed" + }, + { "helpers.h", #include "./cl_kernels/helpers.hembed" }, @@ -321,6 +142,18 @@ const std::map<std::string, std::string> CLKernelLibraryEx::_program_source_map #include "./cl_kernels/helpers_asymm.hembed" }, { + "binary_logical_op.cl", +#include "./cl_kernels/binary_logical_op.clembed" + }, + { + "neg_tensor.cl", +#include "./cl_kernels/neg_tensor.clembed" + }, + { + "pad.cl", +#include "./cl_kernels/pad.clembed" + }, + { "pixelwise_div_float.cl", #include "./cl_kernels/pixelwise_div_float.clembed" }, @@ -329,16 +162,32 @@ const std::map<std::string, std::string> CLKernelLibraryEx::_program_source_map #include "./cl_kernels/pixelwise_div_int.clembed" }, { - "reduce_max.cl", -#include "./cl_kernels/reduce_max.clembed" + "prelu.cl", +#include "./cl_kernels/prelu.clembed" + }, + { + "prelu_quantized.cl", +#include "./cl_kernels/prelu_quantized.clembed" + }, + { + "reduce_operation.cl", +#include "./cl_kernels/reduce_operation.clembed" + }, + { + "space_to_batch.cl", +#include "./cl_kernels/space_to_batch.clembed" }, { - "reduction_mean.cl", -#include "./cl_kernels/reduction_mean.clembed" + "space_to_depth.cl", +#include "./cl_kernels/space_to_depth.clembed" }, { - "strided_slice.cl", -#include "./cl_kernels/strided_slice.clembed" + "squared_difference.cl", +#include "./cl_kernels/squared_difference.clembed" + }, + { + "strided_slice_ex.cl", +#include "./cl_kernels/strided_slice_ex.clembed" }, { "topkv2.cl", @@ -352,6 +201,11 @@ const std::map<std::string, std::string> CLKernelLibraryEx::_program_source_map "topkv2_quicksort.cl", #include "./cl_kernels/topkv2_quicksort.clembed" }, + { + "permute_ex.cl", +#include "./cl_kernels/permute_ex.clembed" + }, + #endif /* EMBEDDED_KERNELS */ }; @@ -359,7 +213,7 @@ CLKernelLibraryEx::CLKernelLibraryEx() : _context(), _device(), _kernel_path("."), _programs_map(), _built_programs_map() { opencl_is_available(); // Make sure the OpenCL symbols are initialised *before* the - // CLKernelLibrary is built + // CLKernelLibraryEx is built } CLKernelLibraryEx &CLKernelLibraryEx::get() @@ -380,7 +234,7 @@ Kernel CLKernelLibraryEx::create_kernel(const std::string &kernel_name, } std::string concat_str; - if (fp16_supported(_device)) + if (fp16_supported()) { concat_str += " -DARM_COMPUTE_OPENCL_FP16_ENABLED=1 "; } @@ -434,6 +288,13 @@ void CLKernelLibraryEx::add_built_program(const std::string &built_program_name, _built_programs_map.emplace(built_program_name, program); } +bool CLKernelLibraryEx::fp16_supported() const { return ::fp16_supported(_device); } + +bool CLKernelLibraryEx::int64_base_atomics_supported() const +{ + return device_supports_extension(_device, "cl_khr_int64_base_atomics"); +} + const Program &CLKernelLibraryEx::load_program(const std::string &program_name) const { const auto program_it = _programs_map.find(program_name); @@ -525,6 +386,7 @@ size_t CLKernelLibraryEx::max_local_workgroup_size(const cl::Kernel &kernel) con cl::NDRange CLKernelLibraryEx::default_ndrange() const { + // GPUTarget _target = get_target_from_device(_device); cl::Device device = cl::Device::getDefault(); GPUTarget _target = get_target_from_device(device); cl::NDRange default_range; diff --git a/libs/ARMComputeEx/src/core/CL/OpenCLEx.cpp b/libs/ARMComputeEx/src/core/CL/OpenCLEx.cpp new file mode 100644 index 000000000..cbda169fb --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/OpenCLEx.cpp @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +#include "arm_compute/core/CL/OpenCLEx.h" + +#include <dlfcn.h> +#include <iostream> + +namespace arm_compute +{ +CLSymbolsEx &CLSymbolsEx::get() +{ + static CLSymbolsEx symbols; + return symbols; +} + +bool CLSymbolsEx::load_default() +{ + static const std::vector<std::string> libraries{"libOpenCL.so", "libGLES_mali.so", "libmali.so"}; + + if (_loaded.first) + { + return _loaded.second; + } + + // Indicate that default loading has been tried + _loaded.first = true; + + for (const auto &lib : libraries) + { + if (load(lib)) + { + return true; + } + } + + std::cerr << "Couldn't find any OpenCL library.\n"; + return false; +} + +bool CLSymbolsEx::load(const std::string &library) +{ + void *handle = dlopen(library.c_str(), RTLD_LAZY | RTLD_LOCAL); + + if (handle == nullptr) + { + std::cerr << "Can't load " << library << ": " << dlerror() << "\n"; + // Set status of loading to failed + _loaded.second = false; + return false; + } + +#define LOAD_FUNCTION_PTR(func_name, handle) \ + func_name##_ptr = reinterpret_cast<decltype(func_name) *>(dlsym(handle, #func_name)); + + LOAD_FUNCTION_PTR(clGetEventInfo, handle); + LOAD_FUNCTION_PTR(clSetEventCallback, handle); + +#undef LOAD_FUNCTION_PTR + + // Don't call dlclose(handle) or all the symbols will be unloaded ! + + // Disable default loading and set status to successful + _loaded = std::make_pair(true, true); + + return true; +} + +} // namespace arm_compute + +cl_int clGetEventInfo(cl_event event, cl_event_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) +{ + arm_compute::CLSymbolsEx::get().load_default(); + auto func = arm_compute::CLSymbolsEx::get().clGetEventInfo_ptr; + if (func != nullptr) + { + return func(event, param_name, param_value_size, param_value, param_value_size_ret); + } + else + { + return CL_OUT_OF_RESOURCES; + } +} + +cl_int clSetEventCallback(cl_event event, cl_int command_exec_callback_type, + void(CL_CALLBACK *pfn_ev_notify)(cl_event ev, cl_int ev_cmd_exec_status, + void *user_data), + void *user_data) +{ + arm_compute::CLSymbolsEx::get().load_default(); + auto func = arm_compute::CLSymbolsEx::get().clSetEventCallback_ptr; + if (func != nullptr) + { + return func(event, command_exec_callback_type, pfn_ev_notify, user_data); + } + else + { + return CL_OUT_OF_RESOURCES; + } +} diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/activation_layer_ex.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/activation_layer_ex.cl new file mode 100644 index 000000000..f54c7bde3 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/activation_layer_ex.cl @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + +#define CONST_ONE 1.f +#define DIV_OP(a, b) ((a) / (b)) +#define RSQRT_OP(a) DIV_OP(CONST_ONE, sqrt((a))) + +// Inverse Square-root Activation +inline TYPE rsqrt_op(TYPE x) +{ + return RSQRT_OP(x); +} + +#define ACTIVATION_OP2(op, x) op##_op(x) +#define ACTIVATION_OP(op, x) ACTIVATION_OP2(op, x) + +#if defined(ACT) + +/** This performs an activation function floating point inputs. + * + * @note In order to perform the activation function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @note Activation function should be given as a preprocessor argument using -DACT=name. e.g. -DACT=TANH + * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively. + * + * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void activation_layer_ex( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get pixels pointer + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load data + TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr); + + // Perform activation + data = ACTIVATION_OP(ACT, data); + + // Store result + VSTORE(VEC_SIZE) + (data, 0, (__global DATA_TYPE *)output.ptr); +} + +#endif /* defined(ACT) */ diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/arg_operation.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/arg_operation.cl new file mode 100644 index 000000000..9a6921d7c --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/arg_operation.cl @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(OP_CODE) +/** Perform arg_max/arg_min + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[out] output_ptr Pointer to the destination image. Supported data types: U32 + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[in] axis Axis through which reduction occurs for max value index + * @param[in] dim Dimension across the axis to be reduced. + */ + +__kernel void arg_op(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + const int axis, + const int dim) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int indices[4] = + { + get_global_id(0), + get_global_id(1), + get_global_id(2) % DEPTH_OUT, + get_global_id(2) / DEPTH_OUT, + }; + + DATA_TYPE value = *((__global DATA_TYPE *)tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3])); + DATA_TYPE tval = value; + int idx = 0; + for(int i = 1; i < dim; ++i) + { + indices[axis] = i; + + #if OP_CODE == 1 // ArgMax + value = max(value, *((__global DATA_TYPE *) + tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3]))); + #elif OP_CODE == 2 //ArgMin + value = min(value, *((__global DATA_TYPE *) + tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3]))); + #else + return; + + #endif + + if(tval!=value) + { + idx = indices[axis]; + tval = value; + } + } + + *((__global uint *)out.ptr) = idx; +} +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(OP_CODE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_ex.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_ex.cl new file mode 100644 index 000000000..2ed698951 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_ex.cl @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifdef SATURATE +#define SUB(x, y) sub_sat((x), (y)) +#else /* SATURATE */ +#define SUB(x, y) (x) - (y) +#endif /* SATURATE */ + +/** This function subtracts one tensors from another. + * + * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: + * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short + * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. + * + * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8, S16 + * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8, S16 + * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8, S16 + * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void arithmetic_sub_ex( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + // Load values + VEC_DATA_TYPE(DATA_TYPE_OUT, 16) + in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); + VEC_DATA_TYPE(DATA_TYPE_OUT, 16) + in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); + + // Calculate and store result + vstore16(SUB(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr); +} diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_quantized.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_quantized.cl index 0c0a9ede6..5cd0a4309 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_quantized.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/arithmetic_op_quantized.cl @@ -2,32 +2,20 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2016, 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers_asymm.h" -#if defined(FIXED_POINT_POSITION) -#include "fixed_point.h" -#endif /* FIXED_POINT_POSITION */ - #ifdef SATURATE #define ADD(x, y) add_sat((x), (y)) #define SUB(x, y) sub_sat((x), (y)) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/batch_to_space_nd.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/batch_to_space_nd.cl new file mode 100644 index 000000000..ad6a48a02 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/batch_to_space_nd.cl @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BLOCK_SIZE0) && defined(BLOCK_SIZE1) && defined(BATCH_OUT) +/** Perform batch to space rearrangement of tensor + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Output tensor batch should be given as a preprocessor argument using -DBATCH_OUT=size. e.g. -DBATCH_OUT=16 + * @attention block size should be given as a preprocessor argument using -DBLOCK_SIZE0=size. e.g. -DBLOCK_SIZE0=1 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p inpu +t_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in +bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void batch_to_space_nd( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output)) + { + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int out_index[4]={0}; + int in_index[4]={0}; + + out_index[0] = get_global_id(0);//W + out_index[1] = get_global_id(1);//H + out_index[2] = get_global_id(2) % DEPTH_OUT;//C + out_index[3] = get_global_id(2) / DEPTH_OUT;//N + + in_index[0] = out_index[0]/BLOCK_SIZE1; + in_index[1] = out_index[1]/BLOCK_SIZE0; + in_index[2] = out_index[2]; + in_index[3] = out_index[3] + ((out_index[1] % BLOCK_SIZE0) * BLOCK_SIZE0 + out_index[0] % BLOCK_SIZE1) * BATCH_OUT; + + *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], in_index[2], in_index[3])); + } +#endif // defined(DATA_TYPE) && defined(DEPTH_IN) && defined(BLOCK_SIZE0) && defined(BLOCK_SIZE1) && defined(BATCH_OUT) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/binary_logical_op.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/binary_logical_op.cl new file mode 100644 index 000000000..bea61f53e --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/binary_logical_op.cl @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(OP_CODE) && defined(DATA_TYPE) +/** returns truth value of the two input tensors for BINARY LOGICAL OP. + * where BINARY LOGICAL OP can be AND, OR. + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=uchar + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input1_ptr Pointer to the source tensor. Supported data types: QASYMM8 + * @param[in] input1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[in] input2_ptr Pointer to the source tensor.Supported data types: QASYMM8 + * @param[in] input2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input2_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input2_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input2_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input2_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: QASYMM8 + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + */ +__kernel void binary_logical_op( + TENSOR3D_DECLARATION(input1), + TENSOR3D_DECLARATION(input2), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input1 = CONVERT_TO_TENSOR3D_STRUCT(input1); + Tensor3D input2 = CONVERT_TO_TENSOR3D_STRUCT(input2); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + #if OP_CODE == 1 // LOGICAL AND + VSTORE(VEC_SIZE) + (CONVERT(VLOAD(VEC_SIZE) + (0, (__global DATA_TYPE *)input1.ptr) && VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input2.ptr), + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), 0, (__global DATA_TYPE *)output.ptr); + + #elif OP_CODE == 2 // LOGICAL OR + VSTORE(VEC_SIZE) + (CONVERT(VLOAD(VEC_SIZE) + (0, (__global DATA_TYPE *)input1.ptr) || VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input2.ptr), + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), 0, (__global DATA_TYPE *)output.ptr); + + #else // OP NOT SUPPORTED + return + + #endif +} +#endif //if defined(OP_CODE) && defined(DATA_TYPE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/cast.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/cast.cl index 113804cca..3d4675e5d 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/cast.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/cast.cl @@ -2,38 +2,34 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" -#ifndef SCALE_IN -#define SCALE_IN 1.0f +#ifndef SCALE +#define SCALE 1.0f +#endif +#ifndef OFFSET +#define OFFSET 0 #endif -#ifndef OFFSET_IN -#define OFFSET_IN 0 +#ifndef VEC_SIZE +#define VEC_SIZE 1 #endif +#if defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) /** Perform a cast operation on an input tensor. * - * @attention Data type can be passed using the -DDATA_TYPE_IN compile flag, e.g. -DDATA_TYPE_IN=float + * @attention Data types of both input and output can be passed using the -DDATA_TYPE_IN and -DDATA_TYPE_OUT compile flag, e.g. -DDATA_TYPE_IN=float, -DDATA_TYPE_OUT=int * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32 @@ -65,9 +61,9 @@ __kernel void cast( 0, (__global DATA_TYPE_OUT *)output.ptr); } - /** Perform a cast operation on an QASYMM8 input tensor. - * + * @attention Data types of both input and output can be passed using the -DDATA_TYPE_IN and -DDATA_TYPE_OUT compile flag, e.g. -DDATA_TYPE_IN=float, -DDATA_TYPE_OUT=int + * @attention Offset and Scale of input should be given as a preprocessor argument using -DOFFSET=int, -DSCALE=float. e.g. -DOFFSET=1, -DSCALE=0.5 * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32 @@ -96,8 +92,8 @@ __kernel void cast_qasymm_in( VEC_DATA_TYPE(DATA_TYPE_IN, VEC_SIZE) in_data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input.ptr); - VEC_DATA_TYPE(int, VEC_SIZE) offset = (VEC_DATA_TYPE(int, VEC_SIZE))(OFFSET_IN); - VEC_DATA_TYPE(float, VEC_SIZE) scale = (VEC_DATA_TYPE(float, VEC_SIZE))(SCALE_IN); + VEC_DATA_TYPE(int, VEC_SIZE) offset = (VEC_DATA_TYPE(int, VEC_SIZE))(OFFSET); + VEC_DATA_TYPE(float, VEC_SIZE) scale = (VEC_DATA_TYPE(float, VEC_SIZE))(SCALE); VEC_DATA_TYPE(int, VEC_SIZE) tmp = CONVERT(in_data, VEC_DATA_TYPE(int, VEC_SIZE)) - offset; VEC_DATA_TYPE(float, VEC_SIZE) out_data = CONVERT(tmp, VEC_DATA_TYPE(float, VEC_SIZE)) * scale; @@ -108,7 +104,8 @@ __kernel void cast_qasymm_in( /** Perform a cast operation on an QASYMM8 output tensor. - * + * @attention Data types of both input and output can be passed using the -DDATA_TYPE_IN and -DDATA_TYPE_OUT compile flag, e.g. -DDATA_TYPE_IN=float, -DDATA_TYPE_OUT=int + * @attention Offset and Scale of output should be given as a preprocessor argument using -DOFFSET=int, -DSCALE=float. e.g. -DOFFSET=1, -DSCALE=0.5 * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32 @@ -137,8 +134,8 @@ __kernel void cast_qasymm_out( VEC_DATA_TYPE(DATA_TYPE_IN, VEC_SIZE) in_data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input.ptr); - VEC_DATA_TYPE(int, VEC_SIZE) offset = (VEC_DATA_TYPE(int, VEC_SIZE))(OFFSET_IN); - VEC_DATA_TYPE(float, VEC_SIZE) scale = (VEC_DATA_TYPE(float, VEC_SIZE))(SCALE_IN); + VEC_DATA_TYPE(int, VEC_SIZE) offset = (VEC_DATA_TYPE(int, VEC_SIZE))(OFFSET); + VEC_DATA_TYPE(float, VEC_SIZE) scale = (VEC_DATA_TYPE(float, VEC_SIZE))(SCALE); VEC_DATA_TYPE(float, VEC_SIZE) tmp = CONVERT(in_data, VEC_DATA_TYPE(float, VEC_SIZE)) / scale; VEC_DATA_TYPE(float, VEC_SIZE) out_data = tmp + CONVERT(offset, VEC_DATA_TYPE(float, VEC_SIZE)); @@ -146,3 +143,4 @@ __kernel void cast_qasymm_out( VSTORE(VEC_SIZE)(CONVERT(out_data, VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)), 0, (__global DATA_TYPE_OUT *)output.ptr); } +#endif // defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op.cl new file mode 100644 index 000000000..765072556 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op.cl @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) && defined(OP_CODE) +/** Returns truth value of comparison operators. + * Comparison operators may be equal, not_equal etc. + * + * @attention The input and output data types need to be passed at compile time using -DDATA_TYPE_IN, -DDATA_TYPE_OUT, + * e.g. -DDATA_TYPE_IN=float, -DDATA_TYPE_OUT = uchar + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input1_ptr Pointer to the source tensor. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[in] input2_ptr Pointer to the source tensor. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input2_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input2_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input2_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input2_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: QASYMM8 + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void comparison_op( + TENSOR3D_DECLARATION(input1), + TENSOR3D_DECLARATION(input2), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input1 = CONVERT_TO_TENSOR3D_STRUCT(input1); + Tensor3D input2 = CONVERT_TO_TENSOR3D_STRUCT(input2); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + #if OP_CODE == 1 //EQUAL + VSTORE(VEC_SIZE) + (CONVERT(VLOAD(VEC_SIZE) + (0, (__global DATA_TYPE_IN *)input1.ptr) == VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input2.ptr), + VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)),0, (__global DATA_TYPE_OUT *)output.ptr); + + #elif OP_CODE == 2 //NOT_EQUAL + VSTORE(VEC_SIZE) + (CONVERT(VLOAD(VEC_SIZE) + (0, (__global DATA_TYPE_IN *)input1.ptr) != VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN *)input2.ptr), + VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)), 0, (__global DATA_TYPE_OUT *)output.ptr); + + #else // OP NOT SUPPORTED + return; + + #endif +} +#endif // defined(DATA_TYPE_IN) && defined(DATA_TYPE_OUT) && defined(OP_CODE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op_quantized.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op_quantized.cl new file mode 100644 index 000000000..1eb305f7b --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/comparison_op_quantized.cl @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" +#define SUB(x, y) (x) - (y) + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(DATA_TYPE_OUT) + +#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) +#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) +#define VEC_OUT VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE) + +/** Returns the truth value of comparison . + * @attention Offset and Scale of both input should be given as a preprocessor argument using -DOFFSET_IN1=int, -DOFFSET_IN2=int, -DSCALE_IN1=float and -DSCALE_IN2=float. e.g. -DOFFSET_IN1=1, -DOFFSET_IN2=0, -DSCALE_IN1=0.5, -DSCALE_IN2=0.5 + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input1_ptr Pointer to the source tensor. Supported data types: QASYMM8 + * @param[in] input1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[in] input2_ptr Pointer to the source tensor. Supported data types: QASYMM8 + * @param[in] input2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input2_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input2_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input2_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input2_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: QASYMM8 + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void comparison_op_qasymm8( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT); + VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT); + + in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1)); + in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2)); + + const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1); + const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2); + + #if OPCODE == 1 //EQUAL QUANTIZED + VSTORE(VEC_SIZE)(CONVERT(in1f32 == in2f32, VEC_OUT), 0, (__global DATA_TYPE_OUT *)out.ptr); + + #elif OPCODE == 2 //NOT EQUAL QUANTIZED + VSTORE(VEC_SIZE)(CONVERT(in1f32 != in2f32, VEC_OUT), 0, (__global DATA_TYPE_OUT *)out.ptr); + + #else // OP NOT SUPPORTED + return; + + #endif +} +#endif // defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(DATA_TYPE_OUT) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/depth_to_space.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/depth_to_space.cl new file mode 100644 index 000000000..fef2243e7 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/depth_to_space.cl @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BLOCK_SIZE) +/** Perform space to depth rearrangement of tensor + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16 + * @attention block size should be given as a preprocessor argument using -DBLOCK_SIZE=size. e.g. -DBLOCK_SIZE=1 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p inpu +t_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in +bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void depth_to_space( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output)) + { + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int out_index[4]={0}; + int in_index[4]={0}; + + out_index[0] = get_global_id(0);//W + out_index[1] = get_global_id(1);//H + out_index[2] = get_global_id(2) % DEPTH_OUT;//C + out_index[3] = get_global_id(2) / DEPTH_OUT;//B + + in_index[0] = out_index[0]/BLOCK_SIZE; + in_index[1] = out_index[1]/BLOCK_SIZE; + in_index[2] = out_index[2] + ((out_index[1] % BLOCK_SIZE) * BLOCK_SIZE + out_index[0] % BLOCK_SIZE) * DEPTH_OUT; + in_index[3] = out_index[3]; + + *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], in_index[2],in_index[3])); + } +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BLOCK_SIZE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/embedding_lookup.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/embedding_lookup.cl new file mode 100644 index 000000000..348458fe9 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/embedding_lookup.cl @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(NUM_DIMS) +/** Perform embedding_lookup of input tensor + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=depth. e.g. -DDEPTH_OUT=16 + * @attention Number of input dimensions are passed as a preprocessor argument using -DNUM_DIMS=size, e.g. -DNUM_DIMS=4 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] lookups_ptr Pointer to the lookups vector. Supported data types: S32 + * @param[in] lookups_stride_x Stride of the lookups vector in X dimension (in bytes) + * @param[in] lookups_step_x lookups_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] lookups_offset_first_element_in_bytes The offset of the first element in the lookups vector + */ + +__kernel void embedding_lookup(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + VECTOR_DECLARATION(lookups)) +{ + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, DEPTH_OUT); + + Vector lups = CONVERT_TO_VECTOR_STRUCT_NO_STEP(lookups); + + //lookup ids for based on the tensor dimensions + int lup_id[4] = {0}; + + lup_id[0] = (NUM_DIMS == 1)?*((__global int *)vector_offset(&lups,get_global_id(0))) + :get_global_id(0); + lup_id[1] = (NUM_DIMS == 2)?*((__global int *)vector_offset(&lups,get_global_id(1))) + :get_global_id(1); + lup_id[2] = (NUM_DIMS == 3)?*((__global int *)vector_offset(&lups,get_global_id(2))) + :get_global_id(2)%DEPTH_OUT; + lup_id[3] = (NUM_DIMS == 4)?*((__global int *)vector_offset(&lups, get_global_id(2) / DEPTH_OUT)) + :get_global_id(2) / DEPTH_OUT; + + in.ptr += input_offset_first_element_in_bytes + lup_id[0] * input_step_x + lup_id[1] * input_step_y + + lup_id[2] * input_step_z + lup_id[3] * input_step_w; + + VSTORE(VEC_SIZE)(CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr), VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), + 0, (__global DATA_TYPE *)out.ptr); +} +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(NUM_DIMS) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/exp.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/exp.cl new file mode 100644 index 000000000..69d94f30a --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/exp.cl @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) +/** Perform an exponential operation on an input tensor. + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @note Can only take floating point data types. + * + * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void exp_layer( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VSTORE(VEC_SIZE) + (exp(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr)), 0, (__global DATA_TYPE *)output.ptr); +} +#endif // defined(DATA_TYPE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/fixed_point.h b/libs/ARMComputeEx/src/core/CL/cl_kernels/fixed_point.h deleted file mode 100644 index 7807533e2..000000000 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/fixed_point.h +++ /dev/null @@ -1,565 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef ARM_COMPUTE_FIXED_POINT_H -#define ARM_COMPUTE_FIXED_POINT_H - -#define TYPE_ALIAS(type, alias) \ - typedef type alias; \ - typedef type alias##x##1; \ - typedef type##2 alias##x##2; \ - typedef type##3 alias##x##3; \ - typedef type##4 alias##x##4; \ - typedef type##8 alias##x##8; \ - typedef type##16 alias##x##16; - -TYPE_ALIAS(char, qs8) -TYPE_ALIAS(short, qs16) -TYPE_ALIAS(int, qs32) - -#define qs8_MIN ((char)CHAR_MIN) -#define qs8_MAX ((char)CHAR_MAX) -#define qs16_MIN ((short)SHRT_MIN) -#define qs16_MAX ((short)SHRT_MAX) -#define qs32_MIN ((int)INT_MIN) -#define qs32_MAX ((int)INT_MAX) - -#define qu8_MIN ((uchar)0) -#define qu8_MAX ((uchar)UCHAR_MAX) -#define qu16_MIN ((ushort)0) -#define qu16_MAX ((ushort)USHRT_MAX) -#define qu32_MIN ((uint)0) -#define qu32_MAX ((uint)UINT_MAX) - -#define qs8_TYPE char -#define qs8x1_TYPE char -#define qs8x2_TYPE char2 -#define qs8x3_TYPE char3 -#define qs8x4_TYPE char4 -#define qs8x8_TYPE char8 -#define qs8x16_TYPE char16 - -#define qs16_TYPE short -#define qs16x1_TYPE short -#define qs16x2_TYPE short2 -#define qs16x3_TYPE short3 -#define qs16x4_TYPE short4 -#define qs16x8_TYPE short8 -#define qs16x16_TYPE short16 - -#define qs32_TYPE int -#define qs32x1_TYPE int -#define qs32x2_TYPE int2 -#define qs32x3_TYPE int3 -#define qs32x4_TYPE int4 -#define qs32x8_TYPE int8 -#define qs32x16_TYPE int16 - -/* All internal constants are represented in the maximum supported fixed point format (QS16), - * thus we define an additional shift parameter required to convert the constant - * from the maximum supported format to the require one. - */ -#define qs8_SHIFT 8 -#define qs16_SHIFT 0 - -#undef VEC_DATA_TYPE_STR -#undef VEC_DATA_TYPE -#undef CONVERT_STR -#undef CONVERT -#undef CONVERT_SAT_STR -#undef CONVERT_SAT - -#define VEC_DATA_TYPE_STR(type, size) type##x##size -#define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size) - -#define CONVERT_STR3(x, type, rtype) (convert_##rtype((x))) -#define CONVERT_STR2(x, type, rtype) CONVERT_STR3(x, type, rtype) -#define CONVERT_STR(x, type) CONVERT_STR2(x, type, type##_TYPE) -#define CONVERT(x, type) CONVERT_STR(x, type) - -#define CONVERT_SAT_STR3(x, type, rtype) (convert_##rtype##_sat((x))) -#define CONVERT_SAT_STR2(x, type, rtype) CONVERT_SAT_STR3(x, type, rtype) -#define CONVERT_SAT_STR(x, type) CONVERT_SAT_STR2(x, type, type##_TYPE) -#define CONVERT_SAT(x, type) CONVERT_SAT_STR(x, type) - -/** Computes saturating absolute value of fixed point vector. - * - * @param[in] type the actual data type. - * - * @return The result of the fixed point absolute value. - */ -#define ABSQ_SAT_IMPL(type) \ - inline type abs_##type##_sat(type VopA) { return CONVERT_SAT(abs(VopA), type); } - -ABSQ_SAT_IMPL(qs8x16) -ABSQ_SAT_IMPL(qs16x8) - -#define ABS_SAT_OP_EXPAND_STR(a, type, size) abs_##type##x##size##_sat((a)) -#define ABS_SAT_OP_EXPAND(a, type, size) ABS_SAT_OP_EXPAND_STR(a, type, size) - -/** Computes max of fixed point types. - * - * @param[in] type the actual data type. - * - * @return The result of the fixed point maximum. - */ -#define MAXQ_IMPL(type) \ - inline type max_##type(type VopA, type VopB) { return max(VopA, VopB); } - -MAXQ_IMPL(qs8x1) -MAXQ_IMPL(qs8x2) -MAXQ_IMPL(qs8x4) -MAXQ_IMPL(qs8x8) -MAXQ_IMPL(qs8x16) -MAXQ_IMPL(qs16x1) -MAXQ_IMPL(qs16x2) -MAXQ_IMPL(qs16x4) -MAXQ_IMPL(qs16x8) -MAXQ_IMPL(qs16x16) - -#define MAX_OP_EXPAND_STR(a, b, type, size) max_##type##x##size((a), (b)) -#define MAX_OP_EXPAND(a, b, type, size) MAX_OP_EXPAND_STR(a, b, type, size) - -/** Computes saturated addition of fixed point types. - * - * @param[in] type the actual data type. - * - * @return The result of the fixed point addition. The result is saturated in case of overflow - */ -#define ADDQ_SAT_IMPL(type) \ - inline type add_sat_##type(type VopA, type VopB) { return add_sat(VopA, VopB); } - -ADDQ_SAT_IMPL(qs8x1) -ADDQ_SAT_IMPL(qs8x2) -ADDQ_SAT_IMPL(qs8x4) -ADDQ_SAT_IMPL(qs8x8) -ADDQ_SAT_IMPL(qs8x16) -ADDQ_SAT_IMPL(qs16x1) -ADDQ_SAT_IMPL(qs16x2) -ADDQ_SAT_IMPL(qs16x4) -ADDQ_SAT_IMPL(qs16x8) -ADDQ_SAT_IMPL(qs16x16) -ADDQ_SAT_IMPL(qs32x1) -ADDQ_SAT_IMPL(qs32x2) -ADDQ_SAT_IMPL(qs32x4) -ADDQ_SAT_IMPL(qs32x8) -ADDQ_SAT_IMPL(qs32x16) - -#define ADD_SAT_OP_EXPAND_STR(a, b, type, size) add_sat_##type##x##size((a), (b)) -#define ADD_SAT_OP_EXPAND(a, b, type, size) ADD_SAT_OP_EXPAND_STR(a, b, type, size) - -/** Computes saturated subtraction of fixed point types. - * - * @param[in] type the actual data type. - * - * @return The result of the fixed point subtraction. The result is saturated in case of overflow - */ -#define SUBQ_SAT_IMPL(type) \ - inline type sub_sat_##type(type VopA, type VopB) { return sub_sat(VopA, VopB); } - -SUBQ_SAT_IMPL(qs8x1) -SUBQ_SAT_IMPL(qs8x2) -SUBQ_SAT_IMPL(qs8x4) -SUBQ_SAT_IMPL(qs8x8) -SUBQ_SAT_IMPL(qs8x16) -SUBQ_SAT_IMPL(qs16x1) -SUBQ_SAT_IMPL(qs16x2) -SUBQ_SAT_IMPL(qs16x4) -SUBQ_SAT_IMPL(qs16x8) -SUBQ_SAT_IMPL(qs16x16) - -#define SUB_SAT_OP_EXPAND_STR(a, b, type, size) sub_sat_##type##x##size((a), (b)) -#define SUB_SAT_OP_EXPAND(a, b, type, size) SUB_SAT_OP_EXPAND_STR(a, b, type, size) - -/* Multiply of two fixed point numbers - * - * @param[in] type the actual data type. - * @param[in] itype the intermediate data type. - * - * @return The result of the fixed point multiplication. - */ -#define MULQ_IMPL(type, itype) \ - inline type mul_##type(type VopA, type VopB, int fixed_point_position) \ - { \ - itype round_val = (itype)(1 << (fixed_point_position - 1)); \ - itype res = CONVERT((VopA), itype) * CONVERT((VopB), itype) + round_val; \ - return CONVERT((res >> (itype)fixed_point_position), type); \ - } - -MULQ_IMPL(qs8x8, qs16x8) -MULQ_IMPL(qs16x8, qs32x8) -MULQ_IMPL(qs8x16, qs16x16) -MULQ_IMPL(qs16x16, qs32x16) - -#define MUL_OP_EXPAND_STR(a, b, type, size, position) mul_##type##x##size((a), (b), (position)) -#define MUL_OP_EXPAND(a, b, type, size, position) MUL_OP_EXPAND_STR(a, b, type, size, position) - -/* Saturate multiply of two fixed point numbers - * - * @param[in] type the actual data type. - * @param[in] itype the intermediate data type. - * - * @return The result of the fixed point multiplication. The result is saturated in case of overflow - */ -#define MULQ_SAT_IMPL(type, itype) \ - inline type mul_sat_##type(type VopA, type VopB, int fixed_point_position) \ - { \ - itype round_val = (itype)(1 << (fixed_point_position - 1)); \ - itype res = mad_sat(CONVERT((VopA), itype), CONVERT((VopB), itype), round_val); \ - return CONVERT_SAT((res >> (itype)fixed_point_position), type); \ - } - -MULQ_SAT_IMPL(qs8x1, qs16x1) -MULQ_SAT_IMPL(qs8x2, qs16x2) -MULQ_SAT_IMPL(qs8x3, qs16x3) -MULQ_SAT_IMPL(qs8x4, qs16x4) -MULQ_SAT_IMPL(qs8x8, qs16x8) -MULQ_SAT_IMPL(qs8x16, qs16x16) -MULQ_SAT_IMPL(qs16x1, qs32x1) -MULQ_SAT_IMPL(qs16x2, qs32x2) -MULQ_SAT_IMPL(qs16x3, qs32x3) -MULQ_SAT_IMPL(qs16x4, qs32x4) -MULQ_SAT_IMPL(qs16x8, qs32x8) -MULQ_SAT_IMPL(qs16x16, qs32x16) - -#define MUL_SAT_OP_EXPAND_STR(a, b, type, size, position) \ - mul_sat_##type##x##size((a), (b), (position)) -#define MUL_SAT_OP_EXPAND(a, b, type, size, position) \ - MUL_SAT_OP_EXPAND_STR(a, b, type, size, position) - -/** Saturate multiply-accumulate - * - * @param[in] type the actual data type. - * @param[in] itype the intermediate data type. - * - * @return The result of the fixed point multiply-accumulate. The result is saturated in case of - * overflow - */ -#define MLAQ_SAT_IMPL(type, itype) \ - type mla_sat_##type(type VopA, type VopB, type VopC, int fixed_point_position) \ - { \ - itype res = mad_sat(CONVERT(VopB, itype), CONVERT(VopC, itype), \ - (itype)(1 << (fixed_point_position - 1))); \ - return add_sat(VopA, CONVERT_SAT(res >> (itype)fixed_point_position, type)); \ - } - -MLAQ_SAT_IMPL(qs8x8, qs16x8) -MLAQ_SAT_IMPL(qs8x16, qs16x16) -MLAQ_SAT_IMPL(qs16x8, qs32x8) - -#define MLA_SAT_OP_EXPAND_STR(a, b, c, type, size, position) \ - mla_sat_##type##x##size((a), (b), (c), (position)) -#define MLA_SAT_OP_EXPAND(a, b, c, type, size, position) \ - MLA_SAT_OP_EXPAND_STR(a, b, c, type, size, position) - -/** Saturate multiply-accumulate long - * - * @param[in] type the actual data type. - * @param[in] itype the intermediate data type. - * - * @return The result of the fixed point multiply-accumulate long. The result is saturated in case - * of overflow - */ -#define MLALQ_SAT_IMPL(type, itype) \ - itype mlal_sat_##type(itype VopA, type VopB, type VopC, int fixed_point_position) \ - { \ - itype res = mad_sat(CONVERT(VopB, itype), CONVERT(VopC, itype), \ - (itype)(1 << (fixed_point_position - 1))); \ - return add_sat(VopA, res >> (itype)fixed_point_position); \ - } - -MLALQ_SAT_IMPL(qs8x8, qs16x8) -MLALQ_SAT_IMPL(qs16x8, qs32x8) - -#define MLAL_SAT_OP_EXPAND_STR(a, b, c, type, size, position) \ - mlal_sat_##type##x##size((a), (b), (c), (position)) -#define MLAL_SAT_OP_EXPAND(a, b, c, type, size, position) \ - MLAL_SAT_OP_EXPAND_STR(a, b, c, type, size, position) - -/** Saturate division of two fixed point vectors - * - * @param[in] stype the actual scalar data type. - * @param[in] type the actual data type. - * @param[in] itype the intermediate data type. - * - * @return The result of the fixed point division. The result is saturated in case of overflow - */ -#define DIVQ_SAT_IMPL(stype, type, itype) \ - inline type div_sat_##type(type VopA, type VopB, int fixed_point_position) \ - { \ - itype conv_a = CONVERT((VopA), itype); \ - itype denominator = CONVERT((VopB), itype); \ - itype numerator = conv_a << (itype)(fixed_point_position); \ - itype res = select((itype)(numerator / denominator), \ - select((itype)stype##_MAX, (itype)stype##_MIN, (itype)(conv_a < (itype)0)), \ - (itype)(denominator == (itype)0)); \ - return CONVERT_SAT((res), type); \ - } - -DIVQ_SAT_IMPL(qs8, qs8x16, qs16x16) -DIVQ_SAT_IMPL(qs16, qs16x8, qs32x8) -DIVQ_SAT_IMPL(qs16, qs16x16, qs32x16) -DIVQ_SAT_IMPL(qs8, qs8, qs16) -DIVQ_SAT_IMPL(qs16, qs16, qs32) - -#define DIV_SAT_OP_EXPAND_STR(a, b, type, position) div_sat_##type((a), (b), (position)) -#define DIV_SAT_OP_EXPAND(a, b, type, position) DIV_SAT_OP_EXPAND_STR(a, b, type, position) - -#define DIV_SAT_OP_VEC_EXPAND_STR(a, b, type, size, position) \ - div_sat_##type##x##size((a), (b), (position)) -#define DIV_SAT_OP_VEC_EXPAND(a, b, type, size, position) \ - DIV_SAT_OP_VEC_EXPAND_STR(a, b, type, size, position) - -/** Saturate exponential of a fixed point vector - * - * @note Implemented approach uses taylor polynomial to approximate the exponential function. - * - * @param[in] stype the actual scalar data type. - * @param[in] type the actual data type. - * @param[in] size the number of the calculated elements. - * - * @return The result of the fixed point exponential. The result is saturated in case of overflow - */ -#define EXPQ_IMPL(stype, type, size) \ - inline type exp_sat_##type(type VopA, int fixed_point_position) \ - { \ - type const_one = (type)(1 << (fixed_point_position)); \ - type ln2 = (type)((((0x58B9 >> (14 - fixed_point_position))) + 1) >> 1); \ - type inv_ln2 = (type)((((0x38AA >> (14 - fixed_point_position)) + 1) >> 1)) | const_one; \ - type A = (type)(((0x7FBA >> (14 - fixed_point_position)) + 1) >> 1); \ - type B = (type)(((0x3FE9 >> (14 - fixed_point_position)) + 1) >> 1); \ - type C = (type)(((0x1693 >> (14 - fixed_point_position)) + 1) >> 1); \ - type D = (type)(((0x0592 >> (14 - fixed_point_position)) + 1) >> 1); \ - type m = MUL_SAT_OP_EXPAND(VopA, inv_ln2, stype, size, fixed_point_position); \ - type dec_m = m >> (type)fixed_point_position; \ - type alpha = MUL_SAT_OP_EXPAND(dec_m << (type)fixed_point_position, ln2, stype, size, \ - fixed_point_position); \ - alpha = CONVERT(abs_diff(VopA, alpha), type); \ - type sum = add_sat(MUL_SAT_OP_EXPAND(alpha, D, stype, size, fixed_point_position), C); \ - sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), B); \ - sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), A); \ - sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), const_one); \ - return select((type)stype##_MAX, select(sum << dec_m, sum >> -dec_m, dec_m < (type)0), \ - clz(sum) > dec_m); /* Saturate result if needed */ \ - } - -EXPQ_IMPL(qs8, qs8x2, 2) -EXPQ_IMPL(qs8, qs8x4, 4) -EXPQ_IMPL(qs8, qs8x8, 8) -EXPQ_IMPL(qs8, qs8x16, 16) -EXPQ_IMPL(qs16, qs16x2, 2) -EXPQ_IMPL(qs16, qs16x4, 4) -EXPQ_IMPL(qs16, qs16x8, 8) -EXPQ_IMPL(qs16, qs16x16, 16) - -#define EXP_OP_EXPAND_STR(a, type, size, position) exp_sat_##type##x##size((a), (position)) -#define EXP_OP_EXPAND(a, type, size, position) EXP_OP_EXPAND_STR(a, type, size, position) - -/** Saturate logarithm of a fixed point vector - * - * @note Implemented approach uses taylor polynomial to approximate the logarithm function. - * - * @param[in] stype the actual scalar data type. - * @param[in] type the actual data type. - * @param[in] size the number of the calculated elements. - * - * @return The result of the fixed point logarithm. The result is saturated in case of overflow - */ -#define LOGQ_IMPL(stype, type, size) \ - inline type log_sat_##type(type VopA, int fixed_point_position) \ - { \ - type const_one = (type)(1 << (fixed_point_position)); \ - type ln2 = (type)(0x58B9 >> (15 - fixed_point_position)); /* 1.4384189 */ \ - type A = (type)(0x5C0F >> (14 - fixed_point_position)); /* 1.4384189 */ \ - type B = -(type)(0x56AE >> (15 - fixed_point_position)); /* -0.6771900 */ \ - type C = (type)(0x2933 >> (15 - fixed_point_position)); /* 0.3218538 */ \ - type D = -(type)(0x0AA7 >> (15 - fixed_point_position)); /* -0.0832229 */ \ - type inter_a = \ - select(VopA, DIV_SAT_OP_VEC_EXPAND(const_one, VopA, stype, size, fixed_point_position), \ - VopA < const_one); \ - type shift_val = (type)(15 - stype##_SHIFT) - clz(inter_a >> (type)fixed_point_position); \ - inter_a = inter_a >> shift_val; \ - inter_a = sub_sat(inter_a, const_one); \ - type sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, D, stype, size, fixed_point_position), C); \ - sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position), B); \ - sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position), A); \ - sum = MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position); \ - sum = MUL_SAT_OP_EXPAND(add_sat(sum, shift_val << (type)fixed_point_position), ln2, stype, \ - size, fixed_point_position); \ - return select(select(sum, -sum, VopA < const_one), (type)0, \ - VopA < (type)0); /* Saturate result if needed */ \ - } - -LOGQ_IMPL(qs8, qs8x16, 16) -LOGQ_IMPL(qs16, qs16x8, 8) -LOGQ_IMPL(qs16, qs16x16, 16) - -#define LOG_OP_EXPAND_STR(a, type, size, position) log_sat_##type##x##size((a), (position)) -#define LOG_OP_EXPAND(a, type, size, position) LOG_OP_EXPAND_STR(a, type, size, position) - -/** Saturate inverse square root of a fixed point vector - * - * @note Implemented approach uses Newton's method to approximate the inverse square root function. - * - * @param[in] stype the actual scalar data type. - * @param[in] type the actual data type. - * @param[in] size the number of the calculated elements. - * - * @return The result of the fixed point inverse square root. The result is saturated in case of - * overflow - */ -#define INVSQRTQ_IMPL(stype, type, size) \ - inline type invsqrt_sat_##type(type VopA, int fixed_point_position) \ - { \ - type const_three = (type)(3 << (fixed_point_position)); \ - type shift_value = (type)(16 - stype##_SHIFT) - (clz(VopA) + (type)fixed_point_position); \ - type temp = select((type)(VopA >> shift_value), \ - select((type)stype##_MAX, (type)(VopA << (-shift_value)), \ - (type)(clz(VopA) > (-shift_value))), \ - (type)(shift_value < (type)0)); \ - type x = temp; \ - x = MUL_SAT_OP_EXPAND( \ - x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, \ - fixed_point_position), \ - temp, stype, size, fixed_point_position)), \ - stype, size, fixed_point_position) >> \ - 1; \ - x = MUL_SAT_OP_EXPAND( \ - x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, \ - fixed_point_position), \ - temp, stype, size, fixed_point_position)), \ - stype, size, fixed_point_position) >> \ - 1; \ - x = MUL_SAT_OP_EXPAND( \ - x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, \ - fixed_point_position), \ - temp, stype, size, fixed_point_position)), \ - stype, size, fixed_point_position) >> \ - 1; \ - if (sizeof((stype)(1)) > 1) /* Perform more iterations if datatype is QS16 */ \ - { \ - x = MUL_SAT_OP_EXPAND( \ - x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, \ - fixed_point_position), \ - temp, stype, size, fixed_point_position)), \ - stype, size, fixed_point_position) >> \ - 1; \ - x = MUL_SAT_OP_EXPAND( \ - x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, \ - fixed_point_position), \ - temp, stype, size, fixed_point_position)), \ - stype, size, fixed_point_position) >> \ - 1; \ - } \ - type shift_value2 = select(shift_value >> 1, (-shift_value) >> 1, shift_value < (type)0); \ - return select((type)(x >> shift_value2), select((type)stype##_MAX, (type)(x << shift_value2), \ - (type)(clz(x) > shift_value2)), \ - (type)(shift_value < (type)0)); /* Saturate result if needed */ \ - } - -INVSQRTQ_IMPL(qs8, qs8x1, 1) -INVSQRTQ_IMPL(qs16, qs16x1, 1) -INVSQRTQ_IMPL(qs8, qs8x16, 16) -INVSQRTQ_IMPL(qs16, qs16x8, 8) - -#define INVSQRT_OP_EXPAND_STR(a, type, size, position) invsqrt_sat_##type##x##size((a), (position)) -#define INVSQRT_OP_EXPAND(a, type, size, position) INVSQRT_OP_EXPAND_STR(a, type, size, position) - -/** Saturate hyperbolic tangent of a fixed point vector - * - * tanh(x) = (e^2x - 1)/(e^2x + 1) - * - * @param[in] stype the actual scalar data type. - * @param[in] type the actual data type. - * @param[in] size the number of the calculated elements. - * - * @return The result of the fixed point hyperbolic tangent. The result is saturated in case of - * overflow - */ -#define TANHQ_IMPL(stype, type, size) \ - inline type tanh_sat_##type(type VopA, int fixed_point_position) \ - { \ - type const_one = (type)(1 << (fixed_point_position)); \ - type const_two = (type)(2 << (fixed_point_position)); \ - type exp2x = \ - EXP_OP_EXPAND(MUL_SAT_OP_EXPAND(const_two, VopA, stype, size, fixed_point_position), \ - stype, size, fixed_point_position); \ - type num = SUB_SAT_OP_EXPAND(exp2x, const_one, stype, size); \ - type den = ADD_SAT_OP_EXPAND(exp2x, const_one, stype, size); \ - return DIV_SAT_OP_VEC_EXPAND(num, den, stype, size, fixed_point_position); \ - } - -TANHQ_IMPL(qs8, qs8x16, 16) -TANHQ_IMPL(qs16, qs16x8, 8) - -#define TANH_OP_EXPAND_STR(a, type, size, position) tanh_sat_##type##x##size((a), (position)) -#define TANH_OP_EXPAND(a, type, size, position) TANH_OP_EXPAND_STR(a, type, size, position) - -#define floatx16 float16 -#define float16_TYPE float16 - -#define CONVERTQ_DOWN_IMPL(in_type, out_type) \ - inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \ - { \ - return CONVERT(a * (1 << fixed_point_position) + \ - select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), \ - out_type); \ - } - -CONVERTQ_DOWN_IMPL(float16, qs8x16) -CONVERTQ_DOWN_IMPL(float16, qs16x16) - -#define CONVERTQ_DOWN_SAT_IMPL(in_type, out_type) \ - inline out_type convert_##out_type##_##in_type##_sat(in_type a, int fixed_point_position) \ - { \ - return CONVERT_SAT(a * (1 << fixed_point_position) + \ - select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), \ - out_type); \ - } - -CONVERTQ_DOWN_SAT_IMPL(float16, qs8x16) -CONVERTQ_DOWN_SAT_IMPL(float16, qs16x16) - -#define CONVERTQ_UP_IMPL(in_type, out_type) \ - inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \ - { \ - return CONVERT(a, out_type) / (1 << fixed_point_position); \ - } - -CONVERTQ_UP_IMPL(qs8x16, float16) -CONVERTQ_UP_IMPL(qs16x16, float16) - -#define SQCVT_SAT_IMPL(type) \ - inline type sqcvt_##type##_sat(float a, int fixed_point_position) \ - { \ - return CONVERT_SAT((a * (1 << fixed_point_position) + ((a < 0) ? -0.5f : 0.5f)), type); \ - } - -SQCVT_SAT_IMPL(qs8) -SQCVT_SAT_IMPL(qs16) - -#define SQCVT_SAT_OP_EXPAND_STR(a, type, position) sqcvt_##type##_sat((a), (position)) -#define SQCVT_SAT_OP_EXPAND(a, type, position) SQCVT_SAT_OP_EXPAND_STR((a), type, position) - -#endif // ARM_COMPUTE_FIXED_POINT_H diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/gather.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/gather.cl index 25e20f5f2..6b767d6c9 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/gather.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/gather.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/hashtable_lookup.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/hashtable_lookup.cl new file mode 100644 index 000000000..ed7409852 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/hashtable_lookup.cl @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(NUM_DIMS) +/** Perform hashtable_lookup of input tensor + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=depth. e.g. -DDEPTH_OUT=16 + * @attention Number of input dimensions are passed as a preprocessor argument using -DNUM_DIMS=size, e.g. -DNUM_DIMS=4 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] lookups_ptr Pointer to the lookups vector. Supported data types: S32 + * @param[in] lookups_stride_x Stride of the lookups vector in X dimension (in bytes) + * @param[in] lookups_step_x lookups_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] lookups_offset_first_element_in_bytes The offset of the first element in the lookups vector + */ +__kernel void hashtable_lookup(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + VECTOR_DECLARATION(lookups)) +{ + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, DEPTH_OUT); + + Vector lups = CONVERT_TO_VECTOR_STRUCT_NO_STEP(lookups); + + int lup_id[4] = {0}; + + lup_id[0] = (NUM_DIMS == 1)?*((__global int *)vector_offset(&lups,get_global_id(0))) + :get_global_id(0); + lup_id[1] = (NUM_DIMS == 2)?*((__global int *)vector_offset(&lups,get_global_id(1))) + :get_global_id(1); + lup_id[2] = (NUM_DIMS == 3)?*((__global int *)vector_offset(&lups,get_global_id(2))) + :get_global_id(2)%DEPTH_OUT; + lup_id[3] = (NUM_DIMS == 4)?*((__global int *)vector_offset(&lups, get_global_id(2) / DEPTH_OUT)) + :get_global_id(2) / DEPTH_OUT; + + if (lup_id[NUM_DIMS-1] < 0) + { + VSTORE(VEC_SIZE)((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))0, 0, (__global DATA_TYPE *)out.ptr); + return; + } + + in.ptr += input_offset_first_element_in_bytes + lup_id[0] * input_step_x + lup_id[1] * input_step_y + + lup_id[2] * input_step_z + lup_id[3] * input_step_w; + + VSTORE(VEC_SIZE)(CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr), VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), + 0, (__global DATA_TYPE *)out.ptr); +} +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(NUM_DIMS) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/helpers.h b/libs/ARMComputeEx/src/core/CL/cl_kernels/helpers.h index 8143d2398..0e123ae0a 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/helpers.h +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/helpers.h @@ -24,15 +24,23 @@ #ifndef ARM_COMPUTE_HELPER_H #define ARM_COMPUTE_HELPER_H -#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) +#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(cl_khr_fp16) #pragma OPENCL EXTENSION cl_khr_fp16 : enable -#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) +#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(cl_khr_fp16) -#if defined(ARM_COMPUTE_DEBUG_ENABLED) -#if defined(cl_arm_printf) +#if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) +#pragma OPENCL EXTENSION cl_arm_integer_dot_product_int8 : enable +#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) + +#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && \ + defined(cl_arm_integer_dot_product_accumulate_int8) +#pragma OPENCL EXTENSION cl_arm_integer_dot_product_accumulate_int8 : enable +#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && + // defined(cl_arm_integer_dot_product_accumulate_int8) + +#if defined(ARM_COMPUTE_DEBUG_ENABLED) && defined(cl_arm_printf) #pragma OPENCL EXTENSION cl_arm_printf : enable -#endif // defined(cl_arm_printf) -#endif // defined(ARM_COMPUTE_DEBUG_ENABLED) +#endif // defined(ARM_COMPUTE_DEBUG_ENABLED) && defined(cl_arm_printf) #define EXPAND(x) x @@ -175,7 +183,7 @@ typedef struct Tensor4D * * @return An image object */ -Vector inline update_vector_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, +inline Vector update_vector_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x) { Vector vector = { @@ -201,7 +209,7 @@ Vector inline update_vector_workitem_ptr(__global uchar *ptr, uint offset_first_ * * @return An image object */ -Image inline update_image_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, +inline Image update_image_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y) { Image img = {.ptr = ptr, @@ -230,7 +238,7 @@ Image inline update_image_workitem_ptr(__global uchar *ptr, uint offset_first_el * * @return A 3D tensor object */ -Image inline update_image_from_tensor3D_workitem_ptr(__global uchar *ptr, +inline Image update_image_from_tensor3D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z) @@ -261,7 +269,7 @@ Image inline update_image_from_tensor3D_workitem_ptr(__global uchar *ptr, * * @return A 3D tensor object */ -Tensor3D inline update_tensor3D_workitem_ptr(__global uchar *ptr, +inline Tensor3D update_tensor3D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z) @@ -276,7 +284,7 @@ Tensor3D inline update_tensor3D_workitem_ptr(__global uchar *ptr, return tensor; } -Tensor4D inline update_tensor4D_workitem_ptr(__global uchar *ptr, +inline Tensor4D update_tensor4D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z, uint stride_w, uint step_w, uint mod_size) @@ -299,7 +307,7 @@ Tensor4D inline update_tensor4D_workitem_ptr(__global uchar *ptr, * @param[in] vec Pointer to the starting position of the buffer * @param[in] x Relative X position */ -__global inline const uchar *vector_offset(const Vector *vec, int x) +inline __global const uchar *vector_offset(const Vector *vec, int x) { return vec->ptr + x * vec->stride_x; } @@ -310,7 +318,7 @@ __global inline const uchar *vector_offset(const Vector *vec, int x) * @param[in] x Relative X position * @param[in] y Relative Y position */ -__global inline uchar *offset(const Image *img, int x, int y) +inline __global uchar *offset(const Image *img, int x, int y) { return img->ptr + x * img->stride_x + y * img->stride_y; } @@ -322,7 +330,7 @@ __global inline uchar *offset(const Image *img, int x, int y) * @param[in] y Relative Y position * @param[in] z Relative Z position */ -__global inline const uchar *tensor3D_offset(const Tensor3D *tensor, int x, int y, int z) +inline __global const uchar *tensor3D_offset(const Tensor3D *tensor, int x, int y, int z) { return tensor->ptr + x * tensor->stride_x + y * tensor->stride_y + z * tensor->stride_z; } @@ -335,7 +343,7 @@ __global inline const uchar *tensor3D_offset(const Tensor3D *tensor, int x, int * @param[in] z Relative Z position * @param[in] w Relative W position */ -__global inline const uchar *tensor4D_offset(const Tensor4D *tensor, int x, int y, int z, int w) +inline __global const uchar *tensor4D_offset(const Tensor4D *tensor, int x, int y, int z, int w) { return tensor->ptr + x * tensor->stride_x + y * tensor->stride_y + z * tensor->stride_z + w * tensor->stride_w; diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/neg_tensor.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/neg_tensor.cl new file mode 100644 index 000000000..e3aa463db --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/neg_tensor.cl @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) +/** Performs a negation of input tensor. + * + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * + * @param[in] in_ptr Pointer to the source image. Supported data types: S16/S32/F16/F32. + * @param[in] in_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] in_step_x in_stride_x * number of elements along X processed per work item (in bytes) + * @param[in] in_offset_first_element_in_bytes Offset of the first element in the source image + * @param[out] out_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] out_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per work item (in bytes) + * @param[in] out_offset_first_element_in_bytes Offset of the first element in the destination image + */ +__kernel void neg_tensor( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VSTORE(VEC_SIZE) + (-VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr), 0, (__global DATA_TYPE *)output.ptr); +} +#endif // defined(DATA_TYPE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/pad.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/pad.cl new file mode 100644 index 000000000..ecf4696e9 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/pad.cl @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(IW) && defined(IH) && defined(ID) && defined(IB) && defined(DEPTH_OUT) && defined(ZERO_VALUE) +/** Perform space to depth rearrangement of tensor + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * @attention Input dimensions should be passed as a preprocessor argument using -DIW(width), -DIH(height), -DID(depth) and -DIB(batch). e.g. -DIW = 4 + * @attention The value to be set by pad value using -DZERO_VALUE=value. e.g. -DZERO_VALUE=0 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p inpu +t_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in +bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + * + * @param[in] pad_values Padding values for each of the dimensions. Only pad values for Up(for + * batch), Top(for height), Left(for width) and Front(for depth) are + * required. Supported data type: S32 + */ + +__kernel void pad( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + const int4 pad_values) + { + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int index[4]={0}; + + index[0] = get_global_id(0);//W + index[1] = get_global_id(1);//H + index[2] = get_global_id(2) % DEPTH_OUT;//C + index[3] = get_global_id(2) / DEPTH_OUT;//N + + if (index[0] < pad_values.x || index[0] >= (IW + pad_values.x) || + index[1] < pad_values.y || index[1] >= (IH + pad_values.y) || + index[2] < pad_values.z || index[2] >= (ID + pad_values.z) || + index[3] < pad_values.w || index[3] >= (IB + pad_values.w)) + { + *((__global DATA_TYPE *)out.ptr) = (DATA_TYPE)ZERO_VALUE; + } + else + { + *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *) + tensor4D_offset(&in, index[0] - pad_values.x, + index[1] - pad_values.y, + index[2] - pad_values.z, + index[3] - pad_values.w)); + } + } + +#endif //if defined(IW) && defined(IH) && defined(ID) && defined(IB) && defined(DEPTH_OUT) && defined(ZERO_VALUE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/permute_ex.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/permute_ex.cl new file mode 100644 index 000000000..7cc8b0354 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/permute_ex.cl @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_IN) && defined(P1) && defined(P2) && defined(P3) && defined(P4) +/** Perform a Generic permute operation on an input tensor of Shape DCHW. + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16 + * @attention Permutation vector is passed as a preprocessor arguement using -DP1, -DP2, -DP3 and -DP4=int, e.g. -DP1=2 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U1 +6/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in b +ytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in b +ytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in b +ytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p inpu +t_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in +bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void permute_generic( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output)) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, DEPTH_IN); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); + + int out_index[4]; + int in_index[4]; + in_index[0] = get_global_id(0);//W + in_index[1] = get_global_id(1);//H + in_index[2] = get_global_id(2) % DEPTH_IN;//C + in_index[3] = get_global_id(2) / DEPTH_IN;//B + out_index[0] = in_index[P1]; + out_index[1] = in_index[P2]; + out_index[2] = in_index[P3]; + out_index[3] = in_index[P4]; + + *((__global DATA_TYPE *)tensor4D_offset(&out, out_index[0],out_index[1],out_index[2],out_index[3])) = *((__global DATA_TYPE *)in.ptr); +} +#endif // defined(DATA_TYPE) && defined(DEPTH_IN) && defined(P1) && defined(P2) && defined(P3) && defined(P4) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_float.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_float.cl index 512c62023..aa05121b1 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_float.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_float.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2016, 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_int.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_int.cl index 82edf3b1d..fdfb78003 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_int.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_div_int.cl @@ -2,40 +2,20 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2016, 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" -#if defined(FIXED_POINT_POSITION) - -#include "fixed_point.h" - -#if defined(SATURATE) -#define DIV_OP(x, y, scale, type, size) DIV_SAT_OP_EXPAND((x), (y), type, size, FIXED_POINT_POSITION) -#else // SATURATE -#define DIV_OP(x, y, scale, type, size) DIV_OP_EXPAND((x), (y), type, size, FIXED_POINT_POSITION) -#endif // SATURATE - -#else // FIXED_POINT_POSITION - #if defined(SATURATE) #define CONVERT_OP_INT_STR(x, type, size) (convert_##type##size##_sat(x)) #else // SATURATE @@ -45,17 +25,14 @@ #define DIV_OP(x, y, scale, type, size) CONVERT_OP_INT((x) / (y) >> scale, type, size) -#endif // FIXED_POINT_POSITION - /** Performs a pixelwise division with integer scale of integer inputs. * * @attention The inputs and output data types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=ushort -DDATA_TYPE_OUT=short * @attention The data_type of the intermediate result of the division should passed as well using -DDATA_TYPE_RES. * e.g. If one of inputs is S16 -DDATA_TYPE_RES=int should be passed else -DDATA_TYPE_RES=short. - * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3 * - * @param[in] in1_ptr Pointer to the source image. Supported data types: U8/QS8/QS16/S16 + * @param[in] in1_ptr Pointer to the source image. Supported data types: U8/S16 * @param[in] in1_stride_x Stride of the source image in X dimension (in bytes) * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] in1_stride_y Stride of the source image in Y dimension (in bytes) @@ -79,7 +56,7 @@ * @param[in] out_stride_z Stride of the destination image in Y dimension (in bytes) * @param[in] out_step_z out_stride_z * number of elements along Y processed per workitem(in bytes) * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination image - * @param[in] scale Integer scaling factor. Supported data types: S32 (ignored for QS8 and QS16 as the assumption is scale = 1). + * @param[in] scale Integer scaling factor. Supported data types: S32 */ __kernel void pixelwise_div_int( TENSOR3D_DECLARATION(in1), diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_mul_quantized.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_mul_quantized.cl index ddc9d5a27..ab1307e64 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_mul_quantized.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/pixelwise_mul_quantized.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2016, 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers_asymm.h" diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu.cl new file mode 100644 index 000000000..68da2ba32 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu.cl @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) +/** Returns result of prelu function implemented as below: + * f(input) = alpha * input for input < 0, f(input) = input for input >= 0. + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @note Can only take floating point data types. + * + * @param[in] input1_ptr Pointer to the source image. Supported Data types : F16/F32 + * @param[in] input1_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[in] alpha_ptr Pointer to the source image. Supported Data types : F16/F32 + * @param[in] alpha_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] alpha_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] alpha_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] alpha_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] alpha_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] alpha_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] alpha_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void prelu( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(alpha), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D alpha = CONVERT_TO_TENSOR3D_STRUCT(alpha); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VSTORE(VEC_SIZE) + (VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr) < 0 ? + VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr) * VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)alpha.ptr) : + VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input.ptr), + 0, (__global DATA_TYPE *)output.ptr); + +} +#endif // defined(DATA_TYPE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu_quantized.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu_quantized.cl new file mode 100644 index 000000000..7e97b7ed6 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/prelu_quantized.cl @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" +#define SUB(x, y) (x) - (y) + +#if defined(OFF_IN1) && defined(OFF_IN2) && defined(OFF_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) && defined(VEC_SIZE) + +#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) +#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) +#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE) +#define CONVERT_RTE(x, type) (convert_##type##_rte((x))) +#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type) + +/** Returns result of prelu function implemented as below: + * f(input) = alpha * input for input < 0, f(input) = input for input >= 0. + * + * @attention Data type can be passed using the -DDATA_TYPE_IN compile flag, e.g. -DDATA_TYPE_IN=uchar + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @note Can only take uchar data types. + * + * @param[in] input1_ptr Pointer to the source image. Supported Data types : QASYMM8 + * @param[in] input1_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[in] alpha_ptr Pointer to the source image. Supported Data types : QASYMM8 + * @param[in] alpha_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] alpha_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] alpha_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] alpha_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] alpha_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] alpha_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] alpha_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void prelu_qasymm8( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(alpha), + TENSOR3D_DECLARATION(output)) +{ + // Get pixels pointer + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D alpha = CONVERT_TO_TENSOR3D_STRUCT(alpha); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)input.ptr), VEC_INT); + VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)alpha.ptr), VEC_INT); + + in_a = SUB(in_a, (VEC_INT)((int)OFF_IN1)); + in_b = SUB(in_b, (VEC_INT)((int)OFF_IN2)); + + const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1); + const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2); + const VEC_FLOAT outf32 = in1f32 < 0 ? in1f32 * in2f32 : in1f32; + const VEC_FLOAT qresf32 = outf32 / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFF_OUT)); + const VEC_UCHAR res = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR); + + VSTORE(VEC_SIZE) + (res, 0, (__global uchar *)output.ptr); +} + +#endif // defined(OFF_IN1) && defined(OFF_IN2) && defined(OFF_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) && defined(VEC_SIZE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_max.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_max.cl deleted file mode 100644 index dfa3b85f4..000000000 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_max.cl +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "helpers.h" - -#if defined(WIDTH) -/** Perform reduce max - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short - * - * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32 - * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor - * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr - * @param[out] output_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[out] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[out] output_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void reduce_max(VECTOR_DECLARATION(input), - VECTOR_DECLARATION(output)) -{ - Vector input = CONVERT_TO_VECTOR_STRUCT(input); - Vector output = CONVERT_TO_VECTOR_STRUCT(output); - - __global float *input_addr = (__global float *)(input.ptr); - __global float *output_addr = (__global float *)(output.ptr); - - float max_value = *input_addr; - for(int x = 1; x < WIDTH; x++) - { - float value = *(input_addr + x); - max_value = max(value, max_value); - } - - // Store max - *output_addr = max_value; -} -#endif // defined(WIDTH) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_operation.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_operation.cl new file mode 100644 index 000000000..8bef49363 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/reduce_operation.cl @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(OP_CODE) +/** Perform reduce max/min + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[in] axis Axis through which reduction occurs + * @param[in] dim Dimension across the axis to be reduced. + */ +__kernel void reduce_min_max(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + const int axis, + const int dim) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int indices[4] = + { + get_global_id(0), + get_global_id(1), + get_global_id(2) % DEPTH_OUT, + get_global_id(2) / DEPTH_OUT, + }; + + DATA_TYPE value = *((__global DATA_TYPE *)tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3])); + for(int i = 1; i < dim; ++i) + { + indices[axis] = i; + + #if OP_CODE == 1 // REDUCE_MAX + value = max(value, *((__global DATA_TYPE *) + tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3]))); + + #elif OP_CODE == 2 // REDUCE_MIN + value = min(value, *((__global DATA_TYPE *) + tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3]))); + + #else // OP NOT SUPPORTED + return; + + #endif + } + + *((__global DATA_TYPE *)out.ptr) = value; +} + +/** Perform reduce sum/mean + * + * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * @attention Operation type(code) specifying which operation to perform should be passed as preprocessor argument using + * -DOP_CODE = number. e.g. -DOP_CODE=1 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[in] axis Axis through which reduction occurs + * @param[in] dim Dimension across the axis to be reduced. + */ +__kernel void reduce_sum_mean(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + const int axis, + const int dim) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int indices[4] = + { + get_global_id(0), + get_global_id(1), + get_global_id(2) % DEPTH_OUT, + get_global_id(2) / DEPTH_OUT, + }; + + DATA_TYPE sum_value = (DATA_TYPE)0; + for(int i = 0; i < dim; ++i) + { + indices[axis] = i; + sum_value += *((__global DATA_TYPE *)tensor4D_offset(&in, indices[0], indices[1], indices[2], indices[3])); + } + + #if OP_CODE == 3 // REDUCE_SUM + *((__global DATA_TYPE *)out.ptr) = sum_value; + + #elif OP_CODE == 4 // REDUCE_MEAN + *((__global DATA_TYPE *)out.ptr) = sum_value / CONVERT(dim, DATA_TYPE); + + #else // OP NOT SUPPORTED + return; + + #endif +} +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(OP_CODE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/reduction_mean.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/reduction_mean.cl deleted file mode 100644 index 1a96eea61..000000000 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/reduction_mean.cl +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016, 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "helpers.h" - -inline DATA_TYPE sum_8(__global const DATA_TYPE *input) -{ - VEC_DATA_TYPE(DATA_TYPE, 8) - in = vload8(0, input); - in.s0123 += in.s4567; - in.s01 += in.s23; - return ((in.s0 + in.s1)); -} - -/** This function calculates the sum and sum of squares of a given input image. - * - * @note To enable calculation sum of squares -DSTDDEV should be passed as a preprocessor argument. - * - * @param[in] src_ptr Pointer to the source image. Supported data types: U8 - * @param[in] src_stride_x Stride of the source image in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image - * @param[out] local_sum Local sum of all elements - * @param[in] height Height of the input image - * @param[in] divider Divider to calculate mean - */ -__kernel void reduction_mean( - IMAGE_DECLARATION(src), - IMAGE_DECLARATION(dst), - __local DATA_TYPE *local_sums, - int height, - int divider) -{ - // Get pixels pointer - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Image dst = CONVERT_TO_IMAGE_STRUCT(dst); - - float8 tmp_sum = 0; - // Calculate partial sum - - for(int i = 0; i < height; i++) - { - local_sums[0] += sum_8((__global DATA_TYPE *)offset(&src, 0, i)); - } - ((__global DATA_TYPE *)offset(&dst, get_global_id(0), get_global_id(1)))[0] = local_sums[0]/divider; -} diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl new file mode 100644 index 000000000..a0fc2d5a9 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl @@ -0,0 +1,163 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && defined(WIDTH_IN) && defined(ZERO_VALUE) +/** Perform space to batch with input of 4D and NCHW format + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * @attention Input tensor batch should be given as a preprocessor argument using -DBATCH_IN=size. e.g. -DBATCH_IN=16 + * @attention Input tensor height should be given as a preprocessor argument using -DHEIGHT_IN=size. e.g. -DHEIGHT_IN=16 + * @attention Input tensor width should be given as a preprocessor argument using -DHEIGHT_IN=size. e.g. -DWIDTH_IN=16 + * @attention The value to be set by pad value using -DZERO_VALUE=value. e.g. -DZERO_VALUE=0 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] block_size_ptr Pointer to the source tensor. Supported data types: S32 + * @param[in] block_size_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] block_size_step_x block_size_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] block_size_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] padding_size_ptr Pointer to the source tensor. Supported data types: S32 + * @param[in] padding_size_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] padding_size_step_x padding_size_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] padding_size_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] padding_size_step_y padding_size_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] padding_size_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void space_to_batch_4d_nchw(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + VECTOR_DECLARATION(block_size), + IMAGE_DECLARATION(padding_size)) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int block_size_x = *((__global int *)(block_size_ptr)); + int block_size_y = *((__global int *)(block_size_ptr + block_size_stride_x)); + int shift_x = (get_global_id(2) / DEPTH_OUT / BATCH_IN) % block_size_x; + int shift_y = (get_global_id(2) / DEPTH_OUT / BATCH_IN) / block_size_x; + + int in_index[4] = {0, }; + in_index[0] = get_global_id(0) * block_size_x + shift_x - *((__global int *)(padding_size_ptr)); + in_index[1] = get_global_id(1) * block_size_y + shift_y - *((__global int *)(padding_size_ptr + padding_size_stride_y)); + in_index[2] = get_global_id(2) % DEPTH_OUT; + in_index[3] = (get_global_id(2) / DEPTH_OUT) % BATCH_IN; + + if (in_index[0] < 0 || in_index[0] >= WIDTH_IN || in_index[1] < 0 || in_index[1] >= HEIGHT_IN) + { + *((__global DATA_TYPE *)out.ptr) = (DATA_TYPE)ZERO_VALUE; + } + else + { + *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], in_index[2], in_index[3])); + } +} +#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && defined(WIDTH_IN) && defined(ZERO_VALUE) + +#if defined(DATA_TYPE) && defined(HEIGHT_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && defined(WIDTH_IN) && defined(ZERO_VALUE) && defined(VEC_SIZE) +/** Perform space to batch with input of 4D and NHWC format + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Output tensor depth should be given as a preprocessor argument using -DHEIGHT_OUT=size. e.g. -DHEIGHT_OUT=16 + * @attention Input tensor batch should be given as a preprocessor argument using -DBATCH_IN=size. e.g. -DBATCH_IN=16 + * @attention Input tensor height should be given as a preprocessor argument using -DHEIGHT_IN=size. e.g. -DHEIGHT_IN=16 + * @attention Input tensor width should be given as a preprocessor argument using -DHEIGHT_IN=size. e.g. -DWIDTH_IN=16 + * @attention The value to be set by pad value using -DZERO_VALUE=value. e.g. -DZERO_VALUE=0 + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: U8/S8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] block_size_ptr Pointer to the source tensor. Supported data types: S32 + * @param[in] block_size_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] block_size_step_x block_size_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] block_size_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] padding_size_ptr Pointer to the source tensor. Supported data types: S32 + * @param[in] padding_size_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] padding_size_step_x padding_size_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] padding_size_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] padding_size_step_y padding_size_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] padding_size_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void space_to_batch_4d_nhwc(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + VECTOR_DECLARATION(block_size), + IMAGE_DECLARATION(padding_size)) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, HEIGHT_OUT); + + int block_size_x = *((__global int *)(block_size_ptr)); + int block_size_y = *((__global int *)(block_size_ptr + block_size_stride_x)); + int shift_x = (get_global_id(2) / HEIGHT_OUT / BATCH_IN) % block_size_x; + int shift_y = (get_global_id(2) / HEIGHT_OUT / BATCH_IN) / block_size_x; + + int in_index[4] = {0, }; + in_index[0] = get_global_id(0) * VEC_SIZE; + in_index[1] = get_global_id(1) * block_size_x + shift_x - *((__global int *)(padding_size_ptr)); + in_index[2] = get_global_id(2) % HEIGHT_OUT * block_size_y + shift_y - *((__global int *)(padding_size_ptr + padding_size_stride_y)); + in_index[3] = (get_global_id(2) / HEIGHT_OUT) % BATCH_IN; + + if (in_index[1] < 0 || in_index[1] >= WIDTH_IN || in_index[2] < 0 || in_index[2] >= HEIGHT_IN) + { + VSTORE(VEC_SIZE)((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))ZERO_VALUE, 0, (__global DATA_TYPE *)out.ptr); + } + else + { + VSTORE(VEC_SIZE)(CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], in_index[2], in_index[3])), + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), + 0, (__global DATA_TYPE *)out.ptr); + } +} + +#endif // defined(DATA_TYPE) && defined(HEIGHT_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && defined(WIDTH_IN) && defined(ZERO_VALUE) && defined(VEC_SIZE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_depth.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_depth.cl new file mode 100644 index 000000000..f6977045a --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/space_to_depth.cl @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016, 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(DATA_TYPE) && defined(DEPTH_IN) && defined(BLOCK_SIZE) +/** Perform space to depth rearrangement of tensor + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16 + * @attention block size should be given as a preprocessor argument using -DBLOCK_SIZE=size. e.g. -DBLOCK_SIZE=1 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p inpu +t_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in +bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void space_to_depth( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output)) + { + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, DEPTH_IN); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(output, 0); + + int out_index[4]={0}; + int in_index[4]={0}; + + in_index[0] = get_global_id(0);//W + in_index[1] = get_global_id(1);//H + in_index[2] = get_global_id(2) % DEPTH_IN;//C + in_index[3] = get_global_id(2) / DEPTH_IN;//B + + out_index[0] = in_index[0]/BLOCK_SIZE; + out_index[1] = in_index[1]/BLOCK_SIZE; + out_index[2] = in_index[2] + ((in_index[1] % BLOCK_SIZE) * BLOCK_SIZE + in_index[0] % BLOCK_SIZE) * DEPTH_IN; + out_index[3] = in_index[3]; + + *((__global DATA_TYPE *)tensor4D_offset(&out, out_index[0],out_index[1],out_index[2],out_index[3])) = *((__global DATA_TYPE *)in.ptr); + } +#endif // defined(DATA_TYPE) && defined(DEPTH_IN) && defined(BLOCK_SIZE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/squared_difference.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/squared_difference.cl new file mode 100644 index 000000000..3e1a5c97f --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/squared_difference.cl @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#ifndef VEC_SIZE +#define VEC_SIZE 1 +#endif + +#if defined(DATA_TYPE) +/** Returns true value of squared_difference of two tensors. + * + * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @note Can only take floating point data types. + * + * @param[in] input1_ptr Pointer to the source image. Supported data types: F16/F32 + * @param[in] input1_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input1_step_x input1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input1_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input1_step_y input1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input1_step_z input1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input1_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[in] input2_ptr Pointer to the source image. Supported data types: F16/F32 + * @param[in] input2_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input2_step_x input2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input2_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input2_step_y input2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input2_step_z input2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input2_offset_first_element_in_bytes The offset of the first element in the source image + * + * @param[out] output_ptr Pointer to the destination image. Supported data types: F16/F32 + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void squared_difference( + TENSOR3D_DECLARATION(input1), + TENSOR3D_DECLARATION(input2), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input1 = CONVERT_TO_TENSOR3D_STRUCT(input1); + Tensor3D input2 = CONVERT_TO_TENSOR3D_STRUCT(input2); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + diff = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input1.ptr)- VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)input2.ptr); + + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + sq_diff = diff * diff; + + VSTORE(VEC_SIZE) + (sq_diff, 0, (__global DATA_TYPE *)output.ptr); +} +#endif // defined(DATA_TYPE) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice.cl deleted file mode 100644 index c5ff82f9e..000000000 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice.cl +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "helpers.h" - - -inline Tensor4D tensor4D_from_vector_no_step(const Vector *vector, int dim_x, int dim_y, int dim_z, int dim_w) -{ - int stride_x = vector->stride_x; - int stride_y = stride_x * dim_x; - int stride_z = stride_y * dim_y; - int stride_w = stride_z * dim_z; - Tensor4D tensor = - { - .ptr = vector->ptr, - .offset_first_element_in_bytes = vector->offset_first_element_in_bytes, - .stride_x = stride_x, - .stride_y = stride_y, - .stride_z = stride_z, - .stride_w = stride_w, - }; - return tensor; -} - -/** Extracts a strided slice up to 4-dimensions - * - * @note Datatype should be given as a preprocessor argument using -DELEMENT_DATA_TYPE=type. e.g. -DELEMENT_DATA_TYPE=short - * @note The size of an element should be given as a preprocessor argument using -DELEMENT_SIZE=size. e.g. -DELEMENT_SIZE=2 - * - * @param[in] input_ptr Pointer to the first source tensor. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/U32/S32/F16/F32 - * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor - * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] dims_in The 4-dimensional dimension of the input. Supported data types: S32 - * @param[in] dims_out The 4-dimensional dimension of the output. Supported data types: S32 - * @param[in] starts The stride of X dimension of input tensor to be sliced. Supported data types: S32 - * @param[in] strides The stride of Y dimension of input tensor to be sliced. Supported data types: S32 - */ -__kernel void strided_slice(VECTOR_DECLARATION(input), - VECTOR_DECLARATION(output), - const int4 dims_in, - const int4 dims_out, - const int4 starts, - const int4 strides) -{ - // TODO: Should be change to CONVERT_TO_TENSOR4D_STRUCT in order to reduce inference of the offset - Vector vec_out = CONVERT_TO_VECTOR_STRUCT_NO_STEP(output); - Vector vec_in = CONVERT_TO_VECTOR_STRUCT_NO_STEP(input); - - // Implemenation - // Infer a Tensor4D from output Vector and output's dimensions info - // Infer a Tensor4D from input Vector and input's dimensions info - // Infer indices of output as 4D from the offset of output vector - // Infer indices of input as 4D from indices of output - // out(offset of output vector) = in(offset of input) - - Tensor4D tensor_out = tensor4D_from_vector_no_step(&vec_out, dims_out.x, dims_out.y, dims_out.z, dims_out.w); - Tensor4D tensor_in = tensor4D_from_vector_no_step(&vec_in, dims_in.x, dims_in.y, dims_in.z, dims_in.w); - - // Must be output_step_x == output_stride_x == an element's size - const int offset_out = get_global_id(0) * output_stride_x; - int4 indices_out = - { - get_global_id(0) % dims_out.x, - (offset_out / tensor_out.stride_y) % dims_out.y, - (offset_out / tensor_out.stride_z) % dims_out.z, - (offset_out / tensor_out.stride_w) % dims_out.w, - }; - - int4 indices_in = - { - starts.x + (strides.x * indices_out.x), - starts.y + (strides.y * indices_out.y), - starts.z + (strides.z * indices_out.z), - starts.w + (strides.w * indices_out.w), - }; - - *((__global ELEMENT_DATA_TYPE *)vector_offset(&vec_out, get_global_id(0))) = *((__global ELEMENT_DATA_TYPE *)tensor4D_offset(&tensor_in, indices_in.x, indices_in.y, indices_in.z, indices_in.w)); -} diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice_ex.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice_ex.cl new file mode 100644 index 000000000..b39c55b96 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/strided_slice_ex.cl @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "helpers.h" + +#if defined(ELEMENT_DATA_TYPE) && defined(DEPTH_OUT) +/** Extracts a strided slice up to 4-dimensions + * + * @note Datatype should be given as a preprocessor argument using -DELEMENT_DATA_TYPE=type. e.g. -DELEMENT_DATA_TYPE=short + * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. e.g. -DDEPTH_OUT=16 + * + * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[in] starts The stride of X dimension of input tensor to be sliced. Supported data types: S32 + * @param[in] strides The stride of Y dimension of input tensor to be sliced. Supported data types: S32 + */ +__kernel void strided_slice_ex(TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output), + const int4 starts, + const int4 strides) +{ + Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); + Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); + + int4 indices_in = + { + starts.x + (strides.x * get_global_id(0)), + starts.y + (strides.y * get_global_id(1)), + starts.z + (strides.z * (get_global_id(2) % DEPTH_OUT)), + starts.w + (strides.w * (get_global_id(2) / DEPTH_OUT)), + }; + *((__global ELEMENT_DATA_TYPE *)out.ptr) = *((__global ELEMENT_DATA_TYPE *)tensor4D_offset(&in, indices_in.x, indices_in.y, indices_in.z, indices_in.w)); +} +#endif // defined(ELEMENT_DATA_TYPE) && defined(DEPTH_OUT) diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2.cl index 0b0cf8218..d97f23a47 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_quicksort.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_quicksort.cl index deadf8412..0292fab04 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_quicksort.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_quicksort.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ #include "helpers.h" diff --git a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_radixsort.cl b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_radixsort.cl index cac0c071e..c2c2d89a4 100644 --- a/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_radixsort.cl +++ b/libs/ARMComputeEx/src/core/CL/cl_kernels/topkv2_radixsort.cl @@ -2,25 +2,17 @@ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved * Copyright (c) 2017 ARM Limited. * - * SPDX-License-Identifier: MIT + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: + * http://www.apache.org/licenses/LICENSE-2.0 * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ // reference: diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLActivationLayerExKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLActivationLayerExKernel.cpp new file mode 100644 index 000000000..1fdd2f98f --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLActivationLayerExKernel.cpp @@ -0,0 +1,211 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLActivationLayerExKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/UtilsEx.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const ActivationLayerInfoEx &act_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, + DataType::F16, DataType::F32); + + // Checks performed when output is configured + if ((output != nullptr) && (output->total_size() != 0)) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + if (output != nullptr) + { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, *input); + } + + const unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); + + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); + bool window_changed = false; + + if (output != nullptr) + { + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, input->valid_region()); + } + else + { + window_changed = update_window_and_padding( + win, AccessWindowHorizontal(input, 0, num_elems_processed_per_iteration)); + } + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLActivationLayerExKernel::CLActivationLayerExKernel() + : _input(nullptr), _output(nullptr), _run_in_place(false) +{ +} + +void CLActivationLayerExKernel::configure(ICLTensor *input, ICLTensor *output, + ActivationLayerInfoEx act_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + + _run_in_place = (output == nullptr) || (output == input); + + if (output != nullptr) + { + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), *input->info()->clone()); + } + + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, act_info)); + + const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size(); + const DataType dt = input->info()->data_type(); + float a_const = act_info.a(); + float b_const = act_info.b(); + int a_const_int = 0; + int b_const_int = 0; + + // Create quantized version of constants a, b if needed + if (is_data_type_quantized(dt)) + { + a_const_int = + input->info()->quantization_info().quantize(a_const, RoundingPolicy::TO_NEAREST_UP); + b_const_int = + input->info()->quantization_info().quantize(b_const, RoundingPolicy::TO_NEAREST_UP); + } + + // Set build options + std::set<std::string> build_opts; + build_opts.emplace( + ("-DACT=" + lower_string(string_from_activation_func_ex(act_info.activation())))); + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(dt))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + + if (is_data_type_quantized(dt)) + { + build_opts.emplace(("-DA_VAL=" + support::cpp11::to_string(a_const_int))); + build_opts.emplace(("-DB_VAL=" + support::cpp11::to_string(b_const_int))); + + const int o1 = input->info()->quantization_info().offset; + // Quantized value of 0 corresponds to the offset o1 + build_opts.emplace(("-DCONST_0=" + support::cpp11::to_string(o1))); + + // Set scale and offset of the input and output if they have different quantization info + if (is_data_type_quantized_asymmetric(dt) && output != nullptr) + { + const float s1 = input->info()->quantization_info().scale; + const float s2 = output->info()->quantization_info().scale; + const int o2 = output->info()->quantization_info().offset; + + if (o1 != o2 || s1 != s2) + { + build_opts.emplace(("-DS1_VAL=" + float_to_string_with_full_precision(s1))); + build_opts.emplace(("-DS2_VAL=" + float_to_string_with_full_precision(s2))); + build_opts.emplace(("-DO1_VAL=" + support::cpp11::to_string(o1))); + build_opts.emplace(("-DO2_VAL=" + support::cpp11::to_string(o2))); + } + } + } + else + { + build_opts.emplace(("-DA_VAL=" + float_to_string_with_full_precision(a_const))); + build_opts.emplace(("-DB_VAL=" + float_to_string_with_full_precision(b_const))); + } + + build_opts.emplace((_run_in_place) ? "-DIN_PLACE" : ""); + + // Create kernel + std::string kernel_name = std::string("activation_layer_ex"); + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + // Make sure _kernel is initialized before calling the parent's configure + _input = input; + _output = output; + + // Configure kernel window + auto win_config = + validate_and_configure_window(input->info(), (_run_in_place) ? nullptr : output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = "activation_layer_ex_"; + _config_id += lower_string(string_from_data_type(dt)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(1)); +} + +Status CLActivationLayerExKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const ActivationLayerInfoEx &act_info) +{ + const bool run_in_place = (output == nullptr) || (output == input); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, act_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_and_configure_window(input->clone().get(), + (run_in_place) ? nullptr : output->clone().get()) + .first); + + return Status{}; +} + +void CLActivationLayerExKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + if (!_run_in_place) + { + add_3D_tensor_argument(idx, _output, slice); + } + enqueue(queue, *this, slice, lws_hint()); + } while (collapsed.slide_window_slice_3D(slice)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLArgMinMaxKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLArgMinMaxKernel.cpp new file mode 100644 index 000000000..c1a2ad0be --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLArgMinMaxKernel.cpp @@ -0,0 +1,159 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLArgMinMaxKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +const TensorShape inferOutputShape(const TensorShape &input_shape, const uint32_t argminmax_axis) +{ + TensorShape out_shape{input_shape}; + + out_shape.set(argminmax_axis, 1); + + return out_shape; +} +} // namespace + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const uint32_t argminmax_axis, ArgOperation op) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F32, + DataType::U8); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(input, output); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0, + "Inputs are not broadcast compatible"); + + const TensorShape output_shape = inferOutputShape(input->tensor_shape(), argminmax_axis); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_shape.total_size() != output->tensor_shape().total_size(), + "output shape's size does not match argminmax_axis"); + + const auto num_dimensions = input->tensor_shape().num_dimensions(); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + argminmax_axis >= 0 && argminmax_axis < num_dimensions, + "argminmax_axis must be greater than or equal to 0 and less than (input's rank)."); + return Status{}; +} + +} // namespace + +CLArgMinMaxKernel::CLArgMinMaxKernel() : _input(nullptr), _output(nullptr), _argminmax_axis() {} + +void CLArgMinMaxKernel::configure(const ICLTensor *input, ICLTensor *output, + const uint32_t argminmax_axis, ArgOperation op) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), argminmax_axis)); + + _input = input; + _output = output; + _argminmax_axis = argminmax_axis; + + std::unique_ptr<ITensorInfo> output_info = output->info()->clone(); + output_info->set_tensor_shape(inferOutputShape(input->info()->tensor_shape(), argminmax_axis)); + + // Construct kernel name for argmax and argmin based on axis + std::string kernel_name = "arg_op"; + int op_code = 0; + if (op == ArgOperation::MAX) + { + op_code = 1; + } + else if (op == ArgOperation::MIN) + { + op_code = 2; + } + else + throw std::runtime_error("Operation not supported, yet"); + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(output_info->data_type())); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output_info->dimension(2))); + build_opts.emplace("-DOP_CODE=" + support::cpp11::to_string(op_code)); + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output_info, Steps()); + + Coordinates coord; + coord.set_num_dimensions(output_info->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output_info->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +Status CLArgMinMaxKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const uint32_t argminmax_axis, ArgOperation op) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, argminmax_axis, op)); + + return Status{}; +} + +void CLArgMinMaxKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &shape_in = _input->info()->tensor_shape(); + + unsigned int idx = 2 * num_arguments_per_4D_tensor(); // Skip the input and output parameters + + _kernel.setArg<cl_int>(idx++, _argminmax_axis); + _kernel.setArg<cl_int>(idx++, shape_in[_argminmax_axis]); + + Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup input slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + // Copy output's shape in order to use for recovering at end of this method + const TensorShape shape_out = _output->info()->tensor_shape(); + _output->info()->set_tensor_shape(inferOutputShape(shape_in, _argminmax_axis)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_out); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out)); + + // Recover output's shape of output tensor + _output->info()->set_tensor_shape(shape_out); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLArithmeticSubtractionExKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLArithmeticSubtractionExKernel.cpp new file mode 100644 index 000000000..1c505b4d5 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLArithmeticSubtractionExKernel.cpp @@ -0,0 +1,216 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionExKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, ConvertPolicy policy) +{ + ARM_COMPUTE_UNUSED(policy); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, + DataType::F16, DataType::F32); + + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + output->data_type() == DataType::U8 && + (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8), + "Output can only be U8 if both inputs are U8"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, + ITensorInfo *output) +{ + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + { + set_shape_if_empty(*output, out_shape); + + if (input1->data_type() == DataType::S16 || input2->data_type() == DataType::S16) + { + set_format_if_unknown(*output, Format::S16); + } + else if (input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16) + { + set_format_if_unknown(*output, Format::F16); + } + else if (input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32) + { + set_format_if_unknown(*output, Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2); + + AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLArithmeticSubtractionExKernel::CLArithmeticSubtractionExKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLArithmeticSubtractionExKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output, ConvertPolicy policy) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(input1->info(), input2->info(), output->info(), policy)); + + // Configure kernel window + auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + + _input1 = input1; + _input2 = input2; + _output = output; + + const bool has_float_out = is_data_type_float(output->info()->data_type()); + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace((policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE"); + build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); + build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); + build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); + + // Create kernel + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel("arithmetic_sub_ex", build_opts)); + + ICLKernel::configure_internal(win_config.second); +} + +Status CLArithmeticSubtractionExKernel::validate(const ITensorInfo *input1, + const ITensorInfo *input2, + const ITensorInfo *output, ConvertPolicy policy) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, policy)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), + input2->clone().get(), + output->clone().get()) + .first); + + return Status{}; +} + +void CLArithmeticSubtractionExKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLArithmeticSubtractionExKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLBatchToSpaceNDKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLBatchToSpaceNDKernel.cpp new file mode 100644 index 000000000..b0016d23c --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLBatchToSpaceNDKernel.cpp @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLBatchToSpaceNDKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const int32_t *block_size) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size[0] >= 1 && block_size[1] >= 1, + "Block size should be greater than or equal to 1."); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) == output->dimension(2), + "Input Depth should be equal to Output Depth"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + output->dimension(3) * block_size[0] * block_size[1] == input->dimension(3), + "Input batch should be equal to (output batch * block size[0] *block size[1])"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(output->dimension(0) % block_size[1]) && + !(output->dimension(1) % block_size[0]), + "Output height and width should be divisible by block size[0] " + "and block_size[1] respectively"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) == input->dimension(0) * block_size[1]) && + (output->dimension(1) == input->dimension(1) * block_size[0]), + "Output height and width should be equal to " + "input_height*blocksize[0] and input_width*blocksize[1] " + "respectively"); + + return Status{}; +} + +} // namespace + +CLBatchToSpaceNDKernel::CLBatchToSpaceNDKernel() : _input(nullptr), _output(nullptr) {} + +void CLBatchToSpaceNDKernel::configure(const ICLTensor *input, ICLTensor *output, + const int32_t *block_size) +{ + + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), block_size)); + + _input = input; + _output = output; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DBLOCK_SIZE0=" + support::cpp11::to_string(block_size[0])); + build_opts.emplace("-DBLOCK_SIZE1=" + support::cpp11::to_string(block_size[1])); + build_opts.emplace("-DBATCH_OUT=" + support::cpp11::to_string(output->info()->dimension(3))); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + + // Create kernel + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel("batch_to_space_nd", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps()); + + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +void CLBatchToSpaceNDKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + + Window slice_in = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_out(slice_in); + slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_out.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_out); + add_4D_tensor_argument(idx, _output, slice_in); + enqueue(queue, *this, slice_in); + } while (window.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLBinaryLogicalOpKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLBinaryLogicalOpKernel.cpp new file mode 100644 index 000000000..3d2f2c702 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLBinaryLogicalOpKernel.cpp @@ -0,0 +1,173 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_parameters(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output) +{ + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QASYMM8); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, + DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + return Status{}; +} +} // namespace + +CLBinaryLogicalOpKernel::CLBinaryLogicalOpKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLBinaryLogicalOpKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output, BinaryLogicalOperation op) +{ + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_parameters(input1->info(), input2->info(), output->info())); + + _input1 = input1; + _input2 = input2; + _output = output; + + // Create kernel + std::string kernel_name = "binary_logical_op"; + std::set<std::string> build_opts; + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type()))); + + int op_code = 0; + switch (op) + { + case BinaryLogicalOperation::AND: + op_code = 1; + break; + case BinaryLogicalOperation::OR: + op_code = 2; + break; + default: + throw std::runtime_error("Operation not supported, yet"); + } + + build_opts.emplace(("-DOP_CODE=" + support::cpp11::to_string(op_code))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info()); + + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1->info()); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info()); + + AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + ICLKernel::configure_internal(win); +} + +void CLBinaryLogicalOpKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLBinaryLogicalOpKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLCastKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLCastKernel.cpp index b019e8c33..bf7ebae3f 100644 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLCastKernel.cpp +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLCastKernel.cpp @@ -17,15 +17,8 @@ #include "arm_compute/core/CL/kernels/CLCastKernel.h" #include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLKernelLibraryEx.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" using namespace arm_compute; @@ -60,8 +53,8 @@ void CLCastKernel::configure(const ICLTensor *input, ICLTensor *output) { const float scale_in = input->info()->quantization_info().scale; const int offset_in = input->info()->quantization_info().offset; - build_opts.emplace("-DSCALE_IN=" + float_to_string_with_full_precision(scale_in)); - build_opts.emplace("-DOFFSET_IN=" + support::cpp11::to_string(offset_in)); + build_opts.emplace("-DSCALE=" + float_to_string_with_full_precision(scale_in)); + build_opts.emplace("-DOFFSET=" + support::cpp11::to_string(offset_in)); _kernel = static_cast<cl::Kernel>( CLKernelLibraryEx::get().create_kernel("cast_qasymm_in", build_opts)); @@ -70,8 +63,8 @@ void CLCastKernel::configure(const ICLTensor *input, ICLTensor *output) { const float scale_in = output->info()->quantization_info().scale; const int offset_in = output->info()->quantization_info().offset; - build_opts.emplace("-DSCALE_IN=" + float_to_string_with_full_precision(scale_in)); - build_opts.emplace("-DOFFSET_IN=" + support::cpp11::to_string(offset_in)); + build_opts.emplace("-DSCALE=" + float_to_string_with_full_precision(scale_in)); + build_opts.emplace("-DOFFSET=" + support::cpp11::to_string(offset_in)); _kernel = static_cast<cl::Kernel>( CLKernelLibraryEx::get().create_kernel("cast_qasymm_out", build_opts)); @@ -88,7 +81,7 @@ void CLCastKernel::configure(const ICLTensor *input, ICLTensor *output) update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, input->info()->valid_region()); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLCastKernel::run(const Window &window, cl::CommandQueue &queue) diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp new file mode 100644 index 000000000..5af5b16ea --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLComparisonOpKernel.cpp @@ -0,0 +1,212 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLComparisonOpKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output) +{ + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::U16, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::U16, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32, DataType::QASYMM8); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + return Status{}; +} +} // namespace + +CLComparisonOpKernel::CLComparisonOpKernel() : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLComparisonOpKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output, const ComparisonOperation &op) +{ + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info())); + + _input1 = input1; + _input2 = input2; + _output = output; + + // Create kernel + std::string kernel_name = "comparison_op"; + int op_code = 0; + + switch (op) + { + case ComparisonOperation::EQUAL: + op_code = 1; + break; + case ComparisonOperation::NOT_EQUAL: + op_code = 2; + break; + default: + throw std::runtime_error(" Operation not supported, yet"); + } + + std::set<std::string> build_opts; + build_opts.emplace(("-DOP_CODE=" + support::cpp11::to_string(op_code))); + build_opts.emplace(("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input1->info()->data_type()))); + build_opts.emplace( + ("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + + if (is_data_type_quantized_asymmetric(input1->info()->data_type()) && + ((input1->info()->quantization_info().offset != input2->info()->quantization_info().offset) || + (input1->info()->quantization_info().scale != input2->info()->quantization_info().scale))) + { + build_opts.emplace("-DOFFSET_IN1=" + + support::cpp11::to_string(input1->info()->quantization_info().offset)); + build_opts.emplace("-DOFFSET_IN2=" + + support::cpp11::to_string(input2->info()->quantization_info().offset)); + build_opts.emplace("-DSCALE_IN1=" + + support::cpp11::to_string(input1->info()->quantization_info().scale)); + build_opts.emplace("-DSCALE_IN2=" + + support::cpp11::to_string(input2->info()->quantization_info().scale)); + kernel_name += "_qasymm8"; + } + + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info()); + + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + { + set_shape_if_empty(*output->info(), out_shape); + + if (input1->info()->data_type() == DataType::S16 || + input2->info()->data_type() == DataType::S16) + { + set_format_if_unknown(*output->info(), Format::S16); + } + else if (input1->info()->data_type() == DataType::F16 && + input2->info()->data_type() == DataType::F16) + { + set_format_if_unknown(*output->info(), Format::F16); + } + else if (input1->info()->data_type() == DataType::F32 || + input2->info()->data_type() == DataType::F32) + { + set_format_if_unknown(*output->info(), Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1->info()); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info()); + + AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + ICLKernel::configure_internal(win); +} + +void CLComparisonOpKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLComparisonOpKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLDepthToSpaceKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLDepthToSpaceKernel.cpp new file mode 100644 index 000000000..c386e3312 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLDepthToSpaceKernel.cpp @@ -0,0 +1,109 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const int32_t block_size) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size >= 1, + "Block size should be greater than or equal to 1."); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) == input->dimension(0) * block_size, + "Output width should be equal to (Input width * block size)"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(1) == input->dimension(1) * block_size, + "Output height should be equal to (Input height * block size)"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) % (block_size * block_size) == 0, + "Input depth should be divisible by (block size * block size)"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + output->dimension(2) == input->dimension(2) / (block_size * block_size), + "Output depth should be equal to (Input depth / (block size * block size))"); + + return Status{}; +} +} // namespace + +CLDepthToSpaceKernel::CLDepthToSpaceKernel() : _input(nullptr), _output(nullptr) +{ + // DO NOTHING +} + +void CLDepthToSpaceKernel::configure(const ICLTensor *input, ICLTensor *output, + const int32_t block_size) +{ + + _input = input; + _output = output; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DBLOCK_SIZE=" + support::cpp11::to_string(block_size)); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("depth_to_space", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps()); + + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +void CLDepthToSpaceKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + + Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup input slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_out); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLEmbeddingLookupKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLEmbeddingLookupKernel.cpp new file mode 100644 index 000000000..0862b78bf --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLEmbeddingLookupKernel.cpp @@ -0,0 +1,114 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLEmbeddingLookupKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, input_access, output_access); + input_access.set_valid_region(win, output->valid_region()); + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLEmbeddingLookupKernel::CLEmbeddingLookupKernel() + : _input(nullptr), _output(nullptr), _lookups(nullptr) +{ +} + +Status CLEmbeddingLookupKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *lookups) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, lookups); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( + input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, + DataType::U32, DataType::S32, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lookups, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + ARM_COMPUTE_ERROR_ON(input->num_dimensions() < 2 && input->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON(lookups->num_dimensions() > 1); + + return Status{}; +} + +void CLEmbeddingLookupKernel::configure(const ICLTensor *input, ICLTensor *output, + const ICLTensor *lookups) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), lookups->info())); + + _input = input; + _output = output; + _lookups = lookups; + + // Set kernel build options + std::stringstream kernel_name; + std::set<std::string> build_opts; + kernel_name << "embedding_lookup"; + + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); + build_opts.emplace("-DNUM_DIMS=" + support::cpp11::to_string(_input->info()->num_dimensions())); + + // Create kernel + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel(kernel_name.str(), build_opts)); + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); +} + +void CLEmbeddingLookupKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + Window slice_in = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + Window win_lookup; + win_lookup.set(Window::DimX, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_in); + add_1D_tensor_argument(idx, _lookups, win_lookup); + + enqueue(queue, *this, slice_in); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_1D(win_lookup)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLExpKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLExpKernel.cpp new file mode 100644 index 000000000..b1ee21bdc --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLExpKernel.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLExpKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +CLExpKernel::CLExpKernel() : _input(nullptr), _output(nullptr) {} + +void CLExpKernel::configure(const ICLTensor *input, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Auto initialize output + auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), + input->info()->quantization_info()); + + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + _input = input; + _output = output; + + constexpr unsigned int num_elems_processed_per_iteration = 4; + + // Create kernel + std::set<std::string> build_opts; + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("exp_layer", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); + AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, input->info()->valid_region()); + + ICLKernel::configure_internal(win); +} + +void CLExpKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice); + } while (collapsed.slide_window_slice_3D(slice)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLGatherKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLGatherKernel.cpp index 23efafa6a..ae2801e2b 100644 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLGatherKernel.cpp +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLGatherKernel.cpp @@ -17,26 +17,14 @@ #include "arm_compute/core/CL/kernels/CLGatherKernel.h" #include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLKernelLibraryEx.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <cmath> -#include <cstdlib> -#include <set> -#include <string> using namespace arm_compute; namespace { -constexpr unsigned int num_elems_processed_per_iteration = 16; +constexpr unsigned int num_elems_processed_per_iteration = 1; Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) @@ -46,6 +34,7 @@ Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S32, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); return Status{}; } @@ -57,8 +46,7 @@ CLGatherKernel::CLGatherKernel() : _input1(nullptr), _input2(nullptr), _output(n void CLGatherKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) { ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::S32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info())); _input1 = input1; _input2 = input2; @@ -89,11 +77,10 @@ void CLGatherKernel::configure(const ICLTensor *input1, const ICLTensor *input2, static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); // Configure kernel window - const unsigned int num_elems_processed_per_iteration = 1; Window win = calculate_max_window(*input2->info(), Steps(num_elems_processed_per_iteration)); output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } Status CLGatherKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLHashtableLookupKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLHashtableLookupKernel.cpp new file mode 100644 index 000000000..cd7b21c6d --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLHashtableLookupKernel.cpp @@ -0,0 +1,177 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLHashtableLookupKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); + AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, input_access, output_access); + input_access.set_valid_region(win, output->valid_region()); + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLHashtableLookupKernel::CLHashtableLookupKernel() + : _input(nullptr), _output(nullptr), _lookups(nullptr) +{ +} + +Status CLHashtableLookupKernel::validate(const ITensorInfo *lookups, const ITensorInfo *keys, + const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *hits) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(lookups, keys, input, output, hits); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( + input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, + DataType::U32, DataType::S32, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lookups, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(keys, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(hits, 1, DataType::U8, DataType::QASYMM8); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0, + "Output's shape was not set"); + + ARM_COMPUTE_ERROR_ON(lookups->dimensions(0) == hits->dimensions(0) && + output->dimension(output->num_dimensions() - 1) == lookups->dimension(0)); + ARM_COMPUTE_ERROR_ON(input->num_dimensions() < 2 && input->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON(lookups->num_dimensions() > 1); + ARM_COMPUTE_ERROR_ON(keys->num_dimensions() > 1); + ARM_COMPUTE_ERROR_ON(hits->num_dimensions() > 1); + + return Status{}; +} + +void CLHashtableLookupKernel::configure(const ICLTensor *lookups, const ICLTensor *keys, + const ICLTensor *input, ICLTensor *output, ICLTensor *hits) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), lookups->info())); + + _lookups = lookups; + _keys = keys; + _input = input; + _output = output; + _hits = hits; + + // Make _lookup_indices tensor + _lookup_indices = arm_compute::support::cpp14::make_unique<CLTensor>(); + _lookup_indices->allocator()->init( + TensorInfo(lookups->info()->tensor_shape(), lookups->info()->num_channels(), DataType::S32)); + _lookup_indices->allocator()->allocate(); + + // Set kernel build options + std::stringstream kernel_name; + std::set<std::string> build_opts; + kernel_name << "hashtable_lookup"; + + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); + build_opts.emplace("-DNUM_DIMS=" + support::cpp11::to_string(_input->info()->num_dimensions())); + + // Create kernel + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel(kernel_name.str(), build_opts)); + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); +} + +void CLHashtableLookupKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + const_cast<ICLTensor *>(_lookups)->map(queue); + const_cast<ICLTensor *>(_keys)->map(queue); + _hits->map(queue); + _lookup_indices->map(queue); + + // Set values of hits + const int32_t *lookups_buf = + reinterpret_cast<int32_t *>(const_cast<ICLTensor *>(_lookups)->buffer()); + const int32_t *keys_buf = reinterpret_cast<int32_t *>(const_cast<ICLTensor *>(_keys)->buffer()); + uint8_t *hits_buf = reinterpret_cast<uint8_t *>(_hits->buffer()); + int32_t *lookup_indices_buf = reinterpret_cast<int32_t *>(_lookup_indices->buffer()); + + std::map<int32_t, size_t> key_map; + const size_t keys_num = _keys->info()->dimension(0); + for (size_t key_index = 0; key_index < keys_num; key_index++) + { + key_map[keys_buf[key_index]] = key_index; + } + + const size_t lookups_num = _lookups->info()->dimension(0); + for (size_t i = 0; i < lookups_num; ++i) + { + const auto lookup_value = lookups_buf[i]; + const auto it = key_map.find(lookup_value); + if (it != key_map.end()) + { +#if defined(DEBUG) + if (it->second >= lookups_num) + ARM_COMPUTE_ERROR("HashTable Lookup: index out of bounds."); +#endif // defined(DEBUG) + lookup_indices_buf[i] = static_cast<int32_t>(it->second); + hits_buf[i] = static_cast<uint8_t>(1); + } + else + { + lookup_indices_buf[i] = -1; + hits_buf[i] = static_cast<uint8_t>(0); + } + } + + const_cast<ICLTensor *>(_lookups)->unmap(queue); + const_cast<ICLTensor *>(_keys)->unmap(queue); + _hits->unmap(queue); + _lookup_indices->unmap(queue); + + Window win = window.collapse(ICLKernel::window(), 2, 4); + + Window win_lookup; + win_lookup.set(Window::DimX, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, win); + add_4D_tensor_argument(idx, _output, win); + add_1D_tensor_argument(idx, _lookup_indices.get(), win_lookup); + + enqueue(queue, *this, win); + } while (window.slide_window_slice_4D(win) && window.slide_window_slice_1D(win_lookup)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLNegKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLNegKernel.cpp new file mode 100644 index 000000000..80d99dd3b --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLNegKernel.cpp @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLNegKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S16, DataType::S32, + DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S16, DataType::S32, + DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(input->info()->tensor_shape(), + output->info()->tensor_shape()); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + return Status{}; +} + +} // namespace + +CLNegKernel::CLNegKernel() : _input(nullptr), _output(nullptr) {} + +void CLNegKernel::configure(const ICLTensor *input, ICLTensor *output) +{ + + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); + + _input = input; + _output = output; + + constexpr unsigned int num_elems_processed_per_iteration = 16; + + // Create kernel + std::set<std::string> build_opts; + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("neg_tensor", build_opts)); + + // Configure window + Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, input->info()->valid_region()); + + ICLKernel::configure_internal(win); +} + +void CLNegKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice, lws_hint()); + } while (collapsed.slide_window_slice_3D(slice)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLNormalizationLayerExKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLNormalizationLayerExKernel.cpp new file mode 100644 index 000000000..12bbe910f --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLNormalizationLayerExKernel.cpp @@ -0,0 +1,166 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLNormalizationLayerExKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + NormalizationLayerInfo norm_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + + // Checks performed when output is configured + if (output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, + NormalizationLayerInfo norm_info) +{ + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, *input->clone()); + + const unsigned int norm_size = norm_info.norm_size(); + bool is_in_map = norm_info.is_in_map(); + + const unsigned int border_width = is_in_map ? std::min(norm_size / 2, 3U) : 0; + const BorderSize border_size = BorderSize(0, border_width); + + const unsigned int num_elems_processed_per_iteration = 4; + const unsigned int num_elems_read_per_iteration = + is_in_map ? (num_elems_processed_per_iteration + 2 * (norm_size / 2)) + : num_elems_processed_per_iteration; + + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); + + // We do not use a Rectangle window for IN_MAP_2D as we clamp the top and bottom accesses inside + // the kernel, avoiding padding + AccessWindowHorizontal input_access(input, -border_size.left, num_elems_read_per_iteration); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, input_access, output_access); + + output_access.set_valid_region(win, input->valid_region()); + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLNormalizationLayerExKernel::CLNormalizationLayerExKernel() + : _input(nullptr), _output(nullptr), _border_size(0), _is_in_map(false) +{ +} + +BorderSize CLNormalizationLayerExKernel::border_size() const { return _border_size; } + +void CLNormalizationLayerExKernel::configure(const ICLTensor *input, ICLTensor *output, + NormalizationLayerInfo norm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output->info(), *input->info()->clone()); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), norm_info)); + + _input = input; + _output = output; + + const unsigned int num_elems_processed_per_iteration = 4; + const bool is_in_map_2D = (norm_info.type() == NormType::IN_MAP_2D); + + // Set build options + CLBuildOptions build_opts; + build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); + build_opts.add_option( + ("-DCOEFF=" + float_to_string_with_full_precision(norm_info.scale_coeff()))); + build_opts.add_option(("-DBETA=" + float_to_string_with_full_precision(norm_info.beta()))); + build_opts.add_option(("-DKAPPA=" + float_to_string_with_full_precision(norm_info.kappa()))); + build_opts.add_option( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + build_opts.add_option(("-DRADIUS=" + support::cpp11::to_string(norm_info.norm_size()))); + build_opts.add_option(("-DNUM_SLICES=" + support::cpp11::to_string(input->info()->dimension(2)))); + build_opts.add_option_if(is_in_map_2D, "-DIN_MAP_2D"); + + // Create kernel + std::string kernel_name = + _is_in_map ? "normalization_layer_in_map" : "normalization_layer_cross_map"; + _kernel = static_cast<cl::Kernel>( + CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), output->info(), norm_info); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = "normalization_layer_"; + _config_id += lower_string(string_from_data_type(input->info()->data_type())); + _config_id += "_"; + _config_id += support::cpp11::to_string( + static_cast<std::underlying_type<NormType>::type>(norm_info.type())); + _config_id += "_"; + _config_id += support::cpp11::to_string(norm_info.norm_size()); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(1)); +} + +Status CLNormalizationLayerExKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + NormalizationLayerInfo norm_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, norm_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_and_configure_window(input->clone().get(), output->clone().get(), norm_info).first); + + return Status{}; +} + +void CLNormalizationLayerExKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + const int collapsed_dimension = _is_in_map ? Window::DimZ : 4; + Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), collapsed_dimension); + Window slice = window_collapsed.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice); + } while (window_collapsed.slide_window_slice_3D(slice)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLPReLUKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLPReLUKernel.cpp new file mode 100644 index 000000000..241f8ae4d --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLPReLUKernel.cpp @@ -0,0 +1,185 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLPReLUKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_info(const ITensorInfo *input, const ITensorInfo *alpha, const ITensorInfo *output) +{ + const TensorShape &out_shape = + TensorShape::broadcast_shape(input->tensor_shape(), alpha->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, + DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(alpha, 1, DataType::F16, DataType::F32, + DataType::QASYMM8); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + return Status{}; +} +} // namespace + +CLPReLUKernel::CLPReLUKernel() : _input(nullptr), _alpha(nullptr), _output(nullptr) {} + +void CLPReLUKernel::configure(const ICLTensor *input, const ICLTensor *alpha, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, alpha); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), alpha->info(), output->info())); + + _input = input; + _alpha = alpha; + _output = output; + + // Create kernel + std::string kernel_name = "prelu"; + std::set<std::string> build_opts; + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + + if (is_data_type_quantized_asymmetric(input->info()->data_type())) + { + build_opts.emplace("-DOFF_IN1=" + + support::cpp11::to_string(input->info()->quantization_info().offset)); + build_opts.emplace("-DOFF_IN2=" + + support::cpp11::to_string(alpha->info()->quantization_info().offset)); + build_opts.emplace("-DOFF_OUT=" + + support::cpp11::to_string(output->info()->quantization_info().offset)); + build_opts.emplace("-DSCALE_IN1=" + + support::cpp11::to_string(input->info()->quantization_info().scale)); + build_opts.emplace("-DSCALE_IN2=" + + support::cpp11::to_string(alpha->info()->quantization_info().scale)); + build_opts.emplace("-DSCALE_OUT=" + + support::cpp11::to_string(output->info()->quantization_info().scale)); + kernel_name += "_qasymm8"; + } + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input->info(), *alpha->info()); + + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + { + set_shape_if_empty(*output->info(), out_shape); + + if (input->info()->data_type() == DataType::F16 && alpha->info()->data_type() == DataType::F16) + { + set_format_if_unknown(*output->info(), Format::F16); + } + else if (input->info()->data_type() == DataType::F32 || + alpha->info()->data_type() == DataType::F32) + { + set_format_if_unknown(*output->info(), Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input->info()); + Window win_input2 = win.broadcast_if_dimension_le_one(*alpha->info()); + + AccessWindowHorizontal input1_access(input->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(alpha->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + ICLKernel::configure_internal(win); +} + +void CLPReLUKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input->info()->tensor_shape(); + const TensorShape &in_shape2 = _alpha->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice_input1); + add_3D_tensor_argument(idx, _alpha, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLPReLUKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input->info()->dimension(0), _alpha->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLPadLayerKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLPadLayerKernel.cpp new file mode 100644 index 000000000..99b54c822 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLPadLayerKernel.cpp @@ -0,0 +1,149 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLPadLayerKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input_info, const ITensorInfo *output_info, + const ITensorInfo *pad_size_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_info, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_info, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(pad_size_info, 1, DataType::S32); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_info->num_dimensions() > 0 && + input_info->num_dimensions() <= 4, + "Pad kernel supports upto 4-D input tensor"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + input_info->num_dimensions() == output_info->num_dimensions(), + "output tensor should have same number of dimensions as input tensor"); + + if (input_info->data_type() == DataType::QASYMM8) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_info->quantization_info() != + output_info->quantization_info(), + "The input and output quantization info are different!"); + } + + return Status{}; +} + +} // namespace + +CLPadLayerKernel::CLPadLayerKernel() : _input(nullptr), _output(nullptr), _pad_size(nullptr) {} + +void CLPadLayerKernel::configure(const ICLTensor *input, ICLTensor *output, ICLTensor *pad_size) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, pad_size); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pad_size->info())); + + _input = input; + _output = output; + _pad_size = pad_size; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.emplace("-DIB=" + support::cpp11::to_string(input->info()->dimension(3))); + build_opts.emplace("-DIW=" + support::cpp11::to_string(input->info()->dimension(0))); + build_opts.emplace("-DIH=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.emplace("-DID=" + support::cpp11::to_string(input->info()->dimension(2))); + if (input->info()->data_type() == DataType::QASYMM8) + { + build_opts.emplace("-DZERO_VALUE=" + + support::cpp11::to_string(input->info()->quantization_info().offset)); + } + else + { + build_opts.emplace("-DZERO_VALUE=" + support::cpp11::to_string(0)); + } + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("pad", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps()); + + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +void CLPadLayerKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + + _pad_size->map(queue); + + // Padding values only for up, top, left and front are required based on the rank of tensor + int rank = _pad_size->info()->dimension(1); + + auto pad_batch_up = + (rank == 4) ? *reinterpret_cast<const int32_t *>(_pad_size->ptr_to_element({0, 0})) : 0; + auto pad_height_top = + (rank >= 2) + ? *reinterpret_cast<const int32_t *>(_pad_size->ptr_to_element({0, (rank == 2) ? 0 : 1})) + : 0; + auto pad_width_left = (rank >= 1) + ? *reinterpret_cast<const int32_t *>( + _pad_size->ptr_to_element({0, (rank == 4) ? 2 : rank - 1})) + : 0; + auto pad_depth_front = + (rank >= 3) + ? *reinterpret_cast<const int32_t *>(_pad_size->ptr_to_element({0, (rank == 3) ? 0 : 3})) + : 0; + + _pad_size->unmap(queue); + + // Pad_values which needs to be passed + const cl_int4 paddingValues = { + {static_cast<cl_int>(pad_width_left), static_cast<cl_int>(pad_height_top), + static_cast<cl_int>(pad_depth_front), static_cast<cl_int>(pad_batch_up)}}; + + Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + _kernel.setArg<cl_int4>(idx++, paddingValues); + enqueue(queue, *this, slice_out); + } while (window.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLPermuteExKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLPermuteExKernel.cpp new file mode 100644 index 000000000..aa094761c --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLPermuteExKernel.cpp @@ -0,0 +1,126 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLPermuteExKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +using namespace arm_compute; + +namespace +{ +TensorShape get_output_shape(const ITensorInfo *input, const PermutationVector &perm) +{ + TensorShape output_shape = input->tensor_shape(); + permute(output_shape, perm); + return output_shape; +} + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const PermutationVector &perm) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( + input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, + DataType::U32, DataType::S32, DataType::F16, DataType::F32); + + const TensorShape output_shape = + misc::shape_calculator::compute_permutation_output_shape(*input, perm); + + // Validate configured output + if (output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + return Status{}; +} +} // namespace + +CLPermuteExKernel::CLPermuteExKernel() : _input(nullptr), _output(nullptr), _perm() {} + +void CLPermuteExKernel::configure(const ICLTensor *input, ICLTensor *output, + const PermutationVector &perm) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm)); + + _input = input; + _output = output; + _perm = perm; + + const TensorShape output_shape = get_output_shape(input->info(), perm); + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); + + // Create kernel + std::set<std::string> build_opts; + + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DDEPTH_IN=" + support::cpp11::to_string(input->info()->dimension(2))); + + // New positions of batch(D), height(H), width(w) and channel(C) based on permutation vector + build_opts.emplace("-DP1=" + support::cpp11::to_string(perm[0])); + build_opts.emplace("-DP2=" + support::cpp11::to_string(perm[1])); + build_opts.emplace("-DP3=" + support::cpp11::to_string(perm[2])); + build_opts.emplace("-DP4=" + support::cpp11::to_string(perm[3])); + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel("permute_generic", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*input->info(), Steps()); + + // The CLPermute doesn't need padding so update_window_and_padding() can be skipped + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +Status CLPermuteExKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const PermutationVector &perm) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, perm)); + + return Status{}; +} + +void CLPermuteExKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + + Window slice_in = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_out(slice_in); + slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_out.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_in); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp index a3e0163de..b985aa737 100644 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLPixelWiseDivisionKernel.cpp @@ -17,20 +17,8 @@ #include "arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h" #include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLKernelLibraryEx.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <cmath> -#include <cstdlib> -#include <set> -#include <string> using namespace arm_compute; @@ -45,12 +33,10 @@ Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, ARM_COMPUTE_UNUSED(overflow_policy); ARM_COMPUTE_UNUSED(rounding_policy); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, - DataType::QS16, DataType::S16, DataType::F16, - DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, - DataType::QS16, DataType::S16, DataType::F16, - DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, + DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, + DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative."); const TensorShape &out_shape = @@ -58,21 +44,11 @@ Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2); - - if (is_data_type_fixed_point(input1->data_type())) - { - // All data types must be all QS8 or all QS16 - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale != 1, - "Unsupported scaling factor for QS8/QS16. Scale must be 1."); - } // Validate in case of configured output if (output->total_size() > 0) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, - DataType::QS16, DataType::S16, + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG( output->data_type() == DataType::U8 && @@ -81,11 +57,6 @@ Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, ARM_COMPUTE_RETURN_ERROR_ON_MSG( detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output"); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, output); - if (is_data_type_fixed_point(input1->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); - } } return Status{}; @@ -191,14 +162,6 @@ void CLPixelWiseDivisionKernel::configure(const ICLTensor *input1, const ICLTens { compute_type = "int"; } - else if (input1->info()->data_type() == DataType::QS8) - { - compute_type = "qs8"; - } - else if (input1->info()->data_type() == DataType::QS16) - { - compute_type = "qs16"; - } else { compute_type = "ushort"; @@ -218,11 +181,6 @@ void CLPixelWiseDivisionKernel::configure(const ICLTensor *input1, const ICLTens : "-DSATURATE"); build_opts.emplace((rounding_policy == RoundingPolicy::TO_ZERO) ? "-DROUND=_rtz" : "-DROUND=_rte"); - if (is_data_type_fixed_point(input1->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + - support::cpp11::to_string(input1->info()->fixed_point_position())); - } build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); @@ -245,7 +203,7 @@ void CLPixelWiseDivisionKernel::configure(const ICLTensor *input1, const ICLTens _kernel.setArg(idx++, scale); } - ICLKernel::configure(win_config.second); + ICLKernel::configure_internal(win_config.second); } Status CLPixelWiseDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLReduceMaxKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLReduceMaxKernel.cpp deleted file mode 100644 index 168b246bf..000000000 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLReduceMaxKernel.cpp +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2016-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/core/CL/kernels/CLReduceMaxKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/CLKernelLibraryEx.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <cmath> -#include <cstdlib> -#include <set> -#include <string> - -using namespace arm_compute; - -namespace -{ -constexpr unsigned int num_elems_processed_per_iteration = 16; - -Status validate_arguments(const ITensorInfo *input, int32_t axis, const ITensorInfo *output) -{ - // We can handle for simple case only - // Input rank: 2 - // Output rank: 1 - // Axis: one axis value, restrict to 1 - - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis != 1, "Axis only allowed 1"); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0, - "Inputs are not broadcast compatible"); - - // Validate in case of configured output - if (output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() != input->data_type(), - "Output same type allowed for input and output"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().num_dimensions() != 1, - "Only support for output dimension 1"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->tensor_shape().num_dimensions() != 2, - "Only support for input dimension 2"); - } - - return Status{}; -} - -} // namespace - -CLReduceMaxKernel::CLReduceMaxKernel() : _input(nullptr), _output(nullptr), _axis(0) {} - -void CLReduceMaxKernel::configure(const ICLTensor *input, int32_t axis, ICLTensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), axis, output->info())); - - _input = input; - _output = output; - _axis = axis; - - // Configure kernel window - int cols = _input->info()->tensor_shape()[0]; - int rows = _input->info()->tensor_shape()[1]; - Window win; - win.set(0, Window::Dimension(0, cols, 1)); - win.set(1, Window::Dimension(0, rows, 1)); - - // Construct kernel name - std::string kernel_name = "reduce_max"; - - // Set kernel build options - std::set<std::string> build_opts; - build_opts.emplace("-DWIDTH=" + support::cpp11::to_string(cols)); - - // Create kernel - _kernel = - static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); - - ICLKernel::configure(win); -} - -Status CLReduceMaxKernel::validate(const ITensorInfo *input, int32_t axis, - const ITensorInfo *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, axis, output)); - - return Status{}; -} - -void CLReduceMaxKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - - Window window_input = window; - Window slice_input = window_input.first_slice_window_1D(); - - do - { - Window slice_output = slice_input.shift_dimensions(1); - unsigned int idx = 0; - add_1D_tensor_argument(idx, _input, slice_input); - add_1D_tensor_argument(idx, _output, slice_output); - enqueue(queue, *this, slice_input); - - } while (window_input.slide_window_slice_1D(slice_input)); -} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLReduceOperationKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLReduceOperationKernel.cpp new file mode 100644 index 000000000..f581780e1 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLReduceOperationKernel.cpp @@ -0,0 +1,181 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLReduceOperationKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; +namespace +{ +// NOTE This is necessary because it is not guaranteed that the axis positions of input and output +// are the same. +const TensorShape inferOutputShape(const TensorShape &input_shape, const uint32_t axis) +{ + TensorShape out_shape{input_shape}; + + out_shape.set(axis, 1); + + return out_shape; +} +} // namespace + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const uint32_t axis, + ReduceOperation op) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + + if (output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, + DataType::F32, DataType::S32); + if (op == ReduceOperation::MEAN || op == ReduceOperation::SUM) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8, + "Not support QASYMM8, yet"); + } + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0, + "Inputs are not broadcast compatible"); + + const auto num_dimensions = input->tensor_shape().num_dimensions(); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + axis >= 0 && axis < num_dimensions, + "axis must be greater than or equal to 0 and less than (input's rank)."); + + const TensorShape output_shape = inferOutputShape(input->tensor_shape(), axis); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_shape.total_size() != output->tensor_shape().total_size(), + "output shape's size does not match axis"); + + return Status{}; +} +} // namespace + +CLReduceOperationKernel::CLReduceOperationKernel() : _input(nullptr), _output(nullptr), _axis() {} + +void CLReduceOperationKernel::configure(const ICLTensor *input, ICLTensor *output, + const uint32_t axis, ReduceOperation op) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op)); + + _input = input; + _output = output; + _axis = axis; + + std::unique_ptr<ITensorInfo> output_info = output->info()->clone(); + output_info->set_tensor_shape(inferOutputShape(input->info()->tensor_shape(), axis)); + + // Construct kernel name + std::string kernel_name; + int op_code = 0; + if (op == ReduceOperation::MAX) + { + kernel_name = "reduce_min_max"; + op_code = 1; + } + else if (op == ReduceOperation::MIN) + { + kernel_name = "reduce_min_max"; + op_code = 2; + } + else if (op == ReduceOperation::SUM) + { + kernel_name = "reduce_sum_mean"; + op_code = 3; + } + else if (op == ReduceOperation::MEAN) + { + kernel_name = "reduce_sum_mean"; + op_code = 4; + } + else + throw std::runtime_error("Operation not supported, yet"); + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(output_info->data_type())); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output_info->dimension(2))); + build_opts.emplace("-DOP_CODE=" + support::cpp11::to_string(op_code)); + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output_info, Steps()); + + Coordinates coord; + coord.set_num_dimensions(output_info->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output_info->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +Status CLReduceOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const uint32_t axis, ReduceOperation op) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op)); + + return Status{}; +} + +void CLReduceOperationKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &shape_in = _input->info()->tensor_shape(); + + unsigned int idx = 2 * num_arguments_per_4D_tensor(); // Skip the input and output parameters + + _kernel.setArg<cl_int>(idx++, _axis); + _kernel.setArg<cl_int>(idx++, shape_in[_axis]); + + // Support dimensions up to 4 + Window slice_out = window.collapse(ICLKernel::window(), 2, 4); + + // Setup input slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + // Copy output's shape in order to use for recovering at end of this method + // TODO Remove changing and recovering output's shape if it is guaranteed that the axis positions + // of input and output are the same + const TensorShape shape_out = _output->info()->tensor_shape(); + _output->info()->set_tensor_shape(inferOutputShape(shape_in, _axis)); + + idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_out); + + // Recover output's shape of output tensor + _output->info()->set_tensor_shape(shape_out); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLReductionMeanKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLReductionMeanKernel.cpp deleted file mode 100644 index 84a77122d..000000000 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLReductionMeanKernel.cpp +++ /dev/null @@ -1,198 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/core/CL/kernels/CLReductionMeanKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/CLKernelLibraryEx.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/FixedPoint.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include "support/ToolchainSupport.h" - -using namespace arm_compute; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, - std::vector<uint32_t> axis) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis.size() >= TensorShape::num_max_dimensions, - "Reduction axis greater than max number of dimensions"); - - std::vector<uint32_t>::const_iterator it; - bool axis_w = false; - bool axis_h = false; - for (it = axis.begin(); it != axis.end(); ++it) - { - if ((*it) == 0) - { - axis_w = true; - } - else if ((*it) == 1) - { - axis_h = true; - } - else - { - ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported axis!"); - } - } - // TODO Other axises (currently, only axises for both width and height are supported.) - if (!axis_w || !axis_h) - { - ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported axis!"); - } - - if (output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON(output->data_layout() != DataLayout::NCHW); - } - - return Status{}; -} - -std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, - std::vector<uint32_t> axis) -{ - // Output tensor auto initialization if not yet initialized - TensorShape output_shape{input->tensor_shape()}; - output_shape.set(0, 1); - output_shape.set(1, 1); - auto_init_if_empty(*output, output_shape, output->num_channels(), input->data_type(), - input->fixed_point_position()); - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration_x = 8; // step - const unsigned int num_elems_processed_per_iteration_y = input->dimension(1); - - Window win = calculate_max_window( - *input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, - num_elems_processed_per_iteration_y); - AccessWindowHorizontal output_access(output, 0, 1); - bool window_changed = update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, output->valid_region()); - - Status err = (window_changed) - ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") - : Status{}; - - return std::make_tuple(err, win); -} -} // namespace - -CLReductionMeanKernel::CLReductionMeanKernel() - : _input(nullptr), _output(nullptr), _reduction_axis(), _border_size() -{ -} - -BorderSize CLReductionMeanKernel::border_size() const { return _border_size; } - -void CLReductionMeanKernel::configure(const ICLTensor *input, ICLTensor *output, - std::vector<uint32_t> axis) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis)); - - _input = input; - _output = output; - _reduction_axis = axis; - - constexpr unsigned int num_elems_processed_per_iteration_x = 8; // step - - // Set border size - _border_size = BorderSize( - ceil_to_multiple(input->info()->dimension(0), num_elems_processed_per_iteration_x) - - input->info()->dimension(0)); - - // Set build options - std::set<std::string> build_opts; - build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); - // build_opts.emplace(("-DVEC_SIZE=" + - // support::cpp11::to_string(num_elems_processed_per_iteration))); - if (is_data_type_fixed_point(input->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + - support::cpp11::to_string(input->info()->fixed_point_position())); - } - - // Create kernel - _kernel = - static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("reduction_mean", build_opts)); - - // Configure kernel window - auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis); - - ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); - - ICLKernel::configure(std::get<1>(win_config)); -} - -Status CLReductionMeanKernel::validate(const ITensorInfo *input, const ITensorInfo *output, - std::vector<uint32_t> axis) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis)); - ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>( - validate_and_configure_window(input->clone().get(), output->clone().get(), axis))); - - return Status{}; -} - -void CLReductionMeanKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - // Set out window - Window out_window(window); - out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); - - // Get first input and output slices - Window in_slice = window.first_slice_window_2D(); - Window out_slice = out_window.first_slice_window_2D(); - - // Set local sums buffer - // TODO work_group - unsigned int local_sum_size = _lws_hint[0] * _input->info()->element_size(); - - unsigned int idx = 2 * num_arguments_per_2D_tensor(); - _kernel.setArg(idx++, local_sum_size, nullptr); - _kernel.setArg<cl_int>(idx++, static_cast<cl_int>(_input->info()->dimension(1))); // height - _kernel.setArg<cl_int>(idx++, static_cast<cl_int>(_input->info()->dimension(0) * - _input->info()->dimension(1))); // divider - - do - { - unsigned int idx = 0; - add_2D_tensor_argument(idx, _input, in_slice); - in_slice.set_dimension_step(Window::DimY, _input->info()->dimension(1)); - add_2D_tensor_argument(idx, _output, out_slice); - enqueue(queue, *this, in_slice); - } while (window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice)); -} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp new file mode 100644 index 000000000..6b0697e89 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp @@ -0,0 +1,238 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *block_size, + const ITensorInfo *padding_size, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(block_size, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(padding_size, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::F16, DataType::S32, + DataType::F32); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != output->num_dimensions(), + "The number of dimensions of input should be equal to output"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() != output->data_layout(), + "The input and output layouts are different!"); + + // TODO Support other cases + if (input->num_dimensions() == 4 && input->data_layout() == DataLayout::NCHW) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != output->dimension(2), + "Input Depth should be equal to Output Depth"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size->dimension(0) != 2 || + padding_size->dimension(1) != 2, + "Only 2-dimensional spatial block's size was wrong"); + } + else if (input->num_dimensions() == 4 && input->data_layout() == DataLayout::NHWC) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(0) != output->dimension(0), + "Input Depth should be equal to Output Depth"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size->dimension(0) != 2 || + padding_size->dimension(1) != 2, + "Only 2-dimensional spatial block's size was wrong"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_MSG("CLSpaceToBatchNDKernel supports only 4-dimensional input"); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() < 2 && input->num_dimensions() > 4, + "CLSpaceToBatchNDKernel supports dimensions up to 4"); + + if (input->data_type() == DataType::QASYMM8) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->quantization_info() != output->quantization_info(), + "The input and output quantization info are different!"); + } + + return Status{}; +} + +} // namespace + +CLSpaceToBatchNDKernel::CLSpaceToBatchNDKernel() : _input(nullptr), _output(nullptr) {} + +void CLSpaceToBatchNDKernel::configure(const ICLTensor *input, const ICLTensor *block_size, + const ICLTensor *padding_size, ICLTensor *output) +{ + + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(input->info(), block_size->info(), padding_size->info(), output->info())); + + _input = input; + _block_size = block_size; + _padding_size = padding_size; + _output = output; + + // Set kernel build options + // TODO Support other cases + std::string kernel_name = "space_to_batch_4d"; + std::set<std::string> build_opts; + Window win; + + if (input->info()->data_layout() == DataLayout::NCHW) + { + kernel_name += "_nchw"; + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.emplace("-DHEIGHT_IN=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.emplace("-DWIDTH_IN=" + support::cpp11::to_string(input->info()->dimension(0))); + + win = calculate_max_window(*output->info(), Steps()); + + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + } + else if (input->info()->data_layout() == DataLayout::NHWC) + { + kernel_name += "_nhwc"; + build_opts.emplace("-DHEIGHT_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + build_opts.emplace("-DHEIGHT_IN=" + support::cpp11::to_string(input->info()->dimension(2))); + build_opts.emplace("-DWIDTH_IN=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.emplace("-DVEC_SIZE=" + + support::cpp11::to_string(num_elems_processed_per_iteration)); + + win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); + AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win, input_access, output_access); + input_access.set_valid_region(win, output->info()->valid_region()); + + if (window_changed) + { + ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!"); + } + } + else + { + ARM_COMPUTE_ERROR("Unsupported layout"); + } + + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DBATCH_IN=" + support::cpp11::to_string(input->info()->dimension(3))); + if (input->info()->data_type() == DataType::QASYMM8) + { + build_opts.emplace("-DZERO_VALUE=" + + support::cpp11::to_string(input->info()->quantization_info().offset)); + } + else + { + build_opts.emplace("-DZERO_VALUE=" + support::cpp11::to_string(0)); + } + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); + + // Configure kernel window + ICLKernel::configure_internal(win); +} + +void CLSpaceToBatchNDKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + +#if defined(DEBUG) + const_cast<ICLTensor *>(_block_size)->map(queue); + const_cast<ICLTensor *>(_padding_size)->map(queue); + + const size_t num_dimensions = _input->info()->num_dimensions(); + const size_t num_spacial_dimensions = _block_size->info()->dimension(0); + int32_t batch_size = _input->info()->dimension(num_dimensions - 1); + for (size_t i = 0; i < num_spacial_dimensions; ++i) + { + const int32_t block_size = *reinterpret_cast<int32_t *>(_block_size->ptr_to_element({i})); + const int32_t padding_size_pre = + *reinterpret_cast<int32_t *>(_padding_size->ptr_to_element({0, i})); + const int32_t padding_size_post = + *reinterpret_cast<int32_t *>(_padding_size->ptr_to_element({1, i})); + + ARM_COMPUTE_ERROR_ON_MSG(block_size < 1, "Block size should be greater than or equal to 1"); + ARM_COMPUTE_ERROR_ON_MSG(padding_size_pre < 0 && padding_size_post < 0, + "Padding size should be greater than or equal to 0"); + + if (num_dimensions == 4 && _input->info()->data_layout() == DataLayout::NCHW) + { + ARM_COMPUTE_ERROR_ON_MSG( + _output->info()->dimension(i) != + (_input->info()->dimension(i) + padding_size_pre + padding_size_post) / block_size, + "Dimension value of spatial block does not match output's dimension value"); + } + else + { + ARM_COMPUTE_ERROR_ON_MSG( + _output->info()->dimension(num_dimensions - num_spacial_dimensions - 1 + i) != + (_input->info()->dimension(num_dimensions - num_spacial_dimensions - 1 + i) + + padding_size_pre + padding_size_post) / + block_size, + "Dimension value of spatial block does not match output's dimension value"); + } + + batch_size *= block_size; + } + ARM_COMPUTE_ERROR_ON_MSG( + _output->info()->dimension(num_dimensions - 1) != batch_size, + "Output batch size should be equal to input batch size * (multiplication of all block size)"); + + const_cast<ICLTensor *>(_block_size)->unmap(queue); + const_cast<ICLTensor *>(_padding_size)->unmap(queue); +#endif // defined(DEBUG) + + Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + // Set block size window + Window win_block = calculate_max_window(*_block_size->info(), Steps()); + + // Set padding size window + Window win_padding = calculate_max_window(*_padding_size->info(), Steps()); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + add_1D_tensor_argument(idx, _block_size, win_block); + add_2D_tensor_argument(idx, _padding_size, win_padding); + enqueue(queue, *this, slice_out); + } while (window.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToDepthKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToDepthKernel.cpp new file mode 100644 index 000000000..5d6329edc --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLSpaceToDepthKernel.cpp @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLSpaceToDepthKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const int32_t block_size) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, + DataType::S16, DataType::S32, DataType::F16, + DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size >= 1, + "Block size should be greater than or equal to 1."); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(3) == output->dimension(3), + "Input batch should be equal to Output batch"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + input->dimension(2) * block_size * block_size == output->dimension(2), + "Output depth should be equal to (input depth * block size *block size)"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(input->dimension(0) % block_size) && + !(input->dimension(1) % block_size), + "Input height and width should be divisible by block size"); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) == (input->dimension(0) / block_size)) && + (output->dimension(1) == (input->dimension(1) / block_size)), + "Output height and width should be equal to " + "input_height/blocksize and input_width/blocksize respectively"); + + return Status{}; +} + +} // namespace + +CLSpaceToDepthKernel::CLSpaceToDepthKernel() : _input(nullptr), _output(nullptr) {} + +void CLSpaceToDepthKernel::configure(const ICLTensor *input, ICLTensor *output, + const int32_t block_size) +{ + + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), block_size)); + + _input = input; + _output = output; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DBLOCK_SIZE=" + support::cpp11::to_string(block_size)); + build_opts.emplace("-DDEPTH_IN=" + support::cpp11::to_string(input->info()->dimension(2))); + + // Create kernel + _kernel = + static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("space_to_depth", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*input->info(), Steps()); + + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + ICLKernel::configure_internal(win); +} + +void CLSpaceToDepthKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); + + Window slice_in = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_out(slice_in); + slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_out.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_in); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp new file mode 100644 index 000000000..260bc39f1 --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp @@ -0,0 +1,170 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + const TensorShape &out_shape = + TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::F16, DataType::F32); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, + "Inputs are not broadcast compatible"); + // Validate in case of configured output + if (output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG( + detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), + "Wrong shape for output"); + } + return Status{}; +} +} // namespace + +CLSquaredDifferenceKernel::CLSquaredDifferenceKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLSquaredDifferenceKernel::configure(const ICLTensor *input1, const ICLTensor *input2, + ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); + ARM_COMPUTE_ERROR_THROW_ON(validate(input1->info(), input2->info(), output->info())); + + _input1 = input1; + _input2 = input2; + _output = output; + + // Create kernel + std::set<std::string> build_opts; + build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type()))); + build_opts.emplace( + ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration))); + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel("squared_difference", build_opts)); + + const std::pair<TensorShape, ValidRegion> broadcast_pair = + ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info()); + + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + { + set_shape_if_empty(*output->info(), out_shape); + + if (input1->info()->data_type() == DataType::F16 && + input2->info()->data_type() == DataType::F16) + { + set_format_if_unknown(*output->info(), Format::F16); + } + else if (input1->info()->data_type() == DataType::F32 || + input2->info()->data_type() == DataType::F32) + { + set_format_if_unknown(*output->info(), Format::F32); + } + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1->info()); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info()); + + AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) || + update_window_and_padding(win_input2, input2_access) || + update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + ICLKernel::configure_internal(win); +} + +void CLSquaredDifferenceKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = + (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = + can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) + : window; + + const TensorShape &in_shape1_collapsed = + has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = + has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } while (collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLSquaredDifferenceKernel::border_size() const +{ + const unsigned int replicateSize = + _output->info()->dimension(0) - + std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = + std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceExKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceExKernel.cpp new file mode 100644 index 000000000..48146a43a --- /dev/null +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceExKernel.cpp @@ -0,0 +1,253 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/CL/kernels/CLStridedSliceExKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibraryEx.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/TensorInfo.h" + +using namespace arm_compute; + +CLStridedSliceExKernel::CLStridedSliceExKernel() + : _input(nullptr), _output(nullptr), _beginData(nullptr), _endData(nullptr), + _stridesData(nullptr), _beginMask(0), _endMask(0), _shrinkAxisMask(0) +{ +} + +Status CLStridedSliceExKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *begin, const ITensorInfo *end, + const ITensorInfo *strides, int32_t beginMask, + int32_t endMask, int32_t shrinkAxisMask) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, begin, end, strides); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( + input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, + DataType::U32, DataType::S32, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(begin, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(end, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(strides, 1, DataType::S32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + + ARM_COMPUTE_ERROR_ON(begin->num_dimensions() != 1 || begin->dimension(0) > 4); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(begin->tensor_shape(), end->tensor_shape(), + strides->tensor_shape()); + + return Status{}; +} + +// Return the index for the first element along that axis. This index will be a +// positive integer between [0, axisSize - 1] that can be used to index +// directly into the data. +inline int32_t StartForAxis(int32_t beginMask, int32_t begin, int32_t stride, + const TensorShape &inputShape, int32_t axis) +{ + // Begin with the specified index + int32_t start = begin; + + // beginMask override + if (beginMask & 1 << axis) + { + if (stride > 0) + { + // Forward iteration - use the first element. These values will get + // clamped below (Note: We could have set them to 0 and axisSize-1, but + // use lowest() and max() to maintain symmetry with StopForAxis()) + start = std::numeric_limits<int32_t>::lowest(); + } + else + { + // Backward iteration - use the last element. + start = std::numeric_limits<int32_t>::max(); + } + } + + // Handle negative indices + int32_t axisSize = inputShape[axis]; + if (start < 0) + { + start += axisSize; + } + + // Clamping + start = arm_compute::utility::clamp(start, 0, axisSize - 1); + + return start; +} + +// Return the "real" index for the end of iteration along that axis. This is an +// "end" in the traditional C sense, in that it points to one past the last +// element. ie. So if you were iterating through all elements of a 1D array of +// size 4, this function would return 4 as the stop, because it is one past the +// "real" indices of 0, 1, 2 & 3. +inline int32_t StopForAxis(int32_t endMask, int32_t end, int32_t stride, + const TensorShape &inputShape, int32_t axis) +{ + // Begin with the specified index + int32_t stop = end; + + // endMask override + if (endMask & (1 << axis)) + { + if (stride > 0) + { + // Forward iteration - use the last element. These values will get + // clamped below + stop = std::numeric_limits<int32_t>::max(); + } + else + { + // Backward iteration - use the first element. + stop = std::numeric_limits<int32_t>::lowest(); + } + } + + // Handle negative indices + int32_t axisSize = inputShape[axis]; + if (stop < 0) + { + stop += axisSize; + } + + // Clamping + // Because the end index points one past the last element, we need slightly + // different clamping ranges depending on the direction. + if (stride > 0) + { + // Forward iteration + stop = arm_compute::utility::clamp(stop, 0, axisSize); + } + else + { + // Backward iteration + stop = arm_compute::utility::clamp(stop, -1, axisSize - 1); + } + + return stop; +} + +inline int32_t getOutDim(int32_t start, int32_t stop, int32_t stride) +{ + int32_t ret = 0; + if (stride > 0) + { + ret = ((stop - start - 1) / stride) + 1; + } + else + { + ret = ((stop - start + 1) / stride) + 1; + } + ARM_COMPUTE_ERROR_ON_MSG(ret < 0, "The dimension must be the natural number"); + return ret; +} + +void CLStridedSliceExKernel::configure(const ICLTensor *input, ICLTensor *output, + ICLTensor *beginData, ICLTensor *endData, + ICLTensor *stridesData, int32_t beginMask, int32_t endMask, + int32_t shrinkAxisMask) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), beginData->info(), + endData->info(), stridesData->info(), beginMask, endMask, + shrinkAxisMask)); + + _input = input; + _output = output; + _beginData = beginData; + _endData = endData; + _stridesData = stridesData; + _beginMask = beginMask; + _endMask = endMask; + _shrinkAxisMask = shrinkAxisMask; + + // Set kernel build options + std::set<std::string> build_opts; + build_opts.emplace("-DELEMENT_DATA_TYPE=" + + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); + + // Create kernel + _kernel = static_cast<cl::Kernel>( + CLKernelLibraryEx::get().create_kernel("strided_slice_ex", build_opts)); + + // Configure kernel window + Window win = calculate_max_window(*output->info(), Steps()); + ICLKernel::configure_internal(win); +} + +void CLStridedSliceExKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + _beginData->map(queue); + _endData->map(queue); + _stridesData->map(queue); + + std::vector<int32_t> starts; + std::vector<int32_t> strides; + + for (uint32_t n = 0; n < _beginData->info()->tensor_shape().total_size(); ++n) + { + const TensorShape shape = _input->info()->tensor_shape(); + starts.emplace_back( + StartForAxis(_beginMask, reinterpret_cast<int32_t *>(_beginData->buffer())[n], + reinterpret_cast<int32_t *>(_stridesData->buffer())[n], shape, n)); + + strides.emplace_back(reinterpret_cast<int32_t *>(_stridesData->buffer())[n]); + } + + for (uint32_t n = _beginData->info()->tensor_shape().total_size(); n < 4; n++) + { + starts.emplace_back(0); + strides.emplace_back(1); + } + // TODO: Apply shrinkAxisMask + + _beginData->unmap(queue); + _stridesData->unmap(queue); + _endData->unmap(queue); + + unsigned int idx = 2 * num_arguments_per_4D_tensor(); // Skip the input and output parameters + const cl_int4 startsArg = {{ + static_cast<cl_int>(starts[0]), static_cast<cl_int>(starts[1]), + static_cast<cl_int>(starts[2]), static_cast<cl_int>(starts[3]), + }}; + _kernel.setArg<cl_int4>(idx++, startsArg); + + const cl_int4 stridesArg = {{ + static_cast<cl_int>(strides[0]), static_cast<cl_int>(strides[1]), + static_cast<cl_int>(strides[2]), static_cast<cl_int>(strides[3]), + }}; + _kernel.setArg<cl_int4>(idx++, stridesArg); + + Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); + + // Setup output slice + Window slice_in(slice_out); + slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); + slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); + slice_in.set(3, Window::Dimension(0, 0, 0)); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice_in); + add_4D_tensor_argument(idx, _output, slice_out); + enqueue(queue, *this, slice_out); + } while (window.slide_window_slice_4D(slice_in) && window.slide_window_slice_4D(slice_out)); +} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceKernel.cpp deleted file mode 100644 index 80ffd423a..000000000 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLStridedSliceKernel.cpp +++ /dev/null @@ -1,304 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/core/CL/kernels/CLStridedSliceKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/CLKernelLibraryEx.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <string> - -using namespace std; -using namespace arm_compute; - -static const int32_t maxDim = 4; - -CLStridedSliceKernel::CLStridedSliceKernel() - : _input(nullptr), _output(nullptr), _beginData(nullptr), _endData(nullptr), - _stridesData(nullptr), _beginMask(0), _endMask(0), _shrinkAxisMask(0) -{ -} - -Status CLStridedSliceKernel::validate(const ITensorInfo *input, const ITensorInfo *output, - const ITensorInfo *begin, const ITensorInfo *end, - const ITensorInfo *strides, int32_t beginMask, - int32_t endMask, int32_t shrinkAxisMask) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, begin, end, strides); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN( - input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, DataType::U16, - DataType::S16, DataType::QS16, DataType::U32, DataType::S32, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(begin, 1, DataType::S32); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(end, 1, DataType::S32); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(strides, 1, DataType::S32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - - ARM_COMPUTE_ERROR_ON(begin->num_dimensions() != 1 || begin->dimension(0) > 4); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(begin->tensor_shape(), end->tensor_shape(), - strides->tensor_shape()); - - return Status{}; -} - -// Return the index for the first element along that axis. This index will be a -// positive integer between [0, axisSize - 1] that can be used to index -// directly into the data. -inline int32_t StartForAxis(int32_t beginMask, int32_t begin, int32_t stride, - const TensorShape &inputShape, int32_t axis) -{ - // Begin with the specified index - int32_t start = begin; - - // beginMask override - if (beginMask & 1 << axis) - { - if (stride > 0) - { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axisSize-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits<int32_t>::lowest(); - } - else - { - // Backward iteration - use the last element. - start = std::numeric_limits<int32_t>::max(); - } - } - - // Handle negative indices - int32_t axisSize = inputShape[axis]; - if (start < 0) - { - start += axisSize; - } - - // Clamping - start = arm_compute::utility::clamp(start, 0, axisSize - 1); - - return start; -} - -// Return the "real" index for the end of iteration along that axis. This is an -// "end" in the traditional C sense, in that it points to one past the last -// element. ie. So if you were iterating through all elements of a 1D array of -// size 4, this function would return 4 as the stop, because it is one past the -// "real" indices of 0, 1, 2 & 3. -inline int32_t StopForAxis(int32_t endMask, int32_t end, int32_t stride, - const TensorShape &inputShape, int32_t axis) -{ - // Begin with the specified index - int32_t stop = end; - - // endMask override - if (endMask & (1 << axis)) - { - if (stride > 0) - { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits<int32_t>::max(); - } - else - { - // Backward iteration - use the first element. - stop = std::numeric_limits<int32_t>::lowest(); - } - } - - // Handle negative indices - int32_t axisSize = inputShape[axis]; - if (stop < 0) - { - stop += axisSize; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (stride > 0) - { - // Forward iteration - stop = arm_compute::utility::clamp(stop, 0, axisSize); - } - else - { - // Backward iteration - stop = arm_compute::utility::clamp(stop, -1, axisSize - 1); - } - - return stop; -} - -inline int32_t offset4D(const TensorShape &shape, int32_t b, int32_t d, int32_t h, int32_t w) -{ - int32_t offset = b * shape[2] * shape[1] * shape[0]; - offset += d * shape[1] * shape[0]; - offset += h * shape[0]; - offset += w; - return offset; -} - -inline int32_t getOutDim(int32_t start, int32_t stop, int32_t stride) -{ - int32_t ret = 0; - if (stride > 0) - { - ret = ((stop - start - 1) / stride) + 1; - } - else - { - ret = ((stop - start + 1) / stride) + 1; - } - ARM_COMPUTE_ERROR_ON_MSG(ret < 0, "The dimension must be the natural number"); - return ret; -} - -void CLStridedSliceKernel::configure(const ICLTensor *input, ICLTensor *output, - ICLTensor *beginData, ICLTensor *endData, - ICLTensor *stridesData, int32_t beginMask, int32_t endMask, - int32_t shrinkAxisMask) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), beginData->info(), - endData->info(), stridesData->info(), beginMask, endMask, - shrinkAxisMask)); - - _input = input; - _output = output; - _beginData = beginData; - _endData = endData; - _stridesData = stridesData; - _beginMask = beginMask; - _endMask = endMask; - _shrinkAxisMask = shrinkAxisMask; - - constexpr unsigned int num_elems_processed_per_iteration = 1; - - // Set kernel build options - std::set<std::string> build_opts; - build_opts.emplace("-DELEMENT_DATA_TYPE=" + - get_cl_type_from_data_type(input->info()->data_type())); - build_opts.emplace("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size())); - - // Create kernel - _kernel = - static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel("strided_slice", build_opts)); - - // Create output's window without padding - TensorShape collapsed = output->info()->tensor_shape(); - collapsed.collapse(4); - TensorInfo info = *output->info(); - info.set_tensor_shape(collapsed); - Window win = calculate_max_window(info, Steps(num_elems_processed_per_iteration)); - - ICLKernel::configure(win); -} - -void CLStridedSliceKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - // Create input window - TensorShape collapsed = _input->info()->tensor_shape(); - collapsed.collapse(4); - TensorInfo info = *_input->info(); - info.set_tensor_shape(collapsed); - Window win_in = calculate_max_window(info, Steps(_input->info()->tensor_shape().total_size())); - - _beginData->map(queue); - _endData->map(queue); - _stridesData->map(queue); - - std::vector<int32_t> dimsIn; - std::vector<int32_t> dimsOut; - std::vector<int32_t> starts; - std::vector<int32_t> stops; - std::vector<int32_t> strides; - - for (uint32_t n = 0; n < _beginData->info()->tensor_shape().total_size(); ++n) - { - const TensorShape shape = _input->info()->tensor_shape(); - starts.emplace_back( - StartForAxis(_beginMask, reinterpret_cast<int32_t *>(_beginData->buffer())[n], - reinterpret_cast<int32_t *>(_stridesData->buffer())[n], shape, n)); - - stops.emplace_back(StopForAxis(_endMask, reinterpret_cast<int32_t *>(_endData->buffer())[n], - reinterpret_cast<int32_t *>(_stridesData->buffer())[n], shape, - n)); - - strides.emplace_back(reinterpret_cast<int32_t *>(_stridesData->buffer())[n]); - dimsIn.emplace_back(shape[n]); - dimsOut.emplace_back(getOutDim(starts[n], stops[n], strides[n])); - } - - for (uint32_t n = _beginData->info()->tensor_shape().total_size(); n < 4; n++) - { - starts.emplace_back(0); - stops.emplace_back(1); - strides.emplace_back(1); - dimsIn.emplace_back(1); - dimsOut.emplace_back(1); - } - // TODO: Apply shrinkAxisMask - - _beginData->unmap(queue); - _stridesData->unmap(queue); - _endData->unmap(queue); - - // Set parameters - unsigned int idx = 2 * num_arguments_per_1D_tensor(); // Skip the input and output parameters - const cl_int4 dimsInArg = {{ - static_cast<cl_int>(dimsIn[0]), static_cast<cl_int>(dimsIn[1]), - static_cast<cl_int>(dimsIn[2]), static_cast<cl_int>(dimsIn[3]), - }}; - _kernel.setArg<cl_int4>(idx++, dimsInArg); - - const cl_int4 dimsOutArg = {{ - static_cast<cl_int>(dimsOut[0]), static_cast<cl_int>(dimsOut[1]), - static_cast<cl_int>(dimsOut[2]), static_cast<cl_int>(dimsOut[3]), - }}; - _kernel.setArg<cl_int4>(idx++, dimsOutArg); - - const cl_int4 startsArg = {{ - static_cast<cl_int>(starts[0]), static_cast<cl_int>(starts[1]), - static_cast<cl_int>(starts[2]), static_cast<cl_int>(starts[3]), - }}; - _kernel.setArg<cl_int4>(idx++, startsArg); - - const cl_int4 stridesArg = {{ - static_cast<cl_int>(strides[0]), static_cast<cl_int>(strides[1]), - static_cast<cl_int>(strides[2]), static_cast<cl_int>(strides[3]), - }}; - _kernel.setArg<cl_int4>(idx++, stridesArg); - - // TODO: Apply slicing output's window - idx = 0; - add_1D_tensor_argument(idx, _input, win_in); - add_1D_tensor_argument(idx, _output, window); - - enqueue(queue, *this, window); -} diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLTopKV2Kernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLTopKV2Kernel.cpp index d95b485b7..073c2f7bb 100644 --- a/libs/ARMComputeEx/src/core/CL/kernels/CLTopKV2Kernel.cpp +++ b/libs/ARMComputeEx/src/core/CL/kernels/CLTopKV2Kernel.cpp @@ -17,15 +17,8 @@ #include "arm_compute/core/CL/kernels/CLTopKV2Kernel.h" #include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLKernelLibraryEx.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include <climits> -#include <cassert> namespace arm_compute { @@ -59,7 +52,7 @@ void CLTopKV2Single::configure(ICLTensor *input, ICLTensor *topk_values, ICLTens // Configure kernel window Window win; win.set(0, Window::Dimension(0, 1, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLTopKV2Single::run(const Window &window, cl::CommandQueue &queue) @@ -102,7 +95,7 @@ void CLTopKV2Init::configure(ICLTensor *input, cl::Buffer *in_key_buf, cl::Buffe // Configure kernel window Window win; win.set(0, Window::Dimension(0, n, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLTopKV2Init::run(const Window &window, cl::CommandQueue &queue) @@ -147,7 +140,7 @@ void CLRadixSortHistogram::configure(cl::Buffer *hist_buf, int bits, int n) // Configure kernel window Window win; win.set(0, Window::Dimension(0, _GROUPS * _ITEMS, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLRadixSortHistogram::run(const Window &window, cl::CommandQueue &queue) @@ -192,7 +185,7 @@ void CLRadixSortScanHistogram::configure(cl::Buffer *hist_buf, cl::Buffer *glob_ // Configure kernel window Window win; win.set(0, Window::Dimension(0, radix * _GROUPS * _ITEMS / 2, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLRadixSortScanHistogram::run(const Window &window, cl::CommandQueue &queue) @@ -236,7 +229,7 @@ void CLRadixSortGlobalScanHistogram::configure(cl::Buffer *glob_sum_buf, cl::Buf // Configure kernel window Window win; win.set(0, Window::Dimension(0, _HISTOSPLIT / 2, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLRadixSortGlobalScanHistogram::run(const Window &window, cl::CommandQueue &queue) @@ -275,7 +268,7 @@ void CLRadixSortPasteHistogram::configure(cl::Buffer *hist_buf, cl::Buffer *glob // Configure kernel window Window win; win.set(0, Window::Dimension(0, radix * _GROUPS * _ITEMS / 2, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLRadixSortPasteHistogram::run(const Window &window, cl::CommandQueue &queue) @@ -322,7 +315,7 @@ void CLRadixSortReorder::configure(cl::Buffer *hist_buf, int bits, int n) // Configure kernel window Window win; win.set(0, Window::Dimension(0, _GROUPS * _ITEMS, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLRadixSortReorder::run(const Window &window, cl::CommandQueue &queue) @@ -365,7 +358,7 @@ void CLTopKV2FindFirstNegative::configure(cl::Buffer *first_negative_idx_buf, in // Configure kernel window Window win; win.set(0, Window::Dimension(0, n, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLTopKV2FindFirstNegative::run(const Window &window, cl::CommandQueue &queue) @@ -404,7 +397,7 @@ void CLTopKV2ReorderNegatives::configure(cl::Buffer *first_negative_idx_buf, int // Configure kernel window Window win; win.set(0, Window::Dimension(0, n, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLTopKV2ReorderNegatives::run(const Window &window, cl::CommandQueue &queue) @@ -449,7 +442,7 @@ void CLTopKV2Store::configure(ICLTensor *values, ICLTensor *indices, int k, int // Configure kernel window Window win; win.set(0, Window::Dimension(0, k, 1)); - ICLKernel::configure(win); + ICLKernel::configure_internal(win); } void CLTopKV2Store::setOutputBuffers(cl::Buffer *out_key_buf, cl::Buffer *out_ind_buf) diff --git a/libs/ARMComputeEx/src/core/NEON/kernels/NENormalizationLayerExKernel.cpp b/libs/ARMComputeEx/src/core/NEON/kernels/NENormalizationLayerExKernel.cpp new file mode 100644 index 000000000..3b5782c25 --- /dev/null +++ b/libs/ARMComputeEx/src/core/NEON/kernels/NENormalizationLayerExKernel.cpp @@ -0,0 +1,294 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/NEON/kernels/NENormalizationLayerExKernel.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/NEON/NEMath.h" + +using namespace arm_compute; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *input_squared, + const ITensorInfo *output, const NormalizationLayerInfo &norm_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_squared, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, input_squared); + + // Checks performed when output is configured + if (output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, + ITensorInfo *input_squared, + ITensorInfo *output, + const NormalizationLayerInfo &norm_info) +{ + unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); + const unsigned int num_elems_read_per_iteration = + num_elems_processed_per_iteration + 2 * (norm_info.norm_size() / 2); + const unsigned int num_rows = + (norm_info.type() == NormType::IN_MAP_2D) ? norm_info.norm_size() : 1; + const unsigned int border_width = + (norm_info.is_cross_map()) ? 0 : std::min<unsigned int>(norm_info.norm_size() / 2, 3U); + BorderSize border_size = BorderSize(0, border_width); + bool window_changed = false; + + // Configure window + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); + + AccessWindowRectangle input_access(input, -border_size.left, 0, num_elems_read_per_iteration, + num_rows); + AccessWindowRectangle input_squared_access(input_squared, -border_size.left, 0, + num_elems_read_per_iteration, num_rows); + + if (output->total_size() != 0) + { + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + window_changed = + update_window_and_padding(win, input_access, input_squared_access, output_access); + output_access.set_valid_region(win, input->valid_region()); + } + else + { + window_changed = update_window_and_padding(win, input_access, input_squared_access); + } + + Status err = (window_changed) + ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") + : Status{}; + return std::make_pair(err, win); +} +} // namespace + +NENormalizationLayerExKernel::NENormalizationLayerExKernel() + : _func(nullptr), _input(nullptr), _input_squared(nullptr), _output(nullptr), + _norm_info(NormType::IN_MAP_1D), _border_size() +{ +} + +BorderSize NENormalizationLayerExKernel::border_size() const { return _border_size; } + +void NENormalizationLayerExKernel::configure(const ITensor *input, const ITensor *input_squared, + ITensor *output, NormalizationLayerInfo norm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_squared, output); + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output->info(), *input->info()); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(input->info(), input_squared->info(), output->info(), norm_info)); + + const unsigned int border_width = + (norm_info.is_cross_map()) ? 0 : std::min<unsigned int>(norm_info.norm_size() / 2, 3U); + + _input = input; + _input_squared = input_squared; + _output = output; + _norm_info = norm_info; + _border_size = BorderSize(0, border_width); + + switch (_input->info()->data_type()) + { + case DataType::F32: + { + switch (norm_info.type()) + { + case NormType::IN_MAP_1D: + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F32, 0, false>; + break; + case NormType::IN_MAP_2D: + // Normalize over X and Y + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F32, 0, true>; + break; + case NormType::CROSS_MAP: + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F32, 2, false>; + break; + default: + break; + } + break; + } + case DataType::F16: + { + switch (norm_info.type()) + { + case NormType::IN_MAP_1D: + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F16, 0, false>; + break; + case NormType::IN_MAP_2D: + // Normalize over X and Y + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F16, 0, true>; + break; + case NormType::CROSS_MAP: + _func = &NENormalizationLayerExKernel::normalize_float<DataType::F16, 2, false>; + break; + default: + break; + } + break; + } + default: + ARM_COMPUTE_ERROR("NOT SUPPORTED!"); + } + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), input_squared->info(), + output->info(), norm_info); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); +} + +template <DataType dt, unsigned int dim, bool do_2D_norm> +void NENormalizationLayerExKernel::normalize_float(const Window &window) +{ + Iterator input(_input, window); + Iterator input_squared(_input_squared, window); + Iterator output(_output, window); + + const int dim_y = 1; + const int radius = _norm_info.norm_size(); + const int total_size = _input->info()->dimension(dim) - 1; + const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim]; + // We account padding across X only and we iterate over rows + const int min_left = (dim == 2) ? 0 : -static_cast<int>(border_size().left); + const int max_right = (dim == 2) ? total_size : total_size + border_size().left; + const int min_top = 0; + const int max_bottom = _input->info()->dimension(dim_y) - 1; + + if (dt == DataType::F32) + { + const float32x4_t coeff_vec = vdupq_n_f32(_norm_info.scale_coeff()); + const float32x4_t beta_vec = vdupq_n_f32(_norm_info.beta()); + const float32x4_t kappa_vec = vdupq_n_f32(_norm_info.kappa()); + + execute_window_loop( + window, + [&](const Coordinates &id) { + // Get range to normalize + const int current_row = do_2D_norm ? id[dim_y] : 0; + const int current_slice = id[dim]; + const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; + const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; + const int first_slice = std::max(current_slice - radius, min_left); + const int last_slice = std::min(current_slice + radius, max_right); + + // Accumulate 2D In-Map values + float32x4_t accu = vdupq_n_f32(0.f); + for (int j = first_row; j <= last_row; j++) + { + // Compute row displacement + const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; + const uint8_t *const input_squared_ptr = + input_squared.ptr() + row - (current_slice * input_squared_stride); + for (int i = first_slice; i <= last_slice; ++i) + { + accu = vaddq_f32(accu, vld1q_f32(reinterpret_cast<const float *>( + input_squared_ptr + i * input_squared_stride))); + } + } + + // Normalize + const float32x4_t normalized = vpowq_f32(vmlaq_f32(kappa_vec, coeff_vec, accu), beta_vec); + const float32x4_t normalized_pixel = vmulq_f32( + vld1q_f32(reinterpret_cast<const float *>(input.ptr())), vinvq_f32(normalized)); + vst1q_f32(reinterpret_cast<float *>(output.ptr()), normalized_pixel); + }, + input, input_squared, output); + } +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + else if (dt == DataType::F16) + { + const float16x8_t coeff_vec = vdupq_n_f16(_norm_info.scale_coeff()); + const float16x8_t beta_vec_f16 = vdupq_n_f16(_norm_info.beta()); + const float16x8_t kappa_vec = vdupq_n_f16(_norm_info.kappa()); + + execute_window_loop( + window, + [&](const Coordinates &id) { + // Get range to normalize + const int current_row = do_2D_norm ? id[dim_y] : 0; + const int current_slice = id[dim]; + const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; + const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; + const int first_slice = std::max(current_slice - radius, min_left); + const int last_slice = std::min(current_slice + radius, max_right); + + // Accumulate 2D In-Map values + float16x8_t accu = vdupq_n_f16(0.f); + for (int j = first_row; j <= last_row; j++) + { + // Compute row displacement + const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; + const uint8_t *const input_squared_ptr = + input_squared.ptr() + row - (current_slice * input_squared_stride); + for (int i = first_slice; i <= last_slice; ++i) + { + accu = vaddq_f16(accu, vld1q_f16(reinterpret_cast<const float16_t *>( + input_squared_ptr + i * input_squared_stride))); + } + } + + const float16x8_t norm_f16 = + vpowq_f16(vaddq_f16(kappa_vec, vmulq_f16(coeff_vec, accu)), beta_vec_f16); + const float16x8_t normalized_pixel = vmulq_f16( + vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), vinvq_f16(norm_f16)); + vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), normalized_pixel); + }, + input, input_squared, output); + } +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + else + { + ARM_COMPUTE_ERROR("Not supported"); + } +} + +Status NENormalizationLayerExKernel::validate(const ITensorInfo *input, + const ITensorInfo *input_squared, + const ITensorInfo *output, + const NormalizationLayerInfo norm_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, input_squared, output, norm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), + input_squared->clone().get(), + output->clone().get(), norm_info) + .first); + + return Status{}; +} + +void NENormalizationLayerExKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + ARM_COMPUTE_ERROR_ON(_func == nullptr); + + // Run function + (this->*_func)(window); +} diff --git a/libs/ARMComputeEx/src/core/UtilsEx.cpp b/libs/ARMComputeEx/src/core/UtilsEx.cpp new file mode 100644 index 000000000..b63093bbb --- /dev/null +++ b/libs/ARMComputeEx/src/core/UtilsEx.cpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/core/UtilsEx.h" + +#include <cstdint> +#include <fstream> +#include <map> +#include <string> + +using namespace arm_compute; + +const std::string & +arm_compute::string_from_activation_func_ex(ActivationLayerInfoEx::ActivationFunction act) +{ + static std::map<ActivationLayerInfoEx::ActivationFunction, const std::string> act_map = { + {ActivationLayerInfoEx::ActivationFunction::RSQRT, "RSQRT"}, + }; + + return act_map[act]; +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLActivationLayerEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLActivationLayerEx.cpp new file mode 100644 index 000000000..1e52fc429 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLActivationLayerEx.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLActivationLayerEx.h" + +#include "arm_compute/core/CL/kernels/CLActivationLayerExKernel.h" + +using namespace arm_compute; + +void CLActivationLayerEx::configure(ICLTensor *input, ICLTensor *output, + ActivationLayerInfoEx act_info) +{ + auto k = arm_compute::support::cpp14::make_unique<CLActivationLayerExKernel>(); + k->configure(input, output, act_info); + _kernel = std::move(k); +} + +Status CLActivationLayerEx::validate(const ITensorInfo *input, const ITensorInfo *output, + const ActivationLayerInfoEx &act_info) +{ + return CLActivationLayerExKernel::validate(input, output, act_info); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLArgMinMax.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLArgMinMax.cpp new file mode 100644 index 000000000..dff743e89 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLArgMinMax.cpp @@ -0,0 +1,120 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLArgMinMax.h" + +#include "arm_compute/core/CL/kernels/CLArgMinMaxKernel.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +namespace arm_compute +{ + +CLArgMinMax::CLArgMinMax() + : _input(nullptr), _output(nullptr), _argminmax_axis(), _interm_tensors(), _argminmax_kernels(), + _num_of_kernels() +{ +} + +void CLArgMinMax::configure(ICLTensor *input, ICLTensor *output, std::vector<uint32_t> axis, + ArgOperation op) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), axis, op)); + _input = input; + _output = output; + _argminmax_axis = axis; + _arg_op = op; + // NOTE The argminmax_axis must have no duplication. + _num_of_kernels = axis.size(); + const size_t num_of_interm_tensors = _num_of_kernels - 1; + + _interm_tensors = arm_compute::support::cpp14::make_unique<CLTensor[]>(num_of_interm_tensors); + _argminmax_kernels = + arm_compute::support::cpp14::make_unique<CLArgMinMaxKernel[]>(_num_of_kernels); + + TensorShape shape{input->info()->tensor_shape()}; + for (size_t i = 0; i < num_of_interm_tensors; i++) + { + shape.set(_argminmax_axis[i], 1); + _interm_tensors[i].allocator()->init( + TensorInfo(shape, input->info()->num_channels(), input->info()->data_type())); + _interm_tensors[i].allocator()->allocate(); + } + + // Set a vector that is ordered ICLTensors sequentially. + std::vector<ICLTensor *> tensors; + tensors.emplace_back(input); + for (size_t i = 0; i < num_of_interm_tensors; i++) + { + tensors.emplace_back(_interm_tensors.get() + i); + } + tensors.emplace_back(output); + + // Apply ArgMinMax on all kernels + for (size_t i = 0; i < _num_of_kernels; i++) + { + _argminmax_kernels[i].configure(tensors[i], tensors[i + 1], _argminmax_axis[i], op); + } +} + +Status CLArgMinMax::validate(const ITensorInfo *input, const std::vector<uint32_t> &argminmax_axis, + const ITensorInfo *output, ArgOperation op) +{ + const size_t num_of_kernels = argminmax_axis.size(); + const size_t num_of_interm_tensors = num_of_kernels - 1; + + // Create temporary tensor infos + auto interm_tensors = + arm_compute::support::cpp14::make_unique<TensorInfo[]>(num_of_interm_tensors); + + // Create intermediate tensor info + TensorShape shape{input->tensor_shape()}; + + for (size_t i = 0; i < num_of_interm_tensors; i++) + { + shape.set(argminmax_axis[i], 1); + interm_tensors[i].set_data_type(input->data_type()); + interm_tensors[i].set_tensor_shape(shape); + interm_tensors[i].set_num_channels(input->num_channels()); + } + + // Set a vector that is ordered ITensorInfo sequentially. + std::vector<const ITensorInfo *> tensors; + tensors.emplace_back(input); + for (size_t i = 0; i < num_of_interm_tensors; i++) + { + tensors.emplace_back(interm_tensors.get() + i); + } + tensors.emplace_back(output); + + // Validate argminmax only on all kernels + for (size_t i = 0; i < num_of_kernels; i++) + { + ARM_COMPUTE_RETURN_ON_ERROR( + CLArgMinMaxKernel::validate(tensors[i], tensors[i + 1], argminmax_axis[i], op)); + } + + return Status{}; +} + +void CLArgMinMax::run() +{ + for (size_t i = 0; i < _num_of_kernels; ++i) + { + CLScheduler::get().enqueue(_argminmax_kernels[i]); + } +} + +} // namespace arm_compute diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLArithmeticSubtractionEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLArithmeticSubtractionEx.cpp new file mode 100644 index 000000000..3f403c80a --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLArithmeticSubtractionEx.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLArithmeticSubtractionEx.h" + +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionExKernel.h" + +using namespace arm_compute; + +void CLArithmeticSubtractionEx::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, + ConvertPolicy policy) +{ + auto k = arm_compute::support::cpp14::make_unique<CLArithmeticSubtractionExKernel>(); + k->configure(input1, input2, output, policy); + _kernel = std::move(k); + + if (output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + + if (broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} + +Status CLArithmeticSubtractionEx::validate(const ITensorInfo *input1, const ITensorInfo *input2, + const ITensorInfo *output, ConvertPolicy policy) +{ + return CLArithmeticSubtractionExKernel::validate(input1, input2, output, policy); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLBatchToSpaceND.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLBatchToSpaceND.cpp new file mode 100644 index 000000000..26e3798cc --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLBatchToSpaceND.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLBatchToSpaceND.h" + +#include "arm_compute/core/CL/kernels/CLBatchToSpaceNDKernel.h" + +using namespace arm_compute; + +void CLBatchToSpaceND::configure(ICLTensor *input, ICLTensor *output, const int32_t *block_size) +{ + auto k = arm_compute::support::cpp14::make_unique<CLBatchToSpaceNDKernel>(); + k->configure(input, output, block_size); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLBinaryLogicalOp.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLBinaryLogicalOp.cpp new file mode 100644 index 000000000..7c5fe5eda --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLBinaryLogicalOp.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLBinaryLogicalOp.h" + +#include "arm_compute/core/CL/kernels/CLBinaryLogicalOpKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +void CLBinaryLogicalOp::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, + BinaryLogicalOperation op) +{ + auto k = arm_compute::support::cpp14::make_unique<CLBinaryLogicalOpKernel>(); + k->configure(input1, input2, output, op); + _kernel = std::move(k); + + if (output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + if (broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLCast.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLCast.cpp index e1059ab53..8e106737c 100644 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLCast.cpp +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLCast.cpp @@ -17,7 +17,6 @@ #include "arm_compute/runtime/CL/functions/CLCast.h" #include "arm_compute/core/CL/kernels/CLCastKernel.h" -#include "support/ToolchainSupport.h" using namespace arm_compute; diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLComparisonOp.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLComparisonOp.cpp new file mode 100644 index 000000000..f6a745a25 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLComparisonOp.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLComparisonOp.h" + +#include "arm_compute/core/CL/kernels/CLComparisonOpKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +void CLComparisonOp::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, + const ComparisonOperation &op) +{ + auto k = arm_compute::support::cpp14::make_unique<CLComparisonOpKernel>(); + k->configure(input1, input2, output, op); + _kernel = std::move(k); + + if (output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + + if (broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLDepthToSpace.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLDepthToSpace.cpp new file mode 100644 index 000000000..c2e4ca9ff --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLDepthToSpace.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLDepthToSpace.h" + +#include "arm_compute/core/CL/kernels/CLDepthToSpaceKernel.h" + +using namespace arm_compute; + +void CLDepthToSpace::configure(ICLTensor *input, ICLTensor *output, const int32_t block_size) +{ + auto k = arm_compute::support::cpp14::make_unique<CLDepthToSpaceKernel>(); + k->configure(input, output, block_size); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLEmbeddingLookup.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLEmbeddingLookup.cpp new file mode 100644 index 000000000..2781784ca --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLEmbeddingLookup.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLEmbeddingLookup.h" + +#include "arm_compute/core/CL/kernels/CLEmbeddingLookupKernel.h" + +using namespace arm_compute; + +void CLEmbeddingLookup::configure(const ICLTensor *input, ICLTensor *output, + const ICLTensor *lookups) +{ + auto k = arm_compute::support::cpp14::make_unique<CLEmbeddingLookupKernel>(); + k->configure(input, output, lookups); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLExp.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLExp.cpp new file mode 100644 index 000000000..411fa8700 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLExp.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLExp.h" + +#include "arm_compute/core/CL/kernels/CLExpKernel.h" + +using namespace arm_compute; + +void CLExp::configure(const ICLTensor *input, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLExpKernel>(); + k->configure(input, output); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLGather.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLGather.cpp index 5552cbc6f..fb056fe45 100644 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLGather.cpp +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLGather.cpp @@ -16,11 +16,7 @@ */ #include "arm_compute/runtime/CL/functions/CLGather.h" -#include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/kernels/CLGatherKernel.h" -#include "support/ToolchainSupport.h" - -#include <utility> using namespace arm_compute; diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLHashtableLookup.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLHashtableLookup.cpp new file mode 100644 index 000000000..7180e9356 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLHashtableLookup.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLHashtableLookup.h" + +#include "arm_compute/core/CL/kernels/CLHashtableLookupKernel.h" + +using namespace arm_compute; + +void CLHashtableLookup::configure(const ICLTensor *lookups, const ICLTensor *keys, + const ICLTensor *input, ICLTensor *output, ICLTensor *hits) +{ + auto k = arm_compute::support::cpp14::make_unique<CLHashtableLookupKernel>(); + k->configure(lookups, keys, input, output, hits); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLNeg.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLNeg.cpp new file mode 100644 index 000000000..be35ea732 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLNeg.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLNeg.h" + +#include "arm_compute/core/CL/kernels/CLNegKernel.h" + +using namespace arm_compute; + +void CLNeg::configure(ICLTensor *input, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLNegKernel>(); + k->configure(input, output); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLNormalizationLayerEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLNormalizationLayerEx.cpp new file mode 100644 index 000000000..276c4557a --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLNormalizationLayerEx.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLNormalizationLayerEx.h" + +#include "arm_compute/runtime/CL/CLScheduler.h" + +using namespace arm_compute; + +CLNormalizationLayerEx::CLNormalizationLayerEx() : _norm_kernel(), _border_handler() {} + +void CLNormalizationLayerEx::configure(ICLTensor *input, ICLTensor *output, + const NormalizationLayerInfo &norm_info) +{ + ARM_COMPUTE_ERROR_ON(input == nullptr); + + // Configure normalization kernel + _norm_kernel.configure(input, output, norm_info); + + // Fill the border by 3 elements since we need vload4 in the IN_MAP normalization kernel + _border_handler.configure(input, _norm_kernel.border_size(), BorderMode::CONSTANT, PixelValue(0)); +} + +Status CLNormalizationLayerEx::validate(const ITensorInfo *input, const ITensorInfo *output, + const NormalizationLayerInfo &norm_info) +{ + return CLNormalizationLayerExKernel::validate(input, output, norm_info); +} + +void CLNormalizationLayerEx::run() +{ + // Run border handler + CLScheduler::get().enqueue(_border_handler, false); + + // Run normalization kernel + CLScheduler::get().enqueue(_norm_kernel); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLPReLU.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLPReLU.cpp new file mode 100644 index 000000000..38adedd10 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLPReLU.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLPReLU.h" + +#include "arm_compute/core/CL/kernels/CLPReLUKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +void CLPReLU::configure(ICLTensor *input, ICLTensor *alpha, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLPReLUKernel>(); + k->configure(input, alpha, output); + _kernel = std::move(k); + + if (output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input->info()->dimension(0) == 1) ? input : alpha; + + if (broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLPadLayerEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLPadLayerEx.cpp new file mode 100644 index 000000000..5265b6c34 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLPadLayerEx.cpp @@ -0,0 +1,28 @@ +/* +* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +* Copyright (c) 2016-2018 ARM Limited. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ +#include "arm_compute/runtime/CL/functions/CLPadLayerEx.h" + +#include "arm_compute/core/CL/kernels/CLPadLayerKernel.h" + +using namespace arm_compute; + +void CLPadLayerEx::configure(ICLTensor *input, ICLTensor *output, ICLTensor *pad_size) +{ + auto k = arm_compute::support::cpp14::make_unique<CLPadLayerKernel>(); + k->configure(input, output, pad_size); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLPermuteEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLPermuteEx.cpp new file mode 100644 index 000000000..fb363270d --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLPermuteEx.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLPermuteEx.h" + +#include "arm_compute/core/CL/kernels/CLPermuteExKernel.h" + +using namespace arm_compute; + +void CLPermuteEx::configure(const ICLTensor *input, ICLTensor *output, + const PermutationVector &perm) +{ + auto k = arm_compute::support::cpp14::make_unique<CLPermuteExKernel>(); + k->configure(input, output, perm); + _kernel = std::move(k); +} + +Status CLPermuteEx::validate(const ITensorInfo *input, const ITensorInfo *output, + const PermutationVector &perm) +{ + ARM_COMPUTE_RETURN_ON_ERROR(CLPermuteExKernel::validate(input, output, perm)); + return Status{}; +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLPixelWiseDivision.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLPixelWiseDivision.cpp index e1add5e90..dc0baa8dd 100644 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLPixelWiseDivision.cpp +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLPixelWiseDivision.cpp @@ -18,9 +18,6 @@ #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/kernels/CLPixelWiseDivisionKernel.h" -#include "support/ToolchainSupport.h" - -#include <utility> using namespace arm_compute; diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceMax.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceMax.cpp deleted file mode 100644 index 3382058db..000000000 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceMax.cpp +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/runtime/CL/functions/CLReduceMax.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "support/ToolchainSupport.h" -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/kernels/CLReduceMaxKernel.h" - -#include <vector> -#include <algorithm> - -#include <utility> - -#define REDUCE_MAX_RUN_ON_CPU 1 - -namespace arm_compute -{ - -CLReduceMax::CLReduceMax() : _axis(0), _input(nullptr), _output(nullptr), _kernel(nullptr) {} - -void CLReduceMax::configure(ICLTensor *input, int axis, ICLTensor *output) -{ - _axis = axis; - - _input = input; - _output = output; - - auto k = arm_compute::support::cpp14::make_unique<CLReduceMaxKernel>(); - k->configure(input, axis, output); - _kernel = std::move(k); - - // We can handle for simple case only - // Output rank: 1 - // Axis: one axis value, restrict to 1 - ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().num_dimensions() != 2); - ARM_COMPUTE_ERROR_ON(output->info()->tensor_shape().num_dimensions() != 1); - ARM_COMPUTE_ERROR_ON(axis != 1); -} - -Status CLReduceMax::validate(const ITensorInfo *input, int32_t axis, const ITensorInfo *output) -{ - return CLReduceMaxKernel::validate(input, axis, output); -} - -void CLReduceMax::run() -{ -#if REDUCE_MAX_RUN_ON_CPU - run_on_cpu(); - - arm_compute::CLScheduler::get().sync(); -#else - arm_compute::CLScheduler::get().enqueue(*_kernel); -#endif -} - -void CLReduceMax::run_on_cpu() -{ - cl::CommandQueue q = CLScheduler::get().queue(); - - _input->map(q); - _output->map(q); - - // Compute by CPU for simple case - // Input rank: 2 - // Output rank: 1 - // Axis: one axis value, restrict to 1 - - float *input_data = (float *)_input->buffer(); - float *output_data = (float *)_output->buffer(); - - std::vector<float> container_max; - int cols = _input->info()->tensor_shape()[0]; - int rows = _input->info()->tensor_shape()[1]; - container_max.resize(rows); - - // Initialize as 1st element in row - float *input_pointer = input_data; - for (int i = 0; i < rows; i++) - { - container_max[i] = *input_pointer; - input_pointer += cols; - } - - // Update max value in row - for (int i = 0; i < rows; i++) - { - float max_in_row = container_max[i]; - for (int j = 1; j < cols; j++) - { - if (max_in_row < input_data[i * cols + j]) - { - max_in_row = input_data[i * cols + j]; - } - } - container_max[i] = max_in_row; - } - - for (int i = 0; i < rows; i++) - { - output_data[i] = container_max[i]; - } - - _input->unmap(q); - _output->unmap(q); -} -} // namespace arm_compute diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp new file mode 100644 index 000000000..2b8d82706 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLReduceOperation.h" + +#include "arm_compute/core/CL/kernels/CLReduceOperationKernel.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +using namespace arm_compute; + +CLReduceOperation::CLReduceOperation() + : _input(nullptr), _output(nullptr), _axis(), _interm_tensors(), _reduce_kernels() +{ +} + +Status CLReduceOperation::validate(const ITensorInfo *input, const ITensorInfo *output, + const std::set<uint32_t> &axis, const ReduceOperation &op) +{ + const size_t num_of_kernels = axis.size(); + const size_t num_of_interm_tensors = num_of_kernels - 1; + + // Create temporary tensor infos + auto interm_tensors = + arm_compute::support::cpp14::make_unique<TensorInfo[]>(num_of_interm_tensors); + + // Create intermediate tensor info + TensorShape shape{input->tensor_shape()}; + + auto it = axis.begin(); + for (size_t i = 0; i < num_of_interm_tensors; ++i, ++it) + { + shape.set(*it, 1); + interm_tensors[i].set_data_type(input->data_type()); + interm_tensors[i].set_tensor_shape(shape); + interm_tensors[i].set_num_channels(input->num_channels()); + } + + // Set a vector that is ordered ITensorInfo sequentially. + std::vector<const ITensorInfo *> tensors; + tensors.emplace_back(input); + for (size_t i = 0; i < num_of_interm_tensors; ++i) + { + tensors.emplace_back(interm_tensors.get() + i); + } + tensors.emplace_back(output); + + // Validate ReduceOperation only on all kernels + it = axis.begin(); + for (size_t i = 0; i < num_of_kernels; ++i, ++it) + { + ARM_COMPUTE_RETURN_ON_ERROR( + CLReduceOperationKernel::validate(tensors[i], tensors[i + 1], *it, op)); + } + + return Status{}; +} + +void CLReduceOperation::configure(ICLTensor *input, ICLTensor *output, + const std::set<uint32_t> &axis, ReduceOperation op) +{ + ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), axis, op)); + + _axis = axis; + + _input = input; + _output = output; + + // NOTE The axis must have no duplication. + const size_t num_of_kernels = axis.size(); + const size_t num_of_interm_tensors = num_of_kernels - 1; + + _interm_tensors = arm_compute::support::cpp14::make_unique<CLTensor[]>(num_of_interm_tensors); + _reduce_kernels = + arm_compute::support::cpp14::make_unique<CLReduceOperationKernel[]>(num_of_kernels); + + TensorShape shape{input->info()->tensor_shape()}; + auto it = axis.begin(); + for (size_t i = 0; i < num_of_interm_tensors; ++i, ++it) + { + shape.set(*it, 1); + _interm_tensors[i].allocator()->init( + TensorInfo(shape, input->info()->num_channels(), input->info()->data_type())); + _interm_tensors[i].allocator()->allocate(); + } + + // Set a vector that is ordered ICLTensors sequentially. + std::vector<ICLTensor *> tensors; + tensors.emplace_back(input); + for (size_t i = 0; i < num_of_interm_tensors; ++i) + { + tensors.emplace_back(_interm_tensors.get() + i); + } + tensors.emplace_back(output); + + // Apply ReduceOperation on all kernels + it = axis.begin(); + for (size_t i = 0; i < num_of_kernels; ++i, ++it) + { + _reduce_kernels[i].configure(tensors[i], tensors[i + 1], *it, op); + } +} + +void CLReduceOperation::run() +{ + const size_t num_of_kernels = _axis.size(); + for (size_t i = 0; i < num_of_kernels; ++i) + { + CLScheduler::get().enqueue(_reduce_kernels[i]); + } +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLReductionMean.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLReductionMean.cpp deleted file mode 100644 index ab724e752..000000000 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLReductionMean.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017-2018 ARM Limited. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/runtime/CL/functions/CLReductionMean.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/kernels/CLReductionMeanKernel.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/PixelValue.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/runtime/CL/CLScheduler.h" -#include "arm_compute/runtime/Tensor.h" -#include "support/ToolchainSupport.h" - -using namespace arm_compute; - -CLReductionMean::CLReductionMean() : _reduction_mean_kernel(), _fill_border_kernel() {} - -Status CLReductionMean::validate(const ITensorInfo *input, const ITensorInfo *output, - std::vector<uint32_t> axis) -{ - ARM_COMPUTE_RETURN_ON_ERROR(CLReductionMeanKernel::validate(input, output, axis)); - return Status{}; -} - -void CLReductionMean::configure(ICLTensor *input, ICLTensor *output, std::vector<uint32_t> axis) -{ - _reduction_mean_kernel.configure(input, output, axis); - _fill_border_kernel.configure(input, _reduction_mean_kernel.border_size(), BorderMode::CONSTANT, - PixelValue(0)); -} - -void CLReductionMean::run() -{ - CLScheduler::get().enqueue(_fill_border_kernel); - CLScheduler::get().enqueue(_reduction_mean_kernel); -} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp new file mode 100644 index 000000000..c03826891 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLSpaceToBatchND.h" + +#include "arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h" + +using namespace arm_compute; + +void CLSpaceToBatchND::configure(const ICLTensor *input, const ICLTensor *block_size, + const ICLTensor *padding_size, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLSpaceToBatchNDKernel>(); + k->configure(input, block_size, padding_size, output); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToDepth.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToDepth.cpp new file mode 100644 index 000000000..0f455f96f --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLSpaceToDepth.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLSpaceToDepth.h" + +#include "arm_compute/core/CL/kernels/CLSpaceToDepthKernel.h" + +using namespace arm_compute; + +void CLSpaceToDepth::configure(ICLTensor *input, ICLTensor *output, const int32_t block_size) +{ + auto k = arm_compute::support::cpp14::make_unique<CLSpaceToDepthKernel>(); + k->configure(input, output, block_size); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLSquaredDifference.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLSquaredDifference.cpp new file mode 100644 index 000000000..dc6e4af44 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLSquaredDifference.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLSquaredDifference.h" + +#include "arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" + +using namespace arm_compute; + +void CLSquaredDifference::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLSquaredDifferenceKernel>(); + k->configure(input1, input2, output); + _kernel = std::move(k); + + if (output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + + if (broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSlice.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSlice.cpp deleted file mode 100644 index cd576cec1..000000000 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSlice.cpp +++ /dev/null @@ -1,307 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (c) 2017 ARM Limited. - * Copyright 2018 The TensorFlow Authors. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "arm_compute/runtime/CL/functions/CLStridedSlice.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/kernels/CLStridedSliceKernel.h" -#include "arm_compute/core/utils/misc/Utility.h" -#include "arm_compute/runtime/CL/CLScheduler.h" -#include "support/ToolchainSupport.h" -#include <vector> - -using namespace arm_compute; - -static const int32_t maxDims = 4; - -// Return the index for the first element along that axis. This index will be a -// positive integer between [0, axisSize - 1] that can be used to index -// directly into the data. -inline int32_t StartForAxis(int32_t beginMask, std::vector<int32_t> const &startIndices, - std::vector<int32_t> const &strides, const TensorShape &inputShape, - int32_t axis) -{ - // Begin with the specified index - int32_t start = startIndices[axis]; - - // beginMask override - if (beginMask & 1 << axis) - { - if (strides[axis] > 0) - { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axisSize-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits<int32_t>::lowest(); - } - else - { - // Backward iteration - use the last element. - start = std::numeric_limits<int32_t>::max(); - } - } - - // Handle negative indices - int32_t axisSize = inputShape[axis]; - if (start < 0) - { - start += axisSize; - } - - // Clamping - start = arm_compute::utility::clamp(start, 0, axisSize - 1); - - return start; -} - -// Return the "real" index for the end of iteration along that axis. This is an -// "end" in the traditional C sense, in that it points to one past the last -// element. ie. So if you were iterating through all elements of a 1D array of -// size 4, this function would return 4 as the stop, because it is one past the -// "real" indices of 0, 1, 2 & 3. -inline int32_t StopForAxis(int32_t endMask, std::vector<int32_t> const &stopIndices, - std::vector<int32_t> const &strides, const TensorShape &inputShape, - int32_t axis) -{ - // Begin with the specified index - int32_t stop = stopIndices[axis]; - - // endMask override - if (endMask & (1 << axis)) - { - if (strides[axis] > 0) - { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits<int32_t>::max(); - } - else - { - // Backward iteration - use the first element. - stop = std::numeric_limits<int32_t>::lowest(); - } - } - - // Handle negative indices - int32_t axisSize = inputShape[axis]; - if (stop < 0) - { - stop += axisSize; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) - { - // Forward iteration - stop = arm_compute::utility::clamp(stop, 0, axisSize); - } - else - { - // Backward iteration - stop = arm_compute::utility::clamp(stop, -1, axisSize - 1); - } - - return stop; -} - -inline int32_t offset4D(const TensorShape &shape, int32_t b, int32_t d, int32_t h, int32_t w) -{ - int32_t offset = b * shape[2] * shape[1] * shape[0]; - offset += d * shape[1] * shape[0]; - offset += h * shape[0]; - offset += w; - return offset; -} - -void CLStridedSlice::configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, - ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, - int32_t endMask, int32_t shrinkAxisMask) -{ - auto k = arm_compute::support::cpp14::make_unique<CLStridedSliceKernel>(); - k->configure(input, output, beginData, endData, stridesData, beginMask, endMask, shrinkAxisMask); - _kernel = std::move(k); -} - -void CLStridedSliceCPU::configure(ICLTensor *input, ICLTensor *output, ICLTensor *beginData, - ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, - int32_t endMask, int32_t shrinkAxisMask) -{ - ARM_COMPUTE_ERROR_THROW_ON(CLStridedSliceKernel::validate( - input->info(), output->info(), beginData->info(), endData->info(), stridesData->info(), - beginMask, endMask, shrinkAxisMask)); - - _input = input; - _output = output; - _beginData = beginData; - _endData = endData; - _stridesData = stridesData; - _beginMask = beginMask; - _endMask = endMask; - _shrinkAxisMask = shrinkAxisMask; -} - -void CLStridedSliceCPU::run() -{ - run_on_cpu(); - - arm_compute::CLScheduler::get().sync(); -} - -inline int32_t getOutDim(int32_t start, int32_t stop, int32_t stride) -{ - if (stride > 0) - { - return ((stop - start - 1) / stride) + 1; - } - else - { - return ((stop - start + 1) / stride) + 1; - } -} - -template <typename T> -inline void StridedSlice(const T *inputData, const TensorShape &inputShape, int32_t beginMask, - int32_t endMask, const std::vector<int32_t> &startIndices, - const std::vector<int32_t> &stopIndices, - const std::vector<int32_t> &strides, T *outputData) -{ - ARM_COMPUTE_ERROR_ON(startIndices.size() != maxDims); - ARM_COMPUTE_ERROR_ON(stopIndices.size() != maxDims); - ARM_COMPUTE_ERROR_ON(strides.size() != maxDims); - - const int32_t start_b = StartForAxis(beginMask, startIndices, strides, inputShape, 3); - const int32_t stop_b = StopForAxis(endMask, stopIndices, strides, inputShape, 3); - const int32_t start_d = StartForAxis(beginMask, startIndices, strides, inputShape, 2); - const int32_t stop_d = StopForAxis(endMask, stopIndices, strides, inputShape, 2); - const int32_t start_h = StartForAxis(beginMask, startIndices, strides, inputShape, 1); - const int32_t stop_h = StopForAxis(endMask, stopIndices, strides, inputShape, 1); - const int32_t start_w = StartForAxis(beginMask, startIndices, strides, inputShape, 0); - const int32_t stop_w = StopForAxis(endMask, stopIndices, strides, inputShape, 0); - - // The shape of outputData may collapse in one-dimension. - // Therefore, it is necessary to create a shape that matches the result of the outputData. - TensorShape outputShape( - getOutDim(start_w, stop_w, strides[0]), getOutDim(start_h, stop_h, strides[1]), - getOutDim(start_d, stop_d, strides[2]), getOutDim(start_b, stop_b, strides[3])); - for (int32_t in_b = start_b, b = 0; strides[3] > 0 ? in_b < stop_b : in_b > stop_b; - in_b += strides[3], b++) - { - for (int32_t in_d = start_d, d = 0; strides[2] > 0 ? in_d < stop_d : in_d > stop_d; - in_d += strides[2], d++) - { - for (int32_t in_h = start_h, h = 0; strides[1] > 0 ? in_h < stop_h : in_h > stop_h; - in_h += strides[1], h++) - { - for (int32_t in_w = start_w, w = 0; strides[0] > 0 ? in_w < stop_w : in_w > stop_w; - in_w += strides[0], w++) - { - outputData[offset4D(outputShape, b, d, h, w)] = - inputData[offset4D(inputShape, in_b, in_d, in_h, in_w)]; - } - } - } - } -} - -void CLStridedSliceCPU::run_on_cpu() -{ - // TODO: Support shrinkAxisMask - cl::CommandQueue q = CLScheduler::get().queue(); - - _input->map(q); - _output->map(q); - _beginData->map(q); - _endData->map(q); - _stridesData->map(q); - - TensorShape inputShape = _input->info()->tensor_shape(); - TensorShape outputShape = _output->info()->tensor_shape(); - - std::vector<int32_t> starts; - std::vector<int32_t> stops; - std::vector<int32_t> strides; - - for (uint32_t idx = 0; idx <= _input->info()->num_dimensions() - 1; ++idx) - { - starts.emplace_back(reinterpret_cast<int32_t *>(_beginData->buffer())[idx]); - stops.emplace_back(reinterpret_cast<int32_t *>(_endData->buffer())[idx]); - strides.emplace_back(reinterpret_cast<int32_t *>(_stridesData->buffer())[idx]); - } - - for (uint32_t i = _input->info()->num_dimensions(); i < maxDims; i++) - { - starts.emplace_back(0); - stops.emplace_back(1); - strides.emplace_back(1); - } - - switch (_input->info()->data_type()) - { - case DataType::U8: - case DataType::QASYMM8: - StridedSlice(reinterpret_cast<const uint8_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, - reinterpret_cast<uint8_t *>(_output->buffer())); - break; - case DataType::S8: - case DataType::QS8: - StridedSlice(reinterpret_cast<const int8_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, reinterpret_cast<int8_t *>(_output->buffer())); - break; - case DataType::U16: - StridedSlice(reinterpret_cast<const uint16_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, - reinterpret_cast<uint16_t *>(_output->buffer())); - break; - case DataType::S16: - case DataType::QS16: - StridedSlice(reinterpret_cast<const int16_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, - reinterpret_cast<int16_t *>(_output->buffer())); - break; - case DataType::F16: - // Not sure this works. - StridedSlice(reinterpret_cast<const half *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, reinterpret_cast<half *>(_output->buffer())); - break; - case DataType::U32: - StridedSlice(reinterpret_cast<const uint32_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, - reinterpret_cast<uint32_t *>(_output->buffer())); - break; - case DataType::S32: - StridedSlice(reinterpret_cast<const int32_t *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, - reinterpret_cast<int32_t *>(_output->buffer())); - break; - case DataType::F32: - StridedSlice(reinterpret_cast<const float *>(_input->buffer()), inputShape, _beginMask, - _endMask, starts, stops, strides, reinterpret_cast<float *>(_output->buffer())); - break; - default: - ARM_COMPUTE_ERROR("DataType not supported"); - break; - } - - _input->unmap(q); - _output->unmap(q); - _beginData->unmap(q); - _endData->unmap(q); - _stridesData->unmap(q); -} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSliceEx.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSliceEx.cpp new file mode 100644 index 000000000..be7353493 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLStridedSliceEx.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2017 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/CL/functions/CLStridedSliceEx.h" + +#include "arm_compute/core/CL/kernels/CLStridedSliceExKernel.h" + +using namespace arm_compute; + +void CLStridedSliceEx::configure(const ICLTensor *input, ICLTensor *output, ICLTensor *beginData, + ICLTensor *endData, ICLTensor *stridesData, int32_t beginMask, + int32_t endMask, int32_t shrinkAxisMask) +{ + auto k = arm_compute::support::cpp14::make_unique<CLStridedSliceExKernel>(); + k->configure(input, output, beginData, endData, stridesData, beginMask, endMask, shrinkAxisMask); + _kernel = std::move(k); +} diff --git a/libs/ARMComputeEx/src/runtime/CL/functions/CLTopKV2.cpp b/libs/ARMComputeEx/src/runtime/CL/functions/CLTopKV2.cpp index 6426364c9..19177497c 100644 --- a/libs/ARMComputeEx/src/runtime/CL/functions/CLTopKV2.cpp +++ b/libs/ARMComputeEx/src/runtime/CL/functions/CLTopKV2.cpp @@ -15,12 +15,9 @@ * limitations under the License. */ #include "arm_compute/runtime/CL/functions/CLTopKV2.h" +#include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/CLHelpers.h" - -#include <vector> -#include <algorithm> #include "../../topk_v2.h" diff --git a/libs/ARMComputeEx/src/runtime/NEON/functions/NENormalizationLayerEx.cpp b/libs/ARMComputeEx/src/runtime/NEON/functions/NENormalizationLayerEx.cpp new file mode 100644 index 000000000..988e92715 --- /dev/null +++ b/libs/ARMComputeEx/src/runtime/NEON/functions/NENormalizationLayerEx.cpp @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2016-2018 ARM Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "arm_compute/runtime/NEON/functions/NENormalizationLayerEx.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" + +using namespace arm_compute; + +NENormalizationLayerEx::NENormalizationLayerEx(std::shared_ptr<IMemoryManager> memory_manager) + : _memory_group(std::move(memory_manager)), _norm_kernel(), _multiply_kernel(), + _border_handler(), _input_squared() +{ +} + +void NENormalizationLayerEx::configure(const ITensor *input, ITensor *output, + const NormalizationLayerInfo &norm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + + TensorInfo tensor_info(input->info()->tensor_shape(), 1, input->info()->data_type(), + input->info()->quantization_info()); + _input_squared.allocator()->init(tensor_info); + + // Manage intermediate buffers + _memory_group.manage(&_input_squared); + + // Configure kernels + _norm_kernel.configure(input, &_input_squared, output, norm_info); + _multiply_kernel.configure(input, input, &_input_squared, 1.0f, ConvertPolicy::SATURATE, + RoundingPolicy::TO_ZERO); + _border_handler.configure(&_input_squared, _norm_kernel.border_size(), BorderMode::CONSTANT, + PixelValue(0.0f)); + + // Allocate the tensor once the configure methods have been called + _input_squared.allocator()->allocate(); +} + +Status NENormalizationLayerEx::validate(const ITensorInfo *input, const ITensorInfo *output, + const NormalizationLayerInfo &norm_info) +{ + // Perform validation step + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + + ARM_COMPUTE_RETURN_ON_ERROR( + NENormalizationLayerExKernel::validate(input, input, output, norm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate( + input, input, output, 1.0f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); + + return Status{}; +} + +void NENormalizationLayerEx::run() +{ + _memory_group.acquire(); + + NEScheduler::get().schedule(&_multiply_kernel, Window::DimY); + NEScheduler::get().schedule(&_border_handler, Window::DimY); + NEScheduler::get().schedule(&_norm_kernel, Window::DimY); + + _memory_group.release(); +} diff --git a/libs/ARMComputeEx/src/runtime/topk_v2.h b/libs/ARMComputeEx/src/runtime/topk_v2.h index a18ff0b0d..f94effea1 100644 --- a/libs/ARMComputeEx/src/runtime/topk_v2.h +++ b/libs/ARMComputeEx/src/runtime/topk_v2.h @@ -15,6 +15,12 @@ * limitations under the License. */ +/** + * @file topk_v2.h + * @brief This file contains TopK method and TopContainer class for TopK operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __NNFW_RT_OPTIMIZED_OPS_TOPK_V2_H__ #define __NNFW_RT_OPTIMIZED_OPS_TOPK_V2_H__ @@ -26,34 +32,62 @@ namespace rt { namespace optimized_ops { -// The follwing codes are impemented and modified while referring to TFLite topk_v2.cc file. -// TopK_v2 of NN Runtime supports TENSOR_FLOAT32, TENSOR_QUANT8_ASYMM, TENSOR_INT32 other than -// TFLite. -//(TFLite additionaly supports kTfLiteInt64.) - -// The class that collects top indexes of k values. Based on template -// tensorflow::gtl::TopN<> but, for optimization, -// it re-uses the same container. +/** + * @brief class to define TopK operation + * @note The follwing codes are impemented and modified while referring to TFLite topk_v2.cc file. + * TopK_v2 of NN Runtime supports TENSOR_FLOAT32, TENSOR_QUANT8_ASYMM, TENSOR_INT32 other than + * TFLite. + * (TFLite additionaly supports kTfLiteInt64.) + * + * The class that collects top indexes of k values. Based on template + * tensorflow::gtl::TopN<> but, for optimization, + * it re-uses the same container. + */ template <typename T> class TopContainer { public: + /** + * @brief Prevent default constructor of of this class + */ TopContainer() = delete; + /** + * @brief Constructor with params + * @param [in] row_size Size of row in data + * @param [in] k The top k predictions + */ TopContainer(int32 k, int32 row_size) : k_(k), container_(), values_(nullptr) { container_.reserve(std::min(k, row_size) + 1); } - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /** + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * @param [in] topContainer To copy + */ TopContainer(const TopContainer &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ + /* + * @brief Prevent instances of this class from being copied (As this class contains pointers) + * @param [in] topContainer To copy + * @return Reference of TopContainer + */ TopContainer &operator=(const TopContainer &) = delete; + /** + * @brief Start collecting + * @param [in] values To set as values + * @return N/A + */ void start_collecting(const T *values) { values_ = values; container_.clear(); } + /** + * @brief Push a value to be compared for topk + * @param [in] a A value to compare + * @return N/A + */ void push(int32 a) { auto comparator = [this](int32 a, int32 b) { return compare_fun(a, b); }; @@ -74,6 +108,10 @@ public: } } + /** + * @brief Get sorted result from pushed values + * @return Reference of vector with sorted values + */ const std::vector<int32> &sorted_result() { auto comparator = [this](int32 a, int32 b) { return compare_fun(a, b); }; @@ -111,6 +149,16 @@ private: } }; +/** + * @brief Operates TopK operation with params + * @param [in] row_size Size of row in data + * @param [in] num_rows The number of rows in data + * @param [in] data To be operated in + * @param [in] k The top k predictions + * @param [out] output_indexes Indexes of targets in the top k predictions + * @param [out] output_values Values of targets in the top k predictions + * @return N/A + */ template <typename T> void TopK(int32 row_size, int32 num_rows, const T *data, int32 k, int32 *output_indexes, T *output_values) diff --git a/libs/CMakeLists.txt b/libs/CMakeLists.txt index 687159725..99d2028f4 100644 --- a/libs/CMakeLists.txt +++ b/libs/CMakeLists.txt @@ -1,3 +1,4 @@ -add_subdirectory(util) -add_subdirectory(support) -add_subdirectory(ARMComputeEx) +# Add all subdirectories. +# Each library in sub-directory must have it's own CMakeLists.txt +# to build library's binaries or to support interface. +add_subdirectories() diff --git a/libs/cpp14/CMakeLists.txt b/libs/cpp14/CMakeLists.txt new file mode 100644 index 000000000..bba9e132d --- /dev/null +++ b/libs/cpp14/CMakeLists.txt @@ -0,0 +1,2 @@ +add_library(nnfw_lib_cpp14 INTERFACE) +target_include_directories(nnfw_lib_cpp14 INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include) diff --git a/libs/cpp14/include/cpp14/memory.h b/libs/cpp14/include/cpp14/memory.h new file mode 100644 index 000000000..b3e678baa --- /dev/null +++ b/libs/cpp14/include/cpp14/memory.h @@ -0,0 +1,29 @@ +/** + * @file memory.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains @c make_unique which is not supported by C++11 + */ +#ifndef __NNFW_CPP14_MEMORY_H__ +#define __NNFW_CPP14_MEMORY_H__ + +#include <memory> + +namespace nnfw +{ +namespace cpp14 +{ +/** + * @brief Provide @c make_unique function supported from C++14 + * @param[in] args List of arguments with which an instance of T will be constructed. + * @return @c std::unique_ptr of an instance of type T + */ +template <typename T, typename... Args> std::unique_ptr<T> make_unique(Args &&... args) +{ + // NOTE std::make_unique is missing in C++11 standard + return std::unique_ptr<T>(new T(std::forward<Args>(args)...)); +} + +} // napesapce cpp14 +} // namespace nnfw + +#endif // __NNFW_CPP14_MEMORY_H__ diff --git a/libs/misc/CMakeLists.txt b/libs/misc/CMakeLists.txt new file mode 100644 index 000000000..cd01695fb --- /dev/null +++ b/libs/misc/CMakeLists.txt @@ -0,0 +1,13 @@ +# Library `nnfw_lib_misc` +set(NNFW_UTILITY_SRCS src/environment.cpp) +list(APPEND NNFW_UTILITY_SRCS src/tensor/Shape.cpp) +list(APPEND NNFW_UTILITY_SRCS src/tensor/NonIncreasingStride.cpp) +list(APPEND NNFW_UTILITY_SRCS src/tensor/IndexFormatter.cpp) +list(APPEND NNFW_UTILITY_SRCS src/tensor/Comparator.cpp) + +add_library(nnfw_lib_misc STATIC ${NNFW_UTILITY_SRCS}) +target_include_directories(nnfw_lib_misc PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) +set_target_properties(nnfw_lib_misc PROPERTIES POSITION_INDEPENDENT_CODE ON) + +add_executable(nnfw_tensor_index_iterator "examples/tensor_index_iterator.cpp") +target_link_libraries(nnfw_tensor_index_iterator nnfw_lib_misc) diff --git a/libs/misc/examples/tensor_index_iterator.cpp b/libs/misc/examples/tensor_index_iterator.cpp new file mode 100644 index 000000000..8a19dac87 --- /dev/null +++ b/libs/misc/examples/tensor_index_iterator.cpp @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "misc/tensor/IndexIterator.h" + +#include <array> + +#include <iostream> +#include <algorithm> + +#include <cassert> + +void test_iterate(void) +{ + const nnfw::misc::tensor::Shape shape{3, 4, 7}; + + std::array<int, 3 * 4 * 7> array; + + array.fill(0); + + using nnfw::misc::tensor::iterate; + using nnfw::misc::tensor::Index; + + iterate(shape) << [&](const Index &index) { + assert(index.rank() == shape.rank()); + + const size_t rank = index.rank(); + + uint32_t offset = index.at(0); + + for (size_t axis = 1; axis < rank; ++axis) + { + offset *= shape.dim(axis); + offset += index.at(axis); + } + + array[offset] += 1; + }; + + assert(std::all_of(array.begin(), array.end(), [](int num) { return num == 1; })); +} + +int main(int argc, char **argv) +{ + test_iterate(); + + nnfw::misc::tensor::Shape shape{3, 4, 3, 4}; + + std::cout << "Iterate over tensor{3, 4, 3, 4}" << std::endl; + + nnfw::misc::tensor::iterate(shape) << [](const nnfw::misc::tensor::Index &index) { + std::cout << "rank: " << index.rank() << std::endl; + + for (size_t d = 0; d < index.rank(); ++d) + { + std::cout << " offset(" << d << ") = " << index.at(d) << std::endl; + } + }; + + return 0; +} diff --git a/libs/misc/include/misc/EnvVar.h b/libs/misc/include/misc/EnvVar.h new file mode 100644 index 000000000..47206d4c0 --- /dev/null +++ b/libs/misc/include/misc/EnvVar.h @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file EnvVar.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::EnvVar class + */ + +#ifndef __NNFW_MISC_ENV_VAR__ +#define __NNFW_MISC_ENV_VAR__ + +#include <algorithm> +#include <array> +#include <cstdlib> +#include <string> + +namespace nnfw +{ +namespace misc +{ +/** + * @brief Class to access environment variable + */ +class EnvVar +{ +public: + /** + * @brief Construct a new EnvVar object + * @param[in] key environment variable + */ + EnvVar(const std::string &key) + { + const char *value = std::getenv(key.c_str()); + if (value == nullptr) + { + // An empty string is considered as an empty value + _value = ""; + } + else + { + _value = value; + } + } + + /** + * @brief Get environment variable of string type + * @param[in] def Default value of environment variable + * @return Defaut value passed as a parameter when there is no environment variable, + * otherwise the value of environment variable passed into constructor + */ + std::string asString(const std::string &def) const + { + if (_value.empty()) + return def; + return _value; + } + + /** + * @brief Get environment variable of boolean type + * @param[in] def Default value of environment variable + * @return Defaut value passed as a parameter when there is no environment variable, + * otherwise the value of environment variable passed into constructor + */ + bool asBool(bool def) const + { + if (_value.empty()) + return def; + static const std::array<std::string, 5> false_list{"0", "OFF", "FALSE", "N", "NO"}; + auto false_found = std::find(false_list.begin(), false_list.end(), _value); + return (false_found == false_list.end()); + } + + /** + * @brief Get environment variable of int type + * @param[in] def Default value of environment variable + * @return Defaut value passed as a parameter when there is no environment variable, + * otherwise the value of environment variable passed into constructor + */ + int asInt(int def) const + { + if (_value.empty()) + return def; + return std::stoi(_value); + } + +private: + std::string _value; +}; + +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_ENV_VAR__ diff --git a/libs/misc/include/misc/benchmark.h b/libs/misc/include/misc/benchmark.h new file mode 100644 index 000000000..fe5b97585 --- /dev/null +++ b/libs/misc/include/misc/benchmark.h @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file benchmark.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::benchmark::Accumulator class + */ +#ifndef __NNFW_MISC_BENCHMARK_H__ +#define __NNFW_MISC_BENCHMARK_H__ + +#include <chrono> + +namespace nnfw +{ +namespace misc +{ +// Benckmark support +namespace benchmark +{ + +/** + * @brief Class to accumulate time during benchmark + */ +template <typename T> class Accumulator +{ +public: + /** + * @brief Construct a new Accumulator object + * @param[in] ref Object to keep time duration + */ + Accumulator(T &ref) : _ref(ref) + { + // DO NOTHING + } + +public: + /** + * @brief Return the reference of @c ref passed to constructor + * @return Reference of @c ref + */ + T &operator()(void) { return _ref; } + +private: + T &_ref; +}; + +/** + * @brief Run passed function and returns accumulated time + * @tparam T Period used by @c std::chrono::duration_cast + * @tparam Callable Function type to benchmark + * @param[in] acc Accumulated time after running @cb + * @param[in] cb Function to run and benchmark + * @return Accumulated time + */ +template <typename T, typename Callable> +Accumulator<T> &operator<<(Accumulator<T> &&acc, Callable cb) +{ + auto begin = std::chrono::steady_clock::now(); + cb(); + auto end = std::chrono::steady_clock::now(); + + acc() += std::chrono::duration_cast<T>(end - begin); + + return acc; +} + +template <typename T> Accumulator<T> measure(T &out) { return Accumulator<T>(out); } + +} // namespace benchmark +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_BENCHMARK_H__ diff --git a/libs/misc/include/misc/environment.h b/libs/misc/include/misc/environment.h new file mode 100644 index 000000000..8e6bd00d5 --- /dev/null +++ b/libs/misc/include/misc/environment.h @@ -0,0 +1,130 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file environment.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains utility functions and classes to access environment variables + */ + +#ifndef __UTIL_ENVIRONMENT_H__ +#define __UTIL_ENVIRONMENT_H__ + +namespace nnfw +{ +namespace misc +{ + +/** + * @brief Get the environment variable of int type + * @param[in] name Name of the environment variable + * @param[in] defaultValue Default value when the value of environment variable does not exist + * @return The int value of the environment variable + */ +int get_env_int(const char *name, int defaultValue = 0); + +/** + * @brief Get the environment variable of bool type + * @param[in] name Name of the environment variable + * @param[in] defaultValue Default value when the value of environment variable does not exist + * @return @c 0 if the value of the environment variable is @c "0", @c 1 in case of other number + */ +bool get_env_bool(const char *name, bool defaultValue = false); +} +} + +#include <string> + +namespace nnfw +{ +namespace misc +{ +namespace env +{ +/** + * @brief Parent struct of @ref IntAccessor and @ref FloatAccessor + * @tparam T Type of the value of environment variable + */ +template <typename T> struct Accessor +{ + /** + * @brief Destroy the Accessor object + */ + virtual ~Accessor() = default; + /** + * @brief Read the value of environment variable + * @param[out] out The value of environment variable + * @return @c true if accessing environment variable is successful, + * @c false if there is exist no such environment variable + */ + virtual bool access(T &out) const = 0; +}; + +/** + * @brief Class to read int environment variable + */ +class IntAccessor : public Accessor<int> +{ +public: + /** + * @brief Construct a new IntAccessor object + * @param[in] tag Name of environment variable + */ + IntAccessor(const std::string &tag); + +public: + /** + * @brief Read the value of environment variable + * @param[out] out The value of environment variable + * @return @c true if accessing environment variable is successful, + * @c false if there is exist no such environment variable + */ + bool access(int &out) const override; + +private: + std::string _tag; +}; + +/** + * @brief Class to read float environment variable + */ +class FloatAccessor : public Accessor<float> +{ +public: + /** + * @brief Construct a new FloatAccessor object + * @param[in] tag Name of environment variable + */ + FloatAccessor(const std::string &tag); + +public: + /** + * @brief Read the value of environment variable + * @param[out] out The value of environment variable + * @return @c true if accessing environment variable is successful, + * @c false if there is exist no such environment variable + */ + bool access(float &out) const override; + +private: + std::string _tag; +}; + +} // namespace env +} // namespace misc +} // namespace nnfw + +#endif // __UTIL_ENVIRONMENT_H__ diff --git a/libs/misc/include/misc/feature/Index.h b/libs/misc/include/misc/feature/Index.h new file mode 100644 index 000000000..a361d8dd2 --- /dev/null +++ b/libs/misc/include/misc/feature/Index.h @@ -0,0 +1,137 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Index.h + * @brief This file contains Index class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_INDEX_H__ +#define __NNFW_MISC_FEATURE_INDEX_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Class to have the index information for calculating the offset. + */ +class Index +{ +public: + /** + * @brief Construct Index object using default constrcutor + */ + Index() = default; + +public: + /** + * @brief Construct Index object with three indexes of dimensions + * @param[in] ch The depth index + * @param[in] row The heigth index + * @param[in] col The width index + */ + Index(int32_t ch, int32_t row, int32_t col) : _batch{1}, _ch{ch}, _row{row}, _col{col} + { + // DO NOTHING + } + /** + * @brief Construct Index object with four indexes of dimensions + * @param[in] batch The batch index + * @param[in] ch The depth index + * @param[in] row The height index + * @param[in] col The width index + */ + Index(int32_t batch, int32_t ch, int32_t row, int32_t col) + : _batch{batch}, _ch{ch}, _row{row}, _col{col} + { + // DO NOTHING + } + +public: + /** + * @brief Get the batch index + * @return The batch index + */ + int32_t batch(void) const { return _batch; } + /** + * @brief Get the depth index + * @return The depth index + */ + int32_t ch(void) const { return _ch; } + /** + * @brief Get the height index + * @return The height index + */ + int32_t row(void) const { return _row; } + /** + * @brief Get the width index + * @return The width index + */ + int32_t col(void) const { return _col; } + +public: + /** + * @brief Get the batch index as the lvalue reference + * @return The reference of the batch value + */ + int32_t &batch(void) { return _batch; } + /** + * @brief Get the depth index as the lvalue reference + * @return The reference of the depth value + */ + int32_t &ch(void) { return _ch; } + /** + * @brief Get the height index as the lvalue reference + * @return The reference of the height value + */ + int32_t &row(void) { return _row; } + /** + * @brief Get the width index as the lvalue reference + * @return The reference of the width value + */ + int32_t &col(void) { return _col; } + +private: + /** + * @brief The batch index + */ + int32_t _batch; + /** + * @brief The depth index + */ + int32_t _ch; + /** + * @brief The height index + */ + int32_t _row; + /** + * @brief The width index + */ + int32_t _col; +}; + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_INDEX_H__ diff --git a/libs/misc/include/misc/feature/IndexIterator.h b/libs/misc/include/misc/feature/IndexIterator.h new file mode 100644 index 000000000..1cf675526 --- /dev/null +++ b/libs/misc/include/misc/feature/IndexIterator.h @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexIterator.h + * @brief This file contains IndexIterator class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_INDEX_ITERATOR_H__ +#define __NNFW_MISC_FEATURE_INDEX_ITERATOR_H__ + +#include "misc/feature/Shape.h" + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Class to iterate Callable with Index of feature + */ +class IndexIterator +{ +public: + /** + * @brief Construct IndexIterator object with Shape of feature + * @param[in] shape Shape reference of feature + */ + IndexIterator(const Shape &shape) : _shape{shape} + { + // DO NOTHING + } + +public: + /** + * @brief Call a function iterated + * @param[in] cb A callback function + * @return Current IndexIterator object + */ + template <typename Callable> IndexIterator &iter(Callable cb) + { + for (int32_t batch = 0; batch < _shape.N; ++batch) + { + for (int32_t ch = 0; ch < _shape.C; ++ch) + { + for (int32_t row = 0; row < _shape.H; ++row) + { + for (int32_t col = 0; col < _shape.W; ++col) + { + cb(batch, ch, row, col); + } + } + } + } + + return (*this); + } + +private: + /** + * @brief Shape for feature + */ + const Shape _shape; +}; + +/** + * @brief Create an object of IndexIterator for feature + * @param[in] Shape reference of feature + * @return Created IndexIterator object + */ +static inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } + +/** + * @brief Call a function iterated using IndexIterator of feature + * Overloaded operator<< + * @param[in] it An IndexIterator reference + * @param[in] cb A callback function + * @return created IndexIterator object + */ +template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) +{ + return it.iter(cb); +} + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_INDEX_ITERATOR_H__ diff --git a/libs/misc/include/misc/feature/Object.h b/libs/misc/include/misc/feature/Object.h new file mode 100644 index 000000000..7af0e28f4 --- /dev/null +++ b/libs/misc/include/misc/feature/Object.h @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Object.h + * @brief This file contains Object class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_OBJECT_H__ +#define __NNFW_MISC_FEATURE_OBJECT_H__ + +#include "misc/feature/Shape.h" +#include "misc/feature/Index.h" +#include "misc/feature/Reader.h" + +#include <vector> + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Class to have information of the operand for feature + */ +template <typename T> class Object final : public Reader<T> +{ +public: + using Generator = std::function<T(const Shape &shape, const Index &index)>; + +public: + /** + * @brief Construct Object object with Shape of feature and set value used by Generator + * @param[in] shape Reference of Shape for feature + * @param[in] fn A function to set values of operand tensor + */ + Object(const Shape &shape, const Generator &fn) : _shape{shape} + { + _value.resize(_shape.C * _shape.H * _shape.W); + + for (int32_t ch = 0; ch < _shape.C; ++ch) + { + for (int32_t row = 0; row < _shape.H; ++row) + { + for (int32_t col = 0; col < _shape.W; ++col) + { + _value.at(offsetOf(ch, row, col)) = fn(_shape, Index{ch, row, col}); + } + } + } + } + +public: + /** + * @brief Get Shape of feature as the reference + * @return The reference of the width value + */ + const Shape &shape(void) const { return _shape; } + +public: + /** + * @brief Get the value used by three indexes + * @param[in] ch The depth index + * @param[in] row The height index + * @param[in] col The width index + * @return The value at the offset + */ + T at(uint32_t ch, uint32_t row, uint32_t col) const override + { + return _value.at(offsetOf(ch, row, col)); + } + +private: + /** + * @brief Get the offset value at three indexes + * @param[in] ch The depth index + * @param[in] row The height index + * @param[in] col The width index + * @return The offset value + */ + uint32_t offsetOf(uint32_t ch, uint32_t row, uint32_t col) const + { + return ch * _shape.H * _shape.W + row * _shape.W + col; + } + +private: + /** + * @brief Shape of operand + */ + Shape _shape; + /** + * @brief The tensor vector of operand + */ + std::vector<T> _value; +}; + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_OBJECT_H__ diff --git a/libs/misc/include/misc/feature/Reader.h b/libs/misc/include/misc/feature/Reader.h new file mode 100644 index 000000000..b09209789 --- /dev/null +++ b/libs/misc/include/misc/feature/Reader.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Reader.h + * @brief This file contains Reader class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_READER_H__ +#define __NNFW_MISC_FEATURE_READER_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Class reads values of feature + * The interface class + */ +template <typename T> struct Reader +{ + /** + * @brief Destruct Reader object using default destructor + */ + virtual ~Reader() = default; + + /** + * @brief Get the value used by three indexes + * @param[in] ch The depth index + * @param[in] row The height index + * @param[in] col The width index + * @return The value at the offset + */ + virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; + /** + * @brief Get the value used by four indexes + * @param[in] batch The batch index + * @param[in] ch The depth index + * @param[in] row The height index + * @param[in] col The width index + * @return The value at the offset + */ + virtual T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const = 0; +}; + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_READER_H__ diff --git a/libs/misc/include/misc/feature/Shape.h b/libs/misc/include/misc/feature/Shape.h new file mode 100644 index 000000000..09881f58b --- /dev/null +++ b/libs/misc/include/misc/feature/Shape.h @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Shape.h + * @brief This file contains Shape class for feature + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_SHAPE_H__ +#define __NNFW_MISC_FEATURE_SHAPE_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Structure to have values of dimensions for feature + */ +struct Shape +{ + int32_t N; /**< The batch value */ + int32_t C; /**< The depth value */ + int32_t H; /**< The height value */ + int32_t W; /**< The width value */ + + /** + * @brief Construct Shape object using default constrcutor + */ + Shape() = default; + /** + * @brief Construct Shape object with three values of dimensions + * @param[in] depth The depth value + * @param[in] height The height value + * @param[in] width The width value + */ + Shape(int32_t depth, int32_t height, int32_t width) : N{1}, C{depth}, H{height}, W{width} + { + // DO NOTHING + } + /** + * @brief Construct Shape object with four values of dimensions + * @param[in] batch The batch value + * @param[in] depth The depth value + * @param[in] height The height value + * @param[in] width The width value + */ + Shape(int32_t batch, int32_t depth, int32_t height, int32_t width) + : N{batch}, C{depth}, H{height}, W{width} + { + // DO NOTHING + } +}; + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_H__ diff --git a/libs/misc/include/misc/feature/TextFormatter.h b/libs/misc/include/misc/feature/TextFormatter.h new file mode 100644 index 000000000..e053f1c61 --- /dev/null +++ b/libs/misc/include/misc/feature/TextFormatter.h @@ -0,0 +1,116 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TextFormatter.h + * @brief This file contains TextFormatter class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FEATURE_TEXT_FORMATTER_H__ +#define __NNFW_MISC_FEATURE_TEXT_FORMATTER_H__ + +#include "misc/feature/Shape.h" +#include "misc/feature/Reader.h" + +#include <ostream> +#include <iomanip> +#include <limits> + +namespace nnfw +{ +namespace misc +{ +namespace feature +{ + +/** + * @brief Class to print operand of feature to ostream in the given string format + */ +template <typename T> class TextFormatter +{ +public: + /** + * @brief Construct TextFormatter object with an operand's information. + * @param[in] shape The shape of an operand + * @param[in] data The data of an operand + */ + TextFormatter(const Shape &shape, const Reader<T> &data) : _shape(shape), _data(data) + { + // DO NOTHING + } + +public: + /** + * @brief Get Shape of feature as the lvalue reference + * @return Shape of feature + */ + const Shape &shape(void) const { return _shape; } + /** + * @brief Get Reader<T> that can read the data of an operand + * @return Reader<T> + */ + const Reader<T> &data(void) const { return _data; } + +private: + /** + * @brief Shape of feature + */ + const Shape &_shape; + /** + * @brief Reader<T> that can read the data of an operand + */ + const Reader<T> &_data; +}; + +/** + * @brief Print operand of feature + * @param[in] os Standard output stream + * @param[in] fmt TextFormatter to print information of an operand + * @return Standard output stream + */ +template <typename T> std::ostream &operator<<(std::ostream &os, const TextFormatter<T> &fmt) +{ + const auto &shape = fmt.shape(); + + for (uint32_t ch = 0; ch < shape.C; ++ch) + { + os << " Channel " << ch << ":" << std::endl; + for (uint32_t row = 0; row < shape.H; ++row) + { + os << " "; + for (uint32_t col = 0; col < shape.W; ++col) + { + const auto value = fmt.data().at(ch, row, col); + os << std::right; + os << std::fixed; + os << std::setw(std::numeric_limits<T>::digits10 + 2); + os << std::setprecision(5); + os << value; + os << " "; + } + os << std::endl; + } + } + + return os; +} + +} // namespace feature +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_TEXT_FORMATTER_H__ diff --git a/libs/misc/include/misc/fp32.h b/libs/misc/include/misc/fp32.h new file mode 100644 index 000000000..c310402ba --- /dev/null +++ b/libs/misc/include/misc/fp32.h @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file fp32.h + * @brief This file contains functions to compare float values + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_FP32_H__ +#define __NNFW_MISC_FP32_H__ + +#include <cmath> +#include <cfloat> +#include <algorithm> +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace fp32 +{ + +/** + * @brief Get the difference between two float values as a relative value. + * @param[in] lhs A float value to be compared + * @param[in] rhs A float value to be compared + * @return A relative value of difference between two float values. + */ +inline float relative_diff(float lhs, float rhs) +{ + const auto diff = std::fabs(lhs - rhs); + const auto base = std::max(std::fabs(lhs), std::fabs(rhs)); + + return diff / base; +} + +/** + * @brief Verify that an obtained float value is equal to the expected float value + * by using FLT_EPSILON + * @param[in] expected An expected float value to be compared + * @param[in] obtained An obtained float value to be compared + * @param[in] tolerance A tolerance value + * @return @c true if both values are equal, otherwise @c false + */ +inline bool epsilon_equal(float expected, float obtained, uint32_t tolerance = 1) +{ + if (std::isnan(expected) && std::isnan(obtained)) + { + return true; + } + + // Let's use relative epsilon comparision + const auto diff = std::fabs(expected - obtained); + const auto max = std::max(std::fabs(expected), std::fabs(obtained)); + + return diff <= (max * FLT_EPSILON * tolerance); +} + +/** + * @brief Verify that an obtained float value is equal to the expected float value + * by comparing absolute tolerance value + * @param[in] expected An expected float value to be compared + * @param[in] obtained An obtained float value to be compared + * @param[in] tolerance A tolerance value + * @return @c true if both values are equal, otherwise @c false + */ +inline bool absolute_epsilon_equal(float expected, float obtained, float tolerance = 0.001) +{ + if (std::isnan(expected) && std::isnan(obtained)) + { + return true; + } + + // Let's use absolute epsilon comparision + const auto diff = std::fabs(expected - obtained); + + return diff <= tolerance; +} + +} // namespace fp32 +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FP32_H__ diff --git a/libs/misc/include/misc/kernel/IndexIterator.h b/libs/misc/include/misc/kernel/IndexIterator.h new file mode 100644 index 000000000..59e0f0095 --- /dev/null +++ b/libs/misc/include/misc/kernel/IndexIterator.h @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexIterator.h + * @brief This file contains IndexIterator class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_KERNEL_INDEX_ITERATOR_H__ +#define __NNFW_MISC_KERNEL_INDEX_ITERATOR_H__ + +#include "misc/kernel/Shape.h" + +namespace nnfw +{ +namespace misc +{ +namespace kernel +{ + +/** + * @brief Class to iterate Callable with Index of kernel + */ +class IndexIterator +{ +public: + /** + * @brief Construct IndexIterator object with Shape of kernel + * @param[in] shape Shape reference of feature + */ + IndexIterator(const Shape &shape) : _shape{shape} + { + // DO NOTHING + } + +public: + /** + * @brief Call a function iterated + * @param[in] cb A callback function + * @return Current IndexIterator object + */ + template <typename Callable> IndexIterator &iter(Callable cb) + { + for (int32_t nth = 0; nth < _shape.N; ++nth) + { + for (int32_t ch = 0; ch < _shape.C; ++ch) + { + for (int32_t row = 0; row < _shape.H; ++row) + { + for (int32_t col = 0; col < _shape.W; ++col) + { + cb(nth, ch, row, col); + } + } + } + } + + return (*this); + } + +private: + const Shape _shape; /**< Shape for kernel */ +}; + +/** + * @brief Create an object of IndexIterator for kernel + * @param[in] shape reference of feature + * @return Created IndexIterator object + */ +inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } + +/** + * @brief Call a function iterated using IndexIterator of kernel + * Overloaded operator<< + * @param[in] it An IndexIterator reference + * @param[in] cb A callback function + * @return Created IndexIterator object + */ +template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) +{ + return it.iter(cb); +} + +} // namespace kernel +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_INDEX_ITERATOR_H__ diff --git a/libs/misc/include/misc/kernel/RandomObject.h b/libs/misc/include/misc/kernel/RandomObject.h new file mode 100644 index 000000000..4b58b0c7f --- /dev/null +++ b/libs/misc/include/misc/kernel/RandomObject.h @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file RandomObject.h + * @brief This file contains RandomObject class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_KERNEL_RANDOM_OBJECT_H__ +#define __NNFW_MISC_KERNEL_RANDOM_OBJECT_H__ + +#include "misc/kernel/Shape.h" +#include "misc/kernel/Reader.h" + +#include <vector> + +namespace nnfw +{ +namespace misc +{ +namespace kernel +{ + +template <typename T> class RandomObject final : public Reader<T> +{ +public: + RandomObject(const Shape &shape) : _shape{shape} + { + const uint32_t size = _shape.N * _shape.C * _shape.H * _shape.W; + + // TODO Use random number + for (uint32_t off = 0; off < size; ++off) + { + _value.emplace_back(static_cast<float>(off)); + } + } + +public: + const Shape &shape(void) const { return _shape; } + +public: + T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override + { + uint32_t index = 0; + + index += nth * _shape.C * _shape.H * _shape.W; + index += ch * _shape.H * _shape.W; + index += row * _shape.W; + index += col; + + return _value.at(index); + } + +private: + const Shape _shape; + std::vector<T> _value; +}; + +} // namespace kernel +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_KERNEL_RANDOM_OBJECT_H__ diff --git a/libs/misc/include/misc/kernel/Reader.h b/libs/misc/include/misc/kernel/Reader.h new file mode 100644 index 000000000..019c809ee --- /dev/null +++ b/libs/misc/include/misc/kernel/Reader.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Reader.h + * @brief This file contains Reader structure + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_KERNEL_READER_H__ +#define __NNFW_MISC_KERNEL_READER_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace kernel +{ + +/** + * @brief Structure to Reader + */ +template <typename T> struct Reader +{ + /** + * @brief Destroy the Reader object as default + */ + virtual ~Reader() = default; + + /** + * @brief Get the value used by four indexes + * @param[in] nth The kernel index + * @param[in] ch The channel index + * @param[in] row The row index + * @param[in] col The column index + * @return The value at the offset + */ + virtual T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const = 0; +}; + +} // namespace kernel +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_KERNEL_READER_H__ diff --git a/libs/misc/include/misc/kernel/Shape.h b/libs/misc/include/misc/kernel/Shape.h new file mode 100644 index 000000000..27d6a8bf0 --- /dev/null +++ b/libs/misc/include/misc/kernel/Shape.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Shape.h + * @brief This file contains Shape structure + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_KERNEL_SHAPE_H__ +#define __NNFW_MISC_KERNEL_SHAPE_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace kernel +{ + +/** + * @brief Structure to Shape + */ +struct Shape +{ + int32_t N; /**< The kernel index */ + int32_t C; /**< The channel index */ + int32_t H; /**< The height index */ + int32_t W; /**< The width index */ + + /** + * @brief Construct a new Shape object as default + */ + Shape() = default; + + /** + * @brief Construct a new Shape object with parameters + * @param[in] count The kernel index + * @param[in] depth The channel index + * @param[in] height The height index + * @param[in] width The width index + */ + Shape(int32_t count, int32_t depth, int32_t height, int32_t width) + : N{count}, C{depth}, H{height}, W{width} + { + // DO NOTHING + } +}; + +} // namespace kernel +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_KERNEL_SHAPE_H__ diff --git a/libs/misc/include/misc/matrix/IndexIterator.h b/libs/misc/include/misc/matrix/IndexIterator.h new file mode 100644 index 000000000..742ed3a65 --- /dev/null +++ b/libs/misc/include/misc/matrix/IndexIterator.h @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexIterator.h + * @brief This file contains IndexIterator class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_MATRIX_INDEX_ITERATOR_H__ +#define __NNFW_MISC_MATRIX_INDEX_ITERATOR_H__ + +#include "misc/matrix/Shape.h" + +namespace nnfw +{ +namespace misc +{ +namespace matrix +{ + +/** + * @brief Class to iterate Callable with Index of matrix + */ +class IndexIterator +{ +public: + /** + * @brief Construct IndexIterator object with Shape of matrix + * @param[in] shape Shape reference of matrix + */ + IndexIterator(const Shape &shape) : _shape{shape} + { + // DO NOTHING + } + +public: + /** + * @brief Call a function iterated + * @param[in] cb A callback function + * @return Current IndexIterator object + */ + template <typename Callable> IndexIterator &iter(Callable cb) + { + for (uint32_t row = 0; row < _shape.H; ++row) + { + for (uint32_t col = 0; col < _shape.W; ++col) + { + cb(row, col); + } + } + + return (*this); + } + +private: + /** + * @brief Shape for matrix + */ + const Shape _shape; +}; + +/** + * @brief Create an object of IndexIterator for matrix + * @param[in] Shape reference of matrix + * @return Created IndexIterator object + */ +inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } + +/** + * @brief Call a function iterated using IndexIterator of matrix + * Overloaded operator<< + * @param[in] it An IndexIterator reference + * @param[in] cb A callback function + * @return created IndexIterator object + */ +template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) +{ + return it.iter(cb); +} + +} // namespace matrix +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_MATRIX_INDEX_ITERATOR_H__ diff --git a/libs/misc/include/misc/matrix/Reader.h b/libs/misc/include/misc/matrix/Reader.h new file mode 100644 index 000000000..ea222c9d1 --- /dev/null +++ b/libs/misc/include/misc/matrix/Reader.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Reader.h + * @brief This file contains Reader class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_MATRIX_READER_H__ +#define __NNFW_MISC_MATRIX_READER_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace matrix +{ + +/** + * @brief Class reads values of matrix + * The interface class + */ +template <typename T> struct Reader +{ + /** + * @brief Destruct Reader object using default destructor + */ + virtual ~Reader() = default; + + /** + * @brief Get the value used by two indexes + * @param[in] row The height index + * @param[in] col The width index + * @return The value at the offset + */ + virtual T at(uint32_t row, uint32_t col) const = 0; +}; + +} // namespace matrix +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_MATRIX_READER_H__ diff --git a/libs/misc/include/misc/matrix/Shape.h b/libs/misc/include/misc/matrix/Shape.h new file mode 100644 index 000000000..8cbcc1e12 --- /dev/null +++ b/libs/misc/include/misc/matrix/Shape.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Shape.h + * @brief This file contains Shape class for matrix + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_MATRIX_SHAPE_H__ +#define __NNFW_MISC_MATRIX_SHAPE_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace matrix +{ + +/** + * @brief Structure to have values of dimensions for matrix + */ +struct Shape +{ + int32_t H; /**< The height value */ + int32_t W; /**< The width value */ + + /** + * @brief Construct Shape object using default constrcutor + */ + Shape() = default; + + /** + * @brief Construct Shape object with two values of dimensions + * @param[in] height The height value + * @param[in] width The width value + */ + Shape(int32_t height, int32_t width) : H{height}, W{width} + { + // DO NOTHING + } +}; + +} // namespace matrix +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_MATRIX_SHAPE_H__ diff --git a/libs/misc/include/misc/tensor/Comparator.h b/libs/misc/include/misc/tensor/Comparator.h new file mode 100644 index 000000000..80f53043c --- /dev/null +++ b/libs/misc/include/misc/tensor/Comparator.h @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Comparator.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Comparator class + */ + +#ifndef __NNFW_MISC_TENSOR_COMPARATOR_H__ +#define __NNFW_MISC_TENSOR_COMPARATOR_H__ + +#include "misc/tensor/Index.h" +#include "misc/tensor/Shape.h" +#include "misc/tensor/Reader.h" +#include "misc/tensor/Diff.h" + +#include <functional> + +#include <vector> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to compare two tensors (expected and obtained to compare) + */ +class Comparator +{ +public: + /** + * @brief Construct a new @c Comparator object + * @param[in] fn Function that compares two float values + */ + Comparator(const std::function<bool(float lhs, float rhs)> &fn) : _compare_fn{fn} + { + // DO NOTHING + } + +public: + /** + * @brief Struct to observe comparison results + */ + struct Observer + { + /** + * @brief Get notification of comparison result at every index of two tensors + * @param[in] index Index of tensors compared + * @param[in] expected Expected value of element at @c index + * @param[in] obtained Obtained value of element at @c index + * @return N/A + */ + virtual void notify(const Index &index, float expected, float obtained) = 0; + }; + +public: + /** + * @brief Compare two tensors + * @param[in] shape Shape of two tensors + * @param[in] expected @c Reader<float> object that accesses expected tensor + * @param[in] obtained @c Reader<float> object that accesses obtained tensor + * @param[in] observer @c Observer notified of expected value and obtained value at every index + * @return @c std::vector<Diff<float>> containing information of failed comparison + */ + // NOTE Observer should live longer than comparator + std::vector<Diff<float>> compare(const Shape &shape, const Reader<float> &expected, + const Reader<float> &obtained, + Observer *observer = nullptr) const; + +private: + std::function<bool(float lhs, float rhs)> _compare_fn; +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_COMPARATOR_H__ diff --git a/libs/misc/include/misc/tensor/Diff.h b/libs/misc/include/misc/tensor/Diff.h new file mode 100644 index 000000000..c41a97987 --- /dev/null +++ b/libs/misc/include/misc/tensor/Diff.h @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Diff.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Diff struct + */ + +#ifndef __NNFW_MISC_TENSOR_DIFF_H__ +#define __NNFW_MISC_TENSOR_DIFF_H__ + +#include "misc/tensor/Index.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Struct to have information after comparing two elements of two tensors + */ +template <typename T> struct Diff +{ + Index index; /**< Index of elements in two tensors, which turn out to be different */ + + T expected; /**< Expected value of element of first tensor */ + T obtained; /**< Obtained value of element of second tensor */ + + /** + * @brief Construct a new @c Diff object + * @param[in] i Initial value of index + */ + Diff(const Index &i) : index(i) + { + // DO NOTHING + } + + /** + * @brief Construct a new @c Diff object + * @param[in] i Index value + * @param[in] e Expected value of element of first tensor + * @param[in] o Obtained value of element of second tensor + */ + Diff(const Index &i, const T &e, const T &o) : index(i), expected{e}, obtained{o} + { + // DO NOTHING + } +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_DIFF_H__ diff --git a/libs/misc/include/misc/tensor/Index.h b/libs/misc/include/misc/tensor/Index.h new file mode 100644 index 000000000..a08d7099e --- /dev/null +++ b/libs/misc/include/misc/tensor/Index.h @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Index.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Index struct + */ +#ifndef __NNFW_MISC_TENSOR_INDEX_H__ +#define __NNFW_MISC_TENSOR_INDEX_H__ + +#include <cstdint> +#include <cstddef> + +#include <vector> +#include <initializer_list> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Struct to represent index of each dimension of a tensor + */ +struct Index +{ +public: + /** + * @brief Construct a new @c Index object + * @param[in] rank Rank of a tensor + */ + Index(size_t rank) { _offsets.resize(rank); } + +public: + /** + * @brief Construct a new @c Index object + * @param[in] offsets Rank of a tensor of @c std::initializer_list<int32_t> type + */ + Index(std::initializer_list<int32_t> offsets) : _offsets{offsets} + { + // DO NOTHING + } + +public: + /** + * @brief Get the rank + * @return Rank that this @c Index object can handle + */ + size_t rank(void) const { return _offsets.size(); } + +public: + /** + * @brief Get the index n'th dimension + * @param[in] n Dimension + * @return index of n'th dimension + */ + int32_t at(size_t n) const { return _offsets.at(n); } + + /** + * @brief Get the reference of the index n'th dimension + * @param[in] n Dimension + * @return reference of index of n'th dimension + */ + int32_t &at(size_t n) { return _offsets.at(n); } + +private: + std::vector<int32_t> _offsets; +}; + +/** + * @brief Copy an @c Index with reversed order + * @param[in] origin @c Index object to copy + * @return an @c Index object with reversed order + * @note This is used to convert NNAPI tensor index to ARM tensor index or vice versa + */ +inline static Index copy_reverse(const Index &origin) +{ + size_t rank = origin.rank(); + Index target(rank); + for (int i = 0; i < rank; i++) + target.at(i) = origin.at(rank - 1 - i); + return target; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_INDEX_H__ diff --git a/libs/misc/include/misc/tensor/IndexEnumerator.h b/libs/misc/include/misc/tensor/IndexEnumerator.h new file mode 100644 index 000000000..4912ea289 --- /dev/null +++ b/libs/misc/include/misc/tensor/IndexEnumerator.h @@ -0,0 +1,131 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexEnumerator.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::IndexEnumerator class + */ + +#ifndef __NNFW_MISC_TENSOR_INDEX_ENUMERATOR_H__ +#define __NNFW_MISC_TENSOR_INDEX_ENUMERATOR_H__ + +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ +/** + * @brief Class to enumerate index of a tensor + * + */ +class IndexEnumerator +{ +public: + /** + * @brief Construct a new @c IndexEnumerator object + * @param[in] shape Shape of tensor of which index will be enumerate + */ + explicit IndexEnumerator(const Shape &shape) : _shape(shape), _index(shape.rank()), _cursor(0) + { + const size_t rank = _shape.rank(); + + for (size_t axis = 0; axis < rank; ++axis) + { + _index.at(axis) = 0; + } + + for (_cursor = 0; _cursor < rank; ++_cursor) + { + if (_index.at(_cursor) < _shape.dim(_cursor)) + { + break; + } + } + } + +public: + /** + * @brief Prevent constructing @c IndexEnumerator object by using R-value reference + */ + IndexEnumerator(IndexEnumerator &&) = delete; + /** + * @brief Prevent copy constructor + */ + IndexEnumerator(const IndexEnumerator &) = delete; + +public: + /** + * @brief Check if more enumeration is available + * @return @c true if more @c advance() is available, otherwise @c false + */ + bool valid(void) const { return _cursor < _shape.rank(); } + +public: + /** + * @brief Get the current index to enumerate + * @return Current index + */ + const Index &curr(void) const { return _index; } + +public: + /** + * @brief Advance index by +1 + */ + void advance(void) + { + const size_t rank = _shape.rank(); + + // Find axis to be updated + while ((_cursor < rank) && !(_index.at(_cursor) + 1 < _shape.dim(_cursor))) + { + ++_cursor; + } + + if (_cursor == rank) + { + return; + } + + // Update index + _index.at(_cursor) += 1; + + for (size_t axis = 0; axis < _cursor; ++axis) + { + _index.at(axis) = 0; + } + + // Update cursor + _cursor = 0; + } + +public: + const Shape _shape; //!< Shape to enumerate + +private: + size_t _cursor; + Index _index; +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_INDEX_ENUMERATOR_H__ diff --git a/libs/misc/include/misc/tensor/IndexFormatter.h b/libs/misc/include/misc/tensor/IndexFormatter.h new file mode 100644 index 000000000..7ae34eec1 --- /dev/null +++ b/libs/misc/include/misc/tensor/IndexFormatter.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexFormatter.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::IndexFormatter class + */ + +#ifndef __NNFW_MISC_TENSOR_INDEX_FORMATTER_H__ +#define __NNFW_MISC_TENSOR_INDEX_FORMATTER_H__ + +#include "misc/tensor/Index.h" + +#include <ostream> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to send @c Index object to output stream + */ +class IndexFormatter +{ +public: + /** + * @brief Construct a new @c IndexFormatter object + * @param[in] index index to be sent to output stream + */ + IndexFormatter(const nnfw::misc::tensor::Index &index) : _index(index) + { + // DO NOTHING + } + +public: + /** + * @brief Get an @c Index object + * @return @c Index object previously passed to the constructor + */ + const nnfw::misc::tensor::Index &index(void) const { return _index; } + +private: + const nnfw::misc::tensor::Index &_index; +}; + +/** + * @brief Send @c IndexFormatter object to output stream + * @param[in] os Output stream + * @param[in] fmt @c IndexFormatter object that is sent to output stream + * @return Output stream + */ +std::ostream &operator<<(std::ostream &os, const IndexFormatter &fmt); + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_INDEX_FORMATTER_H__ diff --git a/libs/misc/include/misc/tensor/IndexIterator.h b/libs/misc/include/misc/tensor/IndexIterator.h new file mode 100644 index 000000000..f6428e19e --- /dev/null +++ b/libs/misc/include/misc/tensor/IndexIterator.h @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file IndexIterator.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::IndexIterator class and + * helper function and operator + */ +#ifndef __NNFW_MISC_TENSOR_INDEX_ITERATOR_H__ +#define __NNFW_MISC_TENSOR_INDEX_ITERATOR_H__ + +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" +#include "misc/tensor/IndexEnumerator.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to iterate indexes available for given shape + */ +class IndexIterator +{ +public: + /** + * @brief Construct a new @c IndexIterator object + * @param[in] shape Shape of tensor of which index will be iterated + */ + IndexIterator(const Shape &shape) : _shape(shape) + { + // DO NOTHING + } + +public: + /** + * @brief Construct a new IndexIterator object using reference + * @param[in] IndexIterator @c IndexIterator object to move + */ + IndexIterator(IndexIterator &&) = default; + + /** + * @brief Prevent copy constructor + */ + IndexIterator(const IndexIterator &) = delete; + +public: + /** + * @brief Iterate all available indexes and run a function for each index + * @param[in] fn Function that requires an index as a parameter. + * @return @c IndexIterator object + */ + template <typename Callable> IndexIterator &iter(Callable fn) + { + for (IndexEnumerator e{_shape}; e.valid(); e.advance()) + { + fn(e.curr()); + } + + return (*this); + } + +private: + const Shape &_shape; +}; + +/** + * @brief Get an @c IndexItator object + * @param[in] shape Shape of tensor of which index will be iterated + * @return @c IndexIterator object + */ +inline IndexIterator iterate(const Shape &shape) { return IndexIterator{shape}; } + +/** + * @brief Iterate all indexes and apply a function + * @param[in] it @c IndexIterator object that is constructed with a tensor shape + * @param[in] cb A function that will receive a specific index. + * Inside the function, the index is used to manipulate tensor element. + * @return @c IndexIterator object + */ +template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) +{ + return it.iter(cb); +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_INDEX_ITERATOR_H__ diff --git a/libs/misc/include/misc/tensor/NonIncreasingStride.h b/libs/misc/include/misc/tensor/NonIncreasingStride.h new file mode 100644 index 000000000..e7ad0857b --- /dev/null +++ b/libs/misc/include/misc/tensor/NonIncreasingStride.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file NonIncreasingStride.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::NonIncreasingStride class + */ +#ifndef __NNFW_MISC_TENSOR_NON_INCREASING_STRIDE_H__ +#define __NNFW_MISC_TENSOR_NON_INCREASING_STRIDE_H__ + +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" + +#include <vector> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to represent strides where stride[N-1] >= stride[N] holds for all N < rank + */ +class NonIncreasingStride +{ +public: + /** + * @brief Initialize the stride data using @c Shape + * @param[in] shape to build stride info + * @return N/A + */ + void init(const Shape &shape) + { + _stride.resize(shape.rank()); + _stride.at(shape.rank() - 1) = 1; + + for (uint32_t axis = shape.rank() - 1; axis > 0; --axis) + { + _stride.at(axis - 1) = _stride.at(axis) * shape.dim(axis); + } + } + +public: + /** + * @brief Get an stride value for specific axis + * @param[in] axis Axis of stride + * @return The value of stride + */ + uint32_t at(uint32_t axis) const { return _stride.at(axis); } + +public: + /** + * @brief Get the 1-D offset of specified index for n-D tensor + * @param index @c Index object + * @return 1-D offset of index + */ + uint32_t offset(const Index &index) const; + +private: + std::vector<uint32_t> _stride; +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_NON_INCREASING_STRIDE_H__ diff --git a/libs/misc/include/misc/tensor/Object.h b/libs/misc/include/misc/tensor/Object.h new file mode 100644 index 000000000..83fbc0bd1 --- /dev/null +++ b/libs/misc/include/misc/tensor/Object.h @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Object.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Object class + */ + +#ifndef __NNFW_MISC_TENSOR_OBJECT_H__ +#define __NNFW_MISC_TENSOR_OBJECT_H__ + +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" +#include "misc/tensor/IndexIterator.h" +#include "misc/tensor/NonIncreasingStride.h" +#include "misc/tensor/Reader.h" + +#include <vector> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to build a tensor using specific generator + * @tparam T Type of tensor element + */ + +template <typename T> class Object final : public Reader<T> +{ +public: + /** + * @brief Function to generate tensor element + */ + using Generator = std::function<T(const Shape &shape, const Index &index)>; + +public: + /** + * @brief Construct a new @c Object object + * @param[in] shape Tensor shape + * @param[in] fn Function to generate tensor elements + */ + Object(const Shape &shape, const Generator &fn) : _shape{shape} + { + // Set 'stride' + _stride.init(shape); + + // Pre-allocate buffer + _values.resize(_shape.dim(0) * _stride.at(0)); + + // Set 'value' + iterate(_shape) << + [this, &fn](const Index &index) { _values.at(_stride.offset(index)) = fn(_shape, index); }; + } + +public: + /** + * @brief Get reference of shape + * @return Reference of shape + */ + const Shape &shape(void) const { return _shape; } + +public: + /** + * @brief Get and element of tensor + * @param[in] index Index of a tensor element + * @return Value of tensor element + */ + T at(const Index &index) const override { return _values.at(_stride.offset(index)); } + +private: + Shape _shape; + NonIncreasingStride _stride; + +private: + std::vector<T> _values; +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_FEATURE_OBJECT_H__ diff --git a/libs/misc/include/misc/tensor/Reader.h b/libs/misc/include/misc/tensor/Reader.h new file mode 100644 index 000000000..9175a913e --- /dev/null +++ b/libs/misc/include/misc/tensor/Reader.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Reader.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Reader struct + */ + +#ifndef __NNFW_MISC_TENSOR_READER_H__ +#define __NNFW_MISC_TENSOR_READER_H__ + +#include "misc/tensor/Index.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Struct to read element of tensor + * @tparam T Type of elements in tensor + */ +template <typename T> struct Reader +{ + /** + * @brief Destroy the Reader object + */ + virtual ~Reader() = default; + + /** + * @brief Get an element of tensor + * @param[in] index Index specifying indexes of tensor element + * @return The value of specificed element + */ + virtual T at(const Index &index) const = 0; +}; + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_READER_H__ diff --git a/libs/misc/include/misc/tensor/Shape.h b/libs/misc/include/misc/tensor/Shape.h new file mode 100644 index 000000000..6e6c23502 --- /dev/null +++ b/libs/misc/include/misc/tensor/Shape.h @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Shape.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Shape class + */ + +#ifndef __NNFW_MISC_TENSOR_SHAPE_H__ +#define __NNFW_MISC_TENSOR_SHAPE_H__ + +#include <cstdint> +#include <cstddef> +#include <deque> +#include <initializer_list> +#include <ostream> +#include <string> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to represent shape of a tensor + */ +class Shape +{ +public: + /** + * @brief Construct a new Shape object + * @param[in] rank Rank of a tensor + */ + Shape(size_t rank) { _dimensions.resize(rank); } + +public: + /** + * @brief Construct a new Shape object + * @param[in] dimensions @c initializer_list<int32_t> of dimensions of tensor + */ + Shape(const std::initializer_list<int32_t> &dimensions) : _dimensions{dimensions} + { + // DO NOTHING + } + + /** + * @brief Construct a new Shape object + * @param[in] origin @c Shape object to copy + */ + Shape(const Shape &origin) = default; + +public: + /** + * @brief Add dimension to the beginning + * @param[in] d dimension to add to the beginning + * @return N/A + */ + void prepend(int32_t d) { _dimensions.emplace_front(d); } + + /** + * @brief Add dimension to the back + * @param[in] d dimension to add to the back + * @return N/A + */ + void append(int32_t d) { _dimensions.emplace_back(d); } + +public: + /** + * @brief Get the rank of this shape + * @return rank + */ + size_t rank(void) const { return _dimensions.size(); } + +public: + /** + * @brief Get specific dimension + * @param[in] n Index of dimension + * @return n'th dimension + */ + int32_t dim(size_t n) const { return _dimensions.at(n); } + + /** + * @brief Get the reference of specific dimension + * @param[in] n Index of dimension + * @return Reference of n'th dimension + */ + int32_t &dim(size_t n) { return _dimensions.at(n); } + +public: + /** + * @brief Get the number of elements specified by this shape + * @return The number of elements + */ + size_t element_nums() const + { + size_t nums = 1; + for (auto d : _dimensions) + { + nums *= d; + } + return nums; + } + +private: + std::deque<int32_t> _dimensions; + +public: + /** + * @brief Get a @c Shape object after parsing string + * @param[in] s String of dimension list. Accepted format is numbers separated by comma. + * @return @c Shape object + */ + static Shape from(const std::string &s); +}; + +/** + * @brief Check equality of two @c Shape + * @param[in] Shape First shape to compare + * @param[in] Shape Second shape to compare + * @return @c true if both shapes are equal, otherwise @c false + */ +bool operator==(const Shape &, const Shape &); + +/** + * @brief Send @c Shape to @c std::ostream + * @param[in] os @c std::ostream to process this @c Shape + * @param[in] shape @c Shape to send to @c ostream + * @return Reference of @c std::ostream + */ +std::ostream &operator<<(std::ostream &os, const Shape &shape); + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_SHAPE_H__ diff --git a/libs/misc/include/misc/tensor/Zipper.h b/libs/misc/include/misc/tensor/Zipper.h new file mode 100644 index 000000000..8f0ec4ab6 --- /dev/null +++ b/libs/misc/include/misc/tensor/Zipper.h @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Zipper.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains nnfw::misc::tensor::Zipper class + */ + +#ifndef __NNFW_MISC_TENSOR_ZIPPER_H__ +#define __NNFW_MISC_TENSOR_ZIPPER_H__ + +#include "misc/tensor/Index.h" +#include "misc/tensor/IndexIterator.h" +#include "misc/tensor/Reader.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +/** + * @brief Class to apply a function with three params: @c Index, elements of a tensor + * at passed index read by @c Reader objects + */ +template <typename T> class Zipper +{ +public: + /** + * @brief Construct a new @c Zipper object + * @param[in] shape Shape of @c lhs and @c rhs + * @param[in] lhs @c Reader object of a tensor + * @param[in] rhs @c Reader object of a tensor + */ + Zipper(const Shape &shape, const Reader<T> &lhs, const Reader<T> &rhs) + : _shape{shape}, _lhs{lhs}, _rhs{rhs} + { + // DO NOTHING + } + +public: + /** + * @brief Apply @c cb to all elements of tensors. Elements of two tensors + * at passed @c index are read by @c lhs and @c rhs + * @param[in] cb Function to apply + * @return N/A + */ + template <typename Callable> void zip(Callable cb) const + { + iterate(_shape) << + [this, &cb](const Index &index) { cb(index, _lhs.at(index), _rhs.at(index)); }; + } + +private: + const Shape &_shape; + const Reader<T> &_lhs; + const Reader<T> &_rhs; +}; + +/** + * @brief Apply @c cb by using @c lhs and @c rhs passed to the constructor of @c zipper + * @param[in] zipper @c Zipper object + * @param[in] cb Function to zpply using @c zip function + * @return @c zipper object after applying @c cb to @c zipper + */ +template <typename T, typename Callable> +const Zipper<T> &operator<<(const Zipper<T> &zipper, Callable cb) +{ + zipper.zip(cb); + return zipper; +} + +/** + * @brief Get @c Zipper object constructed using passed params + * @param shape Shape of @c lhs and @c rhs + * @param lhs @c Reader object of a tensor + * @param rhs @c Reader object of a tensor + * @return @c Zipper object + */ +template <typename T> Zipper<T> zip(const Shape &shape, const Reader<T> &lhs, const Reader<T> &rhs) +{ + return Zipper<T>{shape, lhs, rhs}; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_TENSOR_ZIPPER_H__ diff --git a/libs/misc/include/misc/vector.h b/libs/misc/include/misc/vector.h new file mode 100644 index 000000000..395b08912 --- /dev/null +++ b/libs/misc/include/misc/vector.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file vector.h + * @ingroup COM_AI_RUNTIME + * @brief This file contains @c == operator to check equality of elements in two vectors + */ +#ifndef __NNFW_MISC_VECTOR_H__ +#define __NNFW_MISC_VECTOR_H__ + +#include <vector> + +/** + * @brief Compare elements of two vectors + * @tparam T Type of elements in vectors + * @param[in] lhs First vector to compare + * @param[in] rhs Second vector to compare + * @return @c true if all elements are equal, otherwise @c false. + */ +template <typename T> bool operator==(const std::vector<T> &lhs, const std::vector<T> &rhs) +{ + if (lhs.size() != rhs.size()) + { + return false; + } + + for (size_t ind = 0; ind < lhs.size(); ++ind) + { + if (lhs.at(ind) != rhs.at(ind)) + { + return false; + } + } + + return true; +} + +#endif // __NNFW_MISC_VECTOR_H__ diff --git a/libs/misc/include/misc/vector/Object.h b/libs/misc/include/misc/vector/Object.h new file mode 100644 index 000000000..65d4bc613 --- /dev/null +++ b/libs/misc/include/misc/vector/Object.h @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Object.h + * @brief This file contains Object class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_VECTOR_OBJECT_H__ +#define __NNFW_MISC_VECTOR_OBJECT_H__ + +#include "misc/vector/Reader.h" + +#include <vector> +#include <functional> + +namespace nnfw +{ +namespace misc +{ +namespace vector +{ + +/** + * @brief Class to have information of the operand for vector + */ +template <typename T> class Object final : public Reader<T> +{ +public: + using Generator = std::function<T(int32_t size, int32_t offset)>; + +public: + /** + * @brief Construct Object object with size of vector and set value used by Generator + * @param[in] size The size of vector + * @param[in] gen A function to set values of operand tensor + */ + Object(int32_t size, const Generator &gen) : _size{size} + { + _value.resize(_size); + + for (int32_t offset = 0; offset < size; ++offset) + { + _value.at(offset) = gen(size, offset); + } + } + +public: + /** + * @brief Get size of vector + * @return Size of vector + */ + int32_t size(void) const { return _size; } + +public: + /** + * @brief Get the value used by index + * @param[in] nth The vector index + * @return The value at the offset + */ + T at(uint32_t nth) const override { return _value.at(nth); } + +private: + /** + * @brief Size of vector + */ + const int32_t _size; + /** + * @brief The tensor vector of operand + */ + std::vector<T> _value; +}; + +} // namespace vector +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_VECTOR_OBJECT_H__ diff --git a/libs/misc/include/misc/vector/Reader.h b/libs/misc/include/misc/vector/Reader.h new file mode 100644 index 000000000..eab4c427b --- /dev/null +++ b/libs/misc/include/misc/vector/Reader.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Reader.h + * @brief This file contains Reader class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_MISC_VECTOR_READER_H__ +#define __NNFW_MISC_VECTOR_READER_H__ + +#include <cstdint> + +namespace nnfw +{ +namespace misc +{ +namespace vector +{ + +/** + * @brief Class reads values of vector + * The interface class + */ +template <typename T> struct Reader +{ + /** + * @brief Destruct Reader object using default destructor + */ + virtual ~Reader() = default; + + /** + * @brief Get the value used by the index + * @param[in] nth The vector index + * @return The value at the offset + */ + virtual T at(uint32_t nth) const = 0; +}; + +} // namespace vector +} // namespace misc +} // namespace nnfw + +#endif // __NNFW_MISC_VECTOR_READER_H__ diff --git a/libs/misc/src/environment.cpp b/libs/misc/src/environment.cpp new file mode 100644 index 000000000..e39f18d62 --- /dev/null +++ b/libs/misc/src/environment.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <string.h> +#include <cstdlib> +#include <string> + +#include "misc/environment.h" + +namespace nnfw +{ +namespace misc +{ + +int get_env_int(const char *name, int defaultValue) +{ + const char *value = std::getenv(name); + if (value != nullptr) + return std::stoi(value); + return defaultValue; +} + +bool get_env_bool(const char *name, bool defaultValue) +{ + const char *value = std::getenv(name); + if (value != nullptr) + { + return std::stoi(value) != 0; + } + + return defaultValue; +} + +} // namespace misc +} // namespace nnfw + +namespace nnfw +{ +namespace misc +{ +namespace env +{ + +IntAccessor::IntAccessor(const std::string &tag) : _tag{tag} +{ + // DO NOTHING +} + +bool IntAccessor::access(int &out) const +{ + auto value = std::getenv(_tag.c_str()); + + if (value == nullptr) + { + return false; + } + + out = std::stoi(value); + return true; +} + +FloatAccessor::FloatAccessor(const std::string &tag) : _tag{tag} +{ + // DO NOTHING +} + +bool FloatAccessor::access(float &out) const +{ + auto value = std::getenv(_tag.c_str()); + + if (value == nullptr) + { + return false; + } + + out = std::stof(value); + return true; +} + +} // namespace env +} // namespace misc +} // namespace nnfw diff --git a/libs/misc/src/tensor/Comparator.cpp b/libs/misc/src/tensor/Comparator.cpp new file mode 100644 index 000000000..013c9eed2 --- /dev/null +++ b/libs/misc/src/tensor/Comparator.cpp @@ -0,0 +1,40 @@ +#include "misc/tensor/Comparator.h" +#include "misc/tensor/Zipper.h" + +#include "misc/fp32.h" + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +std::vector<Diff<float>> Comparator::compare(const Shape &shape, const Reader<float> &expected, + const Reader<float> &obtained, + Observer *observer) const +{ + std::vector<Diff<float>> res; + + zip(shape, expected, obtained) << + [&](const Index &index, float expected_value, float obtained_value) { + const auto relative_diff = nnfw::misc::fp32::relative_diff(expected_value, obtained_value); + + if (!_compare_fn(expected_value, obtained_value)) + { + res.emplace_back(index, expected_value, obtained_value); + } + + // Update max_diff_index, if necessary + if (observer != nullptr) + { + observer->notify(index, expected_value, obtained_value); + } + }; + + return res; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw diff --git a/libs/misc/src/tensor/IndexFormatter.cpp b/libs/misc/src/tensor/IndexFormatter.cpp new file mode 100644 index 000000000..c949db7a8 --- /dev/null +++ b/libs/misc/src/tensor/IndexFormatter.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "misc/tensor/IndexFormatter.h" + +#include <cassert> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +std::ostream &operator<<(std::ostream &os, const IndexFormatter &fmt) +{ + const auto rank = fmt.index().rank(); + + assert(rank > 0); + + os << fmt.index().at(0); + + if (rank > 1) + { + for (uint32_t axis = 1; axis < rank; ++axis) + { + os << ", " << fmt.index().at(axis); + } + } + + return os; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw diff --git a/libs/misc/src/tensor/NonIncreasingStride.cpp b/libs/misc/src/tensor/NonIncreasingStride.cpp new file mode 100644 index 000000000..c51ad0324 --- /dev/null +++ b/libs/misc/src/tensor/NonIncreasingStride.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "misc/tensor/NonIncreasingStride.h" + +#include <cassert> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +uint32_t NonIncreasingStride::offset(const Index &index) const +{ + const size_t rank = _stride.size(); + + assert(index.rank() == rank); + + uint32_t offset = 0; + + for (size_t axis = 0; axis < rank; ++axis) + { + offset += _stride.at(axis) * index.at(axis); + } + + return offset; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw diff --git a/libs/misc/src/tensor/Shape.cpp b/libs/misc/src/tensor/Shape.cpp new file mode 100644 index 000000000..675695e8e --- /dev/null +++ b/libs/misc/src/tensor/Shape.cpp @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "misc/tensor/Shape.h" + +#include <cassert> + +namespace nnfw +{ +namespace misc +{ +namespace tensor +{ + +bool operator==(const Shape &lhs, const Shape &rhs) +{ + if (lhs.rank() != rhs.rank()) + { + return false; + } + + for (size_t axis = 0; axis < lhs.rank(); ++axis) + { + if (lhs.dim(axis) != rhs.dim(axis)) + { + return false; + } + } + + return true; +} + +Shape Shape::from(const std::string &str) +{ + Shape shape(0); + + bool pending = false; + int value = 0; + + for (const char *cur = str.c_str(); true; ++cur) + { + if (*cur == ',' || *cur == '\0') + { + if (pending) + { + shape.append(value); + } + + if (*cur == '\0') + { + break; + } + + pending = false; + value = 0; + continue; + } + + assert(*cur >= '0' && *cur <= '9'); + + pending = true; + value *= 10; + value += *cur - '0'; + } + + return shape; +} + +std::ostream &operator<<(std::ostream &os, const Shape &shape) +{ + if (shape.rank() > 0) + { + os << shape.dim(0); + + for (uint32_t axis = 1; axis < shape.rank(); ++axis) + { + os << "," << shape.dim(axis); + } + } + + return os; +} + +} // namespace tensor +} // namespace misc +} // namespace nnfw diff --git a/libs/profiling/CMakeLists.txt b/libs/profiling/CMakeLists.txt new file mode 100644 index 000000000..7169508a1 --- /dev/null +++ b/libs/profiling/CMakeLists.txt @@ -0,0 +1,5 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(nnfw_lib_profiling STATIC ${SOURCES}) +set_property(TARGET nnfw_lib_profiling PROPERTY POSITION_INDEPENDENT_CODE ON) +target_include_directories(nnfw_lib_profiling PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) diff --git a/libs/profiling/include/profiling/profile_buffer.h b/libs/profiling/include/profiling/profile_buffer.h new file mode 100644 index 000000000..83cd3eb2b --- /dev/null +++ b/libs/profiling/include/profiling/profile_buffer.h @@ -0,0 +1,170 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/profiling/profile_buffer.h +#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ +#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ + +#include <cstddef> +#include <cstdint> + +#include "profiling/time.h" + +namespace tflite { +namespace profiling { + +// A profiling event. +struct ProfileEvent { + // Describes the type of event. + // The event_metadata field may contain additional data for interpreting + // the event. + enum class EventType { + // Default event type, the metadata field has no special significance. + DEFAULT = 0, + // The event is an operator invocation and the event_metadata field is the + // index of operator node. + OPERATOR_INVOKE_EVENT = 1 + }; + + // Label of the event. This usually describes the event. + const char* tag; + // Timestamp in microseconds when the event began. + uint64_t begin_timestamp_us; + // Timestamp in microseconds when the event ended. + uint64_t end_timestamp_us; + // The field containing the type of event. This must be one of the event types + // in EventType. + EventType event_type; + // Extra data describing the details of the event. + uint32_t event_metadata; +}; +} // namespace profiling +} // namespace tflite + +#ifdef TFLITE_PROFILING_ENABLED + +#include <sys/time.h> +#include <vector> + +namespace tflite { +namespace profiling { +constexpr uint32_t kInvalidEventHandle = static_cast<uint32_t>(~0) - 1; + +// A ring buffer of profile events. +// This class is not thread safe. +class ProfileBuffer { + public: + ProfileBuffer(uint32_t max_num_entries, bool enabled) + : enabled_(enabled), current_index_(0), event_buffer_(max_num_entries) {} + + // Adds an event to the buffer with begin timestamp set to the current + // timestamp. Returns a handle to event that can be used to call EndEvent. If + // buffer is disabled this has no affect. + // The tag of the event should remain valid till the buffer is valid. + uint32_t BeginEvent(const char* tag, ProfileEvent::EventType event_type, + uint32_t event_metadata) { + if (!enabled_) { + return kInvalidEventHandle; + } + uint64_t timestamp = time::NowMicros(); + int index = current_index_ % event_buffer_.size(); + event_buffer_[index].tag = tag; + event_buffer_[index].event_type = event_type; + event_buffer_[index].event_metadata = event_metadata; + event_buffer_[index].begin_timestamp_us = timestamp; + event_buffer_[index].end_timestamp_us = 0; + current_index_++; + return index; + } + + // Sets the enabled state of buffer to |enabled| + void SetEnabled(bool enabled) { enabled_ = enabled; } + + // Sets the end timestamp for event for the handle to current time. + // If the buffer is disabled or previous event has been overwritten this + // operation has not effect. + void EndEvent(uint32_t event_handle) { + if (!enabled_ || event_handle == kInvalidEventHandle || + event_handle > current_index_) { + return; + } + const uint32_t max_size = event_buffer_.size(); + if (current_index_ > (max_size + event_handle)) { + // Ignore, buffer has already overflowed. + return; + } + + int event_index = event_handle % max_size; + event_buffer_[event_index].end_timestamp_us = time::NowMicros(); + } + + // Returns the size of the buffer. + size_t Size() const { + return (current_index_ >= event_buffer_.size()) ? event_buffer_.size() + : current_index_; + } + + // Resets the buffer. + void Reset() { + enabled_ = false; + current_index_ = 0; + } + + // Returns the profile event at the given index. If the index is invalid a + // nullptr is returned. The return event may get overwritten if more events + // are added to buffer. + const struct ProfileEvent* const At(int index) const { + size_t size = Size(); + if (index >= size) { + return nullptr; + } + const uint32_t max_size = event_buffer_.size(); + uint32_t start = + (current_index_ > max_size) ? current_index_ % max_size : max_size; + index = (index + start) % max_size; + return &event_buffer_[index]; + } + + private: + bool enabled_; + uint32_t current_index_; + std::vector<ProfileEvent> event_buffer_; +}; +} // namespace profiling +} // namespace tflite +#endif // TFLITE_PROFILING_ENABLED +#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_BUFFER_H_ + +// clang-format on diff --git a/libs/profiling/include/profiling/profiler.h b/libs/profiling/include/profiling/profiler.h new file mode 100644 index 000000000..953042da3 --- /dev/null +++ b/libs/profiling/include/profiling/profiler.h @@ -0,0 +1,203 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/profiling/profiler.h +#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ +#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ + +#include <vector> + +#include "profiling/profile_buffer.h" + +#ifdef TFLITE_PROFILING_ENABLED + +namespace tflite { +namespace profiling { +class ScopedProfile; +class ScopedOperatorProfile; + +// Controls whether profiling is enabled or disabled and collects profiles. +// TFLite is used on platforms that don't have posix threads, so the profiler is +// kept as simple as possible. It is designed to be used only on a single +// thread. +// +// Profiles are collected using Scoped*Profile objects that begin and end a +// profile event. +// An example usage is shown in the example below: +// +// Say Worker class has a DoWork method and we are interested in profiling +// the overall execution time for DoWork and time spent in Task1 and Task2 +// functions. +// +// class Worker { +// public: +// void DoWork() { +// ScopedProfile(&controller, "DoWork"); +// Task1(); +// Task2(); +// ..... +// } +// +// void Task1() { +// ScopedProfile(&controller, "Task1"); +// .... +// } +// +// void Task2() { +// ScopedProfile(&controller, "Task2"); +// } +// +// Profiler profiler; +// } +// +// We instrument the functions that need to be profiled. +// +// Profile can be collected by enable profiling and then getting profile +// events. +// +// void ProfileWorker() { +// Worker worker; +// worker.profiler.EnableProfiling(); +// worker.DoWork(); +// worker.profiler.DisableProfiling(); +// // Profiling is complete, extract profiles. +// auto profile_events = worker.profiler.GetProfiles(); +// } +// +// +class Profiler { + public: + Profiler() : buffer_(1024, false) {} + + void StartProfiling() { buffer_.SetEnabled(true); } + void StopProfiling() { buffer_.SetEnabled(false); } + void Reset() { buffer_.Reset(); } + std::vector<const ProfileEvent*> GetProfileEvents() { + std::vector<const ProfileEvent*> profile_events; + profile_events.reserve(buffer_.Size()); + for (size_t i = 0; i < buffer_.Size(); i++) { + profile_events.push_back(buffer_.At(i)); + } + return profile_events; + } + + private: + friend class ScopedProfile; + friend class ScopedOperatorProfile; + ProfileBuffer* GetProfileBuffer() { return &buffer_; } + ProfileBuffer buffer_; +}; + +class ScopedProfile { + public: + // Adds a profile event to profile that begins with the construction + // of object and ends when the object goes out of scope. + // The lifetime of tag should be at least the lifetime of profiler. + + ScopedProfile(Profiler* profiler, const char* tag) + : buffer_(nullptr), event_handle_(0) { + if (profiler) { + buffer_ = profiler->GetProfileBuffer(); + event_handle_ = + buffer_->BeginEvent(tag, ProfileEvent::EventType::DEFAULT, 0); + } + } + ~ScopedProfile() { + if (buffer_) { + buffer_->EndEvent(event_handle_); + } + } + + private: + ProfileBuffer* buffer_; + int32_t event_handle_; +}; + +class ScopedOperatorProfile { + public: + // Adds a profile event to profile that begins with the construction + // of object and ends when the object goes out of scope. + // The lifetime of tag should be at least the lifetime of profiler. + ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index) + : buffer_(nullptr), event_handle_(0) { + if (profiler) { + buffer_ = profiler->GetProfileBuffer(); + event_handle_ = buffer_->BeginEvent( + tag, ProfileEvent::EventType::OPERATOR_INVOKE_EVENT, node_index); + } + } + + ~ScopedOperatorProfile() { + if (buffer_) { + buffer_->EndEvent(event_handle_); + } + } + + private: + ProfileBuffer* buffer_; + int32_t event_handle_; +}; + +} // namespace profiling +} // namespace tflite + +#define VARNAME_UNIQ(name, ctr) name##ctr + +#define SCOPED_OPERATOR_PROFILE(profiler, node_index) \ + tflite::profiling::ScopedOperatorProfile VARNAME_UNIQ( \ + _profile_, __COUNTER__)((profiler), "OpInvoke", (node_index)) +#else + +namespace tflite { +namespace profiling { +// A noop version of profiler when profiling is disabled. +class Profiler { + public: + Profiler() {} + void StartProfiling() {} + void StopProfiling() {} + void Reset() {} + std::vector<const ProfileEvent*> GetProfileEvents() { return {}; } +}; +} // namespace profiling +} // namespace tflite + +#define SCOPED_OPERATOR_PROFILE(profiler, node_index) + +#endif // TFLITE_PROFILING_ENABLED + +#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILER_H_ + +// clang-format on diff --git a/libs/profiling/include/profiling/profiling.h b/libs/profiling/include/profiling/profiling.h new file mode 100644 index 000000000..ee0df1338 --- /dev/null +++ b/libs/profiling/include/profiling/profiling.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_MISC_PROFILING_H__ +#define __NNFW_MISC_PROFILING_H__ + +#include <iostream> + +namespace tflite +{ +namespace profiling +{ +class Profiler; // forward declaration +} +} + +namespace profiling +{ + +class Context +{ +public: + Context() : _sync(false), _profiler(nullptr) {} + +public: + const bool &sync(void) const { return _sync; } + tflite::profiling::Profiler *getProfiler() { return _profiler; } + void setProfiler(tflite::profiling::Profiler *p) { _profiler = p; } + void setSync(void) { _sync = true; } + +private: + bool _sync; + tflite::profiling::Profiler *_profiler; + +public: + static Context &get(void) + { + static Context ctx{}; + return ctx; + } +}; + +} // namespace profiling +#endif // __NNFW_MISC_PROFILING_H__ diff --git a/libs/profiling/include/profiling/time.h b/libs/profiling/include/profiling/time.h new file mode 100644 index 000000000..4b194944d --- /dev/null +++ b/libs/profiling/include/profiling/time.h @@ -0,0 +1,35 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/profiling/time.h +#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ +#define TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ + +#include <cstdint> + +namespace tflite { +namespace profiling { +namespace time { +uint64_t NowMicros(); +} // namespace time +} // namespace profiling +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_TIME_H_ + +// clang-format on diff --git a/libs/profiling/src/profiling/time.cpp b/libs/profiling/src/profiling/time.cpp new file mode 100644 index 000000000..92d8595f8 --- /dev/null +++ b/libs/profiling/src/profiling/time.cpp @@ -0,0 +1,55 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/profiling/time.cpp +#include "profiling/time.h" + +#if defined(_MSC_VER) +#include <chrono> // NOLINT(build/c++11) +#else +#include <sys/time.h> +#endif + +namespace tflite { +namespace profiling { +namespace time { + +#if defined(_MSC_VER) + +uint64_t NowMicros() { + return std::chrono::duration_cast<std::chrono::microseconds>( + std::chrono::system_clock::now().time_since_epoch()) + .count(); +} + +#else + +uint64_t NowMicros() { + struct timeval tv; + gettimeofday(&tv, nullptr); + return static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec; +} + +#endif // defined(_MSC_VER) + +} // namespace time +} // namespace profiling +} // namespace tflite + +// clang-format on diff --git a/libs/support/CMakeLists.txt b/libs/support/CMakeLists.txt deleted file mode 100644 index c91677266..000000000 --- a/libs/support/CMakeLists.txt +++ /dev/null @@ -1,2 +0,0 @@ -add_subdirectory(tflite) -add_subdirectory(nnapi) diff --git a/libs/support/nnapi/CMakeLists.txt b/libs/support/nnapi/CMakeLists.txt deleted file mode 100644 index 193bcbd4e..000000000 --- a/libs/support/nnapi/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cpp") - -add_library(nnfw_support_nnapi ${SOURCES}) -set_property(TARGET nnfw_support_nnapi PROPERTY POSITION_INDEPENDENT_CODE ON) -target_include_directories(nnfw_support_nnapi PUBLIC ${CMAKE_SOURCE_DIR}/include) -target_link_libraries(nnfw_support_nnapi static_nnfw_util) diff --git a/libs/support/nnapi/src/Utils.cpp b/libs/support/nnapi/src/Utils.cpp deleted file mode 100644 index ae1076fd1..000000000 --- a/libs/support/nnapi/src/Utils.cpp +++ /dev/null @@ -1,29 +0,0 @@ -#include "support/nnapi/Utils.h" - -#include <cassert> - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ - -const char *to_string(const PaddingCode &code) -{ - assert((ANEURALNETWORKS_PADDING_SAME == code) || (ANEURALNETWORKS_PADDING_VALID == code)); - - switch (code) - { - case ANEURALNETWORKS_PADDING_SAME: - return "ANEURALNETWORKS_PADDING_SAME"; - case ANEURALNETWORKS_PADDING_VALID: - return "ANEURALNETWORKS_PADDING_VALID"; - } - - return nullptr; -} - -} // namespace nnapi -} // namespace support -} // namespace nnfw diff --git a/libs/support/nnapi/src/feature/Utils.cpp b/libs/support/nnapi/src/feature/Utils.cpp deleted file mode 100644 index 62939ff4a..000000000 --- a/libs/support/nnapi/src/feature/Utils.cpp +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/nnapi/feature/Utils.h" - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ -namespace feature -{ - -uint32_t indexOf(const nnfw::util::feature::Shape &shape, uint32_t ch, uint32_t row, uint32_t col) -{ - uint32_t res = 0; - - // NNAPI assumes that NHWC ordering for feature map - res += row * shape.W * shape.C; - res += col * shape.C; - res += ch; - - return res; -} - -} // namespace feature -} // namespace nnapi -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/CMakeLists.txt b/libs/support/tflite/CMakeLists.txt deleted file mode 100644 index 667b3bc11..000000000 --- a/libs/support/tflite/CMakeLists.txt +++ /dev/null @@ -1,12 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cpp") -file(GLOB_RECURSE TESTS "src/*.test.cpp") -list(REMOVE_ITEM SOURCES ${TESTS}) - -add_library(nnfw_support_tflite STATIC ${SOURCES}) -set_target_properties(nnfw_support_tflite PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(nnfw_support_tflite PUBLIC ${CMAKE_SOURCE_DIR}/include) -target_link_libraries(nnfw_support_tflite tensorflow-lite ${LIB_PTHREAD} dl) -target_link_libraries(nnfw_support_tflite static_nnfw_util) - -add_executable(nnfw_support_tflite_test_TensorView src/TensorView.test.cpp) -target_link_libraries(nnfw_support_tflite_test_TensorView nnfw_support_tflite) diff --git a/libs/support/tflite/src/Diff.cpp b/libs/support/tflite/src/Diff.cpp deleted file mode 100644 index e875571cb..000000000 --- a/libs/support/tflite/src/Diff.cpp +++ /dev/null @@ -1,538 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/Diff.h" -#include "support/tflite/nnapi_delegate.h" - -#include "util/fp32.h" - -#include "util/tensor/IndexIterator.h" -#include "util/tensor/IndexFormatter.h" -#include "util/tensor/Zipper.h" -#include "util/tensor/Comparator.h" - -#include "util/environment.h" - -#include <iostream> -#include <cassert> - -class DiffSummary : public nnfw::util::tensor::Comparator::Observer -{ -public: - DiffSummary() - : max_abs_diff_index(0), max_abs_diff_expected{0.0f}, max_abs_diff_obtained{0.0f}, - max_abs_diff_value{0.0f}, max_rel_diff_index(0), max_rel_diff_expected{0.0f}, - max_rel_diff_obtained{0.0f}, max_rel_diff_value{0.0f} - { - // DO NOTHING - } - -public: - void notify(const nnfw::util::tensor::Index &index, float expected, float obtained) override; - -public: - nnfw::util::tensor::Index max_abs_diff_index; - float max_abs_diff_expected; - float max_abs_diff_obtained; - float max_abs_diff_value; - - nnfw::util::tensor::Index max_rel_diff_index; - float max_rel_diff_expected; - float max_rel_diff_obtained; - float max_rel_diff_value; -}; - -void DiffSummary::notify(const nnfw::util::tensor::Index &index, float expected, float obtained) -{ - const auto abs_diff_value = std::fabs(expected - obtained); - - if (max_abs_diff_value < abs_diff_value) - { - max_abs_diff_index = index; - max_abs_diff_value = abs_diff_value; - max_abs_diff_expected = expected; - max_abs_diff_obtained = obtained; - } - - const auto rel_diff_value = nnfw::util::fp32::relative_diff(expected, obtained); - - if (max_rel_diff_value < rel_diff_value) - { - max_rel_diff_index = index; - max_rel_diff_value = rel_diff_value; - max_rel_diff_expected = expected; - max_rel_diff_obtained = obtained; - } -} - -template <typename T> -bool TfLiteInterpMatchApp::compareSingleTensorView( - const nnfw::support::tflite::TensorView<T> &expected, - const nnfw::support::tflite::TensorView<T> &obtained, int id) const -{ - std::vector<nnfw::util::tensor::Diff<T>> diffs; - assert(expected.shape() == obtained.shape()); - - using nnfw::util::tensor::zip; - using nnfw::util::tensor::Index; - - zip(expected.shape(), expected, obtained) - << [&](const Index &index, T expected_value, T obtained_value) { - if (expected_value != obtained_value) - { - diffs.emplace_back(index, expected_value, obtained_value); - } - }; - - // TODO Unify summary generation code - if (diffs.size() == 0) - { - std::cout << " Tensor #" << id << ": MATCHED" << std::endl; - } - else - { - std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; - std::cout << " " << diffs.size() << " diffs are detected" << std::endl; - } - - if (diffs.size() > 0 && _verbose != 0) - { - std::cout << " ---- Details ---" << std::endl; - for (const auto &diff : diffs) - { - std::cout << " Diff at [" << nnfw::util::tensor::IndexFormatter(diff.index) << "]" - << std::endl; - std::cout << " expected: " << diff.expected << std::endl; - std::cout << " obtained: " << diff.obtained << std::endl; - } - } - - return diffs.size() == 0; -} - -template <> -bool TfLiteInterpMatchApp::compareSingleTensorView<float>( - const nnfw::support::tflite::TensorView<float> &expected, - const nnfw::support::tflite::TensorView<float> &obtained, int id) const -{ - DiffSummary summary; - - assert(expected.shape() == obtained.shape()); - auto diffs = _comparator.compare(expected.shape(), expected, obtained, &summary); - - // TODO Unify summary generation code - if (diffs.size() == 0) - { - std::cout << " Tensor #" << id << ": MATCHED" << std::endl; - } - else - { - std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; - std::cout << " " << diffs.size() << " diffs are detected" << std::endl; - } - - // Print out max_diff - if (summary.max_abs_diff_value > 0) - { - std::cout << " Max absolute diff at [" - << nnfw::util::tensor::IndexFormatter(summary.max_abs_diff_index) << "]" << std::endl; - std::cout << " expected: " << summary.max_abs_diff_expected << std::endl; - std::cout << " obtained: " << summary.max_abs_diff_obtained << std::endl; - std::cout << " absolute diff: " << summary.max_abs_diff_value << std::endl; - } - - if (summary.max_rel_diff_value > 0) - { - const auto tolerance_level = summary.max_rel_diff_value / FLT_EPSILON; - - std::cout << " Max relative diff at [" - << nnfw::util::tensor::IndexFormatter(summary.max_rel_diff_index) << "]" << std::endl; - std::cout << " expected: " << summary.max_rel_diff_expected << std::endl; - std::cout << " obtained: " << summary.max_rel_diff_obtained << std::endl; - std::cout << " relative diff: " << summary.max_rel_diff_value << std::endl; - std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; - } - - if (diffs.size() > 0) - { - if (_verbose != 0) - { - std::cout << " ---- Details ---" << std::endl; - for (const auto &diff : diffs) - { - const auto absolute_diff = std::fabs(diff.expected - diff.obtained); - const auto relative_diff = nnfw::util::fp32::relative_diff(diff.expected, diff.obtained); - const auto tolerance_level = relative_diff / FLT_EPSILON; - - std::cout << " Diff at [" << nnfw::util::tensor::IndexFormatter(diff.index) << "]" - << std::endl; - std::cout << " expected: " << diff.expected << std::endl; - std::cout << " obtained: " << diff.obtained << std::endl; - std::cout << " absolute diff: " << absolute_diff << std::endl; - std::cout << " relative diff: " << relative_diff << std::endl; - std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; - } - } - - return false; - } - return true; -} - -#include <map> - -bool TfLiteInterpMatchApp::run(::tflite::Interpreter &interp, ::tflite::Interpreter &nnapi) const -{ - assert(interp.outputs() == nnapi.outputs()); - - bool all_matched = true; - - using Comparator = std::function<bool(int id, ::tflite::Interpreter &, ::tflite::Interpreter &)>; - - std::map<TfLiteType, Comparator> comparators; - - comparators[kTfLiteUInt8] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<uint8_t>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<uint8_t>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - comparators[kTfLiteInt32] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<int32_t>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<int32_t>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - comparators[kTfLiteFloat32] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<float>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<float>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - for (const auto &id : interp.outputs()) - { - assert(interp.tensor(id)->type == nnapi.tensor(id)->type); - - auto it = comparators.find(interp.tensor(id)->type); - - if (it == comparators.end()) - { - throw std::runtime_error{"Not supported output type"}; - } - - const auto &comparator = it->second; - - if (!comparator(id, interp, nnapi)) - { - all_matched = false; - } - } - - return all_matched; -} - -#include "util/tensor/Object.h" - -using namespace std::placeholders; - -template <> uint8_t RandomGenerator::generate<uint8_t>(void) -{ - // The value of type_range is 255. - float type_range = static_cast<float>(std::numeric_limits<uint8_t>::max()) - - static_cast<float>(std::numeric_limits<uint8_t>::min()); - // Most _dist values range from -5.0 to 5.0. - float min_range = -5.0f; - float max_range = 5.0f; - return static_cast<uint8_t>((_dist(_rand) - min_range) * type_range / (max_range - min_range)); -} - -#include "support/tflite/TensorLogger.h" -// -// Random Test Runner -// -int RandomTestRunner::run(const nnfw::support::tflite::interp::Builder &builder) -{ - auto tfl_interp = builder.build(); - auto nnapi = builder.build(); - - tfl_interp->UseNNAPI(false); - - // Allocate Tensors - tfl_interp->AllocateTensors(); - nnapi->AllocateTensors(); - - assert(tfl_interp->inputs() == nnapi->inputs()); - - using ::tflite::Interpreter; - using Initializer = std::function<void(int id, Interpreter *, Interpreter *)>; - - std::map<TfLiteType, Initializer> initializers; - std::map<TfLiteType, Initializer> reseters; - - // Generate singed 32-bit integer (s32) input - initializers[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteInt32); - assert(nnapi->tensor(id)->type == kTfLiteInt32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<int32_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<int32_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - ++value; - }; - }; - - // Generate singed 32-bit integer (s32) input - reseters[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteInt32); - assert(nnapi->tensor(id)->type == kTfLiteInt32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<int32_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<int32_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - initializers[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); - assert(nnapi->tensor(id)->type == kTfLiteUInt8); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<uint8_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<uint8_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<uint8_t>); - const nnfw::util::tensor::Object<uint8_t> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - assert(tfl_interp_view.shape() == data.shape()); - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - const auto value = data.at(ind); - - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - reseters[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); - assert(nnapi->tensor(id)->type == kTfLiteUInt8); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<uint8_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<uint8_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<uint8_t>); - const nnfw::util::tensor::Object<uint8_t> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - assert(tfl_interp_view.shape() == data.shape()); - - uint8_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - initializers[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); - assert(nnapi->tensor(id)->type == kTfLiteFloat32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<float>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<float>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<float>); - const nnfw::util::tensor::Object<float> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - - assert(tfl_interp_view.shape() == data.shape()); - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - const auto value = data.at(ind); - - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - reseters[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); - assert(nnapi->tensor(id)->type == kTfLiteFloat32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<float>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<float>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<float>); - const nnfw::util::tensor::Object<float> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - - assert(tfl_interp_view.shape() == data.shape()); - - float value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - // Fill IFM with random numbers - for (const auto id : tfl_interp->inputs()) - { - assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); - - auto it = initializers.find(tfl_interp->tensor(id)->type); - - if (it == initializers.end()) - { - throw std::runtime_error{"Not supported input type"}; - } - - it->second(id, tfl_interp.get(), nnapi.get()); - } - - // Fill OFM with 0 - for (const auto id : tfl_interp->outputs()) - { - assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); - - auto it = reseters.find(tfl_interp->tensor(id)->type); - - if (it == reseters.end()) - { - throw std::runtime_error{"Not supported input type"}; - } - - it->second(id, tfl_interp.get(), nnapi.get()); - } - - std::cout << "[NNAPI TEST] Run T/F Lite Interpreter without NNAPI" << std::endl; - tfl_interp->Invoke(); - - std::cout << "[NNAPI TEST] Run T/F Lite Interpreter with NNAPI" << std::endl; - - char *env = getenv("UPSTREAM_DELEGATE"); - - if (env && !std::string(env).compare("1")) - { - nnapi->UseNNAPI(true); - nnapi->Invoke(); - } - else - { - nnfw::NNAPIDelegate d; - - if (d.BuildGraph(nnapi.get())) - { - throw std::runtime_error{"Failed to BuildGraph"}; - } - - if (d.Invoke(nnapi.get())) - { - throw std::runtime_error{"Failed to BuildGraph"}; - } - } - - // Compare OFM - std::cout << "[NNAPI TEST] Compare the result" << std::endl; - - const auto tolerance = _param.tolerance; - - auto equals = [tolerance](float lhs, float rhs) { - // NOTE Hybrid approach - // TODO Allow users to set tolerance for absolute_epsilon_equal - if (nnfw::util::fp32::absolute_epsilon_equal(lhs, rhs)) - { - return true; - } - - return nnfw::util::fp32::epsilon_equal(lhs, rhs, tolerance); - }; - - nnfw::util::tensor::Comparator comparator(equals); - TfLiteInterpMatchApp app(comparator); - - app.verbose() = _param.verbose; - - bool res = app.run(*tfl_interp, *nnapi); - - if (!res) - { - return 255; - } - - std::cout << "[NNAPI TEST] PASSED" << std::endl; - - if (_param.tensor_logging) - nnfw::support::tflite::TensorLogger::instance().save(_param.log_path, *tfl_interp); - - return 0; -} - -RandomTestRunner RandomTestRunner::make(int seed) -{ - RandomTestParam param; - - param.verbose = 0; - param.tolerance = 1; - - nnfw::util::env::IntAccessor("VERBOSE").access(param.verbose); - nnfw::util::env::IntAccessor("TOLERANCE").access(param.tolerance); - - return RandomTestRunner{seed, param}; -} diff --git a/libs/support/tflite/src/FeatureView.cpp b/libs/support/tflite/src/FeatureView.cpp deleted file mode 100644 index 4c7636780..000000000 --- a/libs/support/tflite/src/FeatureView.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/FeatureView.h" -#include "support/tflite/TensorUtils.h" - -#include <cassert> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -nnfw::util::feature::Shape getFeatureShape(const TfLiteTensor *tensor) -{ - nnfw::util::feature::Shape shape{tensor->dims->data[3], tensor->dims->data[1], - tensor->dims->data[2]}; - - return shape; -} - -FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const InputIndex &index) -{ - const auto tensor_index = interp.inputs().at(index.asInt()); - auto tensor_ptr = interp.tensor(tensor_index); - - assert(isFloatTensor(tensor_ptr)); - assert(isFeatureTensor(tensor_ptr)); - - _shape = getFeatureShape(tensor_ptr); - _base = interp.typed_tensor<float>(tensor_index); -} - -FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const OutputIndex &index) -{ - const auto tensor_index = interp.outputs().at(index.asInt()); - auto tensor_ptr = interp.tensor(tensor_index); - - assert(isFloatTensor(tensor_ptr)); - assert(isFeatureTensor(tensor_ptr)); - - _shape = getFeatureShape(tensor_ptr); - _base = interp.typed_tensor<float>(tensor_index); -} - -float FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) const -{ - return *(_base + getElementOffset(ch, row, col)); -} - -float &FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) -{ - return *(_base + getElementOffset(ch, row, col)); -} - -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/Quantization.cpp b/libs/support/tflite/src/Quantization.cpp deleted file mode 100644 index b23204d41..000000000 --- a/libs/support/tflite/src/Quantization.cpp +++ /dev/null @@ -1,22 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/Quantization.h" - -TfLiteQuantizationParams make_default_quantization(void) -{ - return TfLiteQuantizationParams{0.0f, 0}; -} diff --git a/libs/support/tflite/src/TensorShapeUtils.cpp b/libs/support/tflite/src/TensorShapeUtils.cpp deleted file mode 100644 index 611ba920e..000000000 --- a/libs/support/tflite/src/TensorShapeUtils.cpp +++ /dev/null @@ -1,51 +0,0 @@ -#include "support/tflite/TensorShapeUtils.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -nnfw::util::tensor::Shape broadcast(const nnfw::util::tensor::Shape &lhs_shape, - const nnfw::util::tensor::Shape &rhs_shape) -{ - const uint32_t lhs_rank = lhs_shape.rank(); - const uint32_t rhs_rank = rhs_shape.rank(); - const uint32_t out_rank = std::max(lhs_rank, rhs_rank); - - // TODO Simplify implementation - std::vector<int32_t> lhs_normalized_dims; - std::vector<int32_t> rhs_normalized_dims; - - for (uint32_t n = 0; n < out_rank - lhs_rank; ++n) - { - lhs_normalized_dims.emplace_back(1); - } - for (uint32_t axis = 0; axis < lhs_rank; ++axis) - { - lhs_normalized_dims.emplace_back(lhs_shape.dim(axis)); - } - - for (uint32_t n = 0; n < out_rank - rhs_rank; ++n) - { - rhs_normalized_dims.emplace_back(1); - } - for (uint32_t axis = 0; axis < rhs_rank; ++axis) - { - rhs_normalized_dims.emplace_back(rhs_shape.dim(axis)); - } - - nnfw::util::tensor::Shape out_shape(out_rank); - - for (uint32_t axis = 0; axis < out_rank; ++axis) - { - out_shape.dim(axis) = std::max(lhs_normalized_dims.at(axis), rhs_normalized_dims.at(axis)); - } - - return out_shape; -} - -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/TensorView.test.cpp b/libs/support/tflite/src/TensorView.test.cpp deleted file mode 100644 index 1d3a70500..000000000 --- a/libs/support/tflite/src/TensorView.test.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/TensorView.h" - -#include <cassert> - -void int_test(void) -{ - int value[6] = {1, 2, 3, 4, 5, 6}; - - const nnfw::util::tensor::Shape shape{2, 3}; - const nnfw::support::tflite::TensorView<int> view{shape, value}; - - assert(view.at(nnfw::util::tensor::Index{0, 0}) == 1); - assert(view.at(nnfw::util::tensor::Index{0, 1}) == 2); - assert(view.at(nnfw::util::tensor::Index{0, 2}) == 3); - assert(view.at(nnfw::util::tensor::Index{1, 0}) == 4); - assert(view.at(nnfw::util::tensor::Index{1, 1}) == 5); - assert(view.at(nnfw::util::tensor::Index{1, 2}) == 6); -} - -int main(int argc, char **argv) -{ - float value[6] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; - - const nnfw::util::tensor::Shape shape{2, 3}; - const nnfw::support::tflite::TensorView<float> view{shape, value}; - - assert(view.at(nnfw::util::tensor::Index{0, 0}) == 1.0f); - assert(view.at(nnfw::util::tensor::Index{0, 1}) == 2.0f); - assert(view.at(nnfw::util::tensor::Index{0, 2}) == 3.0f); - assert(view.at(nnfw::util::tensor::Index{1, 0}) == 4.0f); - assert(view.at(nnfw::util::tensor::Index{1, 1}) == 5.0f); - assert(view.at(nnfw::util::tensor::Index{1, 2}) == 6.0f); - - int_test(); - - return 0; -} diff --git a/libs/support/tflite/src/interp/FlatBufferBuilder.cpp b/libs/support/tflite/src/interp/FlatBufferBuilder.cpp deleted file mode 100644 index 67df13f34..000000000 --- a/libs/support/tflite/src/interp/FlatBufferBuilder.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/interp/FlatBufferBuilder.h" - -#include "support/tflite/kernels/register.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -std::unique_ptr<::tflite::Interpreter> FlatBufferBuilder::build(void) const -{ - std::unique_ptr<::tflite::Interpreter> interpreter; - - ::tflite::ops::builtin::BuiltinOpResolver resolver; - - ::tflite::InterpreterBuilder builder(_model, resolver); - - builder(&interpreter); - - return std::move(interpreter); -} - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/interp/FunctionBuilder.cpp b/libs/support/tflite/src/interp/FunctionBuilder.cpp deleted file mode 100644 index 65783bd37..000000000 --- a/libs/support/tflite/src/interp/FunctionBuilder.cpp +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/interp/FunctionBuilder.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -std::unique_ptr<::tflite::Interpreter> FunctionBuilder::build(void) const -{ - auto res = std::unique_ptr<::tflite::Interpreter>{new ::tflite::Interpreter}; - - _fn(*res); - - return std::move(res); -} - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/kernels/RSQRT.cpp b/libs/support/tflite/src/kernels/RSQRT.cpp deleted file mode 100644 index 13efe0ed9..000000000 --- a/libs/support/tflite/src/kernels/RSQRT.cpp +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/RSQRT.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <cmath> -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace RSQRT -{ - -void *InitRSQRT(TfLiteContext *context, const char *buffer, size_t length) { return nullptr; } - -void FreeRSQRT(TfLiteContext *context, void *buffer) {} - -TfLiteStatus PrepareRSQRT(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor *input = GetInput(context, node, 0); - TfLiteTensor *output = GetOutput(context, node, 0); - TF_LITE_ENSURE_EQ(context, input->type, output->type); - // Quantized float is not supported yet. - TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input->dims)); -} - -inline TfLiteStatus Eval(TfLiteContext *context, TfLiteNode *node, float float_func(float)) -{ - const TfLiteTensor *input = GetInput(context, node, 0); - TfLiteTensor *output = GetOutput(context, node, 0); - switch (input->type) - { - case kTfLiteFloat32: - { - size_t elements = NumElements(input); - const float *in = input->data.f; - const float *in_end = in + elements; - float *out = output->data.f; - for (; in < in_end; in++, out++) - *out = float_func(*in); - return kTfLiteOk; - } - default: - { - context->ReportError(context, "Input type is %d, requires float32", input->type); - return kTfLiteError; - } - } -} - -TfLiteStatus EvalRSQRT(TfLiteContext *context, TfLiteNode *node) -{ - return Eval(context, node, [](float f) { return 1.f / std::sqrt(f); }); -} - -} // namespace RSQRT -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/SquaredDifference.cpp b/libs/support/tflite/src/kernels/SquaredDifference.cpp deleted file mode 100644 index 25e10a8ed..000000000 --- a/libs/support/tflite/src/kernels/SquaredDifference.cpp +++ /dev/null @@ -1,115 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/SquaredDifference.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace SquaredDifference -{ - -void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length) -{ - return nullptr; -} - -void FreeSquaredDifference(TfLiteContext *context, void *buffer) {} - -TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor *input1 = GetInput(context, node, 0); - const TfLiteTensor *input2 = GetInput(context, node, 1); - TfLiteTensor *output = GetOutput(context, node, 0); - - TF_LITE_ENSURE_EQ(context, input1->type, input2->type); - TF_LITE_ENSURE_EQ(context, input1->type, output->type); - - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input1->dims)); -} - -TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - - const TfLiteTensor *input1 = GetInput(context, node, 0); - const TfLiteTensor *input2 = GetInput(context, node, 1); - - TfLiteTensor *output = GetOutput(context, node, 0); - - size_t elements = NumElements(input1); - - switch (input1->type) - { - case kTfLiteFloat32: - { - const float *in1 = input1->data.f; - const float *in2 = input2->data.f; - const float *in_end1 = in1 + elements; - float *out = output->data.f; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt32: - { - const int *in1 = input1->data.i32; - const int *in2 = input2->data.i32; - const int *in_end1 = in1 + elements; - int *out = output->data.i32; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt64: - { - const int64_t *in1 = input1->data.i64; - const int64_t *in2 = input1->data.i64; - const int64_t *in_end1 = in1 + elements; - int64_t *out = output->data.i64; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - default: - { - context->ReportError(context, "InputType is %d Unsupported", input1->type); - return kTfLiteError; - } - } -} - -} // namespace SquaredDifference -} // nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/TensorFlowMax.cpp b/libs/support/tflite/src/kernels/TensorFlowMax.cpp deleted file mode 100644 index abc6fda4e..000000000 --- a/libs/support/tflite/src/kernels/TensorFlowMax.cpp +++ /dev/null @@ -1,390 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/TensorFlowMax.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace TensorFlowMax -{ - -struct TensorFlowMaxOp -{ - TensorFlowMaxOp(TfLiteContext *context, TfLiteNode *node) - { - input = tflite::GetInput(context, node, 0); - axis = tflite::GetInput(context, node, 1); - output = tflite::GetOutput(context, node, 0); - } - const TfLiteTensor *input; - const TfLiteTensor *axis; - TfLiteTensor *output; -}; - -void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length) -{ - // Creates two temp tensors to store index and axis for internal - // implementation only. - auto *scratch_tensor_index = new int; - context->AddTensors(context, 2, scratch_tensor_index); - return scratch_tensor_index; -} - -void FreeTensorFlowMax(TfLiteContext *context, void *buffer) -{ - delete static_cast<TensorFlowMaxOp *>(buffer); -} - -// Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowMaxOp *op_context, - TfLiteTensor *resolved_axis) -{ - TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); - axis_size->data[0] = static_cast<int>(tflite::NumElements(op_context->axis)); - return context->ResizeTensor(context, resolved_axis, axis_size); -} - -// Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowMaxOp *op_context) -{ - size_t num_axis = tflite::NumElements(op_context->axis); - const TfLiteIntArray *input_dims = op_context->input->dims; - int input_num_dims = tflite::NumDimensions(op_context->input); - const int *axis = op_context->axis->data.i32; - - { - // Calculates size of reducing axis. - int num_reduce_axis = num_axis; - for (int i = 0; i < num_axis; ++i) - { - int current = axis[i]; - if (current < 0) - { - current += input_num_dims; - } - TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); - for (int j = 0; j < i; ++j) - { - int previous = axis[j]; - if (previous < 0) - { - previous += input_num_dims; - } - if (current == previous) - { - --num_reduce_axis; - break; - } - } - } - // Determines output dimensions. - TfLiteIntArray *output_dims = TfLiteIntArrayCreate(input_num_dims - num_reduce_axis); - int num_skip_axis = 0; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) - { - ++num_skip_axis; - is_axis = true; - break; - } - } - if (!is_axis) - { - output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; - } - } - return context->ResizeTensor(context, op_context->output, output_dims); - } -} - -// Initializes temp tensors to store index and resolved axis. -TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, - TensorFlowMaxOp *op_context) -{ - // Creates a temp index to iterate through input data. - int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(2); - node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; - scratch_tensor->type = kTfLiteInt32; - scratch_tensor->allocation_type = kTfLiteArenaRw; - TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); - index_size->data[0] = tflite::NumDimensions(op_context->input); - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); - - // Creates a temp tensor to store resolved axis given input data. - node->temporaries->data[1] = *scratch_tensor_index + 1; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - resolved_axis->type = kTfLiteInt32; - return kTfLiteOk; -} - -TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, tflite::NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, tflite::NumOutputs(node), 1); - - TensorFlowMaxOp op_context(context, node); - TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); - - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Leaves work to Eval if axis is not constant; else resizes output. - if (!tflite::IsConstantTensor(op_context.axis)) - { - tflite::SetTensorToDynamic(op_context.output); - tflite::SetTensorToDynamic(resolved_axis); - return kTfLiteOk; - } - resolved_axis->allocation_type = kTfLiteArenaRw; - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - return ResizeOutputTensor(context, &op_context); -} - -// Gets offset of index if expanded on axis. When expanded, the flattened offset -// will not change, if the output index changes on the given axis. For example, -// if you have a 2D tensor and you are expanding to 3D on axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened -// offset. -inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - int out_idx = 0; - for (int in_idx = 0; in_idx < num_dims; ++in_idx) - { - // if we need to expand this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (in_idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); - out_idx++; - } - else - { - offset = offset * static_cast<size_t>(dims[in_idx]); - } - } - return offset; -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) - { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); - } - } - return offset; -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) -{ - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) - { - int current_val = current[idx] + carry; - TF_LITE_ENSURE(context, (dims[idx] >= current_val)); - if (dims[idx] == current_val) - { - current[idx] = 0; - } - else - { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -template <typename T> -inline TfLiteStatus -CustomMax(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, - T *output_data, const int *output_dims, const int output_num_dims, const int *axis, - const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) -{ - // resolves axis. - int num_resolved_axis = 0; - for (int idx = 0; idx < num_axis_dimensions; ++idx) - { - int current = axis[idx]; - TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); - if (current < 0) - { - current += input_num_dims; - } - bool is_dup = false; - for (int j = 0; j < num_resolved_axis; ++j) - { - if (resolved_axis[j] == current) - { - is_dup = true; - break; - } - } - if (!is_dup) - { - resolved_axis[num_resolved_axis++] = current; - } - } - - TF_LITE_ENSURE(context, (input_num_dims > 0)); - TF_LITE_ENSURE(context, (input_dims != nullptr)); - TF_LITE_ENSURE(context, (temp_index != nullptr)); - - // resets output data. - for (int idx = 0; idx < output_num_dims; ++idx) - { - temp_index[idx] = 0; - } - for (bool has_next = true; has_next; - has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) - { - size_t output_offset = - ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); - size_t input_offset = ExpandedInputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - output_data[output_offset] = input_data[input_offset]; - } - - // resets temp index. - for (int idx = 0; idx < input_num_dims; ++idx) - { - temp_index[idx] = 0; - } - - // iterates through input_data. - for (bool has_next = true; has_next; - has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) - { - size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - if (output_data[output_offset] < input_data[input_offset]) - { - output_data[output_offset] = input_data[input_offset]; - } - } - - return kTfLiteOk; -} - -TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - - TensorFlowMaxOp op_context(context, node); - int num_axis = static_cast<int>(tflite::NumElements(op_context.axis)); - TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Resize the output tensor if the output tensor is dynamic. - if (tflite::IsDynamicTensor(op_context.output)) - { - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - } - - TfLiteStatus returnStatus = kTfLiteOk; - switch (op_context.input->type) - { - case kTfLiteFloat32: - returnStatus = CustomMax<float>( - context, op_context.input->data.f, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt32: - returnStatus = CustomMax<int>(context, op_context.input->data.i32, - op_context.input->dims->data, op_context.input->dims->size, - op_context.output->data.i32, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteUInt8: - returnStatus = CustomMax<uint8_t>( - context, op_context.input->data.uint8, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.uint8, - op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt64: - returnStatus = CustomMax<int64_t>( - context, op_context.input->data.i64, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - default: - returnStatus = kTfLiteError; - } - - return returnStatus; -} -} // namespace TensorFlowMax -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/register.cpp b/libs/support/tflite/src/kernels/register.cpp deleted file mode 100644 index 6700b4de4..000000000 --- a/libs/support/tflite/src/kernels/register.cpp +++ /dev/null @@ -1,169 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE This code is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.cc' -#include "support/tflite/kernels/register.h" -#include "support/tflite/kernels/CustomOps.h" - -// TODO Use namespace nnfw -namespace tflite -{ -namespace ops -{ -namespace builtin -{ - -TfLiteRegistration *Register_RELU(); -TfLiteRegistration *Register_RELU_N1_TO_1(); -TfLiteRegistration *Register_RELU6(); -TfLiteRegistration *Register_TANH(); -TfLiteRegistration *Register_LOGISTIC(); -TfLiteRegistration *Register_AVERAGE_POOL_2D(); -TfLiteRegistration *Register_MAX_POOL_2D(); -TfLiteRegistration *Register_L2_POOL_2D(); -TfLiteRegistration *Register_CONV_2D(); -TfLiteRegistration *Register_DEPTHWISE_CONV_2D(); -TfLiteRegistration *Register_SVDF(); -TfLiteRegistration *Register_RNN(); -TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration *Register_EMBEDDING_LOOKUP(); -TfLiteRegistration *Register_EMBEDDING_LOOKUP_SPARSE(); -TfLiteRegistration *Register_FULLY_CONNECTED(); -TfLiteRegistration *Register_LSH_PROJECTION(); -TfLiteRegistration *Register_HASHTABLE_LOOKUP(); -TfLiteRegistration *Register_SOFTMAX(); -TfLiteRegistration *Register_CONCATENATION(); -TfLiteRegistration *Register_ADD(); -TfLiteRegistration *Register_SPACE_TO_BATCH_ND(); -TfLiteRegistration *Register_DIV(); -TfLiteRegistration *Register_SUB(); -TfLiteRegistration *Register_BATCH_TO_SPACE_ND(); -TfLiteRegistration *Register_MUL(); -TfLiteRegistration *Register_L2_NORMALIZATION(); -TfLiteRegistration *Register_LOCAL_RESPONSE_NORMALIZATION(); -TfLiteRegistration *Register_LSTM(); -TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration *Register_PAD(); -TfLiteRegistration *Register_PADV2(); -TfLiteRegistration *Register_RESHAPE(); -TfLiteRegistration *Register_RESIZE_BILINEAR(); -TfLiteRegistration *Register_SKIP_GRAM(); -TfLiteRegistration *Register_SPACE_TO_DEPTH(); -TfLiteRegistration *Register_GATHER(); -TfLiteRegistration *Register_TRANSPOSE(); -TfLiteRegistration *Register_MEAN(); -TfLiteRegistration *Register_SPLIT(); -TfLiteRegistration *Register_SQUEEZE(); -TfLiteRegistration *Register_STRIDED_SLICE(); -TfLiteRegistration *Register_EXP(); -TfLiteRegistration *Register_TOPK_V2(); -TfLiteRegistration *Register_LOG_SOFTMAX(); -TfLiteRegistration *Register_CAST(); -TfLiteRegistration *Register_DEQUANTIZE(); -TfLiteRegistration *Register_PRELU(); -TfLiteRegistration *Register_MAXIMUM(); -TfLiteRegistration *Register_MINIMUM(); -TfLiteRegistration *Register_ARG_MAX(); -TfLiteRegistration *Register_GREATER(); -TfLiteRegistration *Register_GREATER_EQUAL(); -TfLiteRegistration *Register_LESS(); -TfLiteRegistration *Register_LESS_EQUAL(); -TfLiteRegistration *Register_FLOOR(); -TfLiteRegistration *Register_NEG(); -TfLiteRegistration *Register_SELECT(); -TfLiteRegistration *Register_SLICE(); -TfLiteRegistration *Register_SIN(); -TfLiteRegistration *Register_TRANSPOSE_CONV(); -TfLiteRegistration *Register_SPARSE_TO_DENSE(); - -BuiltinOpResolver::BuiltinOpResolver() -{ - AddBuiltin(BuiltinOperator_RELU, Register_RELU()); - AddBuiltin(BuiltinOperator_RELU_N1_TO_1, Register_RELU_N1_TO_1()); - AddBuiltin(BuiltinOperator_RELU6, Register_RELU6()); - AddBuiltin(BuiltinOperator_TANH, Register_TANH()); - AddBuiltin(BuiltinOperator_LOGISTIC, Register_LOGISTIC()); - AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, Register_AVERAGE_POOL_2D()); - AddBuiltin(BuiltinOperator_MAX_POOL_2D, Register_MAX_POOL_2D()); - AddBuiltin(BuiltinOperator_L2_POOL_2D, Register_L2_POOL_2D()); - AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D()); - AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, Register_DEPTHWISE_CONV_2D()); - AddBuiltin(BuiltinOperator_SVDF, Register_SVDF()); - AddBuiltin(BuiltinOperator_RNN, Register_RNN()); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, Register_BIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, Register_UNIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, Register_EMBEDDING_LOOKUP_SPARSE()); - AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED()); - AddBuiltin(BuiltinOperator_LSH_PROJECTION, Register_LSH_PROJECTION()); - AddBuiltin(BuiltinOperator_HASHTABLE_LOOKUP, Register_HASHTABLE_LOOKUP()); - AddBuiltin(BuiltinOperator_SOFTMAX, Register_SOFTMAX()); - AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION()); - AddBuiltin(BuiltinOperator_ADD, Register_ADD()); - AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND()); - AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, Register_BATCH_TO_SPACE_ND()); - AddBuiltin(BuiltinOperator_MUL, Register_MUL()); - AddBuiltin(BuiltinOperator_L2_NORMALIZATION, Register_L2_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, Register_LOCAL_RESPONSE_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LSTM, Register_LSTM()); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, Register_BIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, Register_UNIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_PAD, Register_PAD()); - AddBuiltin(BuiltinOperator_PADV2, Register_PADV2()); - AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE()); - AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, Register_RESIZE_BILINEAR()); - AddBuiltin(BuiltinOperator_SKIP_GRAM, Register_SKIP_GRAM()); - AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH()); - AddBuiltin(BuiltinOperator_GATHER, Register_GATHER()); - AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE()); - AddBuiltin(BuiltinOperator_MEAN, Register_MEAN()); - AddBuiltin(BuiltinOperator_DIV, Register_DIV()); - AddBuiltin(BuiltinOperator_SUB, Register_SUB()); - AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT()); - AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE()); - AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); - AddBuiltin(BuiltinOperator_EXP, Register_EXP()); - AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); - AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); - AddBuiltin(BuiltinOperator_CAST, Register_CAST()); - AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); - AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); - AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); - AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); - AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); - AddBuiltin(BuiltinOperator_GREATER, Register_GREATER()); - AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL()); - AddBuiltin(BuiltinOperator_LESS, Register_LESS()); - AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL()); - AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR()); - AddBuiltin(BuiltinOperator_NEG, Register_NEG()); - AddBuiltin(BuiltinOperator_SELECT, Register_SELECT()); - AddBuiltin(BuiltinOperator_SLICE, Register_SLICE()); - AddBuiltin(BuiltinOperator_SIN, Register_SIN()); - AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); - AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); - - AddCustom("TensorFlowMax", tflite::ops::custom::nnfw::Register_TensorFlowMax()); - AddCustom("RSQRT", tflite::ops::custom::nnfw::Register_RSQRT()); - AddCustom("SquaredDifference", tflite::ops::custom::nnfw::Register_SquaredDifference()); -} - -} // namespace builtin -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/nnapi_delegate.cpp b/libs/support/tflite/src/nnapi_delegate.cpp deleted file mode 100644 index 1eada4bca..000000000 --- a/libs/support/tflite/src/nnapi_delegate.cpp +++ /dev/null @@ -1,720 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This code is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.cc' -#include "support/tflite/nnapi_delegate.h" -#include <fcntl.h> -#include <sys/mman.h> -#include <sys/stat.h> -#include <sys/types.h> -#include "tensorflow/contrib/lite/builtin_op_data.h" -#include "tensorflow/contrib/lite/error_reporter.h" -#include "tensorflow/contrib/lite/model.h" -#include "NeuralNetworksShim.h" -#include "NeuralNetworksExShim.h" - -#ifdef __ANDROID__ -#include <sys/system_properties.h> -#endif - -namespace nnfw -{ - -// TODO(aselle): FATAL leaves resources hanging. -void FATAL(const char* format, ...) { - va_list args; - va_start(args, format); - vfprintf(stderr, format, args); - va_end(args); - fflush(stderr); - exit(1); -} - -// TODO(aselle): Change the error model to use status codes. -#define CHECK_TFLITE_SUCCESS(x) \ - if (x != kTfLiteOk) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -#define CHECK_NN(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -namespace { - -int32_t GetAndroidSdkVersion() { -#ifdef __ANDROID__ - const char* sdkProp = "ro.build.version.sdk"; - char sdkVersion[PROP_VALUE_MAX]; - int length = __system_property_get(sdkProp, sdkVersion); - if (length != 0) { - for (int i = 0; i < length; ++i) { - int digit = sdkVersion[i] - '0'; - if (digit < 0 || digit > 9) { - // Non-numeric SDK version, assume it's higher then expected; - return 0xFFFF; - } - } - return atoi(sdkVersion); - } - FATAL("No %s prop", sdkProp); -#endif // __ANDROID__ - return 0; -} - -static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion(); - -} // namespace - -NNAPIAllocation::NNAPIAllocation(const char* filename, - ::tflite::ErrorReporter* error_reporter) - : MMAPAllocation(filename, error_reporter) { - if (mmapped_buffer_ != MAP_FAILED) - CHECK_NN(ANeuralNetworksMemory_createFromFd(buffer_size_bytes_, PROT_READ, - mmap_fd_, 0, &handle_)); -} - -NNAPIAllocation::~NNAPIAllocation() { - if (handle_) { - ANeuralNetworksMemory_free(handle_); - } -} - -NNAPIDelegate::~NNAPIDelegate() { - if (nn_compiled_model_) { - ANeuralNetworksCompilation_free(nn_compiled_model_); - nn_compiled_model_ = nullptr; - } - if (nn_model_) { - ANeuralNetworksModel_free(nn_model_); - nn_model_ = nullptr; - // TODO(aselle): Is this thread-safe and callable multiple times? - } - // ANeuralNetworksShutdown(); -} - -// Adds the tensors of the interpreter to the NN API model. -// Returns the number of operands added. -uint32_t addTensorOperands(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, - const std::vector<uint32_t>& skip_list) { - uint32_t next_id = 0; - for (size_t i = 0; i < interpreter->tensors_size(); i++) { - // skip temporaries tensors. - bool shouldSkip = false; - for (auto skip_idx : skip_list) { - if (i == skip_idx) { - shouldSkip = true; - break; - } - } - if (shouldSkip) continue; - - int32_t nn_type = 0; - // NNAPI requires 32-bit float scale to be zero, tflite doesn't care - float scale = 0.0f; - int32_t zeroPoint = 0; - TfLiteTensor* tensor = interpreter->tensor(i); - switch (tensor->type) { - case kTfLiteNoType: - // Tensors added during initialization of Ops don't have a type yet and - // should not be registered with the NNAPI. - continue; - case kTfLiteFloat32: - nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - break; - case kTfLiteUInt8: - nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; - scale = tensor->params.scale; - // FIXME The next line is a workaround because currently zero scale is - // passed down from TF - // Lite. Note that the latest NeuralNetworks.h (see - // https://android.googlesource.com/platform/frameworks/ml/+/master/nn/runtime/include/NeuralNetworks.h) - // requires scale to be greater than zero. Remove this workaround - // when the scale - // value is correctly passed. - scale = (scale == 0.0f) ? 1.0f : scale; - zeroPoint = tensor->params.zero_point; - break; - case kTfLiteInt32: - nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = tensor->params.scale; - zeroPoint = tensor->params.zero_point; - break; - default: - FATAL("Unsupported type."); - } - // TODO(aselle): Note, many of these are intermediate results. Do I need - // to ever specify these sizes. I am currently below doing setValue - // on all of them, but I shouldn't in the future. - // Answer(jeanluc): If all the operators can set the dimension correctly, - // you won't need to. - ANeuralNetworksOperandType operand_type{ - nn_type, static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), scale, zeroPoint}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - // TODO(aselle): Based on Michael's suggestion, limiting this to read - // only memory - if (tensor->allocation_type == kTfLiteMmapRo) { - if (const NNAPIAllocation* alloc = dynamic_cast<const NNAPIAllocation*>( - static_cast<const ::tflite::Allocation*>(tensor->allocation))) { - CHECK_NN(ANeuralNetworksModel_setOperandValueFromMemory( - nn_model, next_id, alloc->memory(), alloc->offset(tensor->data.raw), - tensor->bytes)); - } else { - CHECK_NN(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, tensor->data.raw, tensor->bytes)); - } - } else if (tensor->bytes == 0) { - // These size 0 tensors are optional tensors reserved. - CHECK_NN( - ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); - } - - ++next_id; - } - return next_id; -} - -// Adds the operations and their parameters to the NN API model. -// 'next-id' is the operand ID of the next operand of the model. -void AddOpsAndParams(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, uint32_t next_id, - std::vector<int>* model_state_inputs, - std::vector<int>* model_state_outputs) { - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - const TfLiteRegistration& registration = node_and_registration->second; - tflite::BuiltinOperator builtin = - static_cast<tflite::BuiltinOperator>(registration.builtin_code); - - // Add the parameters. - std::vector<uint32_t> augmented_inputs( - node.inputs->data, node.inputs->data + node.inputs->size); - std::vector<uint32_t> augmented_outputs( - node.outputs->data, node.outputs->data + node.outputs->size); - - auto add_scalar_int32 = [&nn_model, &augmented_inputs, - &next_id](int value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(int32_t))) - augmented_inputs.push_back(next_id++); - }; - - auto add_scalar_float32 = [&nn_model, &augmented_inputs, - &next_id](float value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_FLOAT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(float))) - augmented_inputs.push_back(next_id++); - }; - - // Handle state tensors of RNN, LSTM, SVDF. - // For each state_out tensor, a corresponding state_in operand needs to be - // created for NNAPI. - auto duplicate_state_tensor_float32 = - [interpreter, &nn_model, &next_id, &augmented_inputs, - &model_state_inputs, &model_state_outputs](int tensor_id) { - const TfLiteTensor* tensor = interpreter->tensor(tensor_id); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), - tensor->params.scale, tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_inputs.push_back(next_id); - model_state_inputs->push_back(next_id); - model_state_outputs->push_back(tensor_id); - next_id++; - }; - - auto add_add_params = [&add_scalar_int32]() { add_scalar_int32(0); }; - - auto add_pooling_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLitePoolParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->filter_width); - add_scalar_int32(builtin->filter_height); - add_scalar_int32(builtin->activation); - }; - - auto add_convolution_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->activation); - }; - - auto add_depthwise_conv_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteDepthwiseConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->depth_multiplier); - add_scalar_int32(builtin->activation); - }; - - auto add_fully_connected_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); - add_scalar_int32(builtin->activation); - }; - - auto add_concatenation_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->activation != kTfLiteActNone) { - FATAL("Concatenation does not support fused activation in NNAPI"); - } - }; - - auto add_softmax_params = [&add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteSoftmaxParams*>(data); - add_scalar_float32(builtin->beta); - }; - - auto add_space_to_depth_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSpaceToDepthParams*>(data); - add_scalar_int32(builtin->block_size); - }; - - auto add_lstm_params = [&add_scalar_int32, - &add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteLSTMParams*>(data); - add_scalar_int32(builtin->activation); - add_scalar_float32(builtin->cell_clip); - add_scalar_float32(builtin->proj_clip); - }; - - // LSTM in NNAPI requires scratch tensor as an output operand. - auto add_lstm_scratch_tensor_float32 = [interpreter, &node, &nn_model, - &next_id, &augmented_outputs]() { - int scratch_buffer_index = node.temporaries->data[0]; - const TfLiteTensor* tensor = interpreter->tensor(scratch_buffer_index); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), tensor->params.scale, - tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_outputs.insert(augmented_outputs.begin(), next_id++); - }; - - auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteMeanParams*>(data); - add_scalar_int32(builtin->keep_dims); - }; - - auto add_svdf_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSVDFParams*>(data); - add_scalar_int32(builtin->rank); - add_scalar_int32(builtin->activation); - }; - - auto add_rnn_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteRNNParams*>(data); - add_scalar_int32(builtin->activation); - }; - - // Handle optional input tensors. - auto add_optional_tensors = [&nn_model, &augmented_inputs, - &next_id](int nn_type) { - for (size_t idx = 0; idx < augmented_inputs.size(); idx++) { - if (augmented_inputs[idx] == kOptionalTensor) { - const std::vector<uint32_t> dim = {0, 0}; - ANeuralNetworksOperandType operand_type{nn_type, 2, dim.data(), 0, 0}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, - nullptr, 0)) - augmented_inputs[idx] = next_id++; - } - } - }; - - int nnapi_version = 10; -#include "nnapi_delegate_ex_AddOpsAndParams_lambda.inc" - - ANeuralNetworksOperationType nn_op_type; - - switch (builtin) { - case tflite::BuiltinOperator_ADD: - nn_op_type = ANEURALNETWORKS_ADD; - add_add_params(); - break; - case tflite::BuiltinOperator_MUL: - nn_op_type = ANEURALNETWORKS_MUL; - add_add_params(); - break; - case tflite::BuiltinOperator_AVERAGE_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; - break; - case tflite::BuiltinOperator_MAX_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; - break; - case tflite::BuiltinOperator_L2_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_L2_POOL_2D; - break; - case tflite::BuiltinOperator_CONV_2D: - add_convolution_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONV_2D; - break; - case tflite::BuiltinOperator_RELU: - nn_op_type = ANEURALNETWORKS_RELU; - break; - case tflite::BuiltinOperator_RELU_N1_TO_1: - nn_op_type = ANEURALNETWORKS_RELU1; - break; - case tflite::BuiltinOperator_RELU6: - nn_op_type = ANEURALNETWORKS_RELU6; - break; - case tflite::BuiltinOperator_TANH: - nn_op_type = ANEURALNETWORKS_TANH; - break; - case tflite::BuiltinOperator_FLOOR: - nn_op_type = ANEURALNETWORKS_FLOOR; - break; - case tflite::BuiltinOperator_LOGISTIC: - nn_op_type = ANEURALNETWORKS_LOGISTIC; - break; - case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - add_depthwise_conv_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; - break; - case tflite::BuiltinOperator_CONCATENATION: - add_concatenation_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONCATENATION; - break; - case tflite::BuiltinOperator_SOFTMAX: - add_softmax_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SOFTMAX; - break; - case tflite::BuiltinOperator_FULLY_CONNECTED: - add_fully_connected_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; - break; - case tflite::BuiltinOperator_RESHAPE: - nn_op_type = ANEURALNETWORKS_RESHAPE; - // add_reshape_params(node.builtin_data); - break; - case tflite::BuiltinOperator_RESIZE_BILINEAR: - add_resize_bilinear_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RESIZE_BILINEAR; - break; - case tflite::BuiltinOperator_SPACE_TO_DEPTH: - add_space_to_depth_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; - break; - case tflite::BuiltinOperator_LSTM: { - duplicate_state_tensor_float32( - node.outputs->data[/*kOutputStateTensor*/ 0]); - duplicate_state_tensor_float32( - node.outputs->data[/*kCellStateTensor*/ 1]); - add_lstm_params(node.builtin_data); - add_lstm_scratch_tensor_float32(); - add_optional_tensors(ANEURALNETWORKS_TENSOR_FLOAT32); - nn_op_type = ANEURALNETWORKS_LSTM; - break; - } - case tflite::BuiltinOperator_DEQUANTIZE: - nn_op_type = ANEURALNETWORKS_DEQUANTIZE; - break; - case tflite::BuiltinOperator_SVDF: { - duplicate_state_tensor_float32(node.outputs->data[/*kStateTensor*/ 0]); - add_svdf_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SVDF; - break; - } - case tflite::BuiltinOperator_RNN: { - duplicate_state_tensor_float32( - node.outputs->data[/*kHiddenStateTensor*/ 0]); - add_rnn_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RNN; - break; - } - case tflite::BuiltinOperator_EMBEDDING_LOOKUP: - nn_op_type = ANEURALNETWORKS_EMBEDDING_LOOKUP; - break; - case tflite::BuiltinOperator_PAD: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_PAD; - break; - case tflite::BuiltinOperator_MEAN: - nnapi_version = 11; // require NNAPI 1.1 - add_mean_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MEAN; - break; - case tflite::BuiltinOperator_DIV: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_DIV; - add_add_params(); - break; - case tflite::BuiltinOperator_SUB: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_SUB; - add_add_params(); - break; - case tflite::BuiltinOperator_STRIDED_SLICE: - add_strided_slice_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_STRIDED_SLICE; - break; - case tflite::BuiltinOperator_CAST: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_CAST_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TOPK_V2: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TOPK_V2_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_GATHER: - add_gather_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_GATHER_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_SPLIT: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SPLIT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TRANSPOSE: - nn_op_type = ANEURALNETWORKS_TRANSPOSE; - // param is almost same as reshape - break; - case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: - case tflite::BuiltinOperator_LSH_PROJECTION: - case tflite::BuiltinOperator_HASHTABLE_LOOKUP: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_L2_NORMALIZATION: - case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: - case tflite::BuiltinOperator_PADV2: - case tflite::BuiltinOperator_CALL: - case tflite::BuiltinOperator_SKIP_GRAM: - case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - case tflite::BuiltinOperator_SQUEEZE: - case tflite::BuiltinOperator_EXP: - case tflite::BuiltinOperator_LOG_SOFTMAX: - case tflite::BuiltinOperator_DELEGATE: - case tflite::BuiltinOperator_PRELU: - case tflite::BuiltinOperator_MAXIMUM: - case tflite::BuiltinOperator_MINIMUM: - case tflite::BuiltinOperator_ARG_MAX: - case tflite::BuiltinOperator_GREATER: - case tflite::BuiltinOperator_GREATER_EQUAL: - case tflite::BuiltinOperator_LESS: - case tflite::BuiltinOperator_LESS_EQUAL: - case tflite::BuiltinOperator_NEG: - case tflite::BuiltinOperator_SELECT: - case tflite::BuiltinOperator_SLICE: - case tflite::BuiltinOperator_SIN: - case tflite::BuiltinOperator_TRANSPOSE_CONV: - case tflite::BuiltinOperator_SPARSE_TO_DENSE: - FATAL("Op code %d is currently not delegated to NNAPI", builtin); - nn_op_type = -1; // set to invalid - break; - case tflite::BuiltinOperator_CUSTOM: - std::string custom_name(registration.custom_name); - if (custom_name.compare("TensorFlowMax") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("RSQRT") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_RSQRT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("SquaredDifference") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - - FATAL("Custom operations are not supported when using NNAPI."); - nn_op_type = -1; // set to invalid - break; - } - - //if (nnapi_version == 11 && kAndroidSdkVersion < 28) { - // FATAL("Op %d needs NNAPI1.1", builtin); - //} - - // Add the operation. - CHECK_NN(ANeuralNetworksModel_addOperation( - nn_model, nn_op_type, static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<uint32_t*>(augmented_outputs.data()))); - } -} - -TfLiteStatus NNAPIDelegate::BuildGraph(::tflite::Interpreter* interpreter) { - // TODO(aselle): This is not correct. need to handle resize invalidation. - if (nn_model_ && nn_compiled_model_) return kTfLiteOk; - - if (!nn_model_) { - CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); - - // Find all the temporary tensors and put them in a skip_list. - std::vector<uint32_t> skip_list; - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - if (node.temporaries != nullptr) { - for (int j = 0; j < node.temporaries->size; j++) { - skip_list.push_back(static_cast<uint32_t>(node.temporaries->data[j])); - } - } - } - - uint32_t next_id = addTensorOperands(interpreter, nn_model_, skip_list); - AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_); - - std::vector<int> augmented_inputs = interpreter->inputs(); - std::vector<int> augmented_outputs = interpreter->outputs(); - - // All state tensors input/output need to be treated as model input/output. - augmented_inputs.insert(augmented_inputs.end(), - model_states_inputs_.begin(), - model_states_inputs_.end()); - augmented_outputs.insert(augmented_outputs.end(), - model_states_outputs_.begin(), - model_states_outputs_.end()); - - CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( - nn_model_, static_cast<uint32_t>(augmented_inputs.size()), - reinterpret_cast<const uint32_t*>(augmented_inputs.data()), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<const uint32_t*>(augmented_outputs.data()))); - CHECK_NN(ANeuralNetworksModel_finish(nn_model_)); - } - if (!nn_compiled_model_) { - CHECK_NN(ANeuralNetworksCompilation_create(nn_model_, &nn_compiled_model_)); - CHECK_NN(ANeuralNetworksCompilation_finish(nn_compiled_model_)); - } - return kTfLiteOk; -} - -TfLiteStatus NNAPIDelegate::Invoke(::tflite::Interpreter* interpreter) { - if (!nn_model_) { - TF_LITE_ENSURE_STATUS(BuildGraph(interpreter)); - } - - ANeuralNetworksExecution* execution = nullptr; - CHECK_NN(ANeuralNetworksExecution_create(nn_compiled_model_, &execution)); - - // Currently perform deep copy of input buffer - for (size_t i = 0; i < interpreter->inputs().size(); i++) { - int input = interpreter->inputs()[i]; - // TODO(aselle): Is this what we want or do we want input instead? - // TODO(aselle): This should be called setInputValue maybe to be cons. - TfLiteTensor* tensor = interpreter->tensor(input); - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // Tell nn api where to place final data. - for (size_t i = 0; i < interpreter->outputs().size(); i++) { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->tensor(output); - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // The state_out of previous invocation need to be mapped to state_in of - // current invocation. - for (size_t i = 0; i < model_states_outputs_.size(); i++) { - int state_tensor_idx = model_states_outputs_[i]; - TfLiteTensor* tensor = interpreter->tensor(state_tensor_idx); - // Here we are using a deep copy for state_in tensors so that we are not - // reading and writing into the same buffer during a invocation. - // TODO(miaowang): using double shared buffer to minimize the copies. - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i + interpreter->inputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - // Tell NNAPI where to output the state_out. - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i + interpreter->outputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - } - - // Currently use blocking compute. - ANeuralNetworksEvent* event = nullptr; - CHECK_NN(ANeuralNetworksExecution_startCompute(execution, &event)); - CHECK_NN(ANeuralNetworksEvent_wait(event)); - ANeuralNetworksEvent_free(event); - ANeuralNetworksExecution_free(execution); - -#if 0 - printf("From the NN API:\n"); - TfLiteTensor* tensor = interpreter->tensor(interpreter->outputs()[0]); - if (float* data = - interpreter->typed_tensor<float>(interpreter->outputs()[0])) { - size_t num = tensor->bytes / sizeof(float); - for (float* p = data; p < data + num; p++) { - printf(" %f", *p); - } - printf("\n"); - } -#endif - - return kTfLiteOk; -} - -} // namespace nnfw - -// clang-format on diff --git a/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc b/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc deleted file mode 100644 index ea485fe45..000000000 --- a/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc +++ /dev/null @@ -1,41 +0,0 @@ -// This file is included from AddOpsAndParams defined in nnapi_delegate.cc -// and contains lambda for extened implementation to original Tensorflow Lite. - auto add_resize_bilinear_params = [&add_scalar_int32, &interpreter, &augmented_inputs](void* data) { - auto builtin = reinterpret_cast<TfLiteResizeBilinearParams*>(data); - if (builtin->align_corners) { - FATAL("Resize bilinear does not support align corners in NNAPI"); - } - - TfLiteTensor* tensor = interpreter->tensor(augmented_inputs.back()); - assert(tensor->type == kTfLiteInt32); - assert(tensor->bytes == sizeof(int)*2); - augmented_inputs.pop_back(); - - int height = ((int*)(tensor->data.raw))[1]; - int width = ((int*)(tensor->data.raw))[0]; - add_scalar_int32(height); - add_scalar_int32(width); - }; - - auto add_strided_slice_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteStridedSliceParams*>(data); - add_scalar_int32(builtin->begin_mask); - add_scalar_int32(builtin->end_mask); - // ellipsis_mask and new_axis_mask are not supported on nn runtime - // cf) tflite interpreter supports both operations - if (builtin->ellipsis_mask) { - FATAL("STRIDE_SLICE does not support ellipsis_mask in NNAPI"); - } - if (builtin->new_axis_mask) { - FATAL("STRIDE_SLICE does not support new_axis_mask in NNAPI"); - } - add_scalar_int32(builtin->shrink_axis_mask); - }; - - auto add_gather_ex_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteGatherParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->axis != 0) { - FATAL("GATHER does not support axis>0 in NNAPI"); - } - }; diff --git a/libs/tflite/CMakeLists.txt b/libs/tflite/CMakeLists.txt new file mode 100644 index 000000000..e844d1c68 --- /dev/null +++ b/libs/tflite/CMakeLists.txt @@ -0,0 +1,12 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") +file(GLOB_RECURSE TESTS "src/*.test.cpp") +list(REMOVE_ITEM SOURCES ${TESTS}) + +add_library(nnfw_lib_tflite STATIC ${SOURCES}) +set_target_properties(nnfw_lib_tflite PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(nnfw_lib_tflite PUBLIC ${NNFW_INCLUDE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/include) +target_link_libraries(nnfw_lib_tflite tensorflow-lite ${LIB_PTHREAD} dl) +target_link_libraries(nnfw_lib_tflite nnfw_lib_misc) + +add_executable(nnfw_lib_tflite_test_TensorView src/TensorView.test.cpp) +target_link_libraries(nnfw_lib_tflite_test_TensorView nnfw_lib_tflite) diff --git a/libs/tflite/include/tflite/Assert.h b/libs/tflite/include/tflite/Assert.h new file mode 100644 index 000000000..6d12d37f6 --- /dev/null +++ b/libs/tflite/include/tflite/Assert.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Assert.h + * @brief This file contains helper function of assertion + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_ASSERT_H__ +#define __NNFW_TFLITE_ASSERT_H__ + +#include "tensorflow/contrib/lite/context.h" + +#include <sstream> + +#define STR_DETAIL(value) #value +#define STR(value) STR_DETAIL(value) + +#define TFLITE_ENSURE(exp) \ + { \ + const TfLiteStatus status = (exp); \ + \ + if (status != kTfLiteOk) \ + { \ + std::ostringstream ss; \ + ss << #exp << " failed (" << __FILE__ << ":" << __LINE__ << ")"; \ + throw std::runtime_error{ss.str()}; \ + } \ + } + +#endif // __NNFW_TFLITE_ASSERT_H__ diff --git a/libs/tflite/include/tflite/Diff.h b/libs/tflite/include/tflite/Diff.h new file mode 100644 index 000000000..15c672831 --- /dev/null +++ b/libs/tflite/include/tflite/Diff.h @@ -0,0 +1,199 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Diff.h + * @brief This file contains classes for testing correctess of implementation + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_DIFF_H__ +#define __NNFW_TFLITE_DIFF_H__ + +#include "tensorflow/contrib/lite/interpreter.h" + +#include "misc/tensor/Index.h" +#include "misc/tensor/Diff.h" +#include "misc/tensor/Shape.h" +#include "misc/tensor/Comparator.h" + +#include "tflite/TensorView.h" + +#include <functional> +#include <vector> + +/** + * @brief Class to define TfLite interpreter match application + */ +class TfLiteInterpMatchApp +{ +public: + /** + * @brief Construct a new TfLiteInterpMatchApp object with Comparator + * @param[in] comparator Comparator object for tensor comparation + */ + TfLiteInterpMatchApp(const nnfw::misc::tensor::Comparator &comparator) + : _verbose{false}, _comparator(comparator) + { + // DO NOTHING + } + +public: + /** + * @brief Get reference verbose for debugging information + * @return Reference of verbose value + */ + int &verbose(void) { return _verbose; } + +private: + int _verbose; + +public: + /** + * @brief Run two interpreter and return the output matching + * @param[in] pure Interpreter object of expected(with TfLite) + * @param[in] nnapi Interpreter object of obtained(through NNAPI) + * @return @c true if two Interpreter results are same, otherwise @c false + */ + bool run(::tflite::Interpreter &pure, ::tflite::Interpreter &nnapi) const; + /** + * @brief Compare two TensorView values and return the match result + * @param[in] expected TensorView object to read expected values + * @param[in] obtained TensorView object to read obtained values + * @param[in] id Tensor ID value used for debug message + * @return @c true if two TensorView values are same, otherwise @c false + */ + template <typename T> + bool compareSingleTensorView(const nnfw::tflite::TensorView<T> &expected, + const nnfw::tflite::TensorView<T> &obtained, int id) const; + +private: + const nnfw::misc::tensor::Comparator &_comparator; +}; + +#include "tflite/interp/Builder.h" +#include "tflite/Quantization.h" + +#include <random> + +/** + * @brief Class to generate random values + */ +class RandomGenerator +{ +public: + /** + * @brief Construct a new RandomGenerator object + * @param[in] seed Random seed value + * @param[in] mean Mean value of normal random number generation + * @param[in] stddev Standard deviation of random number generation + * @param[in] quantization TfLiteQuantizationParams type to represent quantization value + * (not used yet) + */ + RandomGenerator(int seed, float mean, float stddev, + const TfLiteQuantizationParams quantization = make_default_quantization()) + : _rand{seed}, _dist{mean, stddev}, _quantization{quantization} + { + // DO NOTHING + } + +public: + /** + * @brief Generate random numbers for type T + * @param[in] s Shape value + * @param[in] i Index value + * @return Random generated value + * @note This is same as T generate(void) as two input parameters are not used + */ + template <typename T> + T generate(const ::nnfw::misc::tensor::Shape &, const ::nnfw::misc::tensor::Index &) + { + return generate<T>(); + } + + /** + * @brief Generate random numbers for type T + * @return Random generated value + */ + template <typename T> T generate(void) { return _dist(_rand); } + +private: + std::minstd_rand _rand; + std::normal_distribution<float> _dist; + const TfLiteQuantizationParams _quantization; +}; + +template <> uint8_t RandomGenerator::generate<uint8_t>(void); + +/** + * @brief Structure for NNAPI correctness test + */ +struct RandomTestParam +{ + int verbose; //!< Verbosity of debug information + int tolerance; //!< Torlerance of value difference + int tensor_logging = 0; //!< Save logging to a file if not 0 + std::string log_path = ""; //!< Path of log file, meaningful only when tensor_logging is 1 +}; + +/** + * @brief Class to define Random test runner + */ +class RandomTestRunner +{ +public: + /** + * @brief Construct a new RandomTestRunner object + * @param[in] seed Random seed value + * @param[in] param RandomTestParam object for test runner + * @param[in] quantization TfLiteQuantizationParams type to represent quantization value + */ + RandomTestRunner(int seed, const RandomTestParam ¶m, + const TfLiteQuantizationParams quantization = make_default_quantization()) + : _randgen{seed, 0.0f, 2.0f, quantization}, _param{param} + { + // DO NOTHING + } + +public: + /** + * @brief Run the random test runner + * @param[in] builder Interpreter Builder used to run + * @return 0 if test succeeds, otherwise failure + */ + int run(const nnfw::tflite::Builder &builder); + +public: + /** + * @brief Get RandomGenerator reference + * @return RandomGenerator reference + */ + RandomGenerator &generator() { return _randgen; }; + +private: + RandomGenerator _randgen; + const RandomTestParam _param; + +public: + /** + * @brief Create a RandomTestRunner object + * @param[in] seed Random seed value + * @return RandomGenerator object + */ + static RandomTestRunner make(int seed); +}; + +#endif // __NNFW_TFLITE_DIFF_H__ diff --git a/libs/tflite/include/tflite/FeatureView.h b/libs/tflite/include/tflite/FeatureView.h new file mode 100644 index 000000000..06cbf4b14 --- /dev/null +++ b/libs/tflite/include/tflite/FeatureView.h @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file FeatureView.h + * @brief This file contains FeatureView class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_FEATURE_VIEW_H__ +#define __NNFW_TFLITE_FEATURE_VIEW_H__ + +#include "tensorflow/contrib/lite/interpreter.h" + +#include "tflite/InputIndex.h" +#include "tflite/OutputIndex.h" + +#include "misc/feature/Shape.h" +#include "misc/feature/Reader.h" + +namespace nnfw +{ +namespace tflite +{ + +template <typename T> class FeatureView; + +/** + * @brief Class to support reading element of float type feature + */ +template <> class FeatureView<float> : public nnfw::misc::feature::Reader<float> +{ +public: + /** + * @brief Construct a new FeatureView object + * @param[in] interp Interpreter to read from + * @param[in] index InputIndex index of input + */ + FeatureView(::tflite::Interpreter &interp, const InputIndex &index); + /** + * @brief Construct a new FeatureView object + * @param[in] interp Interpreter to read from + * @param[in] index OutputIndex index of output + */ + FeatureView(::tflite::Interpreter &interp, const OutputIndex &index); + +public: + /** + * @brief Get value of element using channel, row and column index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ + float at(uint32_t ch, uint32_t row, uint32_t col) const; + /** + * @brief Get reference of element using channel, row and column index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ + float &at(uint32_t ch, uint32_t row, uint32_t col); + +private: + /** + * @brief Get offset of element from channel, row and column index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Offset of element + */ + uint32_t getElementOffset(uint32_t ch, uint32_t row, uint32_t col) const + { + uint32_t res = 0; + + // TensorFlow Lite assumes that NHWC ordering for tessor + res += row * _shape.W * _shape.C; + res += col * _shape.C; + res += ch; + + return res; + } + +private: + nnfw::misc::feature::Shape _shape; + float *_base; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_FEATURE_VIEW_H__ diff --git a/libs/tflite/include/tflite/InputIndex.h b/libs/tflite/include/tflite/InputIndex.h new file mode 100644 index 000000000..f535b2626 --- /dev/null +++ b/libs/tflite/include/tflite/InputIndex.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file InputIndex.h + * @brief This file contains InputIndex class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_INPUT_INDEX_H__ +#define __NNFW_TFLITE_INPUT_INDEX_H__ + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to express index of input + */ +class InputIndex +{ +public: + /** + * @brief Construct a new InputIndex object with index value + * @param [in] index The value of index + */ + InputIndex(int index) : _index(index) + { + // DO NOTHING + } + +public: + /** + * @brief Get index value as int + * @return Index value as int + */ + int asInt(void) const { return _index; } + +private: + int _index; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INPUT_INDEX_H__ diff --git a/libs/tflite/include/tflite/InterpreterSession.h b/libs/tflite/include/tflite/InterpreterSession.h new file mode 100644 index 000000000..deaf05a7f --- /dev/null +++ b/libs/tflite/include/tflite/InterpreterSession.h @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file InterpreterSession.h + * @brief This file contains InterpreterSession class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_INTERPRETER_SESSION_H__ +#define __NNFW_TFLITE_INTERPRETER_SESSION_H__ + +#include "Session.h" + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define TfLite interpreter session which is inherited from Session class + */ +class InterpreterSession final : public Session +{ +public: + /** + * @brief Construct a InterpreterSession object with interpreter of TfLite + * @param[in] interp The TfLite interpreter pointer + */ + InterpreterSession(::tflite::Interpreter *interp) : _interp{interp} + { + // DO NOTHING + } + +public: + /** + * @brief Get TfLite interpreter pointer + * @return The TfLite interpreter + */ + ::tflite::Interpreter *interp(void) override { return _interp; } + +public: + /** + * @brief Prepare the TfLite interpreter session + * @return @c true if tensor preparation is successful, otherwise @c false + */ + bool prepare(void) override + { + _interp->UseNNAPI(false); + + if (kTfLiteOk != _interp->AllocateTensors()) + { + return false; + } + + return true; + } + + /** + * @brief Run the Invoke function of TfLite interpreter + * @return @c true if Invoke() is successful, otherwise @c false + */ + bool run(void) override + { + // Return true if Invoke returns kTfLiteOk + return kTfLiteOk == _interp->Invoke(); + } + + /** + * @brief Tear down TfLite interpreter session + * @return @c true always + */ + bool teardown(void) override + { + // Do NOTHING currently + return true; + } + +private: + ::tflite::Interpreter *const _interp; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INTERPRETER_SESSION_H__ diff --git a/libs/tflite/include/tflite/NNAPISession.h b/libs/tflite/include/tflite/NNAPISession.h new file mode 100644 index 000000000..b2a999d10 --- /dev/null +++ b/libs/tflite/include/tflite/NNAPISession.h @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file NNAPISession.h + * @brief This file contains NNAPISession class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_NNAPI_SESSION_H__ +#define __NNFW_TFLITE_NNAPI_SESSION_H__ + +#include "Session.h" +#include "tflite/ext/nnapi_delegate.h" + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define NNAPI interpreter session which is inherited from Session class + */ +class NNAPISession final : public Session +{ +public: + /** + * @brief Construct a NNAPISession object with interpreter of TfLite + * @param[in] interp The TfLite interpreter pointer + * @note Invoke BuildGraph() of NNAPI delegate from Interpreter + */ + NNAPISession(::tflite::Interpreter *interp) : _interp{interp} + { + // Construct Graph from Interpreter + _delegate.BuildGraph(_interp); + } + +public: + /** + * @brief Get TfLite interpreter pointer + * @return The TfLite interpreter + */ + ::tflite::Interpreter *interp(void) override { return _interp; } + +public: + /** + * @brief Prepare the TfLite interpreter session + * @return @c true if tensor preparation is successful, otherwise @c false + */ + bool prepare(void) override + { + // Explicitly turn off T/F lite internal NNAPI delegation in order to use locally defined + // NNAPI delegation. + _interp->UseNNAPI(false); + + if (kTfLiteOk != _interp->AllocateTensors()) + { + return false; + } + + return true; + } + + /** + * @brief Run the Invoke function of NNAPI delegate + * @return @c true if Invoke() is successful, otherwise @c false + */ + bool run(void) override { return kTfLiteOk == _delegate.Invoke(_interp); } + + /** + * @brief Tear down TfLite interpreter session + * @return @c true always + */ + bool teardown(void) override + { + // DO NOTHING + return true; + } + +private: + ::tflite::Interpreter *const _interp; + nnfw::tflite::NNAPIDelegate _delegate; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_NNAPI_SESSION_H__ diff --git a/libs/tflite/include/tflite/OutputIndex.h b/libs/tflite/include/tflite/OutputIndex.h new file mode 100644 index 000000000..dd1ca8d44 --- /dev/null +++ b/libs/tflite/include/tflite/OutputIndex.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file OutputIndex.h + * @brief This file contains OutputIndex class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_OUTPUT_INDEX_H__ +#define __NNFW_TFLITE_OUTPUT_INDEX_H__ + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define OutputIndex + */ +class OutputIndex +{ +public: + /** + * @brief Construct a OutputIndex object with index value + * @param[in] index The value of index + */ + OutputIndex(int index) : _index(index) + { + // DO NOTHING + } + +public: + /** + * @brief Get index value as int + * @return Index valuel as int + */ + int asInt(void) const { return _index; } + +private: + int _index; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_OUTPUT_INDEX_H__ diff --git a/libs/tflite/include/tflite/Quantization.h b/libs/tflite/include/tflite/Quantization.h new file mode 100644 index 000000000..4a8a0f1ac --- /dev/null +++ b/libs/tflite/include/tflite/Quantization.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Quantization.h + * @brief This file contains BitwiseIntToFloat union and quantization related + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_QUANTIZATION_H__ +#define __NNFW_TFLITE_QUANTIZATION_H__ + +/** + * @brief Union to provide bitwise conversion of integer and float + */ +union BitwiseIntToFloat { + int i; + float f; +}; + +static const float FLOAT_NEAREST_TO_1 = BitwiseIntToFloat{0x3f7fffff}.f; + +#include "tensorflow/contrib/lite/context.h" + +/** + * @brief Get TfLiteQuantizationParams object with default values + * @return TfLiteQuantizationParams object + */ +TfLiteQuantizationParams make_default_quantization(void); + +#endif // __NNFW_TFLITE_QUANTIZATION_H__ diff --git a/libs/tflite/include/tflite/Session.h b/libs/tflite/include/tflite/Session.h new file mode 100644 index 000000000..4f2e5c54d --- /dev/null +++ b/libs/tflite/include/tflite/Session.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Session.h + * @brief This file contains Session class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_SESSION_H__ +#define __NNFW_TFLITE_SESSION_H__ + +#include <tensorflow/contrib/lite/interpreter.h> + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Structure to provide interface methods of interpreter session + */ +struct Session +{ + /** + * @brief Destruct Session object using default destructor + */ + virtual ~Session() = default; + + /** + * @brief Get the Interpreter object pointer + * @return The Interpreter object pointer + */ + virtual ::tflite::Interpreter *interp(void) = 0; + + /** + * @brief Prepare the session + * @return @c true if prepare method succeeded, otherwise @c false + */ + virtual bool prepare(void) = 0; + /** + * @brief Run the session + * @return @c true if run method succeeded, otherwise @c false + */ + virtual bool run(void) = 0; + /** + * @brief Teardown(release) the session + * @return @c true if teardown method succeeded, otherwise @c false + */ + virtual bool teardown(void) = 0; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INTERP_SESSION_H__ diff --git a/libs/tflite/include/tflite/TensorLogger.h b/libs/tflite/include/tflite/TensorLogger.h new file mode 100644 index 000000000..e56a76b58 --- /dev/null +++ b/libs/tflite/include/tflite/TensorLogger.h @@ -0,0 +1,168 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TensorLogger.h + * @brief This file contains TensorLogger class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_TENSOR_LOGGER_H__ +#define __NNFW_TFLITE_TENSOR_LOGGER_H__ + +#include "misc/tensor/IndexIterator.h" +#include "tflite/TensorView.h" + +#include <tensorflow/contrib/lite/interpreter.h> +#include <tensorflow/contrib/lite/context.h> +#include <fstream> +#include <iomanip> + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to write input and output value / shape into a file in python form + * @note This is a utility to write input and output value / shape into a file in python form.\n + * any python app can load this value by running the python code below:\n + * exec(open(filename).read())\n + * generated python code looks like the following: \n + * tensor_shape_gen = []\n + * tensor_value_gen = []\n\n + * tensor_shape_gen.append("{2, 1, 2}")\n + * tensor_value_gen.append([1, 2, 3, 4])\n\n + * tensor_shape_gen.append("{2}")\n + * tensor_value_gen.append([1, 2])\n\n + * tensor_shape_gen.append("{2, 1, 2}")\n + * tensor_value_gen.append([1, 4, 3, 8])\n + */ +class TensorLogger +{ +private: + std::ofstream _outfile; + +public: + /** + * @brief Get TensorLogger instance + * @return The TensorLogger instance + */ + static TensorLogger &instance() + { + static TensorLogger instance; + return instance; + } + + /** + * @brief Save the tensor details to file from interpreter + * @param[in] path The file path to save + * @param[in] interp The TfLite interpreter + */ + void save(const std::string &path, ::tflite::Interpreter &interp) + { + open(path); + + int log_index = 0; + for (const auto id : interp.inputs()) + { + _outfile << "# input tensors" << std::endl; + printTensor(interp, id, log_index++); + } + for (const auto id : interp.outputs()) + { + _outfile << "# output tensors" << std::endl; + printTensor(interp, id, log_index++); + } + close(); + } + +private: + void open(const std::string &path) + { + if (!_outfile.is_open()) + _outfile.open(path, std::ios_base::out); + + _outfile << "# ------ file: " << path << " ------" << std::endl + << "tensor_shape_gen = []" << std::endl + << "tensor_value_gen = []" << std::endl + << std::endl; + } + + void printTensor(::tflite::Interpreter &interp, const int id, const int log_index) + { + const TfLiteTensor *tensor = interp.tensor(id); + + _outfile << "# tensor name: " << tensor->name << std::endl; + _outfile << "# tflite::interpreter.tensor(" << id << ") -> " + "tensor_value_gen[" + << log_index << "]" << std::endl; + + if (tensor->type == kTfLiteInt32) + { + printTensorShape(tensor); + printTensorValue<int32_t>(tensor, tensor->data.i32); + } + else if (interp.tensor(id)->type == kTfLiteUInt8) + { + printTensorShape(tensor); + printTensorValue<uint8_t>(tensor, tensor->data.uint8); + } + else if (tensor->type == kTfLiteFloat32) + { + printTensorShape(tensor); + printTensorValue<float>(tensor, tensor->data.f); + } + } + + void printTensorShape(const TfLiteTensor *tensor) + { + _outfile << "tensor_shape_gen.append('{"; + + size_t r = 0; + for (; r < tensor->dims->size - 1; r++) + { + _outfile << tensor->dims->data[r] << ", "; + } + _outfile << tensor->dims->data[r]; + + _outfile << "}')" << std::endl; + } + + template <typename T> void printTensorValue(const TfLiteTensor *tensor, T *tensor_data_ptr) + { + _outfile << "tensor_value_gen.append(["; + + _outfile << std::fixed << std::setprecision(10); + + const T *end = reinterpret_cast<const T *>(tensor->data.raw_const + tensor->bytes); + for (T *ptr = tensor_data_ptr; ptr < end; ptr++) + _outfile << *ptr << ", "; + + _outfile << "])" << std::endl << std::endl; + } + + void close() + { + _outfile << "# --------- tensor shape and value defined above ---------" << std::endl; + _outfile.close(); + } +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_TENSOR_LOGGER_H__ diff --git a/libs/tflite/include/tflite/TensorShapeUtils.h b/libs/tflite/include/tflite/TensorShapeUtils.h new file mode 100644 index 000000000..ba8687413 --- /dev/null +++ b/libs/tflite/include/tflite/TensorShapeUtils.h @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TensorShapeUtils.h + * @brief This file contains utilities function of tensor shape + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_TENSOR_SHAPE_UTILS_H__ +#define __NNFW_TFLITE_TENSOR_SHAPE_UTILS_H__ + +#include "misc/tensor/Shape.h" + +#include <vector> + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Converts tensor::Shape into a vector + * @param[in] shape The tensor shape to be converted + * @return vector value of given shape object + */ +static inline std::vector<int32_t> as_dims(const nnfw::misc::tensor::Shape &shape) +{ + std::vector<int32_t> dims; + + for (uint32_t axis = 0; axis < shape.rank(); ++axis) + { + dims.emplace_back(shape.dim(axis)); + } + + return dims; +} + +/** + * @brief Broadcasts between two given shapes + * @param[in] lhs_shape The left hand side shape + * @param[in] rhs_shape The right hand side shape + * @return The broadcasted shape + */ +nnfw::misc::tensor::Shape broadcast(const nnfw::misc::tensor::Shape &lhs_shape, + const nnfw::misc::tensor::Shape &rhs_shape); + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_TENSOR_SHAPE_UTILS_H__ diff --git a/libs/tflite/include/tflite/TensorUtils.h b/libs/tflite/include/tflite/TensorUtils.h new file mode 100644 index 000000000..6266c5dff --- /dev/null +++ b/libs/tflite/include/tflite/TensorUtils.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TensorUtils.h + * @brief This file contains utilities function + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_TENSOR_UTILS_H__ +#define __NNFW_TFLITE_TENSOR_UTILS_H__ + +#include <tensorflow/contrib/lite/context.h> + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Get @c true if tensor type is kTfLiteFloat32, otherwise @c false + * @param[in] tensor The tensor object to be compared + * @return @c true if tensor type is kTfLiteFloat32, otherwise @c false + */ +inline bool isFloatTensor(const TfLiteTensor *tensor) { return tensor->type == kTfLiteFloat32; } + +/** + * @brief Get @c true if tensor is 4-D tensor and the first dimension length is 1, + * otherwise @c false + * @param[in] tensor The tensor object to be compared + * @return @c true if tensor is 4-D tensor and the first dimension length is 1, otherwise @c false + */ +inline bool isFeatureTensor(const TfLiteTensor *tensor) +{ + return (tensor->dims->size == 4) && (tensor->dims->data[0] == 1); +} + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_TENSOR_UTILS_H__ diff --git a/libs/tflite/include/tflite/TensorView.h b/libs/tflite/include/tflite/TensorView.h new file mode 100644 index 000000000..79c754c78 --- /dev/null +++ b/libs/tflite/include/tflite/TensorView.h @@ -0,0 +1,120 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TensorView.h + * @brief This file contains TensorView class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_TENSOR_VIEW_H__ +#define __NNFW_TFLITE_TENSOR_VIEW_H__ + +#include "tensorflow/contrib/lite/interpreter.h" + +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" +#include "misc/tensor/Reader.h" +#include "misc/tensor/NonIncreasingStride.h" + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define TensorView which is inherited from nnfw::misc::tensor::Reader<T> class + */ +template <typename T> class TensorView final : public nnfw::misc::tensor::Reader<T> +{ +public: + /** + * @brief Construct a TensorView object with base and shape informations + * @param[in] shape The shape of a tensor + * @param[in] base The base address of a tensor + */ + TensorView(const nnfw::misc::tensor::Shape &shape, T *base) : _shape{shape}, _base{base} + { + // Set 'stride' + _stride.init(_shape); + } + +public: + /** + * @brief Get shape of tensor + * @return Reference of shape + */ + const nnfw::misc::tensor::Shape &shape(void) const { return _shape; } + +public: + /** + * @brief Get value of tensor index + * @param[in] index The tensor index + * @return The value at the index + */ + T at(const nnfw::misc::tensor::Index &index) const override + { + const auto offset = _stride.offset(index); + return *(_base + offset); + } + +public: + /** + * @brief Get reference value of tensor index + * @param[in] index The tensor index + * @return The reference value at the index + */ + T &at(const nnfw::misc::tensor::Index &index) + { + const auto offset = _stride.offset(index); + return *(_base + offset); + } + +private: + nnfw::misc::tensor::Shape _shape; /**< The tensor shape */ + +public: + T *_base; /**< The base address of tensor */ + nnfw::misc::tensor::NonIncreasingStride _stride; /**< The NonIncreasingStride object */ + +public: + // TODO Introduce Operand ID class + /** + * @brief Create TensorView object using given parameters + * @param[in] interp The TfLite interpreter + * @param[in] tensor_index The tensor index + * @return The new TensorView<T> object + */ + static TensorView<T> make(::tflite::Interpreter &interp, int tensor_index) + { + auto tensor_ptr = interp.tensor(tensor_index); + + // Set 'shape' + nnfw::misc::tensor::Shape shape(tensor_ptr->dims->size); + + for (uint32_t axis = 0; axis < shape.rank(); ++axis) + { + shape.dim(axis) = tensor_ptr->dims->data[axis]; + } + + return TensorView<T>(shape, interp.typed_tensor<T>(tensor_index)); + } +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_TENSOR_VIEW_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/Abs.h b/libs/tflite/include/tflite/ext/kernels/Abs.h new file mode 100644 index 000000000..74e4aa658 --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/Abs.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_TFLITE_EXT_KERNELS_ABS_H__ +#define __NNFW_TFLITE_EXT_KERNELS_ABS_H__ + +#include "tensorflow/contrib/lite/context.h" + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace Abs +{ + +void *InitAbs(TfLiteContext *context, const char *buffer, size_t length); +void FreeAbs(TfLiteContext *context, void *buffer); +TfLiteStatus PrepareAbs(TfLiteContext *context, TfLiteNode *node); +TfLiteStatus EvalAbs(TfLiteContext *context, TfLiteNode *node); + +} // namespace Abs +} // namespace custom +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_ABS_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/CustomOps.h b/libs/tflite/include/tflite/ext/kernels/CustomOps.h new file mode 100644 index 000000000..3f9459bb2 --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/CustomOps.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file CustomOps.h + * @brief This file contains registration of custom operands + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ +#define __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ + +#include "tensorflow/contrib/lite/context.h" +#include "tflite/ext/kernels/TensorFlowMax.h" +#include "tflite/ext/kernels/SquaredDifference.h" +#include "tflite/ext/kernels/TensorFlowSum.h" +#include "tflite/ext/kernels/Abs.h" + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ + +#define REGISTER_FUNCTION(Name) \ + TfLiteRegistration *Register_##Name(void) \ + { \ + static TfLiteRegistration r = { \ + Name::Init##Name, Name::Free##Name, Name::Prepare##Name, Name::Eval##Name, \ + }; \ + r.custom_name = #Name; \ + return &r; \ + } + +REGISTER_FUNCTION(TensorFlowMax) +REGISTER_FUNCTION(SquaredDifference) +REGISTER_FUNCTION(TensorFlowSum) +REGISTER_FUNCTION(Abs) + +#undef REGISTER_FUNCTION + +} // namespace custom +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/SquaredDifference.h b/libs/tflite/include/tflite/ext/kernels/SquaredDifference.h new file mode 100644 index 000000000..492523c02 --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/SquaredDifference.h @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file SquaredDifference.h + * @brief This file contains SquaredDifference namespace and SquaredDifference function + * definitions + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ +#define __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ + +#include "tensorflow/contrib/lite/context.h" + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace SquaredDifference +{ + +/** + * @brief Initialize SquaredDifference operand using the contents of buffer + * @param[in] context The TfLite context + * @param[in] buffer The buffer with contents + * @param[in] length The buffer length + * @return The void pointer for user data + */ +void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length); + +/** + * @brief Release any memory it might have allocated via 'InitSquaredDifference' + * @param[in] context The TfLite context + * @param[in] buffer The buffer with contents + * @return N/A + */ +void FreeSquaredDifference(TfLiteContext *context, void *buffer); + +/** + * @brief Prepare the SquaredDifference operand for execution + * @param[in] context The TfLite context + * @param[in] node The operand node + * @return The TfLite status + */ +TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node); + +/** + * @brief Evaluation the SquaredDifference operand for execution + * @param[in] context The TfLite context + * @param[in] node The operand node + * @return The TfLite status + */ +TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node); + +} // namespace SquaredDifference +} // namespace custom +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/TensorFlowMax.h b/libs/tflite/include/tflite/ext/kernels/TensorFlowMax.h new file mode 100644 index 000000000..d31d76483 --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/TensorFlowMax.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file TensorFlowMax.h + * @brief This file contains TensorFlowMax namespace and TensorFlowMax function definitions + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_MAX_H__ +#define __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_MAX_H__ + +#include "tensorflow/contrib/lite/context.h" + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace TensorFlowMax +{ + +/** + * @brief Initialize TensorFlowMax operand using the contents of buffer + * @param[in] context The TfLite context + * @param[in] buffer The buffer with contents + * @param[in] length The buffer length + * @return The void pointer for user data + */ +void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length); + +/** + * @brief Release any memory it might have allocated via 'InitTensorFlowMax' + * @param[in] context The TfLite context + * @param[in] buffer The buffer with contents + * @return N/A + */ +void FreeTensorFlowMax(TfLiteContext *context, void *buffer); + +/** + * @brief Prepare the TensorFlowMax operand for execution + * @param[in] context The TfLite context + * @param[in] node The operand node + * @return The TfLite status + */ +TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node); + +/** + * @brief Evaluation the TensorFlowMax operand for execution + * @param[in] context The TfLite context + * @param[in] node The operand node + * @return The TfLite status + */ +TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node); + +} // namespace TensorFlowMax +} // namespace custom +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_MAX_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/TensorFlowSum.h b/libs/tflite/include/tflite/ext/kernels/TensorFlowSum.h new file mode 100644 index 000000000..66783cf41 --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/TensorFlowSum.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_SUM_H__ +#define __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_SUM_H__ + +#include "tensorflow/contrib/lite/context.h" + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace TensorFlowSum +{ + +void *InitTensorFlowSum(TfLiteContext *context, const char *buffer, size_t length); +void FreeTensorFlowSum(TfLiteContext *context, void *buffer); +TfLiteStatus PrepareTensorFlowSum(TfLiteContext *context, TfLiteNode *node); +TfLiteStatus EvalTensorFlowSum(TfLiteContext *context, TfLiteNode *node); + +} // namespace TensorFlowSum +} // namespace custom +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_TENSORFLOW_SUM_H__ diff --git a/libs/tflite/include/tflite/ext/kernels/register.h b/libs/tflite/include/tflite/ext/kernels/register.h new file mode 100644 index 000000000..124af7abc --- /dev/null +++ b/libs/tflite/include/tflite/ext/kernels/register.h @@ -0,0 +1,42 @@ +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow) +// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.h' +#ifndef __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ +#define __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ + +#include <unordered_map> +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/model.h" + +namespace nnfw { +namespace tflite { + +class BuiltinOpResolver : public ::tflite::MutableOpResolver { + public: + BuiltinOpResolver(); +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ + +// clang-format on diff --git a/libs/tflite/include/tflite/ext/nnapi_delegate.h b/libs/tflite/include/tflite/ext/nnapi_delegate.h new file mode 100644 index 000000000..3aac01af7 --- /dev/null +++ b/libs/tflite/include/tflite/ext/nnapi_delegate.h @@ -0,0 +1,97 @@ +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This header is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.h' +#ifndef __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ +#define __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ + +#include "tensorflow/contrib/lite/allocation.h" +#ifdef OBS_BUILD +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/error_reporter.h" +#else +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#endif +#include "tensorflow/contrib/lite/interpreter.h" +#include "NeuralNetworksShim.h" + +class ANeuralNetworksModel; +class ANeuralNetworksMemory; +class ANeuralNetworksCompilation; + +namespace nnfw { +namespace tflite { + +class NNAPIAllocation : public ::tflite::MMAPAllocation { + public: + NNAPIAllocation(const char* filename, ::tflite::ErrorReporter* error_reporter); + ~NNAPIAllocation(); + + size_t offset(const void* ptr) const { + auto signed_offset = reinterpret_cast<const uint8_t*>(ptr) - + reinterpret_cast<const uint8_t*>(mmapped_buffer_); + + return static_cast<size_t>(signed_offset); + } + + ANeuralNetworksMemory* memory() const { return handle_; } + bool valid() const override { return handle_ != nullptr; } + + private: + mutable ANeuralNetworksMemory* handle_ = nullptr; +}; + +class NNAPIDelegate { + public: + ~NNAPIDelegate(); + + // Convert a tflite graph to NNAPI + TfLiteStatus BuildGraph(::tflite::Interpreter* interpreter); + + // Run + TfLiteStatus Invoke(::tflite::Interpreter* interpreter); + + // Whether the current platform supports NNAPI delegation. + static bool IsSupported(); + + private: + // The NN API model handle + ANeuralNetworksModel* nn_model_ = nullptr; + // The NN API compilation handle + ANeuralNetworksCompilation* nn_compiled_model_ = nullptr; + // Model status + TfLiteStatus model_status_ = kTfLiteOk; + + // List of state tensors for LSTM, RNN, SVDF. + // NN API does not allow ops to maintain states across multiple + // invocations. We need to manually create state input tensors from + // corresponding state output tensors of TFLite operations, and map them + // correctly. + std::vector<int> model_states_inputs_; // holds NNAPI operand ids + std::vector<int> model_states_outputs_; // holds TFLite tensor ids +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ + +// clang-format on diff --git a/libs/tflite/include/tflite/interp/Builder.h b/libs/tflite/include/tflite/interp/Builder.h new file mode 100644 index 000000000..b4d082419 --- /dev/null +++ b/libs/tflite/include/tflite/interp/Builder.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Builder.h + * @brief This file contains Builder structure + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_INTERP_BUILDER_H__ +#define __NNFW_TFLITE_INTERP_BUILDER_H__ + +#include <tensorflow/contrib/lite/interpreter.h> + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Structure to Builder + */ +struct Builder +{ + /** + * @brief Destroy the Builder object + */ + virtual ~Builder() = default; + + /** + * @brief Build a FlatBuffer model + * @return The TfLite interpreter object + */ + virtual std::unique_ptr<::tflite::Interpreter> build(void) const = 0; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INTERP_BUILDER_H__ diff --git a/libs/tflite/include/tflite/interp/FlatBufferBuilder.h b/libs/tflite/include/tflite/interp/FlatBufferBuilder.h new file mode 100644 index 000000000..13470b8c5 --- /dev/null +++ b/libs/tflite/include/tflite/interp/FlatBufferBuilder.h @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file FlatBufferBuilder.h + * @brief This file contains FlatBufferBuilder class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ +#define __NNFW_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ + +#include <tensorflow/contrib/lite/model.h> + +#include "tflite/interp/Builder.h" + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define FlatBufferBuilder which is inherited from Builder + */ +class FlatBufferBuilder final : public Builder +{ +public: + /** + * @brief Construct a FlatBufferBuilder object with FlatBufferModel of TfLite + * @param[in] model The TfLite Flatbuffer model + */ + FlatBufferBuilder(const ::tflite::FlatBufferModel &model) : _model{model} + { + // DO NOTHING + } + +public: + /** + * @brief Build a FlatBuffer model + * @return The TfLite interpreter pointer address + */ + std::unique_ptr<::tflite::Interpreter> build(void) const override; + +private: + const ::tflite::FlatBufferModel &_model; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INTERP_FLAT_BUFFER_BUILDER_H__ diff --git a/libs/tflite/include/tflite/interp/FunctionBuilder.h b/libs/tflite/include/tflite/interp/FunctionBuilder.h new file mode 100644 index 000000000..064375939 --- /dev/null +++ b/libs/tflite/include/tflite/interp/FunctionBuilder.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file FunctionBuilder.h + * @brief This file contains FunctionBuilder class + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NNFW_TFLITE_INTERP_FUNCTION_BUILDER_H__ +#define __NNFW_TFLITE_INTERP_FUNCTION_BUILDER_H__ + +#include <tensorflow/contrib/lite/model.h> + +#include "tflite/interp/Builder.h" + +namespace nnfw +{ +namespace tflite +{ + +/** + * @brief Class to define FunctionBuilder which is inherited from Builder + */ +class FunctionBuilder final : public Builder +{ +public: + using SetupFunc = std::function<void(::tflite::Interpreter &)>; + +public: + /** + * @brief Construct a FunctionBuilder object with SetupFunction + * @param[in] fn The SetupFunc object + */ + FunctionBuilder(const SetupFunc &fn) : _fn{fn} + { + // DO NOTHING + } + +public: + /** + * @brief Build a SetupFunc + * @return The TfLite interpreter pointer address + */ + std::unique_ptr<::tflite::Interpreter> build(void) const override; + +private: + SetupFunc _fn; +}; + +} // namespace tflite +} // namespace nnfw + +#endif // __NNFW_TFLITE_INTERP_FUNCTION_BUILDER_H__ diff --git a/libs/tflite/src/Diff.cpp b/libs/tflite/src/Diff.cpp new file mode 100644 index 000000000..45ef06110 --- /dev/null +++ b/libs/tflite/src/Diff.cpp @@ -0,0 +1,598 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/Diff.h" +#include "tflite/ext/nnapi_delegate.h" + +#include "misc/fp32.h" + +#include "misc/tensor/IndexIterator.h" +#include "misc/tensor/IndexFormatter.h" +#include "misc/tensor/Zipper.h" +#include "misc/tensor/Comparator.h" + +#include "misc/environment.h" + +#include <iostream> +#include <cassert> + +class DiffSummary : public nnfw::misc::tensor::Comparator::Observer +{ +public: + DiffSummary() + : max_abs_diff_index(0), max_abs_diff_expected{0.0f}, max_abs_diff_obtained{0.0f}, + max_abs_diff_value{0.0f}, max_rel_diff_index(0), max_rel_diff_expected{0.0f}, + max_rel_diff_obtained{0.0f}, max_rel_diff_value{0.0f} + { + // DO NOTHING + } + +public: + void notify(const nnfw::misc::tensor::Index &index, float expected, float obtained) override; + +public: + nnfw::misc::tensor::Index max_abs_diff_index; + float max_abs_diff_expected; + float max_abs_diff_obtained; + float max_abs_diff_value; + + nnfw::misc::tensor::Index max_rel_diff_index; + float max_rel_diff_expected; + float max_rel_diff_obtained; + float max_rel_diff_value; +}; + +void DiffSummary::notify(const nnfw::misc::tensor::Index &index, float expected, float obtained) +{ + const auto abs_diff_value = std::fabs(expected - obtained); + + if (max_abs_diff_value < abs_diff_value) + { + max_abs_diff_index = index; + max_abs_diff_value = abs_diff_value; + max_abs_diff_expected = expected; + max_abs_diff_obtained = obtained; + } + + const auto rel_diff_value = nnfw::misc::fp32::relative_diff(expected, obtained); + + if (max_rel_diff_value < rel_diff_value) + { + max_rel_diff_index = index; + max_rel_diff_value = rel_diff_value; + max_rel_diff_expected = expected; + max_rel_diff_obtained = obtained; + } +} + +template <typename T> +bool TfLiteInterpMatchApp::compareSingleTensorView(const nnfw::tflite::TensorView<T> &expected, + const nnfw::tflite::TensorView<T> &obtained, + int id) const +{ + std::vector<nnfw::misc::tensor::Diff<T>> diffs; + assert(expected.shape() == obtained.shape()); + + using nnfw::misc::tensor::zip; + using nnfw::misc::tensor::Index; + + zip(expected.shape(), expected, obtained) + << [&](const Index &index, T expected_value, T obtained_value) { + if (expected_value != obtained_value) + { + diffs.emplace_back(index, expected_value, obtained_value); + } + }; + + // TODO Unify summary generation code + if (diffs.size() == 0) + { + std::cout << " Tensor #" << id << ": MATCHED" << std::endl; + } + else + { + std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; + std::cout << " " << diffs.size() << " diffs are detected" << std::endl; + } + + if (diffs.size() > 0 && _verbose != 0) + { + std::cout << " ---- Details ---" << std::endl; + for (const auto &diff : diffs) + { + std::cout << " Diff at [" << nnfw::misc::tensor::IndexFormatter(diff.index) << "]" + << std::endl; + std::cout << " expected: " << diff.expected << std::endl; + std::cout << " obtained: " << diff.obtained << std::endl; + } + } + + return diffs.size() == 0; +} + +template <> +bool TfLiteInterpMatchApp::compareSingleTensorView<float>( + const nnfw::tflite::TensorView<float> &expected, + const nnfw::tflite::TensorView<float> &obtained, int id) const +{ + DiffSummary summary; + + assert(expected.shape() == obtained.shape()); + auto diffs = _comparator.compare(expected.shape(), expected, obtained, &summary); + + // TODO Unify summary generation code + if (diffs.size() == 0) + { + std::cout << " Tensor #" << id << ": MATCHED" << std::endl; + } + else + { + std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; + std::cout << " " << diffs.size() << " diffs are detected" << std::endl; + } + + // Print out max_diff + if (summary.max_abs_diff_value > 0) + { + std::cout << " Max absolute diff at [" + << nnfw::misc::tensor::IndexFormatter(summary.max_abs_diff_index) << "]" << std::endl; + std::cout << " expected: " << summary.max_abs_diff_expected << std::endl; + std::cout << " obtained: " << summary.max_abs_diff_obtained << std::endl; + std::cout << " absolute diff: " << summary.max_abs_diff_value << std::endl; + } + + if (summary.max_rel_diff_value > 0) + { + const auto tolerance_level = summary.max_rel_diff_value / FLT_EPSILON; + + std::cout << " Max relative diff at [" + << nnfw::misc::tensor::IndexFormatter(summary.max_rel_diff_index) << "]" << std::endl; + std::cout << " expected: " << summary.max_rel_diff_expected << std::endl; + std::cout << " obtained: " << summary.max_rel_diff_obtained << std::endl; + std::cout << " relative diff: " << summary.max_rel_diff_value << std::endl; + std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; + } + + if (diffs.size() > 0) + { + if (_verbose != 0) + { + std::cout << " ---- Details ---" << std::endl; + for (const auto &diff : diffs) + { + const auto absolute_diff = std::fabs(diff.expected - diff.obtained); + const auto relative_diff = nnfw::misc::fp32::relative_diff(diff.expected, diff.obtained); + const auto tolerance_level = relative_diff / FLT_EPSILON; + + std::cout << " Diff at [" << nnfw::misc::tensor::IndexFormatter(diff.index) << "]" + << std::endl; + std::cout << " expected: " << diff.expected << std::endl; + std::cout << " obtained: " << diff.obtained << std::endl; + std::cout << " absolute diff: " << absolute_diff << std::endl; + std::cout << " relative diff: " << relative_diff << std::endl; + std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; + } + } + + return false; + } + return true; +} + +#include <map> + +bool TfLiteInterpMatchApp::run(::tflite::Interpreter &interp, ::tflite::Interpreter &nnapi) const +{ + assert(interp.outputs() == nnapi.outputs()); + + bool all_matched = true; + + using Comparator = std::function<bool(int id, ::tflite::Interpreter &, ::tflite::Interpreter &)>; + + std::map<TfLiteType, Comparator> comparators; + + comparators[kTfLiteUInt8] = [this](int id, ::tflite::Interpreter &interp, + ::tflite::Interpreter &nnapi) { + const auto expected = nnfw::tflite::TensorView<uint8_t>::make(interp, id); + const auto obtained = nnfw::tflite::TensorView<uint8_t>::make(nnapi, id); + + return compareSingleTensorView(expected, obtained, id); + }; + + comparators[kTfLiteInt32] = [this](int id, ::tflite::Interpreter &interp, + ::tflite::Interpreter &nnapi) { + const auto expected = nnfw::tflite::TensorView<int32_t>::make(interp, id); + const auto obtained = nnfw::tflite::TensorView<int32_t>::make(nnapi, id); + + return compareSingleTensorView(expected, obtained, id); + }; + + comparators[kTfLiteFloat32] = [this](int id, ::tflite::Interpreter &interp, + ::tflite::Interpreter &nnapi) { + const auto expected = nnfw::tflite::TensorView<float>::make(interp, id); + const auto obtained = nnfw::tflite::TensorView<float>::make(nnapi, id); + + return compareSingleTensorView(expected, obtained, id); + }; + + comparators[kTfLiteBool] = [this](int id, ::tflite::Interpreter &interp, + ::tflite::Interpreter &nnapi) { + const auto expected = nnfw::tflite::TensorView<bool>::make(interp, id); + const auto obtained = nnfw::tflite::TensorView<bool>::make(nnapi, id); + + return compareSingleTensorView(expected, obtained, id); + }; + + for (const auto &id : interp.outputs()) + { + assert(interp.tensor(id)->type == nnapi.tensor(id)->type); + + auto it = comparators.find(interp.tensor(id)->type); + + if (it == comparators.end()) + { + throw std::runtime_error{"Not supported output type"}; + } + + const auto &comparator = it->second; + + if (!comparator(id, interp, nnapi)) + { + all_matched = false; + } + } + + return all_matched; +} + +#include "misc/tensor/Object.h" + +using namespace std::placeholders; + +template <> uint8_t RandomGenerator::generate<uint8_t>(void) +{ + // The value of type_range is 255. + float type_range = static_cast<float>(std::numeric_limits<uint8_t>::max()) - + static_cast<float>(std::numeric_limits<uint8_t>::min()); + // Most _dist values range from -5.0 to 5.0. + float min_range = -5.0f; + float max_range = 5.0f; + return static_cast<uint8_t>((_dist(_rand) - min_range) * type_range / (max_range - min_range)); +} + +#include "tflite/TensorLogger.h" +// +// Random Test Runner +// +int RandomTestRunner::run(const nnfw::tflite::Builder &builder) +{ + auto tfl_interp = builder.build(); + auto nnapi = builder.build(); + + tfl_interp->UseNNAPI(false); + + // Allocate Tensors + tfl_interp->AllocateTensors(); + nnapi->AllocateTensors(); + + assert(tfl_interp->inputs() == nnapi->inputs()); + + using ::tflite::Interpreter; + using Initializer = std::function<void(int id, Interpreter *, Interpreter *)>; + + std::map<TfLiteType, Initializer> initializers; + std::map<TfLiteType, Initializer> reseters; + + // Generate singed 32-bit integer (s32) input + initializers[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteInt32); + assert(nnapi->tensor(id)->type == kTfLiteInt32); + + auto tfl_interp_view = nnfw::tflite::TensorView<int32_t>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<int32_t>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + int32_t value = 0; + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + ++value; + }; + }; + + // Generate singed 32-bit integer (s32) input + reseters[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteInt32); + assert(nnapi->tensor(id)->type == kTfLiteInt32); + + auto tfl_interp_view = nnfw::tflite::TensorView<int32_t>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<int32_t>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + int32_t value = 0; + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + initializers[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); + assert(nnapi->tensor(id)->type == kTfLiteUInt8); + + auto tfl_interp_view = nnfw::tflite::TensorView<uint8_t>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<uint8_t>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<uint8_t>); + const nnfw::misc::tensor::Object<uint8_t> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + assert(tfl_interp_view.shape() == data.shape()); + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + const auto value = data.at(ind); + + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + reseters[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); + assert(nnapi->tensor(id)->type == kTfLiteUInt8); + + auto tfl_interp_view = nnfw::tflite::TensorView<uint8_t>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<uint8_t>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<uint8_t>); + const nnfw::misc::tensor::Object<uint8_t> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + assert(tfl_interp_view.shape() == data.shape()); + + uint8_t value = 0; + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + initializers[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); + assert(nnapi->tensor(id)->type == kTfLiteFloat32); + + auto tfl_interp_view = nnfw::tflite::TensorView<float>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<float>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<float>); + const nnfw::misc::tensor::Object<float> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + + assert(tfl_interp_view.shape() == data.shape()); + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + const auto value = data.at(ind); + + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + reseters[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); + assert(nnapi->tensor(id)->type == kTfLiteFloat32); + + auto tfl_interp_view = nnfw::tflite::TensorView<float>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<float>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<float>); + const nnfw::misc::tensor::Object<float> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + + assert(tfl_interp_view.shape() == data.shape()); + + float value = 0; + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + initializers[kTfLiteBool] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteBool); + assert(nnapi->tensor(id)->type == kTfLiteBool); + + auto tfl_interp_view = nnfw::tflite::TensorView<bool>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<bool>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<bool (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<bool>); + const nnfw::misc::tensor::Object<bool> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + + assert(tfl_interp_view.shape() == data.shape()); + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + const auto value = data.at(ind); + + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + reseters[kTfLiteBool] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { + assert(tfl_interp->tensor(id)->type == kTfLiteBool); + assert(nnapi->tensor(id)->type == kTfLiteBool); + + auto tfl_interp_view = nnfw::tflite::TensorView<bool>::make(*tfl_interp, id); + auto nnapi_view = nnfw::tflite::TensorView<bool>::make(*nnapi, id); + + assert(tfl_interp_view.shape() == nnapi_view.shape()); + + auto fp = static_cast<bool (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<bool>); + const nnfw::misc::tensor::Object<bool> data(tfl_interp_view.shape(), + std::bind(fp, _randgen, _1, _2)); + + assert(tfl_interp_view.shape() == data.shape()); + + bool value = false; + + nnfw::misc::tensor::iterate(tfl_interp_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + tfl_interp_view.at(ind) = value; + nnapi_view.at(ind) = value; + }; + }; + + // Fill IFM with random numbers + for (const auto id : tfl_interp->inputs()) + { + assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); + + auto it = initializers.find(tfl_interp->tensor(id)->type); + + if (it == initializers.end()) + { + throw std::runtime_error{"Not supported input type"}; + } + + it->second(id, tfl_interp.get(), nnapi.get()); + } + + // Fill OFM with 0 + for (const auto id : tfl_interp->outputs()) + { + assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); + + auto it = reseters.find(tfl_interp->tensor(id)->type); + + if (it == reseters.end()) + { + throw std::runtime_error{"Not supported input type"}; + } + + it->second(id, tfl_interp.get(), nnapi.get()); + } + + std::cout << "[NNAPI TEST] Run T/F Lite Interpreter without NNAPI" << std::endl; + tfl_interp->Invoke(); + + std::cout << "[NNAPI TEST] Run T/F Lite Interpreter with NNAPI" << std::endl; + + char *env = getenv("UPSTREAM_DELEGATE"); + + if (env && !std::string(env).compare("1")) + { + nnapi->UseNNAPI(true); + nnapi->Invoke(); + } + else + { + nnfw::tflite::NNAPIDelegate d; + + if (d.BuildGraph(nnapi.get())) + { + throw std::runtime_error{"Failed to BuildGraph"}; + } + + if (d.Invoke(nnapi.get())) + { + throw std::runtime_error{"Failed to BuildGraph"}; + } + } + + // Compare OFM + std::cout << "[NNAPI TEST] Compare the result" << std::endl; + + const auto tolerance = _param.tolerance; + + auto equals = [tolerance](float lhs, float rhs) { + // NOTE Hybrid approach + // TODO Allow users to set tolerance for absolute_epsilon_equal + if (nnfw::misc::fp32::absolute_epsilon_equal(lhs, rhs)) + { + return true; + } + + return nnfw::misc::fp32::epsilon_equal(lhs, rhs, tolerance); + }; + + nnfw::misc::tensor::Comparator comparator(equals); + TfLiteInterpMatchApp app(comparator); + + app.verbose() = _param.verbose; + + bool res = app.run(*tfl_interp, *nnapi); + + if (!res) + { + return 255; + } + + std::cout << "[NNAPI TEST] PASSED" << std::endl; + + if (_param.tensor_logging) + nnfw::tflite::TensorLogger::instance().save(_param.log_path, *tfl_interp); + + return 0; +} + +RandomTestRunner RandomTestRunner::make(int seed) +{ + RandomTestParam param; + + param.verbose = 0; + param.tolerance = 1; + + nnfw::misc::env::IntAccessor("VERBOSE").access(param.verbose); + nnfw::misc::env::IntAccessor("TOLERANCE").access(param.tolerance); + + return RandomTestRunner{seed, param}; +} diff --git a/libs/tflite/src/FeatureView.cpp b/libs/tflite/src/FeatureView.cpp new file mode 100644 index 000000000..fdf5a4b00 --- /dev/null +++ b/libs/tflite/src/FeatureView.cpp @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/FeatureView.h" +#include "tflite/TensorUtils.h" + +#include <cassert> + +namespace nnfw +{ +namespace tflite +{ + +nnfw::misc::feature::Shape getFeatureShape(const TfLiteTensor *tensor) +{ + nnfw::misc::feature::Shape shape{tensor->dims->data[3], tensor->dims->data[1], + tensor->dims->data[2]}; + + return shape; +} + +FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const InputIndex &index) +{ + const auto tensor_index = interp.inputs().at(index.asInt()); + auto tensor_ptr = interp.tensor(tensor_index); + + assert(isFloatTensor(tensor_ptr)); + assert(isFeatureTensor(tensor_ptr)); + + _shape = getFeatureShape(tensor_ptr); + _base = interp.typed_tensor<float>(tensor_index); +} + +FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const OutputIndex &index) +{ + const auto tensor_index = interp.outputs().at(index.asInt()); + auto tensor_ptr = interp.tensor(tensor_index); + + assert(isFloatTensor(tensor_ptr)); + assert(isFeatureTensor(tensor_ptr)); + + _shape = getFeatureShape(tensor_ptr); + _base = interp.typed_tensor<float>(tensor_index); +} + +float FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) const +{ + return *(_base + getElementOffset(ch, row, col)); +} + +float &FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) +{ + return *(_base + getElementOffset(ch, row, col)); +} + +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/Quantization.cpp b/libs/tflite/src/Quantization.cpp new file mode 100644 index 000000000..9c162c342 --- /dev/null +++ b/libs/tflite/src/Quantization.cpp @@ -0,0 +1,22 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/Quantization.h" + +TfLiteQuantizationParams make_default_quantization(void) +{ + return TfLiteQuantizationParams{0.0f, 0}; +} diff --git a/libs/tflite/src/TensorShapeUtils.cpp b/libs/tflite/src/TensorShapeUtils.cpp new file mode 100644 index 000000000..b5d906719 --- /dev/null +++ b/libs/tflite/src/TensorShapeUtils.cpp @@ -0,0 +1,48 @@ +#include "tflite/TensorShapeUtils.h" + +namespace nnfw +{ +namespace tflite +{ + +nnfw::misc::tensor::Shape broadcast(const nnfw::misc::tensor::Shape &lhs_shape, + const nnfw::misc::tensor::Shape &rhs_shape) +{ + const uint32_t lhs_rank = lhs_shape.rank(); + const uint32_t rhs_rank = rhs_shape.rank(); + const uint32_t out_rank = std::max(lhs_rank, rhs_rank); + + // TODO Simplify implementation + std::vector<int32_t> lhs_normalized_dims; + std::vector<int32_t> rhs_normalized_dims; + + for (uint32_t n = 0; n < out_rank - lhs_rank; ++n) + { + lhs_normalized_dims.emplace_back(1); + } + for (uint32_t axis = 0; axis < lhs_rank; ++axis) + { + lhs_normalized_dims.emplace_back(lhs_shape.dim(axis)); + } + + for (uint32_t n = 0; n < out_rank - rhs_rank; ++n) + { + rhs_normalized_dims.emplace_back(1); + } + for (uint32_t axis = 0; axis < rhs_rank; ++axis) + { + rhs_normalized_dims.emplace_back(rhs_shape.dim(axis)); + } + + nnfw::misc::tensor::Shape out_shape(out_rank); + + for (uint32_t axis = 0; axis < out_rank; ++axis) + { + out_shape.dim(axis) = std::max(lhs_normalized_dims.at(axis), rhs_normalized_dims.at(axis)); + } + + return out_shape; +} + +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/TensorView.test.cpp b/libs/tflite/src/TensorView.test.cpp new file mode 100644 index 000000000..c710b3c33 --- /dev/null +++ b/libs/tflite/src/TensorView.test.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/TensorView.h" + +#include <cassert> + +void int_test(void) +{ + int value[6] = {1, 2, 3, 4, 5, 6}; + + const nnfw::misc::tensor::Shape shape{2, 3}; + const nnfw::tflite::TensorView<int> view{shape, value}; + + assert(view.at(nnfw::misc::tensor::Index{0, 0}) == 1); + assert(view.at(nnfw::misc::tensor::Index{0, 1}) == 2); + assert(view.at(nnfw::misc::tensor::Index{0, 2}) == 3); + assert(view.at(nnfw::misc::tensor::Index{1, 0}) == 4); + assert(view.at(nnfw::misc::tensor::Index{1, 1}) == 5); + assert(view.at(nnfw::misc::tensor::Index{1, 2}) == 6); +} + +int main(int argc, char **argv) +{ + float value[6] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; + + const nnfw::misc::tensor::Shape shape{2, 3}; + const nnfw::tflite::TensorView<float> view{shape, value}; + + assert(view.at(nnfw::misc::tensor::Index{0, 0}) == 1.0f); + assert(view.at(nnfw::misc::tensor::Index{0, 1}) == 2.0f); + assert(view.at(nnfw::misc::tensor::Index{0, 2}) == 3.0f); + assert(view.at(nnfw::misc::tensor::Index{1, 0}) == 4.0f); + assert(view.at(nnfw::misc::tensor::Index{1, 1}) == 5.0f); + assert(view.at(nnfw::misc::tensor::Index{1, 2}) == 6.0f); + + int_test(); + + return 0; +} diff --git a/libs/tflite/src/ext/kernels/Abs.cpp b/libs/tflite/src/ext/kernels/Abs.cpp new file mode 100644 index 000000000..7e9c2338d --- /dev/null +++ b/libs/tflite/src/ext/kernels/Abs.cpp @@ -0,0 +1,103 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/Abs.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +#include <iostream> +#include <cmath> + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace Abs +{ + +void *InitAbs(TfLiteContext *context, const char *buffer, size_t length) { return nullptr; } + +void FreeAbs(TfLiteContext *context, void *buffer) {} + +TfLiteStatus PrepareAbs(TfLiteContext *context, TfLiteNode *node) +{ + TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); + + const TfLiteTensor *input = ::tflite::GetInput(context, node, 0); + TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); + + TF_LITE_ENSURE_EQ(context, input->type, output->type); + + return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input->dims)); +} + +TfLiteStatus EvalAbs(TfLiteContext *context, TfLiteNode *node) +{ + const TfLiteTensor *input = ::tflite::GetInput(context, node, 0); + TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); + size_t elements = ::tflite::NumElements(input); + switch (input->type) + { + case kTfLiteFloat32: + { + auto *in = input->data.f; + auto *in_end = in + elements; + auto *out = output->data.f; + for (; in < in_end; in++, out++) + *out = std::abs(*in); + return kTfLiteOk; + } + case kTfLiteInt32: + { + auto *in = input->data.i32; + auto *in_end = in + elements; + auto *out = output->data.i32; + for (; in < in_end; in++, out++) + *out = std::abs(*in); + return kTfLiteOk; + } + case kTfLiteInt64: + { + auto *in = input->data.i64; + auto *in_end = in + elements; + auto *out = output->data.i64; + for (; in < in_end; in++, out++) + *out = std::abs(*in); + return kTfLiteOk; + } + case kTfLiteUInt8: + { + auto *in = input->data.uint8; + auto *in_end = in + elements; + auto *out = output->data.uint8; + for (; in < in_end; in++, out++) + *out = std::abs(*in); + return kTfLiteOk; + } + default: + { + context->ReportError(context, "Input type %d is not supported", input->type); + return kTfLiteError; + } + } +} + +} // namespace Abs +} // namespace custom +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/SquaredDifference.cpp b/libs/tflite/src/ext/kernels/SquaredDifference.cpp new file mode 100644 index 000000000..8ac2b1de0 --- /dev/null +++ b/libs/tflite/src/ext/kernels/SquaredDifference.cpp @@ -0,0 +1,112 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/SquaredDifference.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +#include <iostream> + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace SquaredDifference +{ + +void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length) +{ + return nullptr; +} + +void FreeSquaredDifference(TfLiteContext *context, void *buffer) {} + +TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node) +{ + TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); + + const TfLiteTensor *input1 = ::tflite::GetInput(context, node, 0); + const TfLiteTensor *input2 = ::tflite::GetInput(context, node, 1); + TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); + + TF_LITE_ENSURE_EQ(context, input1->type, input2->type); + TF_LITE_ENSURE_EQ(context, input1->type, output->type); + + return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input1->dims)); +} + +TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node) +{ + + const TfLiteTensor *input1 = ::tflite::GetInput(context, node, 0); + const TfLiteTensor *input2 = ::tflite::GetInput(context, node, 1); + + TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); + + size_t elements = ::tflite::NumElements(input1); + + switch (input1->type) + { + case kTfLiteFloat32: + { + const float *in1 = input1->data.f; + const float *in2 = input2->data.f; + const float *in_end1 = in1 + elements; + float *out = output->data.f; + + for (; in1 < in_end1; in1++, in2++, out++) + *out = ((*in1 - *in2) * (*in1 - *in2)); + + return kTfLiteOk; + } + case kTfLiteInt32: + { + const int *in1 = input1->data.i32; + const int *in2 = input2->data.i32; + const int *in_end1 = in1 + elements; + int *out = output->data.i32; + + for (; in1 < in_end1; in1++, in2++, out++) + *out = ((*in1 - *in2) * (*in1 - *in2)); + + return kTfLiteOk; + } + case kTfLiteInt64: + { + const int64_t *in1 = input1->data.i64; + const int64_t *in2 = input1->data.i64; + const int64_t *in_end1 = in1 + elements; + int64_t *out = output->data.i64; + + for (; in1 < in_end1; in1++, in2++, out++) + *out = ((*in1 - *in2) * (*in1 - *in2)); + + return kTfLiteOk; + } + default: + { + context->ReportError(context, "InputType is %d Unsupported", input1->type); + return kTfLiteError; + } + } +} + +} // namespace SquaredDifference +} // namespace custom +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/TensorFlowMax.cpp b/libs/tflite/src/ext/kernels/TensorFlowMax.cpp new file mode 100644 index 000000000..d72ad242c --- /dev/null +++ b/libs/tflite/src/ext/kernels/TensorFlowMax.cpp @@ -0,0 +1,405 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/TensorFlowMax.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +#include <iostream> + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace TensorFlowMax +{ + +struct TensorFlowMaxOp +{ + TensorFlowMaxOp(TfLiteContext *context, TfLiteNode *node) + { + input = ::tflite::GetInput(context, node, 0); + axis = ::tflite::GetInput(context, node, 1); + output = ::tflite::GetOutput(context, node, 0); + } + const TfLiteTensor *input; + const TfLiteTensor *axis; + TfLiteTensor *output; +}; + +void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length) +{ + // Creates two temp tensors to store index and axis for internal + // implementation only. + auto *scratch_tensor_index = new int; + context->AddTensors(context, 2, scratch_tensor_index); + return scratch_tensor_index; +} + +void FreeTensorFlowMax(TfLiteContext *context, void *buffer) +{ + delete static_cast<TensorFlowMaxOp *>(buffer); +} + +// Resizes the temp tensor that stores resolved axis. +TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowMaxOp *op_context, + TfLiteTensor *resolved_axis) +{ + TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); + axis_size->data[0] = static_cast<int>(::tflite::NumElements(op_context->axis)); + return context->ResizeTensor(context, resolved_axis, axis_size); +} + +// Resizes output array based on the input size and resolved axis. +TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowMaxOp *op_context) +{ + size_t num_axis = ::tflite::NumElements(op_context->axis); + TfLiteIntArray *input_dims = op_context->input->dims; + int input_num_dims = ::tflite::NumDimensions(op_context->input); + const int *axis = op_context->axis->data.i32; + + { + // Calculates size of reducing axis. + int num_reduce_axis = num_axis; + for (int i = 0; i < num_axis; ++i) + { + int current = axis[i]; + if (current < 0) + { + current += input_num_dims; + } + TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); + for (int j = 0; j < i; ++j) + { + int previous = axis[j]; + if (previous < 0) + { + previous += input_num_dims; + } + if (current == previous) + { + --num_reduce_axis; + break; + } + } + } + // Determines output dimensions. + int output_num_dims = ::tflite::NumDimensions(op_context->output); + TF_LITE_ENSURE(context, (input_num_dims == output_num_dims) || + (input_num_dims - num_reduce_axis == output_num_dims)); + + if (input_num_dims == output_num_dims) + { + TfLiteIntArray *output_dims = TfLiteIntArrayCopy(input_dims); + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + int current = axis[axis_idx]; + output_dims->data[current] = 1; + } + return context->ResizeTensor(context, op_context->output, output_dims); + } + else + { + TfLiteIntArray *output_dims = TfLiteIntArrayCreate(output_num_dims); + int num_skip_axis = 0; + for (int idx = 0; idx < input_num_dims; ++idx) + { + bool is_axis = false; + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) + { + ++num_skip_axis; + is_axis = true; + break; + } + } + if (!is_axis) + { + output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; + } + } + return context->ResizeTensor(context, op_context->output, output_dims); + } + } +} + +// Initializes temp tensors to store index and resolved axis. +TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, + TensorFlowMaxOp *op_context) +{ + // Creates a temp index to iterate through input data. + int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(2); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; + scratch_tensor->type = kTfLiteInt32; + scratch_tensor->allocation_type = kTfLiteArenaRw; + TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); + index_size->data[0] = ::tflite::NumDimensions(op_context->input); + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); + + // Creates a temp tensor to store resolved axis given input data. + node->temporaries->data[1] = *scratch_tensor_index + 1; + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + resolved_axis->type = kTfLiteInt32; + return kTfLiteOk; +} + +TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node) +{ + TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); + + TensorFlowMaxOp op_context(context, node); + TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); + + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Leaves work to Eval if axis is not constant; else resizes output. + if (!::tflite::IsConstantTensor(op_context.axis)) + { + ::tflite::SetTensorToDynamic(op_context.output); + ::tflite::SetTensorToDynamic(resolved_axis); + return kTfLiteOk; + } + resolved_axis->allocation_type = kTfLiteArenaRw; + TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); + return ResizeOutputTensor(context, &op_context); +} + +// Gets offset of index if expanded on axis. When expanded, the flattened offset +// will not change, if the output index changes on the given axis. For example, +// if you have a 2D tensor and you are expanding to 3D on axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened +// offset. +inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, + const int num_axis, const int *axis) +{ + size_t offset = 0; + int out_idx = 0; + for (int in_idx = 0; in_idx < num_dims; ++in_idx) + { + // if we need to expand this axis + bool is_axis = false; + if (axis != nullptr) + { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (in_idx == axis[axis_idx]) + { + is_axis = true; + break; + } + } + } + if (!is_axis) + { + offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); + out_idx++; + } + else + { + offset = offset * static_cast<size_t>(dims[in_idx]); + } + } + return offset; +} + +// Gets offset of index if reducing on axis. When reducing, the flattened offset +// will not change, if the input index changes on the given axis. For example, +// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened +// offset. +// TODO(kanlig): uses Dims to represent dimensions. +inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, + const int num_axis, const int *axis) +{ + size_t offset = 0; + for (int idx = 0; idx < num_dims; ++idx) + { + // if we need to skip this axis + bool is_axis = false; + if (axis != nullptr) + { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (idx == axis[axis_idx]) + { + is_axis = true; + break; + } + } + } + if (!is_axis) + { + offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); + } + } + return offset; +} + +// Gets next index to iterate through a multidimensional array. +inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) +{ + int carry = 1; + for (int idx = num_dims - 1; idx >= 0; --idx) + { + int current_val = current[idx] + carry; + TF_LITE_ENSURE(context, (dims[idx] >= current_val)); + if (dims[idx] == current_val) + { + current[idx] = 0; + } + else + { + current[idx] = current_val; + carry = 0; + break; + } + } + return (carry == 0); +} + +template <typename T> +inline TfLiteStatus +CustomMax(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, + T *output_data, const int *output_dims, const int output_num_dims, const int *axis, + const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) +{ + // resolves axis. + int num_resolved_axis = 0; + for (int idx = 0; idx < num_axis_dimensions; ++idx) + { + int current = axis[idx]; + TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); + if (current < 0) + { + current += input_num_dims; + } + bool is_dup = false; + for (int j = 0; j < num_resolved_axis; ++j) + { + if (resolved_axis[j] == current) + { + is_dup = true; + break; + } + } + if (!is_dup) + { + resolved_axis[num_resolved_axis++] = current; + } + } + + TF_LITE_ENSURE(context, (input_num_dims > 0)); + TF_LITE_ENSURE(context, (input_dims != nullptr)); + TF_LITE_ENSURE(context, (temp_index != nullptr)); + + // resets output data. + for (int idx = 0; idx < output_num_dims; ++idx) + { + temp_index[idx] = 0; + } + for (bool has_next = true; has_next; + has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) + { + size_t output_offset = + ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); + size_t input_offset = ExpandedInputOffset(input_num_dims, input_dims, temp_index, + num_resolved_axis, resolved_axis); + output_data[output_offset] = input_data[input_offset]; + } + + // resets temp index. + for (int idx = 0; idx < input_num_dims; ++idx) + { + temp_index[idx] = 0; + } + + // iterates through input_data. + for (bool has_next = true; has_next; + has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) + { + size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); + size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, + num_resolved_axis, resolved_axis); + if (output_data[output_offset] < input_data[input_offset]) + { + output_data[output_offset] = input_data[input_offset]; + } + } + + return kTfLiteOk; +} + +TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node) +{ + + TensorFlowMaxOp op_context(context, node); + int num_axis = static_cast<int>(::tflite::NumElements(op_context.axis)); + TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Resize the output tensor if the output tensor is dynamic. + if (::tflite::IsDynamicTensor(op_context.output)) + { + TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + + TfLiteStatus returnStatus = kTfLiteOk; + switch (op_context.input->type) + { + case kTfLiteFloat32: + returnStatus = CustomMax<float>( + context, op_context.input->data.f, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, + temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteInt32: + returnStatus = CustomMax<int>(context, op_context.input->data.i32, + op_context.input->dims->data, op_context.input->dims->size, + op_context.output->data.i32, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, + num_axis, false, temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteUInt8: + returnStatus = CustomMax<uint8_t>( + context, op_context.input->data.uint8, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.uint8, + op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, + num_axis, false, temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteInt64: + returnStatus = CustomMax<int64_t>( + context, op_context.input->data.i64, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, + temp_index->data.i32, resolved_axis->data.i32); + break; + default: + returnStatus = kTfLiteError; + } + + return returnStatus; +} + +} // namespace TensorFlowMax +} // namespace custom +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/TensorFlowSum.cpp b/libs/tflite/src/ext/kernels/TensorFlowSum.cpp new file mode 100644 index 000000000..cbf97970c --- /dev/null +++ b/libs/tflite/src/ext/kernels/TensorFlowSum.cpp @@ -0,0 +1,400 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/TensorFlowSum.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +#include <iostream> + +namespace nnfw +{ +namespace tflite +{ +namespace custom +{ +namespace TensorFlowSum +{ + +struct TensorFlowSumOp +{ + TensorFlowSumOp(TfLiteContext *context, TfLiteNode *node) + { + input = ::tflite::GetInput(context, node, 0); + axis = ::tflite::GetInput(context, node, 1); + output = ::tflite::GetOutput(context, node, 0); + } + const TfLiteTensor *input; + const TfLiteTensor *axis; + TfLiteTensor *output; +}; + +void *InitTensorFlowSum(TfLiteContext *context, const char *buffer, size_t length) +{ + // Creates two temp tensors to store index and axis for internal + // implementation only. + auto *scratch_tensor_index = new int; + context->AddTensors(context, 2, scratch_tensor_index); + return scratch_tensor_index; +} + +void FreeTensorFlowSum(TfLiteContext *context, void *buffer) +{ + delete static_cast<TensorFlowSumOp *>(buffer); +} + +// Resizes the temp tensor that stores resolved axis. +TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowSumOp *op_context, + TfLiteTensor *resolved_axis) +{ + TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); + axis_size->data[0] = static_cast<int>(::tflite::NumElements(op_context->axis)); + return context->ResizeTensor(context, resolved_axis, axis_size); +} + +// Resizes output array based on the input size and resolved axis. +TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowSumOp *op_context) +{ + size_t num_axis = ::tflite::NumElements(op_context->axis); + TfLiteIntArray *input_dims = op_context->input->dims; + int input_num_dims = ::tflite::NumDimensions(op_context->input); + const int *axis = op_context->axis->data.i32; + + { + // Calculates size of reducing axis. + int num_reduce_axis = num_axis; + for (int i = 0; i < num_axis; ++i) + { + int current = axis[i]; + if (current < 0) + { + current += input_num_dims; + } + TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); + for (int j = 0; j < i; ++j) + { + int previous = axis[j]; + if (previous < 0) + { + previous += input_num_dims; + } + if (current == previous) + { + --num_reduce_axis; + break; + } + } + } + // Determines output dimensions. + int output_num_dims = ::tflite::NumDimensions(op_context->output); + TF_LITE_ENSURE(context, (input_num_dims == output_num_dims) || + (input_num_dims - num_reduce_axis == output_num_dims)); + + if (input_num_dims == output_num_dims) + { + TfLiteIntArray *output_dims = TfLiteIntArrayCopy(input_dims); + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + int current = axis[axis_idx]; + output_dims->data[current] = 1; + } + return context->ResizeTensor(context, op_context->output, output_dims); + } + else + { + TfLiteIntArray *output_dims = TfLiteIntArrayCreate(output_num_dims); + int num_skip_axis = 0; + for (int idx = 0; idx < input_num_dims; ++idx) + { + bool is_axis = false; + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) + { + ++num_skip_axis; + is_axis = true; + break; + } + } + if (!is_axis) + { + output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; + } + } + return context->ResizeTensor(context, op_context->output, output_dims); + } + } +} + +// Initializes temp tensors to store index and resolved axis. +TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, + TensorFlowSumOp *op_context) +{ + // Creates a temp index to iterate through input data. + int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(2); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; + scratch_tensor->type = kTfLiteInt32; + scratch_tensor->allocation_type = kTfLiteArenaRw; + TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); + index_size->data[0] = ::tflite::NumDimensions(op_context->input); + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); + + // Creates a temp tensor to store resolved axis given input data. + node->temporaries->data[1] = *scratch_tensor_index + 1; + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + resolved_axis->type = kTfLiteInt32; + return kTfLiteOk; +} + +TfLiteStatus PrepareTensorFlowSum(TfLiteContext *context, TfLiteNode *node) +{ + TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); + + TensorFlowSumOp op_context(context, node); + TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); + + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Leaves work to Eval if axis is not constant; else resizes output. + if (!::tflite::IsConstantTensor(op_context.axis)) + { + ::tflite::SetTensorToDynamic(op_context.output); + ::tflite::SetTensorToDynamic(resolved_axis); + return kTfLiteOk; + } + resolved_axis->allocation_type = kTfLiteArenaRw; + TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); + return ResizeOutputTensor(context, &op_context); +} + +// Gets offset of index if expanded on axis. When expanded, the flattened offset +// will not change, if the output index changes on the given axis. For example, +// if you have a 2D tensor and you are expanding to 3D on axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened +// offset. +inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, + const int num_axis, const int *axis) +{ + size_t offset = 0; + int out_idx = 0; + for (int in_idx = 0; in_idx < num_dims; ++in_idx) + { + // if we need to expand this axis + bool is_axis = false; + if (axis != nullptr) + { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (in_idx == axis[axis_idx]) + { + is_axis = true; + break; + } + } + } + if (!is_axis) + { + offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); + out_idx++; + } + else + { + offset = offset * static_cast<size_t>(dims[in_idx]); + } + } + return offset; +} + +// Gets offset of index if reducing on axis. When reducing, the flattened offset +// will not change, if the input index changes on the given axis. For example, +// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened +// offset. +// TODO(kanlig): uses Dims to represent dimensions. +inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, + const int num_axis, const int *axis) +{ + size_t offset = 0; + for (int idx = 0; idx < num_dims; ++idx) + { + // if we need to skip this axis + bool is_axis = false; + if (axis != nullptr) + { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) + { + if (idx == axis[axis_idx]) + { + is_axis = true; + break; + } + } + } + if (!is_axis) + { + offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); + } + } + return offset; +} + +// Gets next index to iterate through a multidimensional array. +inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) +{ + int carry = 1; + for (int idx = num_dims - 1; idx >= 0; --idx) + { + int current_val = current[idx] + carry; + TF_LITE_ENSURE(context, (dims[idx] >= current_val)); + if (dims[idx] == current_val) + { + current[idx] = 0; + } + else + { + current[idx] = current_val; + carry = 0; + break; + } + } + return (carry == 0); +} + +template <typename T> +inline TfLiteStatus +CustomSum(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, + T *output_data, const int *output_dims, const int output_num_dims, const int *axis, + const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) +{ + // resolves axis. + int num_resolved_axis = 0; + for (int idx = 0; idx < num_axis_dimensions; ++idx) + { + int current = axis[idx]; + TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); + if (current < 0) + { + current += input_num_dims; + } + bool is_dup = false; + for (int j = 0; j < num_resolved_axis; ++j) + { + if (resolved_axis[j] == current) + { + is_dup = true; + break; + } + } + if (!is_dup) + { + resolved_axis[num_resolved_axis++] = current; + } + } + + TF_LITE_ENSURE(context, (input_num_dims > 0)); + TF_LITE_ENSURE(context, (input_dims != nullptr)); + TF_LITE_ENSURE(context, (temp_index != nullptr)); + + // resets output data. + for (int idx = 0; idx < output_num_dims; ++idx) + { + temp_index[idx] = 0; + } + for (bool has_next = true; has_next; + has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) + { + size_t output_offset = + ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); + output_data[output_offset] = 0; + } + + // resets temp index. + for (int idx = 0; idx < input_num_dims; ++idx) + { + temp_index[idx] = 0; + } + + // iterates through input_data. + for (bool has_next = true; has_next; + has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) + { + size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); + size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, + num_resolved_axis, resolved_axis); + output_data[output_offset] += input_data[input_offset]; + } + + return kTfLiteOk; +} + +TfLiteStatus EvalTensorFlowSum(TfLiteContext *context, TfLiteNode *node) +{ + + TensorFlowSumOp op_context(context, node); + int num_axis = static_cast<int>(::tflite::NumElements(op_context.axis)); + TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; + TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Resize the output tensor if the output tensor is dynamic. + if (::tflite::IsDynamicTensor(op_context.output)) + { + TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + + TfLiteStatus returnStatus = kTfLiteOk; + switch (op_context.input->type) + { + case kTfLiteFloat32: + returnStatus = CustomSum<float>( + context, op_context.input->data.f, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, + temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteInt32: + returnStatus = CustomSum<int>(context, op_context.input->data.i32, + op_context.input->dims->data, op_context.input->dims->size, + op_context.output->data.i32, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, + num_axis, false, temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteUInt8: + returnStatus = CustomSum<uint8_t>( + context, op_context.input->data.uint8, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.uint8, + op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, + num_axis, false, temp_index->data.i32, resolved_axis->data.i32); + break; + case kTfLiteInt64: + returnStatus = CustomSum<int64_t>( + context, op_context.input->data.i64, op_context.input->dims->data, + op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, + op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, + temp_index->data.i32, resolved_axis->data.i32); + break; + default: + returnStatus = kTfLiteError; + } + + return returnStatus; +} + +} // namespace TensorFlowSum +} // namespace custom +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/register.cpp b/libs/tflite/src/ext/kernels/register.cpp new file mode 100644 index 000000000..b822bd616 --- /dev/null +++ b/libs/tflite/src/ext/kernels/register.cpp @@ -0,0 +1,221 @@ +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This code is derived from the following file (in TensorFlow) +// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.cc' +#include "tflite/ext/kernels/register.h" +#include "tflite/ext/kernels/CustomOps.h" + +namespace tflite { +namespace ops { +namespace builtin { + +TfLiteRegistration *Register_RELU(); +TfLiteRegistration *Register_RELU_N1_TO_1(); +TfLiteRegistration *Register_RELU6(); +TfLiteRegistration *Register_TANH(); +TfLiteRegistration *Register_LOGISTIC(); +TfLiteRegistration *Register_AVERAGE_POOL_2D(); +TfLiteRegistration *Register_MAX_POOL_2D(); +TfLiteRegistration *Register_L2_POOL_2D(); +TfLiteRegistration *Register_CONV_2D(); +TfLiteRegistration *Register_DEPTHWISE_CONV_2D(); +TfLiteRegistration *Register_SVDF(); +TfLiteRegistration *Register_RNN(); +TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_RNN(); +TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_RNN(); +TfLiteRegistration *Register_EMBEDDING_LOOKUP(); +TfLiteRegistration *Register_EMBEDDING_LOOKUP_SPARSE(); +TfLiteRegistration *Register_FULLY_CONNECTED(); +TfLiteRegistration *Register_LSH_PROJECTION(); +TfLiteRegistration *Register_HASHTABLE_LOOKUP(); +TfLiteRegistration *Register_SOFTMAX(); +TfLiteRegistration *Register_CONCATENATION(); +TfLiteRegistration *Register_ADD(); +TfLiteRegistration *Register_SPACE_TO_BATCH_ND(); +TfLiteRegistration *Register_DIV(); +TfLiteRegistration *Register_SUB(); +TfLiteRegistration *Register_BATCH_TO_SPACE_ND(); +TfLiteRegistration *Register_MUL(); +TfLiteRegistration *Register_L2_NORMALIZATION(); +TfLiteRegistration *Register_LOCAL_RESPONSE_NORMALIZATION(); +TfLiteRegistration *Register_LSTM(); +TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_LSTM(); +TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); +TfLiteRegistration *Register_PAD(); +TfLiteRegistration *Register_PADV2(); +TfLiteRegistration *Register_RESHAPE(); +TfLiteRegistration *Register_RESIZE_BILINEAR(); +TfLiteRegistration *Register_SKIP_GRAM(); +TfLiteRegistration *Register_SPACE_TO_DEPTH(); +TfLiteRegistration *Register_GATHER(); +TfLiteRegistration *Register_TRANSPOSE(); +TfLiteRegistration *Register_MEAN(); +TfLiteRegistration *Register_SPLIT(); +TfLiteRegistration *Register_SQUEEZE(); +TfLiteRegistration *Register_STRIDED_SLICE(); +TfLiteRegistration *Register_EXP(); +TfLiteRegistration *Register_TOPK_V2(); +TfLiteRegistration *Register_LOG_SOFTMAX(); +TfLiteRegistration *Register_CAST(); +TfLiteRegistration *Register_DEQUANTIZE(); +TfLiteRegistration *Register_PRELU(); +TfLiteRegistration *Register_MAXIMUM(); +TfLiteRegistration *Register_MINIMUM(); +TfLiteRegistration *Register_ARG_MAX(); +TfLiteRegistration *Register_GREATER(); +TfLiteRegistration *Register_GREATER_EQUAL(); +TfLiteRegistration *Register_LESS(); +TfLiteRegistration *Register_LESS_EQUAL(); +TfLiteRegistration *Register_FLOOR(); +TfLiteRegistration *Register_NEG(); +TfLiteRegistration *Register_SELECT(); +TfLiteRegistration *Register_SLICE(); +TfLiteRegistration *Register_SIN(); +TfLiteRegistration *Register_TRANSPOSE_CONV(); +TfLiteRegistration *Register_SPARSE_TO_DENSE(); +#ifndef OBS_BUILD +TfLiteRegistration *Register_SUM(); +TfLiteRegistration *Register_REDUCE_MAX(); +TfLiteRegistration *Register_REDUCE_MIN(); +TfLiteRegistration *Register_EQUAL(); +TfLiteRegistration *Register_NOT_EQUAL(); +TfLiteRegistration *Register_SQRT(); +TfLiteRegistration *Register_RSQRT(); +TfLiteRegistration *Register_SHAPE(); +TfLiteRegistration *Register_POW(); +TfLiteRegistration *Register_FAKE_QUANT(); +TfLiteRegistration *Register_PACK(); +TfLiteRegistration *Register_ONE_HOT(); +TfLiteRegistration *Register_LOGICAL_OR(); +TfLiteRegistration *Register_LOGICAL_AND(); +TfLiteRegistration *Register_LOGICAL_NOT(); +TfLiteRegistration *Register_UNPACK(); +TfLiteRegistration *Register_FLOOR_DIV(); +TfLiteRegistration *Register_SQUARE(); +TfLiteRegistration *Register_ZEROS_LIKE(); +#endif // OBS_BUILD + +} // namespace builtin +} // namespace ops +} // namespace tflite + +namespace nnfw { +namespace tflite { + +BuiltinOpResolver::BuiltinOpResolver() +{ + // Using namespace directive to minimize diff with upstream tensorflow + using namespace ::tflite::ops::builtin; + using namespace ::tflite; + + AddBuiltin(BuiltinOperator_RELU, Register_RELU()); + AddBuiltin(BuiltinOperator_RELU_N1_TO_1, Register_RELU_N1_TO_1()); + AddBuiltin(BuiltinOperator_RELU6, Register_RELU6()); + AddBuiltin(BuiltinOperator_TANH, Register_TANH()); + AddBuiltin(BuiltinOperator_LOGISTIC, Register_LOGISTIC()); + AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, Register_AVERAGE_POOL_2D()); + AddBuiltin(BuiltinOperator_MAX_POOL_2D, Register_MAX_POOL_2D()); + AddBuiltin(BuiltinOperator_L2_POOL_2D, Register_L2_POOL_2D()); + AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D()); + AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, Register_DEPTHWISE_CONV_2D()); + AddBuiltin(BuiltinOperator_SVDF, Register_SVDF()); + AddBuiltin(BuiltinOperator_RNN, Register_RNN()); + AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, Register_BIDIRECTIONAL_SEQUENCE_RNN()); + AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, Register_UNIDIRECTIONAL_SEQUENCE_RNN()); + AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); + AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, Register_EMBEDDING_LOOKUP_SPARSE()); + AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED()); + AddBuiltin(BuiltinOperator_LSH_PROJECTION, Register_LSH_PROJECTION()); + AddBuiltin(BuiltinOperator_HASHTABLE_LOOKUP, Register_HASHTABLE_LOOKUP()); + AddBuiltin(BuiltinOperator_SOFTMAX, Register_SOFTMAX()); + AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION()); + AddBuiltin(BuiltinOperator_ADD, Register_ADD()); + AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND()); + AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, Register_BATCH_TO_SPACE_ND()); + AddBuiltin(BuiltinOperator_MUL, Register_MUL()); + AddBuiltin(BuiltinOperator_L2_NORMALIZATION, Register_L2_NORMALIZATION()); + AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, Register_LOCAL_RESPONSE_NORMALIZATION()); + AddBuiltin(BuiltinOperator_LSTM, Register_LSTM()); + AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, Register_BIDIRECTIONAL_SEQUENCE_LSTM()); + AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, Register_UNIDIRECTIONAL_SEQUENCE_LSTM()); + AddBuiltin(BuiltinOperator_PAD, Register_PAD()); + AddBuiltin(BuiltinOperator_PADV2, Register_PADV2()); + AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE()); + AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, Register_RESIZE_BILINEAR()); + AddBuiltin(BuiltinOperator_SKIP_GRAM, Register_SKIP_GRAM()); + AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH()); + AddBuiltin(BuiltinOperator_GATHER, Register_GATHER()); + AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE()); + AddBuiltin(BuiltinOperator_MEAN, Register_MEAN()); + AddBuiltin(BuiltinOperator_DIV, Register_DIV()); + AddBuiltin(BuiltinOperator_SUB, Register_SUB()); + AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT()); + AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE()); + AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); + AddBuiltin(BuiltinOperator_EXP, Register_EXP()); + AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); + AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); + AddBuiltin(BuiltinOperator_CAST, Register_CAST()); + AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); + AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); + AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); + AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); + AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); + AddBuiltin(BuiltinOperator_GREATER, Register_GREATER()); + AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL()); + AddBuiltin(BuiltinOperator_LESS, Register_LESS()); + AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL()); + AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR()); + AddBuiltin(BuiltinOperator_NEG, Register_NEG()); + AddBuiltin(BuiltinOperator_SELECT, Register_SELECT()); + AddBuiltin(BuiltinOperator_SLICE, Register_SLICE()); + AddBuiltin(BuiltinOperator_SIN, Register_SIN()); +#ifndef OBS_BUILD + AddBuiltin(BuiltinOperator_SUM, Register_SUM()); + AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX()); + AddBuiltin(BuiltinOperator_REDUCE_MIN, Register_REDUCE_MIN()); + AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); + AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); + AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); + AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL()); + AddBuiltin(BuiltinOperator_SQRT, Register_SQRT()); + AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); + AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); + AddBuiltin(BuiltinOperator_POW, Register_POW()); + AddBuiltin(BuiltinOperator_FAKE_QUANT, Register_FAKE_QUANT(), 1, 2); + AddBuiltin(BuiltinOperator_PACK, Register_PACK()); + AddBuiltin(BuiltinOperator_ONE_HOT, Register_ONE_HOT()); + AddBuiltin(BuiltinOperator_LOGICAL_OR, Register_LOGICAL_OR()); + AddBuiltin(BuiltinOperator_LOGICAL_AND, Register_LOGICAL_AND()); + AddBuiltin(BuiltinOperator_LOGICAL_NOT, Register_LOGICAL_NOT()); + AddBuiltin(BuiltinOperator_UNPACK, Register_UNPACK()); + AddBuiltin(BuiltinOperator_FLOOR_DIV, Register_FLOOR_DIV()); + AddBuiltin(BuiltinOperator_SQUARE, Register_SQUARE()); + AddBuiltin(BuiltinOperator_ZEROS_LIKE, Register_ZEROS_LIKE()); +#endif // OBS_BUILD + + AddCustom("TensorFlowMax", nnfw::tflite::custom::Register_TensorFlowMax()); + AddCustom("SquaredDifference", nnfw::tflite::custom::Register_SquaredDifference()); + AddCustom("TensorFlowSum", nnfw::tflite::custom::Register_TensorFlowSum()); + AddCustom("Abs", nnfw::tflite::custom::Register_Abs()); +} + +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/ext/nnapi_delegate.cpp b/libs/tflite/src/ext/nnapi_delegate.cpp new file mode 100644 index 000000000..25858a7b4 --- /dev/null +++ b/libs/tflite/src/ext/nnapi_delegate.cpp @@ -0,0 +1,1209 @@ +/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// NOTE To minimize diff with upstream tensorflow, disable clang-format +// clang-format off + +// NOTE This code is derived from the following file (in TensorFlow v1.12) +// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.cc' +#include "tflite/ext/nnapi_delegate.h" +#include <fcntl.h> +#include <sys/mman.h> +#include <sys/stat.h> +#include <sys/types.h> +#ifdef OBS_BUILD +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/error_reporter.h" +#else +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#endif +#include "tensorflow/contrib/lite/model.h" +#include "NeuralNetworksShim.h" +#include "NeuralNetworksExShim.h" + +#ifdef __ANDROID__ +#include <android/log.h> +#include <sys/system_properties.h> +#endif + +namespace nnfw { +namespace tflite { + +void logError(const char* format, ...) { + // stderr is convenient for native tests, but is not captured for apps + va_list args_for_stderr; + va_start(args_for_stderr, format); + vfprintf(stderr, format, args_for_stderr); + va_end(args_for_stderr); + fprintf(stderr, "\n"); + fflush(stderr); +#ifdef __ANDROID__ + // produce logcat output for general consumption + va_list args_for_log; + va_start(args_for_log, format); + __android_log_vprint(ANDROID_LOG_ERROR, "tflite", format, args_for_log); + va_end(args_for_log); +#endif +} + +#define FATAL(...) \ + logError(__VA_ARGS__); \ + exit(1); + +// TODO(aselle): Change the error model to use status codes. +#define CHECK_TFLITE_SUCCESS(x) \ + if (x != kTfLiteOk) { \ + FATAL("Aborting since tflite returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ + } + +#define CHECK_NN(x) \ + if (x != ANEURALNETWORKS_NO_ERROR) { \ + FATAL("Aborting since NNAPI returned failure nnapi_delegate.cc:%d", \ + __LINE__); \ + } + +#define RETURN_ERROR_IF_TFLITE_FAILED(x) \ + if (x != kTfLiteOk) { \ + logError( \ + "Returning error since TFLite returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ + return kTfLiteError; \ + } + +#define RETURN_ERROR_IF_NN_FAILED(x) \ + if (x != ANEURALNETWORKS_NO_ERROR) { \ + logError( \ + "Returning error since NNAPI returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ + return kTfLiteError; \ + } + +// Tracking of NNAPI operand ids +static const int64_t kOperandIdNotSet = -1; +static const int64_t kOperandNotNeeded = -2; + +namespace { + +int32_t GetAndroidSdkVersion() { +#ifdef __ANDROID__ + const char* sdkProp = "ro.build.version.sdk"; + char sdkVersion[PROP_VALUE_MAX]; + int length = __system_property_get(sdkProp, sdkVersion); + if (length != 0) { + for (int i = 0; i < length; ++i) { + int digit = sdkVersion[i] - '0'; + if (digit < 0 || digit > 9) { + // Non-numeric SDK version, assume it's higher then expected; + return 0xFFFF; + } + } + return atoi(sdkVersion); + } + FATAL("No %s prop", sdkProp); +#endif // __ANDROID__ + return 0; +} + +int32_t GetAndroidSdkVersionCached() { + static int32_t androidSdkVersion = GetAndroidSdkVersion(); + return androidSdkVersion; +} + +static const uint32_t dimension_for_scalar[1] = {1}; + +} // namespace + +NNAPIAllocation::NNAPIAllocation(const char* filename, + ::tflite::ErrorReporter* error_reporter) + : MMAPAllocation(filename, error_reporter) { + if (mmapped_buffer_ != MAP_FAILED) + CHECK_NN(ANeuralNetworksMemory_createFromFd(buffer_size_bytes_, PROT_READ, + mmap_fd_, 0, &handle_)); +} + +NNAPIAllocation::~NNAPIAllocation() { + if (handle_) { + ANeuralNetworksMemory_free(handle_); + } +} + +NNAPIDelegate::~NNAPIDelegate() { + if (nn_compiled_model_) { + ANeuralNetworksCompilation_free(nn_compiled_model_); + nn_compiled_model_ = nullptr; + } + if (nn_model_) { + ANeuralNetworksModel_free(nn_model_); + nn_model_ = nullptr; + // TODO(aselle): Is this thread-safe and callable multiple times? + } + // ANeuralNetworksShutdown(); +} + +// Adds the tensors of the interpreter to the NN API model. +TfLiteStatus addTensorOperands(::tflite::Interpreter* interpreter, + ANeuralNetworksModel* nn_model, + uint32_t* no_of_operands_added, + std::vector<int64_t>* nnapi_ids) { + uint32_t next_id = 0; + for (size_t i = 0; i < interpreter->tensors_size(); i++) { + // Skip temporaries and RNN back-edges. + if ((*nnapi_ids)[i] == kOperandNotNeeded) continue; + + (*nnapi_ids)[i] = int64_t(next_id); + + int32_t nn_type = 0; + // NNAPI requires 32-bit float scale to be zero, tflite doesn't care + float scale = 0.0f; + int32_t zeroPoint = 0; + TfLiteTensor* tensor = interpreter->tensor(i); + switch (tensor->type) { + case kTfLiteNoType: + // Tensors added during initialization of Ops don't have a type yet and + // should not be registered with the NNAPI. + continue; + case kTfLiteFloat32: + nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; + break; + case kTfLiteUInt8: + nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; + scale = tensor->params.scale; + // FIXME The next line is a workaround because currently zero scale is + // passed down from TF + // Lite. Note that the latest NeuralNetworks.h (see + // https://android.googlesource.com/platform/frameworks/ml/+/master/nn/runtime/include/NeuralNetworks.h) + // requires scale to be greater than zero. Remove this workaround + // when the scale + // value is correctly passed. + scale = (scale == 0.0f) ? 1.0f : scale; + zeroPoint = tensor->params.zero_point; + break; + case kTfLiteInt32: + nn_type = ANEURALNETWORKS_TENSOR_INT32; + scale = tensor->params.scale; + zeroPoint = tensor->params.zero_point; + break; + case kTfLiteBool: + // Workaround to pass bool type under NNAPI + // Use bool type using ANEURALNETWORKS_TENSOR_QUANT8_ASYMM with scale = 1.0f and zero_point = 0 + nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; + scale = 1.0f; + zeroPoint = 0; + break; + default: + logError("Unsupported tensor type %d", tensor->type); + return kTfLiteError; + } + if (tensor->dims->size == 0) { + // WORKAROUND Some model have dimension zero + switch (tensor->type) { + case kTfLiteFloat32: + nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; + break; + case kTfLiteInt32: + nn_type = ANEURALNETWORKS_TENSOR_INT32; + break; + default: + logError("NNAPI doesn't support tensors with rank 0 (index %d name %s)", + i, tensor->name); + return kTfLiteError; + } + } + if (tensor->dims->size > 4) { + logError("NNAPI doesn't support tensors with rank > 4 (index %d name %s)", + i, tensor->name); + return kTfLiteError; + } + // TODO(aselle): Note, many of these are intermediate results. Do I need + // to ever specify these sizes. I am currently below doing setValue + // on all of them, but I shouldn't in the future. + // Answer(jeanluc): If all the operators can set the dimension correctly, + // you won't need to. + ANeuralNetworksOperandType operand_type{ + nn_type, static_cast<uint32_t>(tensor->dims->size), + reinterpret_cast<uint32_t*>(tensor->dims->data), scale, zeroPoint}; + if (tensor->dims->size == 0) { + // WORKAROUND Some model have dimension zero + // Consider scalar as vector size 1 + operand_type.dimensions = dimension_for_scalar; + operand_type.dimensionCount = 1; + } + RETURN_ERROR_IF_NN_FAILED( + ANeuralNetworksModel_addOperand(nn_model, &operand_type)); + // TODO(aselle): Based on Michael's suggestion, limiting this to read + // only memory + if (tensor->allocation_type == kTfLiteMmapRo) { + if (const NNAPIAllocation* alloc = dynamic_cast<const NNAPIAllocation*>( + static_cast<const ::tflite::Allocation*>(tensor->allocation))) { + RETURN_ERROR_IF_NN_FAILED( + ANeuralNetworksModel_setOperandValueFromMemory( + nn_model, next_id, alloc->memory(), + alloc->offset(tensor->data.raw), tensor->bytes)); + } else { + RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_setOperandValue( + nn_model, next_id, tensor->data.raw, tensor->bytes)); + } + } else if (tensor->bytes == 0) { + // These size 0 tensors are optional tensors reserved. + RETURN_ERROR_IF_NN_FAILED( + ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); + } + + ++next_id; + } + *no_of_operands_added = next_id; + return kTfLiteOk; +} + +void MapAndAddTensorIds(const int* from_ids_buf, size_t from_ids_count, + std::vector<uint32_t>* into, + const std::vector<int64_t>& map) { + for (size_t i = 0; i < from_ids_count; i++) { + int from_id = from_ids_buf[i]; + if (from_id == kOptionalTensor) { + into->push_back(from_id); + } else { + into->push_back(map[from_id]); + } + } +} + +// Adds the operations and their parameters to the NN API model. +// 'next-id' is the operand ID of the next operand of the model. +TfLiteStatus AddOpsAndParams( + ::tflite::Interpreter* interpreter, ANeuralNetworksModel* nn_model, + uint32_t next_id, std::vector<int>* model_state_inputs, + std::vector<int>* model_state_outputs, + const std::vector<int64_t>& tensor_id_to_nnapi_id) { + for (size_t i = 0; i < interpreter->nodes_size(); i++) { + const auto* node_and_registration = interpreter->node_and_registration(i); + const TfLiteNode& node = node_and_registration->first; + const TfLiteRegistration& registration = node_and_registration->second; + ::tflite::BuiltinOperator builtin = + static_cast<::tflite::BuiltinOperator>(registration.builtin_code); + + // Add the parameters. + std::vector<uint32_t> augmented_inputs, augmented_outputs; + MapAndAddTensorIds(node.inputs->data, node.inputs->size, &augmented_inputs, + tensor_id_to_nnapi_id); + MapAndAddTensorIds(node.outputs->data, node.outputs->size, + &augmented_outputs, tensor_id_to_nnapi_id); + + auto add_scalar_int32 = [&nn_model, &augmented_inputs, + &next_id](int value) { + ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) + CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, + sizeof(int32_t))) + augmented_inputs.push_back(next_id++); + }; + + auto add_scalar_float32 = [&nn_model, &augmented_inputs, + &next_id](float value) { + ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_FLOAT32}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) + CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, + sizeof(float))) + augmented_inputs.push_back(next_id++); + }; + + auto add_vector_int32 = [&](const int* values, uint32_t num_values) { + ANeuralNetworksOperandType operand_type{ + .type = ANEURALNETWORKS_TENSOR_INT32, + .dimensionCount = 1, + .dimensions = &num_values}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) + CHECK_NN(ANeuralNetworksModel_setOperandValue( + nn_model, next_id, values, sizeof(int32_t) * num_values)); + augmented_inputs.push_back(next_id++); + }; + + // Handle state tensors of RNN, LSTM, SVDF. + // For each state_out tensor, a corresponding state_in operand needs to be + // created for NNAPI. + auto duplicate_state_tensor_float32 = + [interpreter, &nn_model, &next_id, &augmented_inputs, + &model_state_inputs, &model_state_outputs](int tensor_id) { + const TfLiteTensor* tensor = interpreter->tensor(tensor_id); + ANeuralNetworksOperandType operand_type{ + ANEURALNETWORKS_TENSOR_FLOAT32, + static_cast<uint32_t>(tensor->dims->size), + reinterpret_cast<uint32_t*>(tensor->dims->data), + tensor->params.scale, tensor->params.zero_point}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); + augmented_inputs.push_back(next_id); + model_state_inputs->push_back(next_id); + model_state_outputs->push_back(tensor_id); + next_id++; + }; + auto check_and_add_activation = [&add_scalar_int32](int activation) { + if (activation > kTfLiteActRelu6) { + logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); + return kTfLiteError; + } + add_scalar_int32(activation); + return kTfLiteOk; + }; + + auto add_add_params = [&add_scalar_int32](void* data) { + auto* builtin = reinterpret_cast<TfLiteAddParams*>(data); + if (builtin->activation > kTfLiteActRelu6) { + logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); + return kTfLiteError; + } + add_scalar_int32(builtin->activation); + return kTfLiteOk; + }; + + auto add_pooling_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { + auto builtin = reinterpret_cast<TfLitePoolParams*>(data); + add_scalar_int32(builtin->padding); + add_scalar_int32(builtin->stride_width); + add_scalar_int32(builtin->stride_height); + add_scalar_int32(builtin->filter_width); + add_scalar_int32(builtin->filter_height); + return check_and_add_activation(builtin->activation); + }; + + auto add_convolution_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { + auto builtin = reinterpret_cast<TfLiteConvParams*>(data); + add_scalar_int32(builtin->padding); + add_scalar_int32(builtin->stride_width); + add_scalar_int32(builtin->stride_height); + return check_and_add_activation(builtin->activation); + }; + + auto add_depthwise_conv_params = [&add_scalar_int32, + &check_and_add_activation](void* data) { + auto builtin = reinterpret_cast<TfLiteDepthwiseConvParams*>(data); + add_scalar_int32(builtin->padding); + add_scalar_int32(builtin->stride_width); + add_scalar_int32(builtin->stride_height); + add_scalar_int32(builtin->depth_multiplier); + return check_and_add_activation(builtin->activation); + }; + + auto add_fully_connected_params = [&check_and_add_activation](void* data) { + auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); + return check_and_add_activation(builtin->activation); + }; + + auto add_concatenation_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); + add_scalar_int32(builtin->axis); + if (builtin->activation != kTfLiteActNone) { + logError("Concatenation does not support fused activation in NNAPI"); + return kTfLiteError; + } + return kTfLiteOk; + }; + + auto add_softmax_params = [&add_scalar_float32](void* data) { + auto builtin = reinterpret_cast<TfLiteSoftmaxParams*>(data); + add_scalar_float32(builtin->beta); + }; + + auto add_space_to_depth_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteSpaceToDepthParams*>(data); + add_scalar_int32(builtin->block_size); + }; + + auto add_lstm_params = [&add_scalar_int32, + &add_scalar_float32](void* data) { + auto builtin = reinterpret_cast<TfLiteLSTMParams*>(data); + add_scalar_int32(builtin->activation); + add_scalar_float32(builtin->cell_clip); + add_scalar_float32(builtin->proj_clip); + }; + + // LSTM in NNAPI requires scratch tensor as an output operand. + auto add_lstm_scratch_tensor_float32 = [interpreter, &node, &nn_model, + &next_id, &augmented_outputs]() { + if (node.temporaries->size == 0) return; + int scratch_buffer_index = node.temporaries->data[0]; + const TfLiteTensor* tensor = interpreter->tensor(scratch_buffer_index); + ANeuralNetworksOperandType operand_type{ + ANEURALNETWORKS_TENSOR_FLOAT32, + static_cast<uint32_t>(tensor->dims->size), + reinterpret_cast<uint32_t*>(tensor->dims->data), tensor->params.scale, + tensor->params.zero_point}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); + augmented_outputs.insert(augmented_outputs.begin(), next_id++); + }; + + auto add_mean_params = [&add_scalar_int32](void* data) { +#ifdef OBS_BUILD + auto builtin = reinterpret_cast<TfLiteMeanParams*>(data); +#else + auto builtin = reinterpret_cast<TfLiteReducerParams*>(data); +#endif + add_scalar_int32(builtin->keep_dims); + }; + + auto add_svdf_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteSVDFParams*>(data); + add_scalar_int32(builtin->rank); + add_scalar_int32(builtin->activation); + }; + + auto add_rnn_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteRNNParams*>(data); + add_scalar_int32(builtin->activation); + }; + + auto add_squeeze_params = [&](void* data) { + const auto* builtin = reinterpret_cast<TfLiteSqueezeParams*>(data); + // Note that we add the squeeze dimensions even if the dimensions were + // unspecified (empty), as NNAPI requires the operand. + add_vector_int32(builtin->squeeze_dims, + static_cast<uint32_t>(builtin->num_squeeze_dims)); + }; + + // Handle optional input tensors. + auto add_optional_tensors = [&nn_model, &augmented_inputs, + &next_id](int nn_type) { + for (size_t idx = 0; idx < augmented_inputs.size(); idx++) { + if (augmented_inputs[idx] == kOptionalTensor) { + const std::vector<uint32_t> dim = {0, 0}; + ANeuralNetworksOperandType operand_type{nn_type, 2, dim.data(), 0, 0}; + CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) + CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, + nullptr, 0)) + augmented_inputs[idx] = next_id++; + } + } + }; + + int nnapi_version = 10; +#include "nnapi_delegate_ex_AddOpsAndParams_lambda.inc" + + ANeuralNetworksOperationType nn_op_type; + + // Using namespace directive to minimize diff with upstream tensorflow + namespace tflite = ::tflite; + + switch (builtin) { + case tflite::BuiltinOperator_ADD: + nn_op_type = ANEURALNETWORKS_ADD; + RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); + break; + case tflite::BuiltinOperator_MUL: + nn_op_type = ANEURALNETWORKS_MUL; + RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); + break; + case tflite::BuiltinOperator_AVERAGE_POOL_2D: + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; + break; + case tflite::BuiltinOperator_MAX_POOL_2D: + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; + break; + case tflite::BuiltinOperator_L2_POOL_2D: + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_L2_POOL_2D; + break; + case tflite::BuiltinOperator_CONV_2D: { + auto builtin = reinterpret_cast<TfLiteConvParams*>(node.builtin_data); + if (builtin->dilation_width_factor != 1 || + builtin->dilation_height_factor != 1 || node.inputs->size != 3) { + logError("NNAPI does not support dilated Conv2D."); + return kTfLiteError; + } + } + RETURN_ERROR_IF_TFLITE_FAILED( + add_convolution_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_CONV_2D; + break; + case tflite::BuiltinOperator_RELU: + nn_op_type = ANEURALNETWORKS_RELU; + break; + case tflite::BuiltinOperator_RELU_N1_TO_1: + nn_op_type = ANEURALNETWORKS_RELU1; + break; + case tflite::BuiltinOperator_RELU6: + nn_op_type = ANEURALNETWORKS_RELU6; + break; + case tflite::BuiltinOperator_TANH: + nn_op_type = ANEURALNETWORKS_TANH; + break; + case tflite::BuiltinOperator_FLOOR: + nn_op_type = ANEURALNETWORKS_FLOOR; + break; + case tflite::BuiltinOperator_LOGISTIC: + nn_op_type = ANEURALNETWORKS_LOGISTIC; + break; + case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: + RETURN_ERROR_IF_TFLITE_FAILED( + add_depthwise_conv_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; + break; + case tflite::BuiltinOperator_CONCATENATION: + RETURN_ERROR_IF_TFLITE_FAILED( + add_concatenation_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_CONCATENATION; + break; + case tflite::BuiltinOperator_SOFTMAX: + add_softmax_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_SOFTMAX; + break; + case tflite::BuiltinOperator_FULLY_CONNECTED: + RETURN_ERROR_IF_TFLITE_FAILED( + add_fully_connected_params(node.builtin_data)); + nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; + break; + case tflite::BuiltinOperator_RESHAPE: + if (node.inputs->size != 2) { + logError("NNAPI only supports 2-input RESHAPE"); + return kTfLiteError; + } + nn_op_type = ANEURALNETWORKS_RESHAPE; + // add_reshape_params(node.builtin_data); + break; + case tflite::BuiltinOperator_RESIZE_BILINEAR: + add_resize_bilinear_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_RESIZE_BILINEAR; + break; + case tflite::BuiltinOperator_SPACE_TO_DEPTH: + add_space_to_depth_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; + break; + case tflite::BuiltinOperator_LSTM: { + if (node.inputs->size + /* no of params */ 3 != 21) { + logError("NNAPI only supports 21-input LSTMs"); + return kTfLiteError; + } + duplicate_state_tensor_float32( + node.outputs->data[/*kOutputStateTensor*/ 0]); + duplicate_state_tensor_float32( + node.outputs->data[/*kCellStateTensor*/ 1]); + add_lstm_params(node.builtin_data); + add_lstm_scratch_tensor_float32(); + add_optional_tensors(ANEURALNETWORKS_TENSOR_FLOAT32); + nn_op_type = ANEURALNETWORKS_LSTM; + break; + } + case tflite::BuiltinOperator_DEQUANTIZE: + nn_op_type = ANEURALNETWORKS_DEQUANTIZE; + break; + case tflite::BuiltinOperator_SVDF: { + duplicate_state_tensor_float32(node.outputs->data[/*kStateTensor*/ 0]); + add_svdf_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_SVDF; + break; + } + case tflite::BuiltinOperator_RNN: { + duplicate_state_tensor_float32( + node.outputs->data[/*kHiddenStateTensor*/ 0]); + add_rnn_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_RNN; + break; + } + case tflite::BuiltinOperator_EMBEDDING_LOOKUP: + nn_op_type = ANEURALNETWORKS_EMBEDDING_LOOKUP; + break; + case tflite::BuiltinOperator_PAD: + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_PAD; + break; + case tflite::BuiltinOperator_MEAN: + nnapi_version = 11; // require NNAPI 1.1 + add_mean_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_MEAN; + break; + case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: + nn_op_type = ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION; + add_lrn_params(node.builtin_data); + break; + case tflite::BuiltinOperator_DIV: + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_DIV; + RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( + reinterpret_cast<TfLiteDivParams*>(node.builtin_data)->activation)); + break; + case tflite::BuiltinOperator_SUB: + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_SUB; + RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( + reinterpret_cast<TfLiteSubParams*>(node.builtin_data)->activation)); + break; + case tflite::BuiltinOperator_SQUEEZE: + nnapi_version = 11; // requires NNAPI 1.1 + add_squeeze_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_SQUEEZE; + break; + case tflite::BuiltinOperator_TRANSPOSE: + // The permutation input tensor value dictates the output dimensions. + // TODO(b/110888333): Support dynamically-sized tensors in delegates. + if ((node.inputs->size > 1) && + (interpreter->tensor(node.inputs->data[1])->allocation_type != + kTfLiteMmapRo)) { + logError("NNAPI does not yet support dynamic tensors."); + return kTfLiteError; + } + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_TRANSPOSE; + break; + case tflite::BuiltinOperator_L2_NORMALIZATION: + nn_op_type = ANEURALNETWORKS_L2_NORMALIZATION; + if (reinterpret_cast<TfLiteL2NormParams*>(node.builtin_data) + ->activation != kTfLiteActNone) { + logError( + "NNAPI does not support L2Normalization with fused activations"); + return kTfLiteError; + } + if ((node.inputs->size > 0) && + (interpreter->tensor(node.inputs->data[0])->dims->size != 4)) { + logError("NNAPI only supports input rank 4 for L2Normalization"); + return kTfLiteError; + } + break; + case tflite::BuiltinOperator_HASHTABLE_LOOKUP: + if (interpreter->tensor(node.outputs->data[0])->type != + kTfLiteFloat32) { + logError("NNAPI only support HASHTABLE_LOOKUP with float32 output", + builtin); + return kTfLiteError; + } + nn_op_type = ANEURALNETWORKS_HASHTABLE_LOOKUP; + break; + case tflite::BuiltinOperator_STRIDED_SLICE: + add_strided_slice_params(node.builtin_data); + nn_op_type = ANEURALNETWORKS_STRIDED_SLICE; + break; + case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_SPACE_TO_BATCH_ND; + break; + case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_BATCH_TO_SPACE_ND; + check_batch_to_space_params(); + break; + case tflite::BuiltinOperator_CAST: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_CAST_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_TOPK_V2: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_TOPK_V2_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_GATHER: + add_gather_ex_params(node.builtin_data); + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_GATHER_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_SPLIT: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_SPLIT_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_NEG: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_NEG_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_EXP: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_EXP_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_TRANSPOSE_CONV: + add_transpose_conv_params(node.builtin_data); + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_TRANSPOSE_CONV_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_PRELU: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_PRELU_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_ARG_MAX: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_ARGMAX_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; +#ifndef OBS_BUILD + case tflite::BuiltinOperator_PACK: + add_pack_ex_params(node.builtin_data); + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_PACK_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_UNPACK: + add_unpack_ex_params(node.builtin_data); + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_UNPACK_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_SQRT: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_SQRT_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_RSQRT: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_RSQRT_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_EQUAL: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_EQUAL_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_NOT_EQUAL: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_NOT_EQUAL_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_SUM: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_REDUCE_SUM_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_REDUCE_MAX: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_REDUCE_MIN: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_REDUCE_MIN_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_LOGICAL_AND: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_LOGICAL_AND_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + case tflite::BuiltinOperator_LOGICAL_OR: + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_LOGICAL_OR_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; +#endif + case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: + case tflite::BuiltinOperator_LSH_PROJECTION: + case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: + case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: + case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: + case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: + case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: + //case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: + case tflite::BuiltinOperator_PADV2: + //case tflite::BuiltinOperator_RESIZE_BILINEAR: + case tflite::BuiltinOperator_CALL: + case tflite::BuiltinOperator_SKIP_GRAM: + //case tflite::BuiltinOperator_RELU_N1_TO_1: + //case tflite::BuiltinOperator_GATHER: + //case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: + //case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: + //case tflite::BuiltinOperator_TOPK_V2: + //case tflite::BuiltinOperator_SPLIT: + //case tflite::BuiltinOperator_STRIDED_SLICE: + //case tflite::BuiltinOperator_EXP: + case tflite::BuiltinOperator_LOG_SOFTMAX: + //case tflite::BuiltinOperator_DEQUANTIZE: + case tflite::BuiltinOperator_DELEGATE: + //case tflite::BuiltinOperator_CAST: + //case tflite::BuiltinOperator_PRELU: + case tflite::BuiltinOperator_MAXIMUM: + case tflite::BuiltinOperator_MINIMUM: +#ifndef OBS_BUILD + case tflite::BuiltinOperator_ARG_MIN: +#endif + case tflite::BuiltinOperator_GREATER: + case tflite::BuiltinOperator_GREATER_EQUAL: + case tflite::BuiltinOperator_LESS: + case tflite::BuiltinOperator_LESS_EQUAL: + //case tflite::BuiltinOperator_NEG: + case tflite::BuiltinOperator_SELECT: + case tflite::BuiltinOperator_SLICE: + case tflite::BuiltinOperator_SIN: + //case tflite::BuiltinOperator_LOG: + //case tflite::BuiltinOperator_TRANSPOSE_CONV: +#ifndef OBS_BUILD + case tflite::BuiltinOperator_TILE: + case tflite::BuiltinOperator_EXPAND_DIMS: + case tflite::BuiltinOperator_SPARSE_TO_DENSE: + //case tflite::BuiltinOperator_EQUAL: + //case tflite::BuiltinOperator_NOT_EQUAL: + //case tflite::BuiltinOperator_SUM: + //case tflite::BuiltinOperator_REDUCE_MAX: + //case tflite::BuiltinOperator_REDUCE_MIN: + case tflite::BuiltinOperator_REDUCE_PROD: + //case tflite::BuiltinOperator_SQRT: + //case tflite::BuiltinOperator_RSQRT: + case tflite::BuiltinOperator_SHAPE: + case tflite::BuiltinOperator_POW: + case tflite::BuiltinOperator_FAKE_QUANT: + //case tflite::BuiltinOperator_PACK: + //case tflite::BuiltinOperator_LOGICAL_OR: + case tflite::BuiltinOperator_ONE_HOT: + //case tflite::BuiltinOperator_LOGICAL_AND: + case tflite::BuiltinOperator_LOGICAL_NOT: + //case tflite::BuiltinOperator_UNPACK: + case tflite::BuiltinOperator_FLOOR_DIV: + case tflite::BuiltinOperator_REDUCE_ANY: + case tflite::BuiltinOperator_SQUARE: + case tflite::BuiltinOperator_ZEROS_LIKE: + case tflite::BuiltinOperator_FILL: +#endif + logError("Op code %d is currently not delegated to NNAPI", builtin); + return kTfLiteError; + break; + case tflite::BuiltinOperator_CUSTOM: { + std::string custom_name(registration.custom_name); + if (custom_name.compare("TensorFlowMax") == 0) { + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + } + else if (custom_name.compare("SquaredDifference") == 0) { + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + } + else if (custom_name.compare("TensorFlowSum") == 0) { + CHECK_NN(ANeuralNetworksModel_addOperationEx( + nn_model, ANEURALNETWORKS_REDUCE_SUM_EX, + static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(node.outputs->size), + reinterpret_cast<uint32_t*>(node.outputs->data))); + continue; + } + logError("Custom operations are not supported when using NNAPI."); + return kTfLiteError; + break; + } +#ifdef OBS_BUILD + default: + logError("Op code %d is currently not delegated to NNAPI", builtin); + return kTfLiteError; + break; +#endif + } + + //if (nnapi_version == 11 && GetAndroidSdkVersionCached() < 28) { + // FATAL("Op %d needs NNAPI1.1", builtin); + //} + + // Add the operation. + RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_addOperation( + nn_model, nn_op_type, static_cast<uint32_t>(augmented_inputs.size()), + augmented_inputs.data(), + static_cast<uint32_t>(augmented_outputs.size()), + reinterpret_cast<uint32_t*>(augmented_outputs.data()))); + } + return kTfLiteOk; +} + +TfLiteStatus NNAPIDelegate::BuildGraph(::tflite::Interpreter* interpreter) { + if (nn_model_ && nn_compiled_model_) return model_status_; + + // TODO(aselle): This is not correct. need to handle resize invalidation. + if (!nn_model_) { + CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); + + // Find which tensors should be added to NNAPI. TFLite has temporaries + // and RNN back-edges which are are not valid for NNAPI. We look through all + // inputs and outputs and mark the mapping in tensor_id_to_nnapi_id with + // kOperandIdNotSet. addTensorOperands will replace those with the + // corresponding NNAPI operand ids and skip kOperandNotNeeded entries. + std::vector<int64_t> tensor_id_to_nnapi_id(interpreter->tensors_size(), + kOperandNotNeeded); + auto set_ids_to_not_set = [&tensor_id_to_nnapi_id](const int* buf, + size_t count) { + for (int j = 0; j < count; j++) { + auto tensor_id = buf[j]; + if (tensor_id != kOptionalTensor) { + tensor_id_to_nnapi_id[tensor_id] = kOperandIdNotSet; + } + } + }; + for (size_t i = 0; i < interpreter->nodes_size(); i++) { + const auto* node_and_registration = interpreter->node_and_registration(i); + const TfLiteNode& node = node_and_registration->first; + set_ids_to_not_set(node.inputs->data, node.inputs->size); + set_ids_to_not_set(node.outputs->data, node.outputs->size); + } + set_ids_to_not_set(interpreter->inputs().data(), + interpreter->inputs().size()); + set_ids_to_not_set(interpreter->outputs().data(), + interpreter->outputs().size()); + + uint32_t next_id = 0; + RETURN_ERROR_IF_TFLITE_FAILED(addTensorOperands( + interpreter, nn_model_, &next_id, &tensor_id_to_nnapi_id)); + RETURN_ERROR_IF_TFLITE_FAILED( + AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, + &model_states_outputs_, tensor_id_to_nnapi_id)); + + std::vector<uint32_t> augmented_inputs; + MapAndAddTensorIds(interpreter->inputs().data(), + interpreter->inputs().size(), &augmented_inputs, + tensor_id_to_nnapi_id); + augmented_inputs.insert(augmented_inputs.end(), + model_states_inputs_.begin(), + model_states_inputs_.end()); + std::vector<uint32_t> augmented_outputs; + MapAndAddTensorIds(interpreter->outputs().data(), + interpreter->outputs().size(), &augmented_outputs, + tensor_id_to_nnapi_id); + MapAndAddTensorIds(model_states_outputs_.data(), + model_states_outputs_.size(), &augmented_outputs, + tensor_id_to_nnapi_id); + + CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( + nn_model_, static_cast<uint32_t>(augmented_inputs.size()), + reinterpret_cast<const uint32_t*>(augmented_inputs.data()), + static_cast<uint32_t>(augmented_outputs.size()), + reinterpret_cast<const uint32_t*>(augmented_outputs.data()))); + + // TODO Support ANeuralNetworksModel_relaxComputationFloat32toFloat16 + //if (GetAndroidSdkVersionCached() >= 28) { + // CHECK_NN(ANeuralNetworksModel_relaxComputationFloat32toFloat16( + // nn_model_, interpreter->GetAllowFp16PrecisionForFp32())); + //} + CHECK_NN(ANeuralNetworksModel_finish(nn_model_)); + } + if (!nn_compiled_model_) { + CHECK_NN(ANeuralNetworksCompilation_create(nn_model_, &nn_compiled_model_)); + CHECK_NN(ANeuralNetworksCompilation_finish(nn_compiled_model_)); + } + return kTfLiteOk; +} + +#include <unordered_map> + +TfLiteStatus NNAPIDelegate::Invoke(::tflite::Interpreter* interpreter) { + if (!nn_model_) { + model_status_ = BuildGraph(interpreter); + if (model_status_ != kTfLiteOk) { + logError("Failed to build graph for NNAPI"); + } + } + if (model_status_ != kTfLiteOk) { + return model_status_; + } + + ANeuralNetworksExecution* execution = nullptr; + CHECK_NN(ANeuralNetworksExecution_create(nn_compiled_model_, &execution)); + + // Allocate temporary buffer to save casted boolean tensor + std::unordered_map<size_t, uint8_t*> input_boolean_tensors; + std::unordered_map<size_t, uint8_t*> output_boolean_tensors; + for (size_t i = 0; i < interpreter->inputs().size(); i++) + { + int input = interpreter->inputs()[i]; + TfLiteTensor* tensor = interpreter->tensor(input); + if (tensor->type == kTfLiteBool) + { + size_t elements = tensor->bytes / sizeof(bool); + uint8_t* temp_tensor = new uint8_t[tensor->bytes / sizeof(bool)]; + input_boolean_tensors[i] = temp_tensor; + for (size_t idx = 0; idx < elements; idx++) + { + temp_tensor[idx] = (tensor->data.b[idx] ? 0x00 : 0xff); + } + } + } + for (size_t i = 0; i < interpreter->outputs().size(); i++) + { + int output = interpreter->outputs()[i]; + TfLiteTensor* tensor = interpreter->tensor(output); + if (tensor->type == kTfLiteBool) + { + uint8_t* temp_tensor = new uint8_t[tensor->bytes / sizeof(bool)]; + output_boolean_tensors[i] = temp_tensor; + } + } + + // Currently perform deep copy of input buffer + for (size_t i = 0; i < interpreter->inputs().size(); i++) { + int input = interpreter->inputs()[i]; + // TODO(aselle): Is this what we want or do we want input instead? + // TODO(aselle): This should be called setInputValue maybe to be cons. + TfLiteTensor* tensor = interpreter->tensor(input); + if (tensor->type == kTfLiteBool) + { + CHECK_NN(ANeuralNetworksExecution_setInput( + execution, i, nullptr, input_boolean_tensors[i], tensor->bytes * sizeof(uint8_t) / sizeof(bool))); + } + else + { + CHECK_NN(ANeuralNetworksExecution_setInput( + execution, i, nullptr, tensor->data.raw, tensor->bytes)); + } + } + + // Tell nn api where to place final data. + for (size_t i = 0; i < interpreter->outputs().size(); i++) { + int output = interpreter->outputs()[i]; + TfLiteTensor* tensor = interpreter->tensor(output); + + if (tensor->type == kTfLiteBool) + { + CHECK_NN(ANeuralNetworksExecution_setOutput( + execution, i, nullptr, output_boolean_tensors[i], tensor->bytes * sizeof(uint8_t) / sizeof(bool))); + } + else + { + CHECK_NN(ANeuralNetworksExecution_setOutput( + execution, i, nullptr, tensor->data.raw, tensor->bytes)); + } + } + + // The state_out of previous invocation need to be mapped to state_in of + // current invocation. + for (size_t i = 0; i < model_states_outputs_.size(); i++) { + int state_tensor_idx = model_states_outputs_[i]; + TfLiteTensor* tensor = interpreter->tensor(state_tensor_idx); + // Here we are using a deep copy for state_in tensors so that we are not + // reading and writing into the same buffer during a invocation. + // TODO(miaowang): using double shared buffer to minimize the copies. + CHECK_NN(ANeuralNetworksExecution_setInput( + execution, i + interpreter->inputs().size(), nullptr, tensor->data.raw, + tensor->bytes)); + // Tell NNAPI where to output the state_out. + CHECK_NN(ANeuralNetworksExecution_setOutput( + execution, i + interpreter->outputs().size(), nullptr, tensor->data.raw, + tensor->bytes)); + } + + // Currently use blocking compute. + ANeuralNetworksEvent* event = nullptr; + CHECK_NN(ANeuralNetworksExecution_startCompute(execution, &event)); + CHECK_NN(ANeuralNetworksEvent_wait(event)); + ANeuralNetworksEvent_free(event); + ANeuralNetworksExecution_free(execution); + + // Tell nn api where to place final data. + for (size_t i = 0; i < interpreter->inputs().size(); i++) { + int input = interpreter->inputs()[i]; + TfLiteTensor* tensor = interpreter->tensor(input); + + if (tensor->type == kTfLiteBool) + { + uint8_t* temp_tensor = input_boolean_tensors[i]; + input_boolean_tensors[i] = nullptr; + delete temp_tensor; + } + } + for (size_t i = 0; i < interpreter->outputs().size(); i++) { + int output = interpreter->outputs()[i]; + TfLiteTensor* tensor = interpreter->tensor(output); + + if (tensor->type == kTfLiteBool) + { + uint8_t* temp_tensor = output_boolean_tensors[i]; + size_t elements = tensor->bytes / sizeof(bool); + for (size_t idx = 0; idx < elements; idx++) + { + tensor->data.b[idx] = ((temp_tensor[idx] == 0x00) ? false : true); + } + output_boolean_tensors[i] = nullptr; + delete temp_tensor; + } + } + +#if 0 + printf("From the NN API:\n"); + TfLiteTensor* tensor = interpreter->tensor(interpreter->outputs()[0]); + if (float* data = + interpreter->typed_tensor<float>(interpreter->outputs()[0])) { + size_t num = tensor->bytes / sizeof(float); + for (float* p = data; p < data + num; p++) { + printf(" %f", *p); + } + printf("\n"); + } +#endif + + return kTfLiteOk; +} + +bool NNAPIDelegate::IsSupported() { return nnfw::NNAPIExists(); } + +} // namespace tflite +} // namespace nnfw + +// clang-format on diff --git a/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc b/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc new file mode 100644 index 000000000..a91e4de60 --- /dev/null +++ b/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc @@ -0,0 +1,106 @@ +// This file is included from AddOpsAndParams defined in nnapi_delegate.cc +// and contains lambda for extened implementation to original Tensorflow Lite. + auto add_resize_bilinear_params = [&add_scalar_int32, &interpreter, &augmented_inputs](void* data) { + auto builtin = reinterpret_cast<TfLiteResizeBilinearParams*>(data); + if (builtin->align_corners) { + FATAL("Resize bilinear does not support align corners in NNAPI"); + } + + TfLiteTensor* tensor = interpreter->tensor(augmented_inputs.back()); + assert(tensor->type == kTfLiteInt32); + assert(tensor->bytes == sizeof(int)*2); + augmented_inputs.pop_back(); + + int height = ((int*)(tensor->data.raw))[1]; + int width = ((int*)(tensor->data.raw))[0]; + add_scalar_int32(height); + add_scalar_int32(width); + }; + + auto check_l2normalization_params = [interpreter, &node](void* data) { + auto builtin = reinterpret_cast<TfLiteL2NormParams*>(data); + if (builtin->activation != kTfLiteActNone) { + FATAL("NNAPI does not support L2Normalization with fused activations"); + } + if ((node.inputs->size > 0) && + (interpreter->tensor(node.inputs->data[0])->dims->size != 4)) { + FATAL("NNAPI only supports input rank 4 for L2Normalization"); + } + }; + + auto add_transpose_conv_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteTransposeConvParams*>(data); + add_scalar_int32(builtin->padding); + add_scalar_int32(builtin->stride_width); + add_scalar_int32(builtin->stride_height); + }; + + auto add_lrn_params = [&add_scalar_int32, + &add_scalar_float32](void* data) { + auto builtin = reinterpret_cast<TfLiteLocalResponseNormParams*>(data); + add_scalar_int32(builtin->radius); + add_scalar_float32(builtin->bias); + add_scalar_float32(builtin->alpha); + add_scalar_float32(builtin->beta); + }; + + auto add_strided_slice_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteStridedSliceParams*>(data); + add_scalar_int32(builtin->begin_mask); + add_scalar_int32(builtin->end_mask); + // ellipsis_mask and new_axis_mask are not supported on nn runtime + // cf) tflite interpreter supports both operations + if (builtin->ellipsis_mask) { + FATAL("STRIDE_SLICE does not support ellipsis_mask in NNAPI"); + } + if (builtin->new_axis_mask) { + FATAL("STRIDE_SLICE does not support new_axis_mask in NNAPI"); + } + add_scalar_int32(builtin->shrink_axis_mask); + }; + + auto add_gather_ex_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteGatherParams*>(data); + add_scalar_int32(builtin->axis); + if (builtin->axis != 0) { + FATAL("GATHER does not support axis>0 in NNAPI"); + } + }; + +#ifndef OBS_BUILD + auto add_pack_ex_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLitePackParams*>(data); + add_scalar_int32(builtin->values_count); + add_scalar_int32(builtin->axis); + }; + + auto add_unpack_ex_params = [&add_scalar_int32](void* data) { + auto builtin = reinterpret_cast<TfLiteUnpackParams*>(data); + add_scalar_int32(builtin->num); + add_scalar_int32(builtin->axis); + }; +#endif + + auto check_batch_to_space_params = [interpreter, &node, &augmented_inputs]() { + + //If there are 3 inputs, check if crops is having default values {0, 0, 0, 0} + //Else unsupported by NNAPI + + if(augmented_inputs.size() == 3) + { + const uint32_t crops_buffer_index = node.inputs->data[2]; + const TfLiteTensor* crops = interpreter->tensor(crops_buffer_index); + const int *crops_value = crops->data.i32; + + //Check if crops is having default values {0, 0, 0, 0} + if(crops_value[0] != 0 || crops_value[1] != 0 || crops_value[2] != 0 || crops_value[3] != 0) + { + FATAL("BATCH_TO_SPACE_ND does not support Explicit crops in NNAPI"); + } + else + { + //Restrict crops input and pass only other two inputs + augmented_inputs.pop_back(); + } + } + }; diff --git a/libs/tflite/src/interp/FlatBufferBuilder.cpp b/libs/tflite/src/interp/FlatBufferBuilder.cpp new file mode 100644 index 000000000..4b9cde719 --- /dev/null +++ b/libs/tflite/src/interp/FlatBufferBuilder.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/interp/FlatBufferBuilder.h" + +#include "tflite/ext/kernels/register.h" + +namespace nnfw +{ +namespace tflite +{ + +std::unique_ptr<::tflite::Interpreter> FlatBufferBuilder::build(void) const +{ + std::unique_ptr<::tflite::Interpreter> interpreter; + + nnfw::tflite::BuiltinOpResolver resolver; + + ::tflite::InterpreterBuilder builder(_model, resolver); + + builder(&interpreter); + + return std::move(interpreter); +} + +} // namespace tflite +} // namespace nnfw diff --git a/libs/tflite/src/interp/FunctionBuilder.cpp b/libs/tflite/src/interp/FunctionBuilder.cpp new file mode 100644 index 000000000..eab940c18 --- /dev/null +++ b/libs/tflite/src/interp/FunctionBuilder.cpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/interp/FunctionBuilder.h" + +namespace nnfw +{ +namespace tflite +{ + +std::unique_ptr<::tflite::Interpreter> FunctionBuilder::build(void) const +{ + auto res = std::unique_ptr<::tflite::Interpreter>{new ::tflite::Interpreter}; + + _fn(*res); + + return std::move(res); +} + +} // namespace tflite +} // namespace nnfw diff --git a/libs/util/CMakeLists.txt b/libs/util/CMakeLists.txt deleted file mode 100644 index eaa7ae8cf..000000000 --- a/libs/util/CMakeLists.txt +++ /dev/null @@ -1,23 +0,0 @@ -# Library `nnfw_util` -set(NNFW_UTILITY_SRCS src/environment.cpp) -list(APPEND NNFW_UTILITY_SRCS src/tensor/Shape.cpp) -list(APPEND NNFW_UTILITY_SRCS src/tensor/NonIncreasingStride.cpp) -list(APPEND NNFW_UTILITY_SRCS src/tensor/IndexFormatter.cpp) -list(APPEND NNFW_UTILITY_SRCS src/tensor/Comparator.cpp) -if(BUILD_TFLITE_BENCHMARK_MODEL) - list(APPEND NNFW_UTILITY_SRCS src/profiling/time.cc) -endif() - -add_library(nnfw_util SHARED ${NNFW_UTILITY_SRCS}) -target_include_directories(nnfw_util PUBLIC ${NNFW_INCLUDE_DIR}) - -add_library(static_nnfw_util STATIC ${NNFW_UTILITY_SRCS}) -target_include_directories(static_nnfw_util PUBLIC ${NNFW_INCLUDE_DIR}) -set_target_properties(static_nnfw_util PROPERTIES POSITION_INDEPENDENT_CODE ON) - -install(TARGETS nnfw_util - RUNTIME DESTINATION bin COMPONENT libraries - LIBRARY DESTINATION lib COMPONENT libraries) - -add_executable(nnfw_util_tensor_index_iterator "examples/tensor_index_iterator.cpp") -target_link_libraries(nnfw_util_tensor_index_iterator nnfw_util) diff --git a/libs/util/examples/tensor_index_iterator.cpp b/libs/util/examples/tensor_index_iterator.cpp deleted file mode 100644 index 284e04aa0..000000000 --- a/libs/util/examples/tensor_index_iterator.cpp +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/tensor/IndexIterator.h" - -#include <array> - -#include <iostream> -#include <algorithm> - -#include <cassert> - -void test_iterate(void) -{ - const nnfw::util::tensor::Shape shape{3, 4, 7}; - - std::array<int, 3 * 4 * 7> array; - - array.fill(0); - - using nnfw::util::tensor::iterate; - using nnfw::util::tensor::Index; - - iterate(shape) << [&](const Index &index) { - assert(index.rank() == shape.rank()); - - const size_t rank = index.rank(); - - uint32_t offset = index.at(0); - - for (size_t axis = 1; axis < rank; ++axis) - { - offset *= shape.dim(axis); - offset += index.at(axis); - } - - array[offset] += 1; - }; - - assert(std::all_of(array.begin(), array.end(), [](int num) { return num == 1; })); -} - -int main(int argc, char **argv) -{ - test_iterate(); - - nnfw::util::tensor::Shape shape{3, 4, 3, 4}; - - std::cout << "Iterate over tensor{3, 4, 3, 4}" << std::endl; - - nnfw::util::tensor::iterate(shape) << [](const nnfw::util::tensor::Index &index) { - std::cout << "rank: " << index.rank() << std::endl; - - for (size_t d = 0; d < index.rank(); ++d) - { - std::cout << " offset(" << d << ") = " << index.at(d) << std::endl; - } - }; - - return 0; -} diff --git a/libs/util/src/environment.cpp b/libs/util/src/environment.cpp deleted file mode 100644 index 4b18b409f..000000000 --- a/libs/util/src/environment.cpp +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <string.h> -#include <cstdlib> -#include <string> - -#include "util/environment.h" - -namespace nnfw -{ -namespace util -{ - -int get_env_int(const char *name, int defaultValue) -{ - const char *value = std::getenv(name); - if (value != nullptr) - return std::stoi(value); - return defaultValue; -} - -bool get_env_bool(const char *name, bool defaultValue) -{ - const char *value = std::getenv(name); - if (value != nullptr) - { - return std::stoi(value) != 0; - } - - return defaultValue; -} - -} // namespace util -} // namespace nnfw - -namespace nnfw -{ -namespace util -{ -namespace env -{ - -IntAccessor::IntAccessor(const std::string &tag) : _tag{tag} -{ - // DO NOTHING -} - -bool IntAccessor::access(int &out) const -{ - auto value = std::getenv(_tag.c_str()); - - if (value == nullptr) - { - return false; - } - - out = std::stoi(value); - return true; -} - -FloatAccessor::FloatAccessor(const std::string &tag) : _tag{tag} -{ - // DO NOTHING -} - -bool FloatAccessor::access(float &out) const -{ - auto value = std::getenv(_tag.c_str()); - - if (value == nullptr) - { - return false; - } - - out = std::stof(value); - return true; -} - -} // namespace env -} // namespace util -} // namespace nnfw diff --git a/libs/util/src/profiling/time.cc b/libs/util/src/profiling/time.cc deleted file mode 100644 index 6fe1b54dc..000000000 --- a/libs/util/src/profiling/time.cc +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "util/profiling/time.h" - -#include <sys/time.h> - -namespace tflite -{ -namespace profiling -{ -namespace time -{ -uint64_t NowMicros() -{ - struct timeval tv; - gettimeofday(&tv, nullptr); - return static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec; -} -} // namespace time -} // namespace profiling -} // namespace tflite diff --git a/libs/util/src/tensor/Comparator.cpp b/libs/util/src/tensor/Comparator.cpp deleted file mode 100644 index 89cd687e9..000000000 --- a/libs/util/src/tensor/Comparator.cpp +++ /dev/null @@ -1,40 +0,0 @@ -#include "util/tensor/Comparator.h" -#include "util/tensor/Zipper.h" - -#include "util/fp32.h" - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -std::vector<Diff<float>> Comparator::compare(const Shape &shape, const Reader<float> &expected, - const Reader<float> &obtained, - Observer *observer) const -{ - std::vector<Diff<float>> res; - - zip(shape, expected, obtained) << - [&](const Index &index, float expected_value, float obtained_value) { - const auto relative_diff = nnfw::util::fp32::relative_diff(expected_value, obtained_value); - - if (!_compare_fn(expected_value, obtained_value)) - { - res.emplace_back(index, expected_value, obtained_value); - } - - // Update max_diff_index, if necessary - if (observer != nullptr) - { - observer->notify(index, expected_value, obtained_value); - } - }; - - return res; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw diff --git a/libs/util/src/tensor/IndexFormatter.cpp b/libs/util/src/tensor/IndexFormatter.cpp deleted file mode 100644 index 66ff80771..000000000 --- a/libs/util/src/tensor/IndexFormatter.cpp +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/tensor/IndexFormatter.h" - -#include <cassert> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -std::ostream &operator<<(std::ostream &os, const IndexFormatter &fmt) -{ - const auto rank = fmt.index().rank(); - - assert(rank > 0); - - os << fmt.index().at(0); - - if (rank > 1) - { - for (uint32_t axis = 1; axis < rank; ++axis) - { - os << ", " << fmt.index().at(axis); - } - } - - return os; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw diff --git a/libs/util/src/tensor/NonIncreasingStride.cpp b/libs/util/src/tensor/NonIncreasingStride.cpp deleted file mode 100644 index 3774ded83..000000000 --- a/libs/util/src/tensor/NonIncreasingStride.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/tensor/NonIncreasingStride.h" - -#include <cassert> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -uint32_t NonIncreasingStride::offset(const Index &index) const -{ - const size_t rank = _stride.size(); - - assert(index.rank() == rank); - - uint32_t offset = 0; - - for (size_t axis = 0; axis < rank; ++axis) - { - offset += _stride.at(axis) * index.at(axis); - } - - return offset; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw diff --git a/libs/util/src/tensor/Shape.cpp b/libs/util/src/tensor/Shape.cpp deleted file mode 100644 index f1de26fdc..000000000 --- a/libs/util/src/tensor/Shape.cpp +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/tensor/Shape.h" - -#include <cassert> - -namespace nnfw -{ -namespace util -{ -namespace tensor -{ - -bool operator==(const Shape &lhs, const Shape &rhs) -{ - if (lhs.rank() != rhs.rank()) - { - return false; - } - - for (size_t axis = 0; axis < lhs.rank(); ++axis) - { - if (lhs.dim(axis) != rhs.dim(axis)) - { - return false; - } - } - - return true; -} - -Shape Shape::from(const std::string &str) -{ - Shape shape(0); - - bool pending = false; - int value = 0; - - for (const char *cur = str.c_str(); true; ++cur) - { - if (*cur == ',' || *cur == '\0') - { - if (pending) - { - shape.append(value); - } - - if (*cur == '\0') - { - break; - } - - pending = false; - value = 0; - continue; - } - - assert(*cur >= '0' && *cur <= '9'); - - pending = true; - value *= 10; - value += *cur - '0'; - } - - return shape; -} - -std::ostream &operator<<(std::ostream &os, const Shape &shape) -{ - if (shape.rank() > 0) - { - os << shape.dim(0); - - for (uint32_t axis = 1; axis < shape.rank(); ++axis) - { - os << "," << shape.dim(axis); - } - } - - return os; -} - -} // namespace tensor -} // namespace util -} // namespace nnfw diff --git a/packaging/nnfw.spec b/packaging/nnfw.spec index 0af7a91cf..3649e8e20 100644 --- a/packaging/nnfw.spec +++ b/packaging/nnfw.spec @@ -11,6 +11,7 @@ Source1: %{name}.manifest %ifarch arm armv7l aarch64 BuildRequires: cmake BuildRequires: python +BuildRequires: python3 BuildRequires: boost-devel BuildRequires: gtest-devel BuildRequires: tensorflow-lite-devel @@ -33,7 +34,6 @@ Requires: nnfw %description test NNFW test rpm. It does not depends on nnfw rpm since it contains nnfw runtime. -%define install_prefix /usr %define test_install_prefix /opt/usr/nnfw-test %ifarch %{arm} @@ -48,9 +48,9 @@ NNFW test rpm. It does not depends on nnfw rpm since it contains nnfw runtime. %{!?coverage_build: %define coverage_build 0} %if %{coverage_build} == 1 -%define build_options COVERAGE_BUILD=1 OBS_BUILD=1 BUILD_TYPE=Debug TARGET_ARCH=%{target_arch} TARGET_OS=tizen UPDATE_MODULE=0 +%define build_options COVERAGE_BUILD=1 OBS_BUILD=1 BUILD_TYPE=Debug TARGET_ARCH=%{target_arch} TARGET_OS=tizen %else -%define build_options OBS_BUILD=1 BUILD_TYPE=%{build_type} INSTALL_PATH=%{buildroot}%{install_prefix} TARGET_ARCH=%{target_arch} TARGET_OS=tizen UPDATE_MODULE=0 +%define build_options OBS_BUILD=1 BUILD_TYPE=%{build_type} INSTALL_PATH=%{buildroot}%{_prefix} TARGET_ARCH=%{target_arch} TARGET_OS=tizen %endif %prep @@ -66,19 +66,23 @@ cp %{SOURCE1} . %ifarch arm armv7l aarch64 %{build_options} make install +%ifarch aarch64 +mv %{buildroot}%{_prefix}/lib %{buildroot}%{_libdir} +%endif + %if %{coverage_build} == 0 # nnfw-test rpm(like test-suite on cross build) ## install Product mkdir -p %{buildroot}%{test_install_prefix}/Product/out -mv %{buildroot}%{install_prefix}/unittest %{buildroot}%{test_install_prefix}/Product/out -mv %{buildroot}%{install_prefix}/bin %{buildroot}%{test_install_prefix}/Product/out -cp -rf %{buildroot}%{install_prefix}/lib %{buildroot}%{test_install_prefix}/Product/out -rm -rf %{buildroot}%{install_prefix}/lib/pureacl +mv %{buildroot}%{_prefix}/unittest %{buildroot}%{test_install_prefix}/Product/out +mv %{buildroot}%{_prefix}/bin %{buildroot}%{test_install_prefix}/Product/out +cp -rf %{buildroot}%{_libdir} %{buildroot}%{test_install_prefix}/Product/out +rm -rf %{buildroot}%{_libdir}/pureacl ## install tests -cp -rf ./tests %{buildroot}%{test_install_prefix}/. +cp -rf ./tests/framework %{buildroot}%{test_install_prefix}/framework ## install tools mkdir -p %{buildroot}%{test_install_prefix}/tools -cp -rf ./tools/test_driver %{buildroot}%{test_install_prefix}/tools +cp -rf ./tests/scripts %{buildroot}%{test_install_prefix}/tools %else %{build_options} make build_coverage_suite mkdir -p %{buildroot}%{test_install_prefix} @@ -95,9 +99,10 @@ rm -rf %{buildroot}%{test_install_prefix}/coverage-suite.tar.gz %manifest %{name}.manifest %defattr(-,root,root,-) %ifarch arm armv7l aarch64 -%{install_prefix}/lib/* -%exclude %{install_prefix}/bin/* -%exclude %{install_prefix}/unittest/* +%{_libdir}/* +%exclude %{_prefix}/bin/* +%exclude %{_prefix}/unittest/* +%exclude %{_libdir}/debug %endif %files test @@ -105,6 +110,7 @@ rm -rf %{buildroot}%{test_install_prefix}/coverage-suite.tar.gz %defattr(-,root,root,-) %ifarch arm armv7l aarch64 %{test_install_prefix}/* +%exclude %{_libdir}/debug %endif %changelog @@ -1,22 +1,36 @@ #!/bin/bash +NNFW_SCRIPT_RPATH="scripts" +NNFW_COMMAND_RPATH="${NNFW_SCRIPT_RPATH}/command" + +NNFW_PROJECT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +NNFW_SCRIPT_PATH="${NNFW_PROJECT_PATH}/${NNFW_SCRIPT_RPATH}" + +function Usage() +{ + echo "Usage: $0 [COMMAND] ..." + echo "Command:" + for file in "$NNFW_COMMAND_RPATH"/*; + do + echo " $(basename "$file")" + done +} + COMMAND=$1; shift if [[ -z "${COMMAND}" ]]; then - echo "USAGE: $0 [COMMAND] ..." + Usage exit 255 fi -NNFW_PROJECT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -NNFW_COMMAND_RPATH="scripts/command" - -NNFW_COMMAND_FILE="${NNFW_PROJECT_PATH}/${NNFW_COMMAND_RPATH}/${COMMAND}" +COMMAND_FILE="${NNFW_PROJECT_PATH}/${NNFW_COMMAND_RPATH}/${COMMAND}" -if [[ ! -f "${NNFW_COMMAND_FILE}" ]]; then +if [[ ! -f "${COMMAND_FILE}" ]]; then echo "ERROR: '${COMMAND}' is not supported" exit 255 fi export NNFW_PROJECT_PATH +export NNFW_SCRIPT_PATH -source "${NNFW_COMMAND_FILE}" "$@" +source "${COMMAND_FILE}" "$@" diff --git a/runtimes/CMakeLists.txt b/runtimes/CMakeLists.txt index 3e1bdeb06..e8ee9ffc7 100644 --- a/runtimes/CMakeLists.txt +++ b/runtimes/CMakeLists.txt @@ -1,5 +1,3 @@ -set(BUILD_RUNTIME_TESTS OFF) - if(BUILD_NEURUN) set(LIB_NEURUN neurun) add_subdirectory(neurun) @@ -8,12 +6,8 @@ endif(BUILD_NEURUN) if(BUILD_PURE_ARM_COMPUTE) set(LIB_PUREACL_RUNTIME nnapi_pure_arm_compute) add_subdirectory(pure_arm_compute) - set(BUILD_RUNTIME_TESTS ON) endif() -if(BUILD_RUNTIME_TESTS) - add_subdirectory(tests) -endif(BUILD_RUNTIME_TESTS) - -add_subdirectory(template) -add_subdirectory(logging) +if(NOT OBS_BUILD) + add_subdirectory(logging) +endif(NOT OBS_BUILD) diff --git a/runtimes/neurun/CMakeLists.txt b/runtimes/neurun/CMakeLists.txt index 2cbd702b0..92547da2c 100644 --- a/runtimes/neurun/CMakeLists.txt +++ b/runtimes/neurun/CMakeLists.txt @@ -18,29 +18,23 @@ file(GLOB SOURCES_BACKEND "src/backend/*.cc") file(GLOB_RECURSE SOURCES_INTERNAL "src/internal/*.cc") file(GLOB_RECURSE SOURCES_GRAPH "src/graph/*.cc") file(GLOB_RECURSE SOURCES_LINEAR "src/linear/*.cc") -file(GLOB_RECURSE SOURCES_CODEGEN "src/codegen/*.cc") +file(GLOB_RECURSE SOURCES_DUMPER "src/dumper/*.cc") +file(GLOB_RECURSE SOURCES_COMPILER "src/compiler/*.cc") file(GLOB_RECURSE SOURCES_VERIFIER "src/verifier/*.cc") +file(GLOB_RECURSE SOURCES_UTIL "src/util/*.cc") +file(GLOB_RECURSE SOURCES_MODEL "src/model/*.cc") -set(SOURCES ${SOURCES} ${SOURCES_FRONTEND} ${SOURCES_BACKEND} ${SOURCES_INTERNAL} ${SOURCES_GRAPH} ${SOURCES_LINEAR} ${SOURCES_CODEGEN} ${SOURCES_VERIFIER}) - -# NOTE For now ARMCompute is necessary -# TODO Remove required package below(should be optional) -nnfw_find_package(ARMCompute REQUIRED) +set(SOURCES ${SOURCES} ${SOURCES_FRONTEND} ${SOURCES_BACKEND} ${SOURCES_INTERNAL} ${SOURCES_GRAPH} ${SOURCES_LINEAR} ${SOURCES_DUMPER} ${SOURCES_COMPILER} ${SOURCES_VERIFIER} ${SOURCES_UTIL} ${SOURCES_MODEL}) add_library(${LIB_NEURUN} SHARED ${SOURCES}) target_include_directories(${LIB_NEURUN} PUBLIC ${NNFW_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN} PUBLIC ${NEURUN_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN} PUBLIC ${CMAKE_SOURCE_DIR}/externals/tensorflow) -target_link_libraries(${LIB_NEURUN} arm_compute) target_link_libraries(${LIB_NEURUN} tensorflow-lite) -target_link_libraries(${LIB_NEURUN} nnfw_util) -target_link_libraries(${LIB_NEURUN} nnfw_support_nnapi) - -# TODO This should be optional -target_link_libraries(${LIB_NEURUN} ${LIB_NEURUN_BACKEND_CPU}) -target_link_libraries(${LIB_NEURUN} ${LIB_NEURUN_BACKEND_ACL_CL}) +target_link_libraries(${LIB_NEURUN} nnfw_lib_misc) +target_link_libraries(${LIB_NEURUN} nnfw_lib_cpp14) -target_compile_options(${LIB_NEURUN} PRIVATE -Wall -Wextra -Werror) +target_compile_options(${LIB_NEURUN} PRIVATE -Wall -Wextra -Werror -Wno-unused-parameter) set_target_properties(${LIB_NEURUN} PROPERTIES OUTPUT_NAME neuralnetworks) @@ -58,6 +52,8 @@ target_link_libraries(${TEST_NEURUN} ${LIB_NEURUN}) target_link_libraries(${TEST_NEURUN} gtest) target_link_libraries(${TEST_NEURUN} gtest_main) target_link_libraries(${TEST_NEURUN} ${LIB_PTHREAD}) +target_link_libraries(${TEST_NEURUN} ${LIB_NEURUN_BACKEND_CPU}) +target_link_libraries(${TEST_NEURUN} ${LIB_NEURUN_BACKEND_ACL_CL}) add_test(${TEST_NEURUN} ${TEST_NEURUN}) install(TARGETS ${TEST_NEURUN} DESTINATION unittest) diff --git a/runtimes/neurun/src/backend/BackendManager.cc b/runtimes/neurun/src/backend/BackendManager.cc index fb7d69108..5d19d4015 100644 --- a/runtimes/neurun/src/backend/BackendManager.cc +++ b/runtimes/neurun/src/backend/BackendManager.cc @@ -14,36 +14,28 @@ * limitations under the License. */ +#include <dlfcn.h> #include "BackendManager.h" -#include "backend/acl_cl/BackendConfig.h" -#include "backend/acl_cl/TensorBuilder.h" -#include "backend/acl_cl/InitializerGenerator.h" -#include "backend/acl_cl/StageGenerator.h" -#include "backend/cpu/BackendConfig.h" -#include "backend/cpu/TensorBuilder.h" -#include "backend/cpu/InitializerGenerator.h" -#include "backend/cpu/StageGenerator.h" +#include "backend/interface/IConfig.h" +#include "backend/interface/ITensorBuilder.h" +#include "backend/interface/IStageGenerator.h" +#include "util/logging.h" +#include "util/config/ConfigManager.h" namespace neurun { namespace backend { -Backend::Backend(const std::shared_ptr<neurun::backend::IBackendConfig> &backend_config, - const std::shared_ptr<neurun::backend::IInitializerGenerator> &initializer_gen, +Backend::Backend(const std::shared_ptr<neurun::backend::IConfig> &backend_config, const std::shared_ptr<neurun::backend::IStageGenerator> &stage_gen) - : _config(backend_config), _initializer_gen(initializer_gen), _stage_gen(stage_gen) + : _config(backend_config), _stage_gen(stage_gen) { backend_config->initialize(); } -const std::shared_ptr<neurun::backend::IBackendConfig> Backend::config() const { return _config; } - -const std::shared_ptr<neurun::backend::IInitializerGenerator> Backend::initializer_gen() const -{ - return _initializer_gen; -} +const std::shared_ptr<neurun::backend::IConfig> Backend::config() const { return _config; } const std::shared_ptr<neurun::backend::IStageGenerator> Backend::stage_gen() const { @@ -55,34 +47,72 @@ const std::shared_ptr<neurun::backend::ITensorBuilder> Backend::tensor_builder() return _stage_gen->tensor_builder(); } -BackendManager::BackendManager(const neurun::graph::operand::Set &operands) +template <typename T, class... Types> +void BackendManager::loadObjectFromPlugin(std::shared_ptr<T> &object_of_plugin_class, + const std::string obj_creator_func_name, void *handle, + Types &&... args) +{ + T *(*allocate_obj)(Types && ... Args); + // load object creator function + allocate_obj = (T * (*)(Types && ... Args))dlsym(handle, obj_creator_func_name.c_str()); + if (allocate_obj == nullptr) + { + fprintf(stderr, "BackendManager: unable to open function %s: %s\n", + obj_creator_func_name.c_str(), dlerror()); + abort(); + } + + object_of_plugin_class.reset(allocate_obj(args...)); +} + +void BackendManager::loadBackend(const std::string &backend, + const neurun::model::operand::Set &operands) { - // Add arm_compute backend + const std::string backend_plugin = "libbackend_" + backend + ".so"; + void *handle = dlopen(backend_plugin.c_str(), RTLD_LAZY | RTLD_LOCAL); + if (handle == nullptr) { - using namespace ::neurun::backend::acl_cl; - auto acl_backend_initializer = std::make_shared<BackendConfig>(); - auto acl_tensor_builder = std::make_shared<TensorBuilder>(); - auto acl_initializer_gen = std::make_shared<InitializerGenerator>(operands); - auto acl_stage_gen = std::make_shared<StageGenerator>(operands, acl_tensor_builder); - - // TODO Do not use magic string for backend id - _gen_map["acl_cl"] = {acl_backend_initializer, acl_initializer_gen, acl_stage_gen}; + fprintf(stderr, "BackendManager::loadBackend failed to load plugin of %s backend: %s\n", + backend.c_str(), dlerror()); + abort(); } + VERBOSE(BackendManager::loadBackend) << "loaded " << backend_plugin << " as a plugin of " + << backend << " backend\n"; + + // load Config + std::shared_ptr<neurun::backend::IConfig> config; + loadObjectFromPlugin(config, std::string("allocate_Config"), handle); + + // load TensorBuilder + std::shared_ptr<neurun::backend::ITensorBuilder> tensor_builder; + loadObjectFromPlugin(tensor_builder, std::string("allocate_TensorBuilder"), handle); + + // load StageGenerator + std::shared_ptr<neurun::backend::IStageGenerator> stage_gen; + loadObjectFromPlugin(stage_gen, std::string("allocate_StageGenerator"), handle, operands, + tensor_builder); + _gen_map[config->id()] = {config, stage_gen}; +} - // Add CPU backend +BackendManager::BackendManager(const neurun::model::operand::Set &operands) +{ + const auto backends = config::ConfigManager::instance().get<std::string>("BACKENDS"); + size_t prev_pos = 0; + auto pos = backends.find(";"); + while (pos != std::string::npos) + { + loadBackend(backends.substr(prev_pos, pos - prev_pos), operands); + prev_pos = pos + 1; + pos = backends.find(";", prev_pos); + } + // if backends doesn't terminate with ";" + if (prev_pos < backends.size()) { - using namespace ::neurun::backend::cpu; - auto cpu_backend_initializer = std::make_shared<BackendConfig>(); - auto cpu_tensor_builder = std::make_shared<TensorBuilder>(); - auto cpu_initializer_gen = std::make_shared<InitializerGenerator>(operands); - auto cpu_stage_gen = std::make_shared<StageGenerator>(operands, cpu_tensor_builder); - - // TODO Do not use magic string for backend id - _gen_map["cpu"] = {cpu_backend_initializer, cpu_initializer_gen, cpu_stage_gen}; + loadBackend(backends.substr(prev_pos), operands); } } -Backend BackendManager::get(const std::string &key) { return _gen_map.at(key); } +Backend *BackendManager::get(const std::string &key) { return &_gen_map.at(key); } } // namespace backend } // namespace neurun diff --git a/runtimes/neurun/src/backend/BackendManager.h b/runtimes/neurun/src/backend/BackendManager.h index 6f862ffe6..428542b1e 100644 --- a/runtimes/neurun/src/backend/BackendManager.h +++ b/runtimes/neurun/src/backend/BackendManager.h @@ -20,51 +20,72 @@ #include <memory> #include <map> -#include "graph/operand/Set.h" +#include "model/operand/Set.h" namespace neurun { namespace backend { -struct IBackendConfig; -struct IInitializerGenerator; +struct IConfig; struct IStageGenerator; struct ITensorBuilder; class Backend { public: - Backend(const std::shared_ptr<neurun::backend::IBackendConfig> &backend_config, - const std::shared_ptr<neurun::backend::IInitializerGenerator> &initializer_gen, + Backend(const std::shared_ptr<neurun::backend::IConfig> &backend_config, const std::shared_ptr<neurun::backend::IStageGenerator> &stage_gen); - Backend(void) : _config(nullptr), _initializer_gen(nullptr), _stage_gen(nullptr) + Backend(void) : _config(nullptr), _stage_gen(nullptr) { // DO NOTHING } public: - const std::shared_ptr<neurun::backend::IBackendConfig> config() const; - const std::shared_ptr<neurun::backend::IInitializerGenerator> initializer_gen() const; + const std::shared_ptr<neurun::backend::IConfig> config() const; const std::shared_ptr<neurun::backend::IStageGenerator> stage_gen() const; const std::shared_ptr<neurun::backend::ITensorBuilder> tensor_builder() const; private: - std::shared_ptr<neurun::backend::IBackendConfig> _config; - std::shared_ptr<neurun::backend::IInitializerGenerator> _initializer_gen; + std::shared_ptr<neurun::backend::IConfig> _config; std::shared_ptr<neurun::backend::IStageGenerator> _stage_gen; }; class BackendManager { public: - BackendManager(const neurun::graph::operand::Set &operands); + BackendManager(const neurun::model::operand::Set &operands); - Backend get(const std::string &key); + Backend *get(const std::string &key); private: std::map<std::string, Backend> _gen_map; + /** + * @brief Allocate an object of a class of a plugin by loading a plugin function, that does + * allocation, and calling it + * + * @param object_of_plugin_class target object + * @param obj_creator_func_name name of the plugin function, that allocates an object + * @param handle handle of the plugin + * @param args arguments to pass to constructor of the plugin class + * + * @return + */ + template <typename T, class... Types> + void loadObjectFromPlugin(std::shared_ptr<T> &object_of_plugin_class, + const std::string obj_creator_func_name, void *handle, + Types &&... args); + + /** + * @brief load backend plugin + * + * @param backend backend to be loaded + * @param operands operands to construct StageGenerator + * + * @return + */ + void loadBackend(const std::string &backend, const neurun::model::operand::Set &operands); }; } // namespace backend diff --git a/runtimes/neurun/src/backend/IBackendConfig.h b/runtimes/neurun/src/backend/IBackendConfig.h deleted file mode 100644 index a6c7ce517..000000000 --- a/runtimes/neurun/src/backend/IBackendConfig.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_IBACKEND_CONFIG_H__ -#define __INTERNAL_IBACKEND_CONFIG_H__ - -#include "graph/operand/Layout.h" - -namespace neurun -{ -namespace backend -{ - -struct IBackendConfig -{ - virtual ~IBackendConfig() = default; - - virtual void initialize() = 0; - // NOTE Assume backend has only one type of operand layout - virtual graph::operand::Layout getOperandLayout() = 0; -}; - -} // namespace backend -} // namespace neurun - -#endif // __INTERNAL_IBACKEND_CONFIG_H__ diff --git a/runtimes/neurun/src/backend/IInitializerGenerator.h b/runtimes/neurun/src/backend/IInitializerGenerator.h deleted file mode 100644 index 83cf87a52..000000000 --- a/runtimes/neurun/src/backend/IInitializerGenerator.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_IINITIALIZER_GENERATOR_H__ -#define __INTERNAL_IINITIALIZER_GENERATOR_H__ - -#include "arm_compute/core/ITensor.h" - -#include "graph/operation/Conv2D.h" -#include "graph/operation/FullyConnected.h" - -using Initializer = std::function<void(::arm_compute::ITensor &)>; - -namespace neurun -{ -namespace backend -{ - -struct IInitializerGenerator -{ - virtual ~IInitializerGenerator() = default; - - virtual Initializer generateWeight(const graph::operation::Conv2D::Implicit::Node &node) = 0; - virtual Initializer generateWeight(const graph::operation::FullyConnected::Node &node) = 0; - - virtual Initializer generateBias(const graph::operation::Conv2D::Implicit::Node &node) = 0; - virtual Initializer generateBias(const graph::operation::FullyConnected::Node &node) = 0; -}; - -} // namespace backend -} // namespace neurun - -#endif // __INTERNAL_IINITIALIZER_GENERATOR_H__ diff --git a/runtimes/neurun/src/backend/IObject.h b/runtimes/neurun/src/backend/IObject.h deleted file mode 100644 index f7d511095..000000000 --- a/runtimes/neurun/src/backend/IObject.h +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ -#define __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ - -#include <functional> - -#include <arm_compute/core/ITensor.h> - -namespace neurun -{ -namespace backend -{ -namespace operand -{ - -struct IObject -{ - virtual ~IObject() = default; - virtual ::arm_compute::ITensor *ptr(void) const = 0; - virtual void access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const = 0; -}; - -} // namespace operand -} // namespace backend -} // namespace neurun - -#endif // __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ diff --git a/runtimes/neurun/src/backend/IStageGenerator.h b/runtimes/neurun/src/backend/IStageGenerator.h deleted file mode 100644 index 05959e2b1..000000000 --- a/runtimes/neurun/src/backend/IStageGenerator.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_ISTAGE_GENERATOR_H__ -#define __INTERNAL_ISTAGE_GENERATOR_H__ - -#include <memory> -#include <functional> - -#include <arm_compute/runtime/IFunction.h> - -#include "backend/ITensorBuilder.h" -#include "graph/operation/Conv2D.h" -#include "graph/operation/MaxPool2D.h" -#include "graph/operation/AvgPool2D.h" -#include "graph/operation/Concat.h" -#include "graph/operation/FullyConnected.h" -#include "graph/operation/Reshape.h" -#include "graph/operation/Softmax.h" -#include "graph/operation/NOP.h" - -struct IExecutionBuilder -{ - virtual ~IExecutionBuilder() = default; - - virtual void append(std::unique_ptr<::arm_compute::IFunction> &&f) = 0; -}; - -using Stage = std::function<void(IExecutionBuilder &)>; - -namespace neurun -{ -namespace backend -{ - -struct IStageGenerator -{ - virtual ~IStageGenerator() = default; - - virtual std::shared_ptr<ITensorBuilder> tensor_builder() = 0; - - virtual Stage generate(const graph::operation::Conv2D::Implicit::Node &node) = 0; - virtual Stage generate(const graph::operation::MaxPool2D::Implicit::Node &node) = 0; - virtual Stage generate(const graph::operation::AvgPool2D::Implicit::Node &node) = 0; - virtual Stage generate(const graph::operation::Concat::Node &node) = 0; - virtual Stage generate(const graph::operation::FullyConnected::Node &node) = 0; - virtual Stage generate(const graph::operation::Reshape::Node &node) = 0; - virtual Stage generate(const graph::operation::Softmax::Node &node) = 0; - virtual Stage generate(const graph::operation::NOP::Node &node) = 0; -}; - -} // namespace backend -} // namespace neurun - -#endif // __INTERNAL_ISTAGE_GENERATOR_H__ diff --git a/runtimes/neurun/src/backend/ITensorBuilder.h b/runtimes/neurun/src/backend/ITensorBuilder.h deleted file mode 100644 index c3a07ffeb..000000000 --- a/runtimes/neurun/src/backend/ITensorBuilder.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_ITENSOR_BUILDER_H__ -#define __INTERNAL_ITENSOR_BUILDER_H__ - -#include <map> -#include <arm_compute/core/TensorInfo.h> - -#include "graph/operand/Index.h" -#include "codegen/Plan.h" - -namespace neurun -{ -namespace backend -{ - -struct ITensorBuilder -{ - virtual ~ITensorBuilder(void) = default; - virtual void mark(const ::neurun::graph::operand::Index &ind) = 0; - // TODO Add an interface for adding subsumption info - virtual void prepare(codegen::Plan &plan, - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx) = 0; - virtual void allocate(void) = 0; -}; - -} // namespace backend -} // namespace neurun - -#include <set> -#include <memory> - -namespace neurun -{ -namespace backend -{ - -using TensorBuilderSet = std::set<std::shared_ptr<backend::ITensorBuilder>>; - -} // namespace backend -} // namespace neurun - -#endif // __INTERNAL_ITENSOR_BUILDER_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/BackendConfig.cc b/runtimes/neurun/src/backend/acl_cl/BackendConfig.cc deleted file mode 100644 index 6b3e6b3a3..000000000 --- a/runtimes/neurun/src/backend/acl_cl/BackendConfig.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <arm_compute/runtime/CL/CLScheduler.h> - -#include "backend/acl_cl/BackendConfig.h" - -namespace neurun -{ -namespace backend -{ -namespace acl_cl -{ - -void BackendConfig::initialize() { arm_compute::CLScheduler::get().default_init(); } - -} // namespace acl_cl -} // namespace backend -} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/BackendConfig.h b/runtimes/neurun/src/backend/acl_cl/BackendConfig.h deleted file mode 100644 index 8eec9e795..000000000 --- a/runtimes/neurun/src/backend/acl_cl/BackendConfig.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_BACKEND_ACL_CL_BACKEND_CONFIG_H__ -#define __NEURUN_BACKEND_ACL_CL_BACKEND_CONFIG_H__ - -#include "backend/IBackendConfig.h" - -namespace neurun -{ -namespace backend -{ -namespace acl_cl -{ - -class BackendConfig : public IBackendConfig -{ -public: - BackendConfig() - { - // DO NOTHING - } - - virtual void initialize() override; - virtual graph::operand::Layout getOperandLayout() { return graph::operand::Layout::NCHW; } -}; - -} // namespace acl_cl -} // namespace backend -} // namespace neurun - -#endif // __NEURUN_BACKEND_ACL_CL_BACKEND_CONFIG_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/CMakeLists.txt b/runtimes/neurun/src/backend/acl_cl/CMakeLists.txt index d64c23a80..f1ea22bc5 100644 --- a/runtimes/neurun/src/backend/acl_cl/CMakeLists.txt +++ b/runtimes/neurun/src/backend/acl_cl/CMakeLists.txt @@ -1,17 +1,15 @@ file(GLOB_RECURSE SOURCES "*.cc") -add_library(${LIB_NEURUN_BACKEND_ACL_CL} STATIC ${SOURCES}) +add_library(${LIB_NEURUN_BACKEND_ACL_CL} SHARED ${SOURCES}) target_include_directories(${LIB_NEURUN_BACKEND_ACL_CL} PUBLIC ${NNFW_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_BACKEND_ACL_CL} PUBLIC ${NEURUN_INCLUDE_DIR}) -target_include_directories(${LIB_NEURUN_BACKEND_ACL_CL} PUBLIC ${CMAKE_SOURCE_DIR}/externals/tensorflow) # TODO Remove this file. We should not need this. target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} arm_compute) -target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} nnfw_support_nnapi) target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} ${LIB_NEURUN_KERNEL_ACL_CL}) +target_link_libraries(${LIB_NEURUN_BACKEND_ACL_CL} ${LIB_NEURUN}) -target_compile_options(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE -Wall -Wextra -Werror) +target_compile_options(${LIB_NEURUN_BACKEND_ACL_CL} PRIVATE -Wall -Wextra -Werror -Wno-unused-parameter) -set_target_properties(${LIB_NEURUN_BACKEND_ACL_CL} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${LIB_NEURUN_BACKEND_ACL_CL} PROPERTIES OUTPUT_NAME backend_acl_cl) install(TARGETS ${LIB_NEURUN_BACKEND_ACL_CL} DESTINATION lib/neurun) diff --git a/runtimes/neurun/src/backend/acl_cl/Config.cc b/runtimes/neurun/src/backend/acl_cl/Config.cc new file mode 100644 index 000000000..cad9b8988 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/Config.cc @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <arm_compute/runtime/CL/CLScheduler.h> + +#include "backend/acl_cl/Config.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ + +void Config::initialize() { arm_compute::CLScheduler::get().default_init(); } + +} // namespace acl_cl +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/Config.h b/runtimes/neurun/src/backend/acl_cl/Config.h new file mode 100644 index 000000000..cb43bfbe0 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/Config.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_CONFIG_H__ +#define __NEURUN_BACKEND_ACL_CL_CONFIG_H__ + +#include "backend/interface/IConfig.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ + +class Config : public IConfig +{ +public: + Config() + { + // DO NOTHING + } + + virtual std::string id() override { return "acl_cl"; } + virtual void initialize() override; + virtual graph::operand::Layout getOperandLayout() { return graph::operand::Layout::NCHW; } + virtual bool SupportSubTensorAlloc() override { return true; } +}; + +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_CONFIG_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/Convert.cc b/runtimes/neurun/src/backend/acl_cl/Convert.cc new file mode 100644 index 000000000..ed0a089c4 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/Convert.cc @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Convert.h" + +#include "Swizzle.h" +#include "model/operand/DataType.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ + +::arm_compute::TensorShape asTensorShape(const ::neurun::model::operand::Shape &shape, + bool apply_dim_correction) +{ + const uint32_t rank = shape.rank(); + + ::arm_compute::TensorShape res{}; + + res.set_num_dimensions(rank); + + for (uint32_t axis = 0; axis < rank; ++axis) + { + // NOTE In some cases, in incorrect dimensions is required. + // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of + // LSTM is used as the weight of the FullyConnected. + // The FullyConnected's weight must be greater or equal than 2-dimensions. + // However, if the dimension correction is applied to input_to_input_weights with input_size + // equal to 1, it will be changed to 1-D. + // So input_to_input_weights is not used by the weight of FullyConnected. + res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction); + } + + return res; +} + +::arm_compute::DataType asDataType(const ::neurun::model::operand::DataType &type) +{ + switch (type) + { + case ::neurun::model::operand::DataType::SCALAR_FLOAT32: + case ::neurun::model::operand::DataType::TENSOR_FLOAT32: + return ::arm_compute::DataType::F32; + case ::neurun::model::operand::DataType::SCALAR_INT32: + case ::neurun::model::operand::DataType::TENSOR_INT32: + return ::arm_compute::DataType::S32; + case ::neurun::model::operand::DataType::SCALAR_UINT32: + return ::arm_compute::DataType::U32; + case ::neurun::model::operand::DataType::TENSOR_QUANT8_ASYMM: + return ::arm_compute::DataType::QASYMM8; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset) +{ + return ::arm_compute::QuantizationInfo(scale, offset); +} + +::arm_compute::TensorInfo asTensorInfo(const ::neurun::model::operand::Shape &shape, + const ::neurun::model::operand::TypeInfo &typeInfo) +{ + return ::arm_compute::TensorInfo(asTensorShape(shape), 1, asDataType(typeInfo.type()), + asQuantizationInfo(typeInfo.scale(), typeInfo.offset())); +} + +} // namespace acl_cl +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/Convert.h b/runtimes/neurun/src/backend/acl_cl/Convert.h new file mode 100644 index 000000000..1a233fb87 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/Convert.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_CONVERT_H__ +#define __NEURUN_BACKEND_ACL_CL_CONVERT_H__ + +#include <arm_compute/core/TensorInfo.h> +#include <arm_compute/core/SubTensorInfo.h> +#include <arm_compute/core/TensorShape.h> + +#include "model/operand/Object.h" +#include "model/operand/Shape.h" +#include "model/operand/TypeInfo.h" +#include "misc/feature/Shape.h" +#include "misc/kernel/Shape.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ + +::arm_compute::TensorShape asTensorShape(const ::neurun::model::operand::Shape &shape, + bool apply_dim_correction = true); +::arm_compute::DataType asDataType(const ::neurun::model::operand::DataType &type); +::arm_compute::TensorInfo asTensorInfo(const ::neurun::model::operand::Shape &shape, + const ::neurun::model::operand::TypeInfo &typeInfo); + +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_CONVERT_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc b/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc deleted file mode 100644 index 9a681b3de..000000000 --- a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc +++ /dev/null @@ -1,144 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/acl_cl/InitializerGenerator.h" - -#include <arm_compute/core/Coordinates.h> - -#include "backend/acl_cl/kernel/View.h" -#include "internal/nnapi/kernel/Reader.h" -#include "util/kernel/IndexIterator.h" - -namespace neurun -{ -namespace backend -{ -namespace acl_cl -{ - -InitializerGenerator::InitializerGenerator(const neurun::graph::operand::Set &ctx) : _ctx(ctx) -{ - // DO NOTHING -} - -Initializer -InitializerGenerator::generateWeight(const graph::operation::Conv2D::Implicit::Node &node) -{ - const ::neurun::graph::operand::Index ker_index{node.getInputs().at(1)}; - - const auto ker_shape = _ctx.at(ker_index).shape().asKernel(); - auto ker_base = _ctx.at(ker_index).data().base(); - auto ker_size = _ctx.at(ker_index).data().size(); - - return [ker_shape, ker_base, ker_size](::arm_compute::ITensor &tensor) { - const ::internal::nnapi::kernel::Reader<float> from{ker_shape, ker_base, ker_size}; - ::internal::arm_compute::kernel::View<float> into{&tensor}; - - ::nnfw::util::kernel::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - into.at(nth, ch, row, col) = value; - }; - }; -} - -Initializer InitializerGenerator::generateWeight(const graph::operation::FullyConnected::Node &node) -{ - const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - - const auto num_output = _ctx.at(weight_index).shape().dim(0); - auto weight_base = _ctx.at(weight_index).data().base(); - auto weight_size = _ctx.at(weight_index).data().size(); - - // NOTE We assume that input is a feature map - // TODO Remove this restriction! - const auto ifm_shape = _ctx.at(input_index).shape().asFeature(); - - return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) { - const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, ifm_shape.W}; - const ::internal::nnapi::kernel::Reader<float> from{ker_shape, weight_base, weight_size}; - - ::nnfw::util::kernel::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - - uint32_t offset = 0; - - // ARM Compute Library uses 'NCHW' ordering - offset += nth * ifm_shape.C * ifm_shape.H * ifm_shape.W; - offset += ch * ifm_shape.H * ifm_shape.W; - offset += row * ifm_shape.W; - offset += col; - - const ::arm_compute::Coordinates coordinate{offset}; - - auto into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - *into = value; - }; - }; -} - -Initializer InitializerGenerator::generateBias(const graph::operation::Conv2D::Implicit::Node &node) -{ - // TODO Refactor so we can reuse the common code - - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - - auto bias_base = _ctx.at(bias_index).data().base(); - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - return [bias_base, bias_size](::arm_compute::ITensor &tensor) { - for (int32_t n = 0; n < bias_size; ++n) - { - const ::arm_compute::Coordinates coordinate{n}; - - float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - const float *from = reinterpret_cast<const float *>(bias_base) + n; - const auto value = *from; - - *into = value; - } - }; -} - -Initializer InitializerGenerator::generateBias(const graph::operation::FullyConnected::Node &node) -{ - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - - auto bias_base = _ctx.at(bias_index).data().base(); - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - return [bias_base, bias_size](::arm_compute::ITensor &tensor) { - for (int32_t n = 0; n < bias_size; ++n) - { - const ::arm_compute::Coordinates coordinate{n}; - - float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - const float *from = reinterpret_cast<const float *>(bias_base) + n; - const auto value = *from; - - *into = value; - } - }; -} - -} // namespace acl_cl -} // namespace backend -} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h b/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h deleted file mode 100644 index 78b7efb5e..000000000 --- a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__ -#define __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__ - -#include "backend/IInitializerGenerator.h" - -#include "graph/operand/Set.h" - -namespace neurun -{ -namespace backend -{ -namespace acl_cl -{ - -class InitializerGenerator : public IInitializerGenerator -{ -public: - InitializerGenerator(const neurun::graph::operand::Set &ctx); - - Initializer generateWeight(const graph::operation::Conv2D::Implicit::Node &node) override; - Initializer generateWeight(const graph::operation::FullyConnected::Node &node) override; - - Initializer generateBias(const graph::operation::Conv2D::Implicit::Node &node) override; - Initializer generateBias(const graph::operation::FullyConnected::Node &node) override; - -private: - const neurun::graph::operand::Set &_ctx; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace neurun - -#endif // __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/PluginClassesAllocator.cc b/runtimes/neurun/src/backend/acl_cl/PluginClassesAllocator.cc new file mode 100644 index 000000000..f33e71d33 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/PluginClassesAllocator.cc @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <memory> +#include "TensorBuilder.h" +#include "StageGenerator.h" +#include "Config.h" +#include "util/logging.h" + +extern "C" { +neurun::backend::acl_cl::TensorBuilder *allocate_TensorBuilder() +{ + VERBOSE(allocate_TensorBuilder) << "loaded from acl_cl\n"; + return new neurun::backend::acl_cl::TensorBuilder; +} + +neurun::backend::acl_cl::StageGenerator *allocate_StageGenerator( + const neurun::model::operand::Set &operand_ctx, + const std::shared_ptr<neurun::backend::acl_cl::TensorBuilder> &tensor_builder) +{ + VERBOSE(allocate_StageGenerator) << "loaded from acl_cl\n"; + return new neurun::backend::acl_cl::StageGenerator(operand_ctx, tensor_builder); +} + +neurun::backend::acl_cl::Config *allocate_Config() +{ + VERBOSE(allocate_Config) << "loaded from acl_cl\n"; + return new neurun::backend::acl_cl::Config; +} +} diff --git a/runtimes/neurun/src/backend/acl_cl/StageGenerator.cc b/runtimes/neurun/src/backend/acl_cl/StageGenerator.cc index c63698fd8..89bbd7bd2 100644 --- a/runtimes/neurun/src/backend/acl_cl/StageGenerator.cc +++ b/runtimes/neurun/src/backend/acl_cl/StageGenerator.cc @@ -16,6 +16,8 @@ #include "backend/acl_cl/StageGenerator.h" +#include "kernel/acl_cl/CLFunction.h" + #include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h> #include <arm_compute/runtime/CL/functions/CLPoolingLayer.h> #include <arm_compute/runtime/CL/functions/CLActivationLayer.h> @@ -25,20 +27,27 @@ #include "kernel/acl_cl/ConcatLayer.h" -#include "internal/Padding.h" +#include "util/Padding.h" -#include "graph/operand/Index.h" +#include "model/operand/Index.h" -#include "logging.h" +#include "util/logging.h" #include "NeuralNetworks.h" -#include "support/nnapi/Utils.h" +#include "util/Utils.h" template <typename T> std::unique_ptr<T> make_layer(void) { return std::unique_ptr<T>{new T}; } -::arm_compute::PadStrideInfo asPadStringInfo(const ::internal::Padding &padding, - const ::internal::Stride &stride) +std::unique_ptr<::neurun::kernel::acl_cl::CLFunction> +make_cl_function(std::unique_ptr<::arm_compute::IFunction> &&layer) +{ + return std::unique_ptr<::neurun::kernel::acl_cl::CLFunction>( + new ::neurun::kernel::acl_cl::CLFunction(std::move(layer))); +} + +::arm_compute::PadStrideInfo asPadStringInfo(const neurun::util::Padding &padding, + const neurun::util::Stride &stride) { return ::arm_compute::PadStrideInfo{stride.horizontal, stride.vertical, @@ -86,7 +95,9 @@ void ActivationBuilder::appendReLU(::arm_compute::ICLTensor *ifm_alloc) fn->configure(ifm_alloc, nullptr, act_info); - _builder.append(std::move(fn)); + auto acl_fn = make_cl_function(std::move(fn)); + + _builder.append(std::move(acl_fn)); } void ActivationBuilder::append(FuseCode code, ::arm_compute::ICLTensor *ifm_alloc) @@ -113,25 +124,27 @@ void ActivationBuilder::append(FuseCode code, ::arm_compute::ICLTensor *ifm_allo // // StageGenerator // -StageGenerator::StageGenerator(const neurun::graph::operand::Set &ctx, +StageGenerator::StageGenerator(const neurun::model::operand::Set &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder) : _ctx(ctx), _tensor_builder(tensor_builder) { // DO NOTHING } -Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::Conv2DNode &node) { - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index ker_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; + using model::operation::Conv2DNode; + + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(Conv2DNode::Input::INPUT)}; + const auto ker_index{node.getInputs().at(Conv2DNode::Input::KERNEL)}; + const auto bias_index{node.getInputs().at(Conv2DNode::Input::BIAS)}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + const auto padding_index{node.param().padding_index}; + const auto activation_index{node.param().activation_index}; const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); @@ -143,7 +156,7 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || (ANEURALNETWORKS_PADDING_VALID == padding_type)); - ::internal::Stride stride; + neurun::util::Stride stride; stride.vertical = _ctx.at(vstride_index).asScalar<int32_t>(); stride.horizontal = _ctx.at(hstride_index).asScalar<int32_t>(); @@ -151,64 +164,67 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; - int ker_index; - int bias_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; + model::operand::Index ker_index; + model::operand::Index bias_index; - ::internal::Padding padding; - ::internal::Stride stride; + neurun::util::Padding padding; + neurun::util::Stride stride; FuseCode activation; }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); - param.ker_index = ker_index.asInt(); - param.bias_index = bias_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; + param.ker_index = ker_index; + param.bias_index = bias_index; param.stride = stride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(ifm_shape, ofm_shape, stride, ker_shape.W, ker_shape.H) - : ::internal::valid_padding(); + ? neurun::util::same_padding(ifm_shape, ofm_shape, stride, ker_shape.W, ker_shape.H) + : neurun::util::valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}).get(); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}).get(); - auto ker_alloc = tensors->at(::neurun::graph::operand::Index{param.ker_index}).get(); - auto bias_alloc = tensors->at(::neurun::graph::operand::Index{param.bias_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index).get(); + auto ifm_alloc = tensors->at(param.ifm_index).get(); + auto ker_alloc = tensors->at(param.ker_index).get(); + auto bias_alloc = tensors->at(param.bias_index).get(); const auto conv_info = asPadStringInfo(param.padding, param.stride); std::unique_ptr<::arm_compute::CLConvolutionLayer> fn{new ::arm_compute::CLConvolutionLayer}; - fn->configure(ifm_alloc, ker_alloc, bias_alloc, ofm_alloc, conv_info); + fn->configure(ifm_alloc->handle(), ker_alloc->handle(), bias_alloc->handle(), + ofm_alloc->handle(), conv_info); - builder.append(std::move(fn)); + auto acl_fn = make_cl_function(std::move(fn)); - ActivationBuilder{builder}.append(param.activation, ofm_alloc); - }; + builder.append(std::move(acl_fn)); + + ActivationBuilder{builder}.append(param.activation, ofm_alloc->handle()); + }); } -Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::MaxPool2DNode &node) { - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(model::operation::MaxPool2DNode::Input::INPUT)}; - const ::neurun::graph::operand::Index kh_index{node.param().kh_index}; - const ::neurun::graph::operand::Index kw_index{node.param().kw_index}; + const auto kh_index{node.param().kh_index}; + const auto kw_index{node.param().kw_index}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; + const auto padding_index{node.param().padding_index}; const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); @@ -225,22 +241,22 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; uint32_t kw; uint32_t kh; - ::internal::Padding padding; - ::internal::Stride stride; + neurun::util::Padding padding; + neurun::util::Stride stride; // TODO Add 'activation' field }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; param.kh = kh; param.kw = kw; @@ -249,8 +265,8 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node param.stride.horizontal = hstride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) - : ::internal::valid_padding(); + ? neurun::util::same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) + : neurun::util::valid_padding(); VERBOSE(MaxPool2D) << "IFM_H: " << ifm_shape.H << std::endl; VERBOSE(MaxPool2D) << "IFM_W: " << ifm_shape.W << std::endl; @@ -267,9 +283,9 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}).get(); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index).get(); + auto ifm_alloc = tensors->at(param.ifm_index).get(); ::arm_compute::PoolingLayerInfo info{::arm_compute::PoolingType::MAX, ::arm_compute::Size2D{param.kw, param.kh}, @@ -277,24 +293,26 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node std::unique_ptr<::arm_compute::CLPoolingLayer> fn{new ::arm_compute::CLPoolingLayer}; - fn->configure(ifm_alloc, ofm_alloc, info); + fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info); - builder.append(std::move(fn)); - }; + auto acl_fn = make_cl_function(std::move(fn)); + + builder.append((std::move(acl_fn))); + }); } -Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::AvgPool2DNode &node) { - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(model::operation::AvgPool2DNode::Input::INPUT)}; - const ::neurun::graph::operand::Index kh_index{node.param().kh_index}; - const ::neurun::graph::operand::Index kw_index{node.param().kw_index}; + const auto kh_index{node.param().kh_index}; + const auto kw_index{node.param().kw_index}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; + const auto padding_index{node.param().padding_index}; const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); @@ -314,22 +332,22 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; uint32_t kw; uint32_t kh; - ::internal::Padding padding; - ::internal::Stride stride; + neurun::util::Padding padding; + neurun::util::Stride stride; // TODO Add 'activation' field }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; param.kh = kh; param.kw = kw; @@ -338,8 +356,8 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node param.stride.horizontal = hstride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) - : ::internal::valid_padding(); + ? neurun::util::same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) + : neurun::util::valid_padding(); VERBOSE(AvgPool2D) << "IFM_H: " << ifm_shape.H << std::endl; VERBOSE(AvgPool2D) << "IFM_W: " << ifm_shape.W << std::endl; @@ -349,7 +367,7 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node VERBOSE(AvgPool2D) << "KER_W: " << kw << std::endl; VERBOSE(AvgPool2D) << "STRIDE_H: " << vstride << std::endl; VERBOSE(AvgPool2D) << "STRIDE_W: " << hstride << std::endl; - VERBOSE(AvgPool2D) << "PAD: " << ::nnfw::support::nnapi::to_string(padding_type) << std::endl; + VERBOSE(AvgPool2D) << "PAD: " << neurun::util::to_string(padding_type) << std::endl; VERBOSE(AvgPool2D) << "PAD(T): " << param.padding.top << std::endl; VERBOSE(AvgPool2D) << "PAD(B): " << param.padding.bottom << std::endl; VERBOSE(AvgPool2D) << "PAD(L): " << param.padding.left << std::endl; @@ -357,9 +375,9 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}).get(); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index).get(); + auto ifm_alloc = tensors->at(param.ifm_index).get(); ::arm_compute::PoolingLayerInfo info{ ::arm_compute::PoolingType::AVG, ::arm_compute::Size2D{param.kw, param.kh}, @@ -367,170 +385,207 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node std::unique_ptr<::arm_compute::CLPoolingLayer> fn{new ::arm_compute::CLPoolingLayer}; - fn->configure(ifm_alloc, ofm_alloc, info); + fn->configure(ifm_alloc->handle(), ofm_alloc->handle(), info); - builder.append(std::move(fn)); - }; + auto acl_fn = make_cl_function(std::move(fn)); + + builder.append((std::move(acl_fn))); + }); } -Stage StageGenerator::generate(const graph::operation::Concat::Node &node) +void StageGenerator::visit(const model::operation::ConcatNode &node) { - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index axis_index{node.param().axis_index}; + const auto ofm_index{node.getOutputs().at(0)}; + const auto axis_index{node.param().axis_index}; struct Param { - int32_t output_index; - std::vector<int32_t> input_indexes; + model::operand::Index output_index; + std::vector<model::operand::Index> input_indexes; int32_t axis; }; Param param; - param.output_index = ofm_index.asInt(); + param.output_index = ofm_index; for (const auto &e : node.getInputs()) { - param.input_indexes.emplace_back(e.asInt()); + param.input_indexes.emplace_back(e); } param.axis = _ctx.at(axis_index).asScalar<int32_t>(); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + // If tensor allocator allocate as subtensor + bool canEliminate = true; + for (auto ifm_ind : param.input_indexes) + { + if (!tensors->isSubTensorOf(param.output_index, ifm_ind)) + { + canEliminate = false; + break; + } + } + if (canEliminate) + { + // If concat eliminated, return with nothing to do + return; + } + + auto output_alloc = tensors->at(param.output_index).get(); - std::vector<::arm_compute::ICLTensor *> input_allocs; + std::vector<::neurun::backend::acl_cl::operand::ICLTensor *> input_allocs; for (auto ifm_ind : param.input_indexes) { - input_allocs.emplace_back(tensors->at(::neurun::graph::operand::Index{ifm_ind}).get()); + input_allocs.emplace_back( + dynamic_cast<::neurun::backend::acl_cl::operand::CLTensor *>(tensors->at(ifm_ind).get())); } std::unique_ptr<::neurun::kernel::acl_cl::ConcatLayer> fn{ new ::neurun::kernel::acl_cl::ConcatLayer}; - fn->configure(input_allocs, param.axis, output_alloc); + fn->configure(input_allocs, param.axis, + dynamic_cast<::neurun::backend::acl_cl::operand::CLTensor *>(output_alloc)); - builder.append(std::move(fn)); - }; + auto acl_fn = make_cl_function(std::move(fn)); + + builder.append(std::move(acl_fn)); + }); } -Stage StageGenerator::generate(const graph::operation::FullyConnected::Node &node) +void StageGenerator::visit(const model::operation::FullyConnectedNode &node) { - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + using model::operation::FullyConnectedNode; + + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(FullyConnectedNode::Input::INPUT)}; + const auto weight_index{node.getInputs().at(FullyConnectedNode::Input::WEIGHT)}; + const auto bias_index{node.getInputs().at(FullyConnectedNode::Input::BIAS)}; + const auto activation_index{node.param().activation_index}; // Construct operation parameters struct Param { - int output_index; + model::operand::Index output_index; - int input_index; - int weight_index; - int bias_index; + model::operand::Index input_index; + model::operand::Index weight_index; + model::operand::Index bias_index; FuseCode activation; }; Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); - param.weight_index = weight_index.asInt(); - param.bias_index = bias_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; + param.weight_index = weight_index; + param.bias_index = bias_index; param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); - auto weight_alloc = tensors->at(::neurun::graph::operand::Index{param.weight_index}).get(); - auto bias_alloc = tensors->at(::neurun::graph::operand::Index{param.bias_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); + auto weight_alloc = tensors->at(param.weight_index).get(); + auto bias_alloc = tensors->at(param.bias_index).get(); auto fn = make_layer<::arm_compute::CLFullyConnectedLayer>(); - fn->configure(input_alloc, weight_alloc, bias_alloc, output_alloc); + fn->configure(input_alloc->handle(), weight_alloc->handle(), bias_alloc->handle(), + output_alloc->handle()); - builder.append(std::move(fn)); + auto acl_fn = make_cl_function(std::move(fn)); - ActivationBuilder{builder}.append(param.activation, output_alloc); - }; + builder.append(std::move(acl_fn)); + + ActivationBuilder{builder}.append(param.activation, output_alloc->handle()); + }); } -Stage StageGenerator::generate(const graph::operation::Reshape::Node &node) +void StageGenerator::visit(const model::operation::ReshapeNode &node) { - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(model::operation::ReshapeNode::Input::INPUT)}; struct Param { - int output_index; - int input_index; + model::operand::Index output_index; + model::operand::Index input_index; }; Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); auto fn = make_layer<::arm_compute::CLReshapeLayer>(); - fn->configure(input_alloc, output_alloc); + fn->configure(input_alloc->handle(), output_alloc->handle()); - builder.append(std::move(fn)); - }; + auto acl_fn = make_cl_function(std::move(fn)); + + builder.append(std::move(acl_fn)); + }); } -Stage StageGenerator::generate(const graph::operation::Softmax::Node &node) +void StageGenerator::visit(const model::operation::SoftmaxNode &node) { - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index scale_index{node.param().scale_index}; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(model::operation::SoftmaxNode::Input::INPUT)}; + const auto scale_index{node.param().scale_index}; assert(_ctx.at(scale_index).shape().rank() == 0); struct Param { - int output_index; - int input_index; + model::operand::Index output_index; + model::operand::Index input_index; float scale; }; Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; param.scale = _ctx.at(scale_index).asScalar<float>(); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); auto fn = make_layer<::arm_compute::CLSoftmaxLayer>(); - fn->configure(input_alloc, output_alloc, param.scale); + fn->configure(input_alloc->handle(), output_alloc->handle(), param.scale); - builder.append(std::move(fn)); - }; + auto acl_fn = make_cl_function(std::move(fn)); + + builder.append(std::move(acl_fn)); + }); } -Stage StageGenerator::generate(const graph::operation::NOP::Node & /* node */) +void StageGenerator::visit(const model::operation::PermuteNode & /* node */) { - // DO NOTHING - return nullptr; + throw "Unsupported"; +} + +void StageGenerator::visit(const model::operation::AddNode &) +{ + VERBOSE(Add) << "generate CPU Add" << std::endl; + + throw std::runtime_error("NYI"); } } // namespace acl_cl diff --git a/runtimes/neurun/src/backend/acl_cl/StageGenerator.h b/runtimes/neurun/src/backend/acl_cl/StageGenerator.h index 921604649..1dac2592b 100644 --- a/runtimes/neurun/src/backend/acl_cl/StageGenerator.h +++ b/runtimes/neurun/src/backend/acl_cl/StageGenerator.h @@ -17,9 +17,9 @@ #ifndef __NEURUN_BACKEND_ACL_CL_STAGE_GENERATOR_H__ #define __NEURUN_BACKEND_ACL_CL_STAGE_GENERATOR_H__ -#include "backend/IStageGenerator.h" +#include "backend/interface/IStageGenerator.h" -#include "graph/operand/Set.h" +#include "model/operand/Set.h" #include "backend/acl_cl/TensorBuilder.h" namespace neurun @@ -32,22 +32,18 @@ namespace acl_cl class StageGenerator : public IStageGenerator { public: - StageGenerator(const neurun::graph::operand::Set &ctx, + StageGenerator(const neurun::model::operand::Set &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder); virtual std::shared_ptr<ITensorBuilder> tensor_builder() override { return _tensor_builder; } - virtual Stage generate(const graph::operation::Conv2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::MaxPool2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::AvgPool2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::Concat::Node &node) override; - virtual Stage generate(const graph::operation::FullyConnected::Node &node) override; - virtual Stage generate(const graph::operation::Reshape::Node &node) override; - virtual Stage generate(const graph::operation::Softmax::Node &node) override; - virtual Stage generate(const graph::operation::NOP::Node &node) override; +#define OP(InternalName, IsNnApi, NnApiName) \ + virtual void visit(const model::operation::InternalName &) override; +#include "model/operation/Op.lst" +#undef OP private: - const neurun::graph::operand::Set &_ctx; + const neurun::model::operand::Set &_ctx; std::shared_ptr<TensorBuilder> _tensor_builder; }; diff --git a/runtimes/neurun/src/backend/acl_cl/Swizzle.h b/runtimes/neurun/src/backend/acl_cl/Swizzle.h new file mode 100644 index 000000000..838e57162 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/Swizzle.h @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_SWIZZLE_H__ +#define __NEURUN_BACKEND_ACL_CL_SWIZZLE_H__ + +#include <cassert> + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ + +class ARMComputeAxis +{ +public: + ARMComputeAxis() = default; + +public: + explicit ARMComputeAxis(uint32_t value) : _value{value} + { + // DO NOTHING + } + +public: + uint32_t value(void) const { return _value; } + +private: + uint32_t _value; +}; + +// Convert T/F Lite / NNAPI axis (based on ...NHWC) to ARMCompute axis (WHCN...) +inline ARMComputeAxis ToARMComputeAxis(uint32_t rank, uint32_t axis) +{ + assert(rank > axis); + const ARMComputeAxis reversed{(rank - axis) - 1}; + + if (rank < 4) + { + return reversed; + } + + // DEPTH + if (0 == reversed.value()) + { + return ARMComputeAxis{2}; + } + // WIDTH + if (1 == reversed.value()) + { + return ARMComputeAxis{0}; + } + // HEIGHT + if (2 == reversed.value()) + { + return ARMComputeAxis{1}; + } + + // ELSE + return reversed; +} + +template <typename T> inline T ReorderBits(T in, size_t numOfBits) +{ + assert(numOfBits > 0); + T out = 0; + for (int32_t i = numOfBits - 1; i >= 0; --i) + { + const uint32_t toShift = numOfBits - ToARMComputeAxis(numOfBits, i).value() - 1; + out += ((in & 1) << toShift); + in >>= 1; + } + return out; +} + +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_SWIZZLE_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/TensorBuilder.cc b/runtimes/neurun/src/backend/acl_cl/TensorBuilder.cc index 05943c26a..b5c038200 100644 --- a/runtimes/neurun/src/backend/acl_cl/TensorBuilder.cc +++ b/runtimes/neurun/src/backend/acl_cl/TensorBuilder.cc @@ -17,8 +17,12 @@ #include "backend/acl_cl/TensorBuilder.h" #include <cassert> +#include <stack> #include "operand/Object.h" +#include "Convert.h" + +#include "util/logging.h" namespace neurun { @@ -32,34 +36,124 @@ TensorBuilder::TensorBuilder() // DO NOTHING } -void TensorBuilder::mark(const ::neurun::graph::operand::Index &ind) +void TensorBuilder::registerTensorInfo(const model::operand::Index &ind, + const compiler::TensorInfo &info) +{ + assert(_tensors.size() == 0); + + _tensor_info_map.insert({ind, info}); +} + +void TensorBuilder::registerSubTensorInfo(const model::operand::Index &ind, + const compiler::SubTensorInfo &info) { assert(_tensors.size() == 0); - _inds.insert(ind); + _subtensor_info_map.insert({ind, info}); +} + +void TensorBuilder::notifyFirstUse(const model::operand::Index &) +{ + // DO NOTHING +} + +void TensorBuilder::notifyLastUse(const model::operand::Index &) +{ + // DO NOTHING } -void TensorBuilder::prepare(codegen::Plan &plan, - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx) +void TensorBuilder::prepare(void) { assert(_tensors.size() == 0); // TODO Handle SubTensor(subsumption) // Currently this TensorBuilder does not have subsumption info yet + // Allocated subtensor will be mapped to _subtensors instead of _tensors + assert(_subtensors.size() == 0); - for (auto ind_int : _inds) + for (auto &entry : _tensor_info_map) { - ::neurun::graph::operand::Index ind{ind_int}; - auto tensor = std::make_shared<::arm_compute::CLTensor>(); - tensor->allocator()->init(tensor_info_ctx.at(ind.asInt())); - plan.operands().set(ind, std::make_shared<operand::Object>(tensor)); + auto ind = entry.first; + const auto &info = entry.second; + auto tensor = std::make_shared<::neurun::backend::acl_cl::operand::CLTensor>(info); _tensors[ind] = tensor; } + + // To make subtensor, parent tensor must be made first + // For this condition, use stack + // 1) Push one subtensor index to stack (iterate subtensors) + // 2) If tensor at stack top is already made, pop and go to 4) + // 3) If tensor pushed at 1) is not made, check parent tensor + // 3-1) If parent tensor is already made, we can make child tensor + // Make child tensor and pop, go to 4) + // 3-2) If parent tensor is not made, we can't make child tensor yet + // Push parent tensor index to stack and return to 4) + // 4) If stack is empty, return to 1), else return to 2) + for (auto &entry : _subtensor_info_map) + { + model::operand::Index ind = entry.first; + + std::stack<model::operand::Index> stack; + stack.push(ind); + + while (!stack.empty()) + { + const auto current = stack.top(); + const auto &info = _subtensor_info_map.at(current); + + // Already generated CLSubTensor + if (_subtensors.find(current) != _subtensors.end()) + { + stack.pop(); + continue; + } + + auto parent = info.parent(); + std::shared_ptr<::neurun::backend::acl_cl::operand::ICLTensor> parent_tensor; + + if (_tensors.find(parent) != _tensors.end()) + { + // Parent is allocated as tensor + parent_tensor = _tensors[parent]; + } + else if (_subtensors.find(parent) != _subtensors.end()) + { + // Parent is allocated as subtensor + parent_tensor = _subtensors[parent]; + } + else + { + // Cannot find allocated parent tensor: allocate parent first + assert(_subtensor_info_map.find(parent) != _subtensor_info_map.end()); + stack.push(parent); + continue; + } + assert(parent_tensor != nullptr); + + // Child's type should be same with parent + assert(info.type().offset() == parent_tensor->info()->quantization_info().offset); + assert(info.type().scale() == parent_tensor->info()->quantization_info().scale); + assert(asDataType(info.type().type()) == parent_tensor->info()->data_type()); + auto shape = asTensorShape(info.shape()); + + // Only support axis: 3 (channel) + ::arm_compute::Coordinates coordinates; + coordinates.set_num_dimensions(4); + assert(info.offset().h() == 0); + assert(info.offset().n() == 0); + assert(info.offset().w() == 0); + coordinates[2] = info.offset().c(); + auto tensor = std::make_shared<::neurun::backend::acl_cl::operand::CLSubTensor>( + parent_tensor.get(), shape, coordinates, true); + _subtensors[current] = tensor; + stack.pop(); + } + } } void TensorBuilder::allocate(void) { - assert(_inds.size() == _tensors.size()); + assert(_tensor_info_map.size() == _tensors.size()); for (const auto &tensor_entry : _tensors) { @@ -68,10 +162,83 @@ void TensorBuilder::allocate(void) } } -std::shared_ptr<::arm_compute::CLTensor> -TensorBuilder::at(const ::neurun::graph::operand::Index &ind) +std::shared_ptr<::neurun::backend::operand::ITensor> +TensorBuilder::tensorAt(const model::operand::Index &ind) +{ + if (_tensors.find(ind) != _tensors.end()) + { + return _tensors.at(ind); + } + else + { + return _subtensors.at(ind); + } +} + +std::shared_ptr<backend::operand::IObject> +TensorBuilder::wrapTensor(const model::operand::Index &ind) { - return _tensors.at(ind); + if (_objects.find(ind) != _objects.end()) + { + return _objects.at(ind); + } + else + { + if (_tensors.find(ind) != _tensors.end()) + { + return _objects[ind] = std::make_shared<operand::Object>(_tensors.at(ind)); + } + else + { + return _objects[ind] = std::make_shared<operand::Object>(_subtensors.at(ind)); + } + } +} + +void TensorBuilder::iterate(const IterateFunction &fn) +{ + for (auto it : _tensors) + { + fn(it.first); + } + for (auto it : _subtensors) + { + fn(it.first); + } +} + +std::shared_ptr<::neurun::backend::acl_cl::operand::ICLTensor> +TensorBuilder::at(const ::neurun::model::operand::Index &ind) +{ + if (_tensors.find(ind) != _tensors.end()) + { + return _tensors.at(ind); + } + else + { + return _subtensors.at(ind); + } +} + +bool TensorBuilder::isSubTensorOf(const model::operand::Index &parent, + const model::operand::Index &child) +{ + if (_subtensor_info_map.find(child) == _subtensor_info_map.end()) + { + return false; + } + + if (_subtensors.find(child) == _subtensors.end()) + { + return false; + } + + if (_subtensor_info_map.at(child).parent() != parent) + { + return false; + } + + return true; } } // namespace acl_cl diff --git a/runtimes/neurun/src/backend/acl_cl/TensorBuilder.h b/runtimes/neurun/src/backend/acl_cl/TensorBuilder.h index 0a0f4e9ca..64d81721a 100644 --- a/runtimes/neurun/src/backend/acl_cl/TensorBuilder.h +++ b/runtimes/neurun/src/backend/acl_cl/TensorBuilder.h @@ -17,12 +17,12 @@ #ifndef __NEURUN_BACKEND_ACL_CL_TENSOR_BUILDER_H__ #define __NEURUN_BACKEND_ACL_CL_TENSOR_BUILDER_H__ -#include "backend/ITensorBuilder.h" +#include "backend/interface/ITensorBuilder.h" +#include "backend/acl_cl/operand/CLTensor.h" +#include "backend/acl_cl/operand/CLSubTensor.h" +#include "backend/acl_cl/operand/Object.h" #include <unordered_map> -#include <unordered_set> - -#include <arm_compute/runtime/CL/CLTensor.h> namespace neurun { @@ -31,23 +31,60 @@ namespace backend namespace acl_cl { -class Plan; - class TensorBuilder : public ITensorBuilder { public: TensorBuilder(); - virtual void mark(const ::neurun::graph::operand::Index &ind) override; - virtual void prepare(codegen::Plan &plan, - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx) override; + /** + * @brief Register tensor information to allocate on ACL-CL backend + * @param[in] ind Operand index + * @param[in] info Tensor information + */ + virtual void registerTensorInfo(const model::operand::Index &ind, + const compiler::TensorInfo &info) override; + /** + * @brief Register subtensor information to allocate on ACL-CL backend + * @param[in] ind Operand index + * @param[in] info Tensor information + */ + virtual void registerSubTensorInfo(const model::operand::Index &ind, + const compiler::SubTensorInfo &info) override; + + virtual void notifyFirstUse(const model::operand::Index &) override; + virtual void notifyLastUse(const model::operand::Index &) override; + + virtual void prepare(void) override; virtual void allocate(void) override; - std::shared_ptr<::arm_compute::CLTensor> at(const ::neurun::graph::operand::Index &ind); + virtual std::shared_ptr<::neurun::backend::operand::ITensor> + tensorAt(const model::operand::Index &ind) override; + virtual std::shared_ptr<backend::operand::IObject> + wrapTensor(const model::operand::Index &ind) override; + virtual void iterate(const IterateFunction &fn) override; + + std::shared_ptr<::neurun::backend::acl_cl::operand::ICLTensor> + at(const ::neurun::model::operand::Index &ind); + /** + * @brief Check child tensor is allocated as subtensor of parent tensor + * @param[in] parent Index of parent + * @param[in] child Index of child + * @return @c true if child is allocated as subtensor of parent, otherwise @c false + */ + bool isSubTensorOf(const model::operand::Index &parent, const model::operand::Index &child); private: - std::unordered_set<graph::operand::Index> _inds; - std::unordered_map<graph::operand::Index, std::shared_ptr<::arm_compute::CLTensor>> _tensors; + std::unordered_map<model::operand::Index, compiler::TensorInfo> _tensor_info_map; + std::unordered_map<model::operand::Index, compiler::SubTensorInfo> _subtensor_info_map; + std::unordered_map<model::operand::Index, + std::shared_ptr<::neurun::backend::acl_cl::operand::CLTensor>> + _tensors; + std::unordered_map<model::operand::Index, + std::shared_ptr<::neurun::backend::acl_cl::operand::CLSubTensor>> + _subtensors; + std::unordered_map<model::operand::Index, + std::shared_ptr<::neurun::backend::acl_cl::operand::Object>> + _objects; }; } // namespace acl_cl diff --git a/runtimes/neurun/src/backend/acl_cl/feature/View.h b/runtimes/neurun/src/backend/acl_cl/feature/View.h deleted file mode 100644 index 12025ce01..000000000 --- a/runtimes/neurun/src/backend/acl_cl/feature/View.h +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ -#define __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ - -#include "util/feature/Reader.h" - -#include <arm_compute/core/ITensor.h> - -#include <cassert> - -namespace internal -{ -namespace arm_compute -{ -namespace feature -{ - -template <typename T> class View; - -template <> class View<float> final : public nnfw::util::feature::Reader<float> -{ -public: - View(::arm_compute::ITensor *tensor) : _tensor{tensor} - { - assert(tensor->info()->data_type() == ::arm_compute::DataType::F32); - - // TODO Validate whether tensor is a feature map, or not - - _shape.C = tensor->info()->dimension(2); - _shape.H = tensor->info()->dimension(1); - _shape.W = tensor->info()->dimension(0); - } - -public: - const ::nnfw::util::feature::Shape &shape(void) const { return _shape; } - -public: - float at(uint32_t ch, uint32_t row, uint32_t col) const override - { - const auto offset = feature_index_to_byte_offset(ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - float at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override - { - const auto offset = feature_index_to_byte_offset(batch, ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - -public: - float &at(uint32_t ch, uint32_t row, uint32_t col) - { - const auto offset = feature_index_to_byte_offset(ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - float &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) - { - const auto offset = feature_index_to_byte_offset(batch, ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - -private: - size_t feature_index_to_byte_offset(uint32_t ch, uint32_t row, uint32_t col) const - { - // ARM Compute uses CHW ordering - return _tensor->info()->offset_element_in_bytes(::arm_compute::Coordinates{col, row, ch}); - } - size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const - { - // ARM Compute uses CHW ordering - return _tensor->info()->offset_element_in_bytes( - ::arm_compute::Coordinates{col, row, ch, batch}); - } - -private: - ::nnfw::util::feature::Shape _shape; - ::arm_compute::ITensor *_tensor; -}; - -} // namespace feature -} // namespace arm_compute -} // namespace internal - -#endif // __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/kernel/View.h b/runtimes/neurun/src/backend/acl_cl/kernel/View.h deleted file mode 100644 index aec9a8892..000000000 --- a/runtimes/neurun/src/backend/acl_cl/kernel/View.h +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_ARM_COMPUTE_KERNEL_VIEW_H__ -#define __INTERNAL_ARM_COMPUTE_KERNEL_VIEW_H__ - -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" - -#include <arm_compute/core/ITensor.h> - -#include <cassert> - -namespace internal -{ -namespace arm_compute -{ -namespace kernel -{ - -template <typename T> class View; - -template <> class View<float> final : public nnfw::util::kernel::Reader<float> -{ -public: - View(::arm_compute::ITensor *tensor) : _tensor{tensor} - { - assert(tensor->info()->data_type() == ::arm_compute::DataType::F32); - - _shape.N = tensor->info()->dimension(3); - _shape.C = tensor->info()->dimension(2); - _shape.H = tensor->info()->dimension(1); - _shape.W = tensor->info()->dimension(0); - } - -public: - const ::nnfw::util::kernel::Shape &shape(void) const { return _shape; } - -public: - float at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override - { - const auto offset = kernel_index_to_byte_offset(nth, ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - -public: - float &at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) - { - const auto offset = kernel_index_to_byte_offset(nth, ch, row, col); - - float *ptr = reinterpret_cast<float *>(_tensor->buffer() + offset); - - return *ptr; - } - -private: - size_t kernel_index_to_byte_offset(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const - { - return _tensor->info()->offset_element_in_bytes(::arm_compute::Coordinates{col, row, ch, nth}); - } - -private: - ::nnfw::util::kernel::Shape _shape; - ::arm_compute::ITensor *_tensor; -}; - -} // namespace kernel -} // namespace arm_compute -} // namespace internal - -#endif // __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.cc b/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.cc new file mode 100644 index 000000000..f64b521dd --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.cc @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CLSubTensor.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +CLSubTensor::CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape, + const arm_compute::Coordinates &coords, bool extend_parent) + : _cl_sub_tensor(std::make_shared<arm_compute::CLSubTensor>(parent->handle(), tensor_shape, + coords, extend_parent)) +{ + // DO NOTHING +} + +arm_compute::CLSubTensor *CLSubTensor::handle() const { return _cl_sub_tensor.get(); } + +arm_compute::CLSubTensor *CLSubTensor::handle() { return _cl_sub_tensor.get(); } + +void CLSubTensor::map(bool blocking) { _cl_sub_tensor->map(blocking); } + +void CLSubTensor::unmap() { _cl_sub_tensor->unmap(); } + +uint8_t *CLSubTensor::doMap(cl::CommandQueue &q, bool blocking) +{ + assert(cl_buffer().get() == nullptr); + return static_cast<uint8_t *>(q.enqueueMapBuffer(cl_buffer(), blocking ? CL_TRUE : CL_FALSE, + CL_MAP_READ | CL_MAP_WRITE, 0, + info()->total_size())); +} + +void CLSubTensor::doUnmap(cl::CommandQueue &q) +{ + assert(cl_buffer().get() == nullptr); + q.enqueueUnmapMemObject(cl_buffer(), buffer()); +} + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.h b/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.h new file mode 100644 index 000000000..cef78c196 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/CLSubTensor.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ +#define __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ + +#include <arm_compute/runtime/CL/CLSubTensor.h> +#include "ICLTensor.h" +#include "compiler/SubTensorInfo.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +class CLSubTensor : public ICLTensor +{ +public: + CLSubTensor() = delete; + +public: + CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape, + const arm_compute::Coordinates &coords, bool extend_parent = false); + +public: + arm_compute::CLSubTensor *handle() const override; + arm_compute::CLSubTensor *handle() override; + +public: + void map(bool blocking = true); + void unmap(); + +protected: + uint8_t *doMap(cl::CommandQueue &q, bool blocking) override; + virtual void doUnmap(cl::CommandQueue &q) override; + +private: + std::shared_ptr<arm_compute::CLSubTensor> _cl_sub_tensor; +}; + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.cc b/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.cc new file mode 100644 index 000000000..e7b718df3 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.cc @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <arm_compute/runtime/CL/CLScheduler.h> +#include <arm_compute/runtime/CL/CLMemory.h> +#include <arm_compute/runtime/CL/CLMemoryRegion.h> +#include "CLTensor.h" + +#include "backend/acl_cl/Convert.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +CLTensor::CLTensor(const compiler::TensorInfo &info) + : _cl_tensor(std::make_shared<arm_compute::CLTensor>()) +{ + auto acl_cl_info = asTensorInfo(info.shape(), info.typeInfo()); + allocator()->init(acl_cl_info); +} + +arm_compute::CLTensor *CLTensor::handle() const { return _cl_tensor.get(); } + +arm_compute::CLTensor *CLTensor::handle() { return _cl_tensor.get(); } + +arm_compute::CLTensorAllocator *CLTensor::allocator() { return _cl_tensor->allocator(); } + +void CLTensor::map(bool blocking) { _cl_tensor->map(blocking); } + +void CLTensor::unmap() { _cl_tensor->unmap(); } + +uint8_t *CLTensor::doMap(cl::CommandQueue &q, bool blocking) +{ + return allocator()->map(q, blocking); +} + +void CLTensor::doUnmap(cl::CommandQueue &q) { allocator()->unmap(q, buffer()); } + +// handle() is Deprecated on acl v18.11 +// TODO Update this +#if 0 +void CLTensor::setBuffer(void *host_ptr) +{ + // create empty MemoryRegion: just context. Since flag isn't used here, no matter which flag to + // pass + auto memory = arm_compute::CLMemory(std::make_shared<arm_compute::CLBufferMemoryRegion>( + arm_compute::CLScheduler::get().context(), CL_MEM_USE_HOST_PTR | CL_MEM_READ_WRITE, 0)); + + // set buffer + auto mem = reinterpret_cast<cl::Buffer *>(memory.region()->handle()); + *mem = cl::Buffer(arm_compute::CLScheduler::get().context(), + CL_MEM_USE_HOST_PTR | CL_MEM_READ_WRITE, info()->total_size(), host_ptr); + // set correct buffer size + memory.region()->set_size(info()->total_size()); + // import memory + allocator()->import_memory(memory); +} +#endif + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.h b/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.h new file mode 100644 index 000000000..31c96e201 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/CLTensor.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ +#define __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ + +#include <arm_compute/core/TensorInfo.h> +#include <arm_compute/runtime/CL/CLTensor.h> +#include <arm_compute/runtime/CL/CLScheduler.h> +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "ICLTensor.h" +#include "compiler/TensorInfo.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +class CLTensor : public ICLTensor +{ +public: + CLTensor() = delete; + +public: + CLTensor(const compiler::TensorInfo &info); + +public: + arm_compute::CLTensor *handle() const override; + arm_compute::CLTensor *handle() override; + +public: + arm_compute::CLTensorAllocator *allocator(); + void map(bool blocking = true); + void unmap(); + void setBuffer(void *host_ptr); + +protected: + uint8_t *doMap(cl::CommandQueue &q, bool blocking) override; + void doUnmap(cl::CommandQueue &q) override; + +private: + std::shared_ptr<arm_compute::CLTensor> _cl_tensor; +}; + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.cc b/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.cc new file mode 100644 index 000000000..23d723de4 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.cc @@ -0,0 +1,48 @@ +#include "ICLTensor.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +size_t ICLTensor::total_size() const { return info()->total_size(); } + +size_t ICLTensor::dimension(size_t index) const { return info()->dimension(index); } + +size_t ICLTensor::num_dimensions() const { return info()->num_dimensions(); } + +size_t ICLTensor::calcOffset(const neurun::util::feature::Coordinate4D &coords) +{ + int32_t N = coords.n(); + int32_t C = coords.c(); + int32_t H = coords.h(); + int32_t W = coords.w(); + + ::arm_compute::Coordinates coordinates{W, H, C, N}; + return info()->offset_element_in_bytes(coordinates); +} + +arm_compute::DataType ICLTensor::data_type() const { return info()->data_type(); } + +uint8_t *ICLTensor::buffer() const { return handle()->buffer(); } + +const cl::Buffer &ICLTensor::cl_buffer() const { return handle()->cl_buffer(); } + +arm_compute::ITensorInfo *ICLTensor::info() const { return handle()->info(); } + +arm_compute::ITensorInfo *ICLTensor::info() { return handle()->info(); } + +void ICLTensor::map(cl::CommandQueue &q, bool blocking) { return handle()->map(q, blocking); } + +void ICLTensor::unmap(cl::CommandQueue &q) { return handle()->unmap(q); } + +void ICLTensor::clear(cl::CommandQueue &q) { return handle()->clear(q); } + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.h b/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.h new file mode 100644 index 000000000..226fbf814 --- /dev/null +++ b/runtimes/neurun/src/backend/acl_cl/operand/ICLTensor.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ +#define __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ + +#include <arm_compute/core/ITensorInfo.h> +#include <arm_compute/core/CL/ICLTensor.h> +#include "backend/interface/operand/ITensor.h" + +namespace neurun +{ +namespace backend +{ +namespace acl_cl +{ +namespace operand +{ + +class ICLTensor : public ::neurun::backend::operand::ITensor +{ +public: + ICLTensor() = default; + ICLTensor(const ICLTensor &) = delete; + ICLTensor &operator=(const ICLTensor &) = delete; + ICLTensor(ICLTensor &&) = default; + ICLTensor &operator=(ICLTensor &&) = default; + virtual ~ICLTensor() = default; + +public: + virtual arm_compute::ICLTensor *handle() = 0; + virtual arm_compute::ICLTensor *handle() const = 0; + +public: + uint8_t *buffer() const override; + size_t total_size() const override; + size_t dimension(size_t index) const override; + size_t num_dimensions() const override; + size_t calcOffset(const neurun::util::feature::Coordinate4D &coords) override; + +public: + arm_compute::DataType data_type() const; + const cl::Buffer &cl_buffer() const; + arm_compute::ITensorInfo *info() const; + arm_compute::ITensorInfo *info(); + void map(cl::CommandQueue &q, bool blocking = true); + void unmap(cl::CommandQueue &q); + void clear(cl::CommandQueue &q); + +protected: + virtual uint8_t *doMap(cl::CommandQueue &q, bool blocking) = 0; + virtual void doUnmap(cl::CommandQueue &q) = 0; +}; + +} // namespace operand +} // namespace acl_cl +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ diff --git a/runtimes/neurun/src/backend/acl_cl/operand/Object.cc b/runtimes/neurun/src/backend/acl_cl/operand/Object.cc index 98b96a11a..a84fa2366 100644 --- a/runtimes/neurun/src/backend/acl_cl/operand/Object.cc +++ b/runtimes/neurun/src/backend/acl_cl/operand/Object.cc @@ -27,7 +27,8 @@ namespace acl_cl namespace operand { -void Object::access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const +void Object::access( + const std::function<void(::neurun::backend::operand::ITensor &tensor)> &fn) const { auto &queue = ::arm_compute::CLScheduler::get().queue(); diff --git a/runtimes/neurun/src/backend/acl_cl/operand/Object.h b/runtimes/neurun/src/backend/acl_cl/operand/Object.h index da33c0549..4ba22b269 100644 --- a/runtimes/neurun/src/backend/acl_cl/operand/Object.h +++ b/runtimes/neurun/src/backend/acl_cl/operand/Object.h @@ -18,9 +18,9 @@ #define __NEURUN_BACKEND_ACL_CL_OPERAND_OBJECT_H__ #include <memory> -#include <arm_compute/core/CL/ICLTensor.h> -#include "backend/IObject.h" +#include "backend/interface/operand/IObject.h" +#include "backend/acl_cl/operand/ICLTensor.h" namespace neurun { @@ -37,19 +37,21 @@ public: Object() = default; public: - Object(const std::shared_ptr<::arm_compute::ICLTensor> &tensor) : _tensor{tensor} + Object(const std::shared_ptr<::neurun::backend::acl_cl::operand::ICLTensor> &tensor) + : _tensor{tensor} { // DO NOTHING } public: - ::arm_compute::ICLTensor *ptr(void) const override { return _tensor.get(); } + ::neurun::backend::acl_cl::operand::ICLTensor *ptr(void) const override { return _tensor.get(); } private: - std::shared_ptr<::arm_compute::ICLTensor> _tensor; + std::shared_ptr<::neurun::backend::acl_cl::operand::ICLTensor> _tensor; public: - void access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const override; + void + access(const std::function<void(::neurun::backend::operand::ITensor &tensor)> &fn) const override; }; } // namespace operand diff --git a/runtimes/neurun/src/backend/cpu/BackendConfig.cc b/runtimes/neurun/src/backend/cpu/BackendConfig.cc deleted file mode 100644 index 34fc3491a..000000000 --- a/runtimes/neurun/src/backend/cpu/BackendConfig.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/cpu/BackendConfig.h" - -namespace neurun -{ -namespace backend -{ -namespace cpu -{ - -void BackendConfig::initialize() -{ - // DO NOTHING -} - -} // namespace cpu -} // namespace backend -} // namespace neurun diff --git a/runtimes/neurun/src/backend/cpu/BackendConfig.h b/runtimes/neurun/src/backend/cpu/BackendConfig.h deleted file mode 100644 index 109235bb1..000000000 --- a/runtimes/neurun/src/backend/cpu/BackendConfig.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_BACKEND_CPU_BACKEND_CONFIG_H__ -#define __NEURUN_BACKEND_CPU_BACKEND_CONFIG_H__ - -#include "backend/IBackendConfig.h" - -namespace neurun -{ -namespace backend -{ -namespace cpu -{ - -class BackendConfig : public IBackendConfig -{ -public: - BackendConfig() - { - // DO NOTHING - } - - virtual void initialize() override; - virtual graph::operand::Layout getOperandLayout() { return graph::operand::Layout::NHWC; } -}; - -} // namespace cpu -} // namespace backend -} // namespace neurun - -#endif // __NEURUN_BACKEND_CPU_BACKEND_CONFIG_H__ diff --git a/runtimes/neurun/src/backend/cpu/CMakeLists.txt b/runtimes/neurun/src/backend/cpu/CMakeLists.txt index 95e9af687..dc4406a65 100644 --- a/runtimes/neurun/src/backend/cpu/CMakeLists.txt +++ b/runtimes/neurun/src/backend/cpu/CMakeLists.txt @@ -1,19 +1,18 @@ file(GLOB_RECURSE SOURCES "*.cc") -add_library(${LIB_NEURUN_BACKEND_CPU} STATIC ${SOURCES}) +add_library(${LIB_NEURUN_BACKEND_CPU} SHARED ${SOURCES}) target_include_directories(${LIB_NEURUN_BACKEND_CPU} PUBLIC ${NNFW_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_BACKEND_CPU} PUBLIC ${NEURUN_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_BACKEND_CPU} PUBLIC ${CMAKE_SOURCE_DIR}/externals/tensorflow) -target_link_libraries(${LIB_NEURUN_BACKEND_CPU} arm_compute) # TODO We should not need this target_link_libraries(${LIB_NEURUN_BACKEND_CPU} tensorflow-lite) -target_link_libraries(${LIB_NEURUN_BACKEND_CPU} nnfw_util) -target_link_libraries(${LIB_NEURUN_BACKEND_CPU} nnfw_support_nnapi) +target_link_libraries(${LIB_NEURUN_BACKEND_CPU} nnfw_lib_misc) +target_link_libraries(${LIB_NEURUN_BACKEND_CPU} nnfw_lib_cpp14) target_link_libraries(${LIB_NEURUN_BACKEND_CPU} ${LIB_NEURUN_KERNEL_CPU}) +target_link_libraries(${LIB_NEURUN_BACKEND_CPU} ${LIB_NEURUN}) target_compile_options(${LIB_NEURUN_BACKEND_CPU} PRIVATE -Wall -Wextra -Werror) -set_target_properties(${LIB_NEURUN_BACKEND_CPU} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${LIB_NEURUN_BACKEND_CPU} PROPERTIES OUTPUT_NAME backend_cpu) install(TARGETS ${LIB_NEURUN_BACKEND_CPU} DESTINATION lib/neurun) diff --git a/runtimes/neurun/src/backend/cpu/Config.cc b/runtimes/neurun/src/backend/cpu/Config.cc new file mode 100644 index 000000000..001ba9d02 --- /dev/null +++ b/runtimes/neurun/src/backend/cpu/Config.cc @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "backend/cpu/Config.h" + +namespace neurun +{ +namespace backend +{ +namespace cpu +{ + +void Config::initialize() +{ + // DO NOTHING +} + +} // namespace cpu +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/cpu/Config.h b/runtimes/neurun/src/backend/cpu/Config.h new file mode 100644 index 000000000..ad9ca0ee8 --- /dev/null +++ b/runtimes/neurun/src/backend/cpu/Config.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_CPU_CONFIG_H__ +#define __NEURUN_BACKEND_CPU_CONFIG_H__ + +#include "backend/interface/IConfig.h" + +namespace neurun +{ +namespace backend +{ +namespace cpu +{ + +class Config : public IConfig +{ +public: + Config() + { + // DO NOTHING + } + + virtual std::string id() override { return "cpu"; } + virtual void initialize() override; + virtual graph::operand::Layout getOperandLayout() { return graph::operand::Layout::NHWC; } + virtual bool SupportSubTensorAlloc() override + { + // NOTE CPU allocator cannot support subtensor allocation yet + return false; + } +}; + +} // namespace cpu +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_CPU_CONFIG_H__ diff --git a/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc b/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc deleted file mode 100644 index 7b08c7131..000000000 --- a/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc +++ /dev/null @@ -1,208 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "InitializerGenerator.h" - -#include "internal/nnapi/kernel/Reader.h" -#include "internal/nnapi/kernel/View.h" -#include "util/kernel/IndexIterator.h" - -#include "NeuralNetworks.h" - -namespace neurun -{ -namespace backend -{ -namespace cpu -{ - -InitializerGenerator::InitializerGenerator(const neurun::graph::operand::Set &ctx) : _ctx(ctx) -{ - // DO NOTHING -} - -Initializer -InitializerGenerator::generateWeight(const graph::operation::Conv2D::Implicit::Node &node) -{ - const ::neurun::graph::operand::Index ker_index{node.getInputs().at(1)}; - - const auto ker_shape = _ctx.at(ker_index).shape().asKernel(); - auto ker_base = _ctx.at(ker_index).data().base(); - auto ker_size = _ctx.at(ker_index).data().size(); - - return [ker_shape, ker_base, ker_size](::arm_compute::ITensor &tensor) { - const ::internal::nnapi::kernel::Reader<float> from{ker_shape, ker_base, ker_size}; - ::internal::nnapi::kernel::View<float> into{&tensor}; - - ::nnfw::util::kernel::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - into.at(nth, row, col, ch) = value; - }; - }; -} - -Initializer InitializerGenerator::generateWeight(const graph::operation::FullyConnected::Node &node) -{ - const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - - const auto num_output = _ctx.at(weight_index).shape().dim(0); - auto weight_base = _ctx.at(weight_index).data().base(); - auto weight_size = _ctx.at(weight_index).data().size(); - auto weight_type = _ctx.at(weight_index).typeInfo().type(); - - // NOTE We assume that input is a feature map - // TODO Remove this restriction! - const auto ifm_shape = _ctx.at(input_index).shape().asFeature(); - - switch (weight_type) - { - case ::neurun::graph::operand::DataType::TENSOR_FLOAT32: - { - return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) { - const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, - ifm_shape.W}; - const ::internal::nnapi::kernel::Reader<float> from{ker_shape, weight_base, weight_size}; - - ::nnfw::util::kernel::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - - uint32_t offset = 0; - - // NNAPI uses NHWC ordering - offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C; - offset += row * ifm_shape.W * ifm_shape.C; - offset += col * ifm_shape.C; - offset += ch; - - const ::arm_compute::Coordinates coordinate{offset}; - - auto into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - *into = value; - }; - }; - } - case ::neurun::graph::operand::DataType::TENSOR_QUANT8_ASYMM: - { - return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) { - const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, - ifm_shape.W}; - const ::internal::nnapi::kernel::Reader<uint8_t> from{ker_shape, weight_base, weight_size}; - ::nnfw::util::kernel::iterate(ker_shape) - << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(nth, ch, row, col); - uint32_t offset = 0; - - // NNAPI uses NHWC ordering - offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C; - offset += row * ifm_shape.W * ifm_shape.C; - offset += col * ifm_shape.C; - offset += ch; - - const ::arm_compute::Coordinates coordinate{offset}; - - auto into = reinterpret_cast<uint8_t *>(tensor.ptr_to_element(coordinate)); - - *into = value; - }; - }; - } - default: - { - throw std::runtime_error("Not supported weight type"); - } - } -} - -Initializer InitializerGenerator::generateBias(const graph::operation::Conv2D::Implicit::Node &node) -{ - // TODO Refactor so we can reuse the common code - - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - - auto bias_base = _ctx.at(bias_index).data().base(); - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - return [bias_base, bias_size](::arm_compute::ITensor &tensor) { - for (int32_t n = 0; n < bias_size; ++n) - { - const ::arm_compute::Coordinates coordinate{n}; - - float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - const float *from = reinterpret_cast<const float *>(bias_base) + n; - const auto value = *from; - - *into = value; - } - }; -} - -Initializer InitializerGenerator::generateBias(const graph::operation::FullyConnected::Node &node) -{ - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - - auto bias_base = _ctx.at(bias_index).data().base(); - auto bias_type = _ctx.at(bias_index).typeInfo().type(); - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - switch (bias_type) - { - case ::neurun::graph::operand::DataType::TENSOR_FLOAT32: - { - return [bias_base, bias_size](::arm_compute::ITensor &tensor) { - for (int32_t n = 0; n < bias_size; ++n) - { - const ::arm_compute::Coordinates coordinate{n}; - - float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate)); - - const float *from = reinterpret_cast<const float *>(bias_base) + n; - const auto value = *from; - - *into = value; - } - }; - } - case ::neurun::graph::operand::DataType::TENSOR_QUANT8_ASYMM: - { - return [bias_base, bias_size](::arm_compute::ITensor &tensor) { - for (int32_t n = 0; n < bias_size; ++n) - { - const ::arm_compute::Coordinates coordinate{n}; - - uint8_t *into = reinterpret_cast<uint8_t *>(tensor.ptr_to_element(coordinate)); - - const uint8_t *from = reinterpret_cast<const uint8_t *>(bias_base) + n; - const auto value = *from; - - *into = value; - } - }; - } - default: - { - throw std::runtime_error("Not supported bias type"); - } - } -} - -} // namespace cpu -} // namespace backend -} // namespace neurun diff --git a/runtimes/neurun/src/backend/cpu/InitializerGenerator.h b/runtimes/neurun/src/backend/cpu/InitializerGenerator.h deleted file mode 100644 index 42d37f48b..000000000 --- a/runtimes/neurun/src/backend/cpu/InitializerGenerator.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__ -#define __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__ - -#include "backend/IInitializerGenerator.h" - -#include "graph/operand/Set.h" - -namespace neurun -{ -namespace backend -{ -namespace cpu -{ - -class InitializerGenerator : public IInitializerGenerator -{ -public: - InitializerGenerator(const neurun::graph::operand::Set &ctx); - - Initializer generateWeight(const graph::operation::Conv2D::Implicit::Node &node) override; - Initializer generateWeight(const graph::operation::FullyConnected::Node &node) override; - - Initializer generateBias(const graph::operation::Conv2D::Implicit::Node &node) override; - Initializer generateBias(const graph::operation::FullyConnected::Node &node) override; - -private: - const neurun::graph::operand::Set &_ctx; -}; - -} // namespace cpu -} // namespace backend -} // namespace neurun - -#endif // __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__ diff --git a/runtimes/neurun/src/backend/cpu/MemoryAllocator.cc b/runtimes/neurun/src/backend/cpu/MemoryAllocator.cc deleted file mode 100644 index 13d2a7ffc..000000000 --- a/runtimes/neurun/src/backend/cpu/MemoryAllocator.cc +++ /dev/null @@ -1,17 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -//#include "internal/cpu/MemoryAllocator.h" diff --git a/runtimes/neurun/src/backend/cpu/MemoryAllocator.h b/runtimes/neurun/src/backend/cpu/MemoryAllocator.h deleted file mode 100644 index e3550ac07..000000000 --- a/runtimes/neurun/src/backend/cpu/MemoryAllocator.h +++ /dev/null @@ -1,123 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_CPU_MEMORY_ALLOCATOR_H__ -#define __INTERNAL_CPU_MEMORY_ALLOCATOR_H__ - -#include "arm_compute/runtime/ITensorAllocator.h" -#include "arm_compute/runtime/Memory.h" - -#include <cstdint> -#include <memory> -#include <vector> - -namespace arm_compute -{ -class Coordinates; -class TensorInfo; -class Tensor; -}; - -/** Basic implementation of a CPU memory tensor allocator. */ -class TensorAllocator : public ITensorAllocator -{ -public: - /** Default constructor. */ - TensorAllocator(Tensor *owner = nullptr); - /** Default destructor */ - ~TensorAllocator(); - - /** Make ITensorAllocator's init methods available */ - using ITensorAllocator::init; - - /** Shares the same backing memory with another tensor allocator, while the tensor info might be - * different. - * In other words this can be used to create a sub-tensor from another tensor while sharing the - * same memory. - * - * @note TensorAllocator have to be of the same specialized type. - * - * @param[in] allocator The allocator that owns the backing memory to be shared. Ownership becomes - * shared afterwards. - * @param[in] coords The starting coordinates of the new tensor inside the parent tensor. - * @param[in] sub_info The new tensor information (e.g. shape etc) - */ - void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo sub_info); - - /** Returns the pointer to the allocated data. */ - uint8_t *data() const; - - /** Allocate size specified by TensorInfo of CPU memory. - * - * @note The tensor must not already be allocated when calling this function. - * - */ - void allocate() override; - - /** Free allocated CPU memory. - * - * @note The tensor must have been allocated when calling this function. - * - */ - void free() override; - /** Import an existing memory as a tensor's backing memory - * - * @warning If the tensor is flagged to be managed by a memory manager, - * this call will lead to an error. - * @warning Ownership of memory depends on the way the @ref Memory object was constructed - * @note Calling free on a tensor with imported memory will just clear - * the internal pointer value. - * - * @param[in] memory Memory to import - * - * @return error status - */ - arm_compute::Status import_memory(Memory memory); - /** Associates the tensor with a memory group - * - * @param[in] associated_memory_group Memory group to associate the tensor with - */ - void set_associated_memory_group(MemoryGroup *associated_memory_group); - -protected: - /** No-op for CPU memory - * - * @return A pointer to the beginning of the tensor's allocation. - */ - uint8_t *lock() override; - - /** No-op for CPU memory. */ - void unlock() override; - -private: - MemoryGroup *_associated_memory_group; /**< Registered memory manager */ - Memory _memory; /**< CPU memory */ - Tensor *_owner; /**< Owner of the allocator */ -}; - -namespace internal -{ -namespace cpu -{ - -class MemoryAllocator : public -{ -}; - -} // namespace cpu -} // namespace internal - -#endif // __INTERNAL_CPU_MEMORY_ALLOCATOR_H__ diff --git a/runtimes/neurun/src/backend/cpu/MemoryPlanner.cc b/runtimes/neurun/src/backend/cpu/MemoryPlanner.cc new file mode 100644 index 000000000..2d0995b8a --- /dev/null +++ b/runtimes/neurun/src/backend/cpu/MemoryPlanner.cc @@ -0,0 +1,127 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MemoryPlanner.h" +#include "util/logging.h" +#include <cassert> + +namespace neurun +{ +namespace backend +{ +namespace cpu +{ + +Allocator::Allocator(uint32_t capacity) +{ + assert(!_base && capacity != 0); + + _base = new uint8_t[capacity]; + + VERBOSE(ALLOC) << "allocation capacity: " << capacity << std::endl; + VERBOSE(ALLOC) << "base pointer: " << static_cast<void *>(_base) << std::endl; +} + +Allocator::~Allocator() { delete[] _base; } + +void BumpPlanner::claim(const model::operand::Index &ind, size_t size) +{ + assert(size != 0); + + Block blk{_capacity, size}; + _mem_plans[ind] = blk; + _capacity += size; + + VERBOSE(BP_PLANNER) << "CLAIM(#" << ind.value() << "): " << blk.offset << ", " << blk.size + << std::endl; +} + +void BumpPlanner::release(const model::operand::Index &ind) +{ + VERBOSE(BP_PLANNER) << "RELEASE(#" << ind.value() << "): " + << "NOTHING does" << std::endl; +} + +// There are some assumptions for claiming memory(== making a reservation for memory). +// 1. About _claim_table(std::map). +// - The table's data structure is std::map so that it always sorts +// value(model::operand::Index) by key(base_offset). +// - This claim() inserts key/value into _claim_table and the release() removes the key/value from +// _claim_table. +// - _claim_table shows the memory status at a certain point in time. Therefore, +// - If _claim_table has an offset and a certain size at a certain point in time, +// it means the place at the offset has been already claimed(== can't claim now. need to find +// someplace new). +// - If _claim_table doesn't have any element for an offset and a certain size at a certain +// point in time, it means the place at the offset can be claimed. +// 2. In the loop for _claim_table, we can assume the current claim_base_offset value is bigger than +// the previous claim_base_offset. +void FirstFitPlanner::claim(const model::operand::Index &ind, size_t size) +{ + assert(size != 0); + + // Find the right position for claiming + uint32_t next_offset = 0; + for (auto &mem_claim : _claim_table) + { + auto claimed_base_offset = mem_claim.first; + auto claimed_size = _mem_plans[mem_claim.second].size; + if (next_offset + size <= claimed_base_offset) + { + break; + } + else + { + next_offset = claimed_base_offset + claimed_size; + } + } + + // Now next_offset is set to the proper offset + _claim_table[next_offset] = ind; + _mem_plans[ind] = {next_offset, size}; + + VERBOSE(FF_PLANNER) << "claim(#" << ind.value() << "): [+" << next_offset << ", " << size << "sz]" + << std::endl; + + if (_capacity < next_offset + size) + { + _capacity = next_offset + size; + } +} + +void FirstFitPlanner::release(const model::operand::Index &ind) +{ + for (auto it = _claim_table.cbegin(); it != _claim_table.cend(); ++it) + { + if (it->second == ind) + { + uint32_t offset = it->first; + uint32_t index = ind.value(); + uint32_t size = _mem_plans[ind].size; + + _claim_table.erase(it); + + VERBOSE(FF_PLANNER) << "release(#" << index << "): [+" << offset << ", " << size << "sz]" + << std::endl; + return; + } + } + assert(!"Cannot release for given index. It has been not claimed or released already."); +} + +} // namespace cpu +} // namespace backend +} // namespace neurun diff --git a/runtimes/neurun/src/backend/cpu/MemoryPlanner.h b/runtimes/neurun/src/backend/cpu/MemoryPlanner.h new file mode 100644 index 000000000..4b2661223 --- /dev/null +++ b/runtimes/neurun/src/backend/cpu/MemoryPlanner.h @@ -0,0 +1,166 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file MemoryPlanner.h + * @brief This file contains Memory Planning related classes + */ + +#ifndef __NEURUN_BACKEND_CPU_MEMORY_PLANNER_H__ +#define __NEURUN_BACKEND_CPU_MEMORY_PLANNER_H__ + +#include <map> +#include <unordered_map> + +#include "model/operand/Index.h" + +namespace neurun +{ +namespace backend +{ +namespace cpu +{ + +/** + * @brief Structure to have memory offset and size + */ +struct Block +{ + uint32_t offset; + uint32_t size; +}; + +/** + * @brief Class to allocate memory + */ +class Allocator +{ +public: + Allocator(uint32_t capacity); + ~Allocator(); + /** + * @brief Get memory base pointer + * @return base pointer + */ + uint8_t *base() const { return _base; } + +private: + uint8_t *_base = nullptr; +}; + +/** + * @brief Interface to plan memory + */ +struct IMemoryPlanner +{ + using MemoryPlans = std::unordered_map<model::operand::Index, Block>; + + /** + * @brief Claim memory for operand + * @param[in] index The operand index + * @param[in] size The size of the memory + */ + virtual void claim(const model::operand::Index &, size_t) = 0; + /** + * @brief Release memory for operand + * @param[in] index The operand index + */ + virtual void release(const model::operand::Index &) = 0; + /** + * @brief Get capacity for memory planning + * @return The value of capacity + */ + virtual uint32_t capacity() = 0; + /** + * @brief Get MemoryPlans + * @return MemoryPlans + */ + virtual MemoryPlans &memory_plans() = 0; +}; + +/** + * @brief Class to plan memory by bump way + */ +class BumpPlanner : public IMemoryPlanner +{ +public: + /** + * @brief Claim memory for operand by bump way + * @param[in] index The operand index + * @param[in] size The size of the memory + */ + virtual void claim(const model::operand::Index &, size_t) override; + /** + * @brief Release memory for operand by bump way + * @param[in] index The operand index + */ + virtual void release(const model::operand::Index &) override; + /** + * @brief Get capacity for memory planning + * @return The value of capacity + */ + virtual uint32_t capacity() override { return _capacity; } + /** + * @brief Get MemoryPlans + * @return MemoryPlans + */ + virtual MemoryPlans &memory_plans() override { return _mem_plans; } + +private: + uint32_t _capacity = 0; + MemoryPlans _mem_plans; +}; + +/** + * @brief Class to plan memory by firstfit way + */ +class FirstFitPlanner : public IMemoryPlanner +{ +public: + /** + * @brief Claim memory for operand by firstfit way + * @param[in] index The operand index + * @param[in] size The size of the memory + */ + virtual void claim(const model::operand::Index &, size_t) override; + /** + * @brief Release memory for operand by firstfit way + * @param[in] index The operand index + */ + virtual void release(const model::operand::Index &) override; + /** + * @brief Get capacity for memory planning + * @return The value of capacity + */ + virtual uint32_t capacity() override { return _capacity; } + /** + * @brief Get MemoryPlans + * @return MemoryPlans + */ + virtual MemoryPlans &memory_plans() override { return _mem_plans; } + +private: + uint32_t _capacity = 0; + MemoryPlans _mem_plans; + // Use std::map because claim() assumes that _claim_table is sorted by uint32_t(base_offset) + std::map<uint32_t, model::operand::Index> _claim_table; +}; + +} // namespace cpu +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_CPU_MEMORY_PLANNER_H__ diff --git a/runtimes/neurun/src/backend/cpu/PluginClassesAllocator.cc b/runtimes/neurun/src/backend/cpu/PluginClassesAllocator.cc new file mode 100644 index 000000000..26d4d8858 --- /dev/null +++ b/runtimes/neurun/src/backend/cpu/PluginClassesAllocator.cc @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <memory> +#include "TensorBuilder.h" +#include "StageGenerator.h" +#include "Config.h" +#include "util/logging.h" + +extern "C" { +neurun::backend::cpu::TensorBuilder *allocate_TensorBuilder() +{ + VERBOSE(allocate_TensorBuilder) << "loaded from CPU\n"; + return new neurun::backend::cpu::TensorBuilder; +} + +neurun::backend::cpu::StageGenerator * +allocate_StageGenerator(const neurun::model::operand::Set &operand_ctx, + const std::shared_ptr<neurun::backend::cpu::TensorBuilder> &tensor_builder) +{ + VERBOSE(allocate_StageGenerator) << "loaded from CPU\n"; + return new neurun::backend::cpu::StageGenerator(operand_ctx, tensor_builder); +} + +neurun::backend::cpu::Config *allocate_Config() +{ + VERBOSE(allocate_Config) << "loaded from CPU\n"; + return new neurun::backend::cpu::Config; +} +} diff --git a/runtimes/neurun/src/backend/cpu/StageGenerator.cc b/runtimes/neurun/src/backend/cpu/StageGenerator.cc index b7a3fa24a..c53b320a4 100644 --- a/runtimes/neurun/src/backend/cpu/StageGenerator.cc +++ b/runtimes/neurun/src/backend/cpu/StageGenerator.cc @@ -18,7 +18,8 @@ #include <stdexcept> -#include "internal/Padding.h" +#include "cpp14/memory.h" +#include "util/Padding.h" #include "kernel/cpu/OperationUtils.h" #include "kernel/cpu/ConvolutionLayer.h" #include "kernel/cpu/AvgPoolLayer.h" @@ -27,12 +28,13 @@ #include "kernel/cpu/FullyConnectedLayer.h" #include "kernel/cpu/ReshapeLayer.h" #include "kernel/cpu/SoftMaxLayer.h" +#include "kernel/cpu/PermuteLayer.h" +#include "backend/BackendManager.h" +#include "backend/interface/IConfig.h" -#include "logging.h" +#include "util/logging.h" -#include "support/nnapi/Utils.h" - -#include "logging.h" +#include "util/Utils.h" namespace neurun { @@ -41,25 +43,27 @@ namespace backend namespace cpu { -StageGenerator::StageGenerator(const neurun::graph::operand::Set &operand_ctx, +StageGenerator::StageGenerator(const neurun::model::operand::Set &operand_ctx, const std::shared_ptr<TensorBuilder> &tensor_builder) : _ctx(operand_ctx), _tensor_builder(tensor_builder) { // DO NOTHING } -Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::Conv2DNode &node) { - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index ker_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; + using model::operation::Conv2DNode; + + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(Conv2DNode::Input::INPUT)}; + const auto ker_index{node.getInputs().at(Conv2DNode::Input::KERNEL)}; + const auto bias_index{node.getInputs().at(Conv2DNode::Input::BIAS)}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + const auto padding_index{node.param().padding_index}; + const auto activation_index{node.param().activation_index}; const PaddingCode padding_type = static_cast<PaddingCode>(_ctx.at(padding_index).asScalar<int32_t>()); @@ -67,7 +71,7 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || (ANEURALNETWORKS_PADDING_VALID == padding_type)); - ::internal::Stride stride; + util::Stride stride; stride.vertical = _ctx.at(vstride_index).asScalar<int32_t>(); stride.horizontal = _ctx.at(hstride_index).asScalar<int32_t>(); @@ -75,28 +79,28 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; - int ker_index; - int bias_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; + model::operand::Index ker_index; + model::operand::Index bias_index; ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; ::neurun::kernel::cpu::Shape ker_shape; ::neurun::kernel::cpu::Shape bias_shape; - ::internal::Padding padding; - ::internal::Stride stride; + util::Padding padding; + util::Stride stride; FuseCode activation; }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); - param.ker_index = ker_index.asInt(); - param.bias_index = bias_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; + param.ker_index = ker_index; + param.bias_index = bias_index; param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ofm_index)); param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(ifm_index)); @@ -105,21 +109,21 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n param.stride = stride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), stride, - _ctx.at(ker_index).shape().asKernel().W, - _ctx.at(ker_index).shape().asKernel().H) - : ::internal::valid_padding(); + ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), + _ctx.at(ofm_index).shape().asFeature(), stride, + _ctx.at(ker_index).shape().asKernel().W, + _ctx.at(ker_index).shape().asKernel().H) + : util::valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}); - auto ker_alloc = tensors->at(::neurun::graph::operand::Index{param.ker_index}); - auto bias_alloc = tensors->at(::neurun::graph::operand::Index{param.bias_index}); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index); + auto ifm_alloc = tensors->at(param.ifm_index); + auto ker_alloc = tensors->at(param.ker_index); + auto bias_alloc = tensors->at(param.bias_index); std::unique_ptr<::neurun::kernel::cpu::ConvolutionLayer> fn{ new ::neurun::kernel::cpu::ConvolutionLayer}; @@ -130,24 +134,22 @@ Stage StageGenerator::generate(const graph::operation::Conv2D::Implicit::Node &n param.stride.vertical, param.activation, ofm_alloc->buffer(), param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::MaxPool2DNode &node) { - VERBOSE(MaxPool2D) << "generate CPU MaxPool2D" << std::endl; + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(model::operation::MaxPool2DNode::Input::INPUT)}; - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; + const auto kh_index{node.param().kh_index}; + const auto kw_index{node.param().kw_index}; - const ::neurun::graph::operand::Index kh_index{node.param().kh_index}; - const ::neurun::graph::operand::Index kw_index{node.param().kw_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; - - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + const auto padding_index{node.param().padding_index}; + const auto activation_index{node.param().activation_index}; const int32_t kh = _ctx.at(kh_index).asScalar<int32_t>(); const int32_t kw = _ctx.at(kw_index).asScalar<int32_t>(); @@ -161,8 +163,8 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; uint32_t kw; uint32_t kh; @@ -170,16 +172,16 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; - ::internal::Padding padding; - ::internal::Stride stride; + util::Padding padding; + util::Stride stride; FuseCode activation; }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; param.kh = kh; param.kw = kw; @@ -192,30 +194,17 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) - : ::internal::valid_padding(); + ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), + _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) + : util::valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - VERBOSE(MaxPool2D) << "IFM_H: " << _ctx.at(ifm_index).shape().asFeature().H << std::endl; - VERBOSE(MaxPool2D) << "IFM_W: " << _ctx.at(ifm_index).shape().asFeature().W << std::endl; - VERBOSE(MaxPool2D) << "OFM_H: " << _ctx.at(ofm_index).shape().asFeature().H << std::endl; - VERBOSE(MaxPool2D) << "OFM_W: " << _ctx.at(ofm_index).shape().asFeature().W << std::endl; - VERBOSE(MaxPool2D) << "KER_H: " << kh << std::endl; - VERBOSE(MaxPool2D) << "KER_W: " << kw << std::endl; - VERBOSE(MaxPool2D) << "STRIDE_H: " << vstride << std::endl; - VERBOSE(MaxPool2D) << "STRIDE_W: " << hstride << std::endl; - VERBOSE(MaxPool2D) << "PAD(T): " << param.padding.top << std::endl; - VERBOSE(MaxPool2D) << "PAD(B): " << param.padding.bottom << std::endl; - VERBOSE(MaxPool2D) << "PAD(L): " << param.padding.left << std::endl; - VERBOSE(MaxPool2D) << "PAD(R): " << param.padding.right << std::endl; - auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}).get(); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index).get(); + auto ifm_alloc = tensors->at(param.ifm_index).get(); std::unique_ptr<::neurun::kernel::cpu::MaxPoolLayer> fn{ new ::neurun::kernel::cpu::MaxPoolLayer}; @@ -226,24 +215,22 @@ Stage StageGenerator::generate(const graph::operation::MaxPool2D::Implicit::Node param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node &node) +void StageGenerator::visit(const model::operation::AvgPool2DNode &node) { - VERBOSE(AvgPool2D) << "generate CPU AvgPool2D" << std::endl; - - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(model::operation::AvgPool2DNode::Input::INPUT)}; - const ::neurun::graph::operand::Index kh_index{node.param().kh_index}; - const ::neurun::graph::operand::Index kw_index{node.param().kw_index}; + const auto kh_index{node.param().kh_index}; + const auto kw_index{node.param().kw_index}; - const ::neurun::graph::operand::Index vstride_index{node.param().vstride_index}; - const ::neurun::graph::operand::Index hstride_index{node.param().hstride_index}; + const auto vstride_index{node.param().vstride_index}; + const auto hstride_index{node.param().hstride_index}; - const ::neurun::graph::operand::Index padding_index{node.param().padding_index}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + const auto padding_index{node.param().padding_index}; + const auto activation_index{node.param().activation_index}; const int32_t kh = _ctx.at(kh_index).asScalar<int32_t>(); const int32_t kw = _ctx.at(kw_index).asScalar<int32_t>(); @@ -260,8 +247,8 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node // Construct operation parameters struct Param { - int ofm_index; - int ifm_index; + model::operand::Index ofm_index; + model::operand::Index ifm_index; uint32_t kw; uint32_t kh; @@ -269,16 +256,16 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; - ::internal::Padding padding; - ::internal::Stride stride; + util::Padding padding; + util::Stride stride; FuseCode activation; }; Param param; - param.ofm_index = ofm_index.asInt(); - param.ifm_index = ifm_index.asInt(); + param.ofm_index = ofm_index; + param.ifm_index = ifm_index; param.kh = kh; param.kw = kw; @@ -291,31 +278,17 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? ::internal::same_padding(_ctx.at(ifm_index).shape().asFeature(), - _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) - : ::internal::valid_padding(); + ? util::same_padding(_ctx.at(ifm_index).shape().asFeature(), + _ctx.at(ofm_index).shape().asFeature(), param.stride, kw, kh) + : util::valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - VERBOSE(AvgPool2D) << "IFM_H: " << _ctx.at(ifm_index).shape().asFeature().H << std::endl; - VERBOSE(AvgPool2D) << "IFM_W: " << _ctx.at(ifm_index).shape().asFeature().W << std::endl; - VERBOSE(AvgPool2D) << "OFM_H: " << _ctx.at(ofm_index).shape().asFeature().H << std::endl; - VERBOSE(AvgPool2D) << "OFM_W: " << _ctx.at(ofm_index).shape().asFeature().W << std::endl; - VERBOSE(AvgPool2D) << "KER_H: " << kh << std::endl; - VERBOSE(AvgPool2D) << "KER_W: " << kw << std::endl; - VERBOSE(AvgPool2D) << "STRIDE_H: " << vstride << std::endl; - VERBOSE(AvgPool2D) << "STRIDE_W: " << hstride << std::endl; - VERBOSE(AvgPool2D) << "PAD: " << ::nnfw::support::nnapi::to_string(padding_type) << std::endl; - VERBOSE(AvgPool2D) << "PAD(T): " << param.padding.top << std::endl; - VERBOSE(AvgPool2D) << "PAD(B): " << param.padding.bottom << std::endl; - VERBOSE(AvgPool2D) << "PAD(L): " << param.padding.left << std::endl; - VERBOSE(AvgPool2D) << "PAD(R): " << param.padding.right << std::endl; - auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto ofm_alloc = tensors->at(::neurun::graph::operand::Index{param.ofm_index}).get(); - auto ifm_alloc = tensors->at(::neurun::graph::operand::Index{param.ifm_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto ofm_alloc = tensors->at(param.ofm_index).get(); + auto ifm_alloc = tensors->at(param.ifm_index).get(); std::unique_ptr<::neurun::kernel::cpu::AvgPoolLayer> fn{ new ::neurun::kernel::cpu::AvgPoolLayer}; @@ -326,20 +299,18 @@ Stage StageGenerator::generate(const graph::operation::AvgPool2D::Implicit::Node param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::Concat::Node &node) +void StageGenerator::visit(const model::operation::ConcatNode &node) { - VERBOSE(Concat) << "generate CPU Concat" << std::endl; - - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index axis_index{node.param().axis_index}; + const auto ofm_index{node.getOutputs().at(0)}; + const auto axis_index{node.param().axis_index}; struct Param { - int32_t output_index; - std::vector<int32_t> input_indexes; + model::operand::Index output_index; + std::vector<model::operand::Index> input_indexes; int32_t axis; @@ -349,10 +320,10 @@ Stage StageGenerator::generate(const graph::operation::Concat::Node &node) Param param; - param.output_index = ofm_index.asInt(); + param.output_index = ofm_index; for (const auto &e : node.getInputs()) { - param.input_indexes.emplace_back(e.asInt()); + param.input_indexes.emplace_back(e); } param.axis = _ctx.at(axis_index).asScalar<int32_t>(); @@ -365,14 +336,13 @@ Stage StageGenerator::generate(const graph::operation::Concat::Node &node) auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); std::vector<const uint8_t *> input_buffers; for (auto ifm_ind : param.input_indexes) { - input_buffers.emplace_back( - tensors->at(::neurun::graph::operand::Index{ifm_ind}).get()->buffer()); + input_buffers.emplace_back(tensors->at(ifm_ind).get()->buffer()); } std::unique_ptr<::neurun::kernel::cpu::ConcatLayer> fn{new ::neurun::kernel::cpu::ConcatLayer}; @@ -381,26 +351,26 @@ Stage StageGenerator::generate(const graph::operation::Concat::Node &node) param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::FullyConnected::Node &node) +void StageGenerator::visit(const model::operation::FullyConnectedNode &node) { - VERBOSE(FullyConnected) << "generate CPU FullyConnected" << std::endl; + using model::operation::FullyConnectedNode; - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(FullyConnectedNode::Input::INPUT)}; + const auto weight_index{node.getInputs().at(FullyConnectedNode::Input::WEIGHT)}; + const auto bias_index{node.getInputs().at(FullyConnectedNode::Input::BIAS)}; + const auto activation_index{node.param().activation_index}; // Construct operation parameters struct Param { - int output_index; - int input_index; - int weight_index; - int bias_index; + model::operand::Index output_index; + model::operand::Index input_index; + model::operand::Index weight_index; + model::operand::Index bias_index; ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; @@ -412,10 +382,10 @@ Stage StageGenerator::generate(const graph::operation::FullyConnected::Node &nod Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); - param.weight_index = weight_index.asInt(); - param.bias_index = bias_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; + param.weight_index = weight_index; + param.bias_index = bias_index; param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); @@ -426,11 +396,11 @@ Stage StageGenerator::generate(const graph::operation::FullyConnected::Node &nod auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); - auto weight_alloc = tensors->at(::neurun::graph::operand::Index{param.weight_index}).get(); - auto bias_alloc = tensors->at(::neurun::graph::operand::Index{param.bias_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); + auto weight_alloc = tensors->at(param.weight_index).get(); + auto bias_alloc = tensors->at(param.bias_index).get(); std::unique_ptr<::neurun::kernel::cpu::FullyConnectedLayer> fn{ new ::neurun::kernel::cpu::FullyConnectedLayer}; @@ -440,18 +410,18 @@ Stage StageGenerator::generate(const graph::operation::FullyConnected::Node &nod output_alloc->buffer(), param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::Reshape::Node &node) +void StageGenerator::visit(const model::operation::ReshapeNode &node) { - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(model::operation::ReshapeNode::Input::INPUT)}; struct Param { - int output_index; - int input_index; + model::operand::Index output_index; + model::operand::Index input_index; ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; @@ -459,17 +429,17 @@ Stage StageGenerator::generate(const graph::operation::Reshape::Node &node) Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); std::unique_ptr<::neurun::kernel::cpu::ReshapeLayer> fn{ new ::neurun::kernel::cpu::ReshapeLayer}; @@ -477,21 +447,19 @@ Stage StageGenerator::generate(const graph::operation::Reshape::Node &node) fn->configure(input_alloc->buffer(), param.ifm_shape, output_alloc->buffer(), param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::Softmax::Node &node) +void StageGenerator::visit(const model::operation::SoftmaxNode &node) { - VERBOSE(Softmax) << "generate CPU Softmax" << std::endl; - - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index scale_index{node.param().scale_index}; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(model::operation::SoftmaxNode::Input::INPUT)}; + const auto scale_index{node.param().scale_index}; struct Param { - int output_index; - int input_index; + model::operand::Index output_index; + model::operand::Index input_index; ::neurun::kernel::cpu::Shape ofm_shape; ::neurun::kernel::cpu::Shape ifm_shape; @@ -501,8 +469,8 @@ Stage StageGenerator::generate(const graph::operation::Softmax::Node &node) Param param; - param.output_index = output_index.asInt(); - param.input_index = input_index.asInt(); + param.output_index = output_index; + param.input_index = input_index; param.ofm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(output_index)); param.ifm_shape = ::neurun::kernel::cpu::getShape(_ctx.at(input_index)); @@ -511,9 +479,9 @@ Stage StageGenerator::generate(const graph::operation::Softmax::Node &node) auto tensors = _tensor_builder; - return [tensors, param](IExecutionBuilder &builder) { - auto output_alloc = tensors->at(::neurun::graph::operand::Index{param.output_index}).get(); - auto input_alloc = tensors->at(::neurun::graph::operand::Index{param.input_index}).get(); + returnStage([tensors, param](IExecutionBuilder &builder) { + auto output_alloc = tensors->at(param.output_index).get(); + auto input_alloc = tensors->at(param.input_index).get(); std::unique_ptr<::neurun::kernel::cpu::SoftMaxLayer> fn{ new ::neurun::kernel::cpu::SoftMaxLayer}; @@ -522,15 +490,58 @@ Stage StageGenerator::generate(const graph::operation::Softmax::Node &node) param.ofm_shape); builder.append(std::move(fn)); - }; + }); } -Stage StageGenerator::generate(const graph::operation::NOP::Node & /* node */) +void StageGenerator::visit(const model::operation::PermuteNode &node) { - // DO NOTHING - return nullptr; + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(0)}; + + using PermuteType = model::operation::PermuteNode::Type; + + struct Param + { + model::operand::Index output_index; + model::operand::Index input_index; + + model::operand::Shape shape; + + PermuteType type{PermuteType::COPY}; + }; + + Param param; + + param.output_index = output_index; + param.input_index = input_index; + + param.shape = _ctx.at(output_index).shape(); + param.type = node.param().type; + + // assert(param.shape == _ctx.at(input_index)); + + const auto &input_li = _ctx.at(input_index).lower_info(); + const auto &output_li = _ctx.at(output_index).lower_info(); + const auto input_backend = input_li->def_backends().getOnlyElement(); + const auto output_backend = output_li->def_backends().getOnlyElement(); + + const auto input_tensors = input_backend->tensor_builder(); + const auto output_tensors = output_backend->tensor_builder(); + + returnStage([input_tensors, output_tensors, param](IExecutionBuilder &builder) { + auto output_object = output_tensors->wrapTensor(param.output_index); + auto input_object = input_tensors->wrapTensor(param.input_index); + + auto fn = nnfw::cpp14::make_unique<::neurun::kernel::cpu::PermuteLayer>(); + + fn->configure(input_object, output_object, param.shape, param.type); + + builder.append(std::move(fn)); + }); } +void StageGenerator::visit(const model::operation::AddNode &) { throw std::runtime_error("NYI"); } + } // namespace neurun } // namespace backend } // namespace cpu diff --git a/runtimes/neurun/src/backend/cpu/StageGenerator.h b/runtimes/neurun/src/backend/cpu/StageGenerator.h index acdd2c8b2..6a0e387da 100644 --- a/runtimes/neurun/src/backend/cpu/StageGenerator.h +++ b/runtimes/neurun/src/backend/cpu/StageGenerator.h @@ -17,9 +17,9 @@ #ifndef __NEURUN_BACKEND_CPU_STAGE_GENERATOR_H__ #define __NEURUN_BACKEND_CPU_STAGE_GENERATOR_H__ -#include "backend/IStageGenerator.h" +#include "backend/interface/IStageGenerator.h" -#include "graph/operand/Set.h" +#include "model/operand/Set.h" #include "backend/cpu/operand/Tensor.h" #include "TensorBuilder.h" @@ -33,22 +33,18 @@ namespace cpu class StageGenerator : public IStageGenerator { public: - StageGenerator(const neurun::graph::operand::Set &ctx, + StageGenerator(const neurun::model::operand::Set &ctx, const std::shared_ptr<TensorBuilder> &tensor_builder); virtual std::shared_ptr<ITensorBuilder> tensor_builder() override { return _tensor_builder; } - virtual Stage generate(const graph::operation::Conv2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::MaxPool2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::AvgPool2D::Implicit::Node &node) override; - virtual Stage generate(const graph::operation::Concat::Node &node) override; - virtual Stage generate(const graph::operation::FullyConnected::Node &node) override; - virtual Stage generate(const graph::operation::Reshape::Node &node) override; - virtual Stage generate(const graph::operation::Softmax::Node &node) override; - virtual Stage generate(const graph::operation::NOP::Node &node) override; +#define OP(InternalName, IsNnApi, NnApiName) \ + virtual void visit(const model::operation::InternalName &) override; +#include "model/operation/Op.lst" +#undef OP private: - const neurun::graph::operand::Set &_ctx; + const neurun::model::operand::Set &_ctx; std::shared_ptr<TensorBuilder> _tensor_builder; }; diff --git a/runtimes/neurun/src/backend/cpu/TensorBuilder.cc b/runtimes/neurun/src/backend/cpu/TensorBuilder.cc index 1b972a830..9c39b9c00 100644 --- a/runtimes/neurun/src/backend/cpu/TensorBuilder.cc +++ b/runtimes/neurun/src/backend/cpu/TensorBuilder.cc @@ -19,6 +19,7 @@ #include <cassert> #include "operand/Object.h" +#include "util/logging.h" namespace neurun { @@ -27,43 +28,93 @@ namespace backend namespace cpu { -TensorBuilder::TensorBuilder() +TensorBuilder::TensorBuilder() : _mem_planner(std::make_shared<FirstFitPlanner>()) { // DO NOTHING } -void TensorBuilder::mark(const ::neurun::graph::operand::Index &ind) +void TensorBuilder::registerTensorInfo(const model::operand::Index &ind, + const compiler::TensorInfo &info) { - assert(_tensors.size() == 0); + _tensor_info_map.insert({ind, info}); +} + +void TensorBuilder::registerSubTensorInfo(const model::operand::Index &, + const compiler::SubTensorInfo &) +{ + // Not supported yet + assert(false); +} + +void TensorBuilder::notifyFirstUse(const model::operand::Index &ind) +{ + assert(_tensor_info_map.find(ind) != _tensor_info_map.end()); + const auto &info = _tensor_info_map.at(ind); - _inds.insert(ind); + const auto size = info.total_size(); + _mem_planner->claim(ind, size); } -void TensorBuilder::prepare(codegen::Plan &plan, - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx) +void TensorBuilder::notifyLastUse(const model::operand::Index &ind) { _mem_planner->release(ind); } + +void TensorBuilder::prepare(void) { assert(_tensors.size() == 0); - for (auto ind_int : _inds) + _mem_alloc = std::make_shared<Allocator>(_mem_planner->capacity()); + assert(_mem_alloc->base()); + + for (auto &mem_plan : _mem_planner->memory_plans()) { - ::neurun::graph::operand::Index ind{ind_int}; - auto tensor = std::make_shared<operand::Tensor>(tensor_info_ctx.at(ind.asInt())); - // TODO Fix allocation here. When Tensor object is created the memory for tensor is also - // allocated, and this must be fixed. - plan.operands().set(ind, std::make_shared<operand::Object>(tensor)); + auto ind = mem_plan.first; + auto mem_blk = mem_plan.second; + const auto &info = _tensor_info_map[ind]; + + uint8_t *buffer = _mem_alloc->base() + mem_blk.offset; + auto tensor = std::make_shared<operand::Tensor>(info); + tensor->setBuffer(buffer); _tensors[ind] = tensor; + + VERBOSE(CPU_TENSORBUILDER) << "TENSOR(#" << ind.value() << "): " << static_cast<void *>(buffer) + << std::endl; + + // If we do not make tensor here currently, stages would cause segment fault } } void TensorBuilder::allocate(void) { - assert(_inds.size() == _tensors.size()); - // NOTE For now nothing to do. Allocation is done in prepare stage, which is wrong - // See also: comment in `prepare()` } -std::shared_ptr<operand::Tensor> TensorBuilder::at(const ::neurun::graph::operand::Index &ind) +std::shared_ptr<::neurun::backend::operand::ITensor> +TensorBuilder::tensorAt(const model::operand::Index &ind) +{ + return _tensors.at(ind); +} + +std::shared_ptr<backend::operand::IObject> +TensorBuilder::wrapTensor(const model::operand::Index &ind) +{ + if (_objects.find(ind) != _objects.end()) + { + return _objects.at(ind); + } + else + { + return _objects[ind] = std::make_shared<operand::Object>(_tensors.at(ind)); + } +} + +void TensorBuilder::iterate(const IterateFunction &fn) +{ + for (auto it : _tensors) + { + fn(it.first); + } +} + +std::shared_ptr<operand::Tensor> TensorBuilder::at(const ::neurun::model::operand::Index &ind) { return _tensors.at(ind); } diff --git a/runtimes/neurun/src/backend/cpu/TensorBuilder.h b/runtimes/neurun/src/backend/cpu/TensorBuilder.h index f61a930fe..2715d57f0 100644 --- a/runtimes/neurun/src/backend/cpu/TensorBuilder.h +++ b/runtimes/neurun/src/backend/cpu/TensorBuilder.h @@ -18,11 +18,12 @@ #define __NEURUN_BACKEND_CPU_TENSOR_BUILDER_H__ #include <unordered_map> -#include <unordered_set> -#include "backend/ITensorBuilder.h" +#include "backend/interface/ITensorBuilder.h" #include "backend/cpu/operand/Tensor.h" -#include "graph/operand/Index.h" +#include "backend/cpu/operand/Object.h" +#include "model/operand/Index.h" +#include "MemoryPlanner.h" namespace neurun { @@ -31,23 +32,47 @@ namespace backend namespace cpu { -class Plan; - class TensorBuilder : public ITensorBuilder { public: TensorBuilder(); - virtual void mark(const ::neurun::graph::operand::Index &ind) override; - virtual void prepare(codegen::Plan &plan, - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx) override; + /** + * @brief Register tensor information to allocate on CPU backend + * @param[in] ind Operand index + * @param[in] info Tensor information + */ + virtual void registerTensorInfo(const model::operand::Index &ind, + const compiler::TensorInfo &info) override; + /** + * @brief Register subtensor information to allocate on CPU backend + * @param[in] ind Operand index + * @param[in] info Tensor information + */ + virtual void registerSubTensorInfo(const model::operand::Index &ind, + const compiler::SubTensorInfo &info) override; + + virtual void notifyFirstUse(const model::operand::Index &) override; + virtual void notifyLastUse(const model::operand::Index &) override; + + virtual void prepare(void) override; virtual void allocate(void) override; - std::shared_ptr<operand::Tensor> at(const ::neurun::graph::operand::Index &ind); + virtual std::shared_ptr<::neurun::backend::operand::ITensor> + tensorAt(const model::operand::Index &ind) override; + virtual std::shared_ptr<backend::operand::IObject> + wrapTensor(const model::operand::Index &ind) override; + virtual void iterate(const IterateFunction &fn) override; + + std::shared_ptr<operand::Tensor> at(const ::neurun::model::operand::Index &ind); private: - std::unordered_set<graph::operand::Index> _inds; - std::unordered_map<graph::operand::Index, std::shared_ptr<operand::Tensor>> _tensors; + std::unordered_map<model::operand::Index, compiler::TensorInfo> _tensor_info_map; + std::unordered_map<model::operand::Index, std::shared_ptr<operand::Tensor>> _tensors; + std::unordered_map<model::operand::Index, std::shared_ptr<operand::Object>> _objects; + std::unordered_map<model::operand::Index, Block> _tensor_mem_map; + std::shared_ptr<IMemoryPlanner> _mem_planner; + std::shared_ptr<Allocator> _mem_alloc; }; } // namespace cpu diff --git a/runtimes/neurun/src/backend/cpu/operand/Object.cc b/runtimes/neurun/src/backend/cpu/operand/Object.cc index 52b63fba7..011747a8c 100644 --- a/runtimes/neurun/src/backend/cpu/operand/Object.cc +++ b/runtimes/neurun/src/backend/cpu/operand/Object.cc @@ -25,7 +25,8 @@ namespace cpu namespace operand { -void Object::access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const +void Object::access( + const std::function<void(::neurun::backend::operand::ITensor &tensor)> &fn) const { fn(*_tensor); } diff --git a/runtimes/neurun/src/backend/cpu/operand/Object.h b/runtimes/neurun/src/backend/cpu/operand/Object.h index 08f63f3dc..5ef7c4fbf 100644 --- a/runtimes/neurun/src/backend/cpu/operand/Object.h +++ b/runtimes/neurun/src/backend/cpu/operand/Object.h @@ -18,9 +18,9 @@ #define __NEURUN_BACKEND_CPU_OPERAND_OBJECT_H__ #include <memory> -#include <arm_compute/core/ITensor.h> +#include "backend/interface/operand/ITensor.h" -#include "backend/IObject.h" +#include "backend/interface/operand/IObject.h" namespace neurun { @@ -37,19 +37,20 @@ public: Object() = default; public: - Object(const std::shared_ptr<::arm_compute::ITensor> &tensor) : _tensor{tensor} + Object(const std::shared_ptr<::neurun::backend::operand::ITensor> &tensor) : _tensor{tensor} { // DO NOTHING } public: - ::arm_compute::ITensor *ptr(void) const override { return _tensor.get(); } + ::neurun::backend::operand::ITensor *ptr(void) const override { return _tensor.get(); } private: - std::shared_ptr<::arm_compute::ITensor> _tensor; + std::shared_ptr<::neurun::backend::operand::ITensor> _tensor; public: - void access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const override; + void + access(const std::function<void(::neurun::backend::operand::ITensor &tensor)> &fn) const override; }; } // namespace operand diff --git a/runtimes/neurun/src/backend/cpu/operand/Tensor.cc b/runtimes/neurun/src/backend/cpu/operand/Tensor.cc index 0e4f34aac..a5251292e 100644 --- a/runtimes/neurun/src/backend/cpu/operand/Tensor.cc +++ b/runtimes/neurun/src/backend/cpu/operand/Tensor.cc @@ -16,6 +16,8 @@ #include "Tensor.h" +#define NO_USE(a) (void)(a) + namespace neurun { namespace backend @@ -25,7 +27,11 @@ namespace cpu namespace operand { -// NO IMPLEMENTATION YET +size_t Tensor::calcOffset(const neurun::util::feature::Coordinate4D &coords) +{ + NO_USE(coords); + throw std::runtime_error("offset_element_in_bytes is not supported for cpu::Tensor now."); +} } // namespace operand } // namespace cpu diff --git a/runtimes/neurun/src/backend/cpu/operand/Tensor.h b/runtimes/neurun/src/backend/cpu/operand/Tensor.h index 83a99acf2..7500f890f 100644 --- a/runtimes/neurun/src/backend/cpu/operand/Tensor.h +++ b/runtimes/neurun/src/backend/cpu/operand/Tensor.h @@ -17,8 +17,8 @@ #ifndef __NEURUN_BACKEND_CPU_OPERAND_TENSOR_H__ #define __NEURUN_BACKEND_CPU_OPERAND_TENSOR_H__ -#include <arm_compute/core/ITensor.h> -#include <arm_compute/core/TensorInfo.h> +#include "backend/interface/operand/ITensor.h" +#include "compiler/TensorInfo.h" namespace neurun { @@ -29,38 +29,40 @@ namespace cpu namespace operand { -class Tensor : public ::arm_compute::ITensor +class Tensor : public ::neurun::backend::operand::ITensor { public: - Tensor() = default; + Tensor() = delete; - Tensor(::arm_compute::TensorInfo info) : _info(info) - { - // TODO Do not allocate buffer here. This tensor is just an abstract Tensor object for cpu. - uint32_t size = _info.total_size(); // NOTE This size may not be accurate - _buffer = new uint8_t[size]; // NOTE The allocated buffer is never deallocated. - } - - Tensor(uint8_t *buffer) : _buffer(buffer) +public: + Tensor(const compiler::TensorInfo &info) : _info(info) { // DO NOTHING } public: void setBuffer(uint8_t *buffer) { _buffer = buffer; } + ::neurun::model::operand::DataType data_type() const { return _info.typeInfo().type(); } public: - ::arm_compute::TensorInfo *info() const override - { - return const_cast<::arm_compute::TensorInfo *>(&_info); - } - - ::arm_compute::TensorInfo *info() override { return &_info; } - uint8_t *buffer() const override { return _buffer; } + /** + * @brief Get dimension by index + * + * @param index Index to get diemension + * @return size_t Dimension at index + * @note N : dimension(0) + * H : dimension(1) + * W : dimension(2) + * C : dimension(3) + */ + size_t dimension(size_t index) const override { return _info.shape().dim(index); } + size_t num_dimensions() const override { return _info.shape().dims().size(); } + size_t total_size() const override { return _info.total_size(); } + size_t calcOffset(const neurun::util::feature::Coordinate4D &coords) override; private: - ::arm_compute::TensorInfo _info; + compiler::TensorInfo _info; uint8_t *_buffer = nullptr; }; diff --git a/runtimes/neurun/src/backend/interface/IConfig.h b/runtimes/neurun/src/backend/interface/IConfig.h new file mode 100644 index 000000000..82789d0ff --- /dev/null +++ b/runtimes/neurun/src/backend/interface/IConfig.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ICONFIG_H__ +#define __NEURUN_BACKEND_ICONFIG_H__ + +#include <string> + +#include "graph/operand/Layout.h" + +namespace neurun +{ +namespace backend +{ + +struct IConfig +{ + virtual ~IConfig() = default; + + virtual std::string id() = 0; + virtual void initialize() = 0; + // NOTE Assume backend has only one type of operand layout + virtual graph::operand::Layout getOperandLayout() = 0; + // Support subtensor allocation + virtual bool SupportSubTensorAlloc() = 0; +}; + +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ICONFIG_H__ diff --git a/runtimes/neurun/src/backend/interface/IStageGenerator.h b/runtimes/neurun/src/backend/interface/IStageGenerator.h new file mode 100644 index 000000000..878a50e3f --- /dev/null +++ b/runtimes/neurun/src/backend/interface/IStageGenerator.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ISTAGE_GENERATOR_H__ +#define __NEURUN_BACKEND_ISTAGE_GENERATOR_H__ + +#include <memory> +#include <functional> + +#include "exec/interface/IFunction.h" + +#include "backend/interface/ITensorBuilder.h" +#include "model/operation/NodeVisitor.h" + +struct IExecutionBuilder +{ + virtual ~IExecutionBuilder() = default; + + virtual void append(std::unique_ptr<::neurun::exec::IFunction> &&f) = 0; +}; + +using Stage = std::function<void(IExecutionBuilder &)>; + +namespace neurun +{ +namespace backend +{ + +class IStageGenerator : model::operation::NodeVisitor +{ +public: + virtual ~IStageGenerator() = default; + + virtual std::shared_ptr<ITensorBuilder> tensor_builder() = 0; + +protected: +#define OP(InternalName, IsNnApi, NnApiName) \ + virtual void visit(const model::operation::InternalName &) override {} +#include "model/operation/Op.lst" +#undef OP + +protected: + void returnStage(const Stage &stage) { _return = stage; } + +public: + Stage generate(const model::operation::Node &node) + { + node.accept(std::move(*this)); + return _return; + } + +private: + Stage _return = nullptr; +}; + +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ISTAGE_GENERATOR_H__ diff --git a/runtimes/neurun/src/backend/interface/ITensorBuilder.h b/runtimes/neurun/src/backend/interface/ITensorBuilder.h new file mode 100644 index 000000000..354a270e6 --- /dev/null +++ b/runtimes/neurun/src/backend/interface/ITensorBuilder.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_ITENSOR_BUILDER_H__ +#define __NEURUN_BACKEND_ITENSOR_BUILDER_H__ + +#include <map> + +#include "model/operand/Index.h" +#include "operand/IObject.h" +#include "compiler/SubTensorInfo.h" +#include "compiler/TensorInfo.h" +#include "backend/interface/operand/ITensor.h" + +namespace neurun +{ +namespace backend +{ + +struct ITensorBuilder +{ + using IterateFunction = std::function<void(const model::operand::Index &)>; + + virtual ~ITensorBuilder(void) = default; + + // TODO Merge registerTensorInfo and registerSubTensorInfo using abstraction by internal class + /** + * @brief Register tensor information to allocate on backend + */ + virtual void registerTensorInfo(const model::operand::Index &, const compiler::TensorInfo &) = 0; + /** + * @brief Register subtensor information to allocate on backend + */ + virtual void registerSubTensorInfo(const model::operand::Index &, + const compiler::SubTensorInfo &) = 0; + + virtual void notifyFirstUse(const model::operand::Index &) = 0; + virtual void notifyLastUse(const model::operand::Index &) = 0; + + virtual void prepare(void) = 0; + virtual void allocate(void) = 0; + + virtual std::shared_ptr<::neurun::backend::operand::ITensor> + tensorAt(const model::operand::Index &ind) = 0; + virtual std::shared_ptr<backend::operand::IObject> + wrapTensor(const model::operand::Index &ind) = 0; + virtual void iterate(const IterateFunction &fn) = 0; +}; + +} // namespace backend +} // namespace neurun + +#include <set> +#include <memory> + +namespace neurun +{ +namespace backend +{ + +using TensorBuilderSet = std::set<std::shared_ptr<backend::ITensorBuilder>>; + +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_ITENSOR_BUILDER_H__ diff --git a/runtimes/neurun/src/backend/interface/operand/IObject.h b/runtimes/neurun/src/backend/interface/operand/IObject.h new file mode 100644 index 000000000..44b33b080 --- /dev/null +++ b/runtimes/neurun/src/backend/interface/operand/IObject.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ +#define __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ + +#include <functional> + +#include "ITensor.h" + +namespace neurun +{ +namespace backend +{ +namespace operand +{ + +struct IObject +{ + virtual ~IObject() = default; + virtual ::neurun::backend::operand::ITensor *ptr(void) const = 0; + virtual void + access(const std::function<void(::neurun::backend::operand::ITensor &tensor)> &fn) const = 0; +}; + +} // namespace operand +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_OPERAND_I_OBJECT_H__ diff --git a/runtimes/neurun/src/backend/interface/operand/ITensor.h b/runtimes/neurun/src/backend/interface/operand/ITensor.h new file mode 100644 index 000000000..8bc3ff465 --- /dev/null +++ b/runtimes/neurun/src/backend/interface/operand/ITensor.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_BACKEND_OPERAND_I_TENSOR_H__ +#define __NEURUN_BACKEND_OPERAND_I_TENSOR_H__ + +#include <cstring> +#include <cstdint> + +#include "util/feature/Coordinate4D.h" + +namespace neurun +{ +namespace backend +{ +namespace operand +{ + +class ITensor +{ +public: + virtual ~ITensor() = default; + +public: + virtual uint8_t *buffer() const = 0; + virtual size_t total_size() const = 0; + virtual size_t dimension(size_t index) const = 0; + virtual size_t num_dimensions() const = 0; + virtual size_t calcOffset(const neurun::util::feature::Coordinate4D &coords) = 0; +}; + +} // namespace operand +} // namespace backend +} // namespace neurun + +#endif // __NEURUN_BACKEND_OPERAND_I_TENSOR_H__ diff --git a/runtimes/neurun/src/codegen/BackendResolver.cc b/runtimes/neurun/src/codegen/BackendResolver.cc deleted file mode 100644 index 4037a2813..000000000 --- a/runtimes/neurun/src/codegen/BackendResolver.cc +++ /dev/null @@ -1,27 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "BackendResolver.h" - -namespace neurun -{ -namespace codegen -{ - -// NOT IMPLEMENTED - -} // namespace codegen -} // namespace neurun diff --git a/runtimes/neurun/src/codegen/BackendResolver.h b/runtimes/neurun/src/codegen/BackendResolver.h deleted file mode 100644 index 02f22b278..000000000 --- a/runtimes/neurun/src/codegen/BackendResolver.h +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_BACKEND_RESOLVER_H__ -#define __NEURUN_CODEGEN_BACKEND_RESOLVER_H__ - -#include <set> -#include <unordered_map> -#include <typeindex> - -#include "logging.h" -#include "util/EnvVar.h" -#include "backend/BackendManager.h" -#include "backend/IInitializerGenerator.h" -#include "backend/IStageGenerator.h" - -namespace neurun -{ -namespace codegen -{ - -class BackendResolver -{ -public: - BackendResolver(const neurun::graph::operand::Set &operands) - { - _backend_manager = std::make_shared<backend::BackendManager>(operands); - - const auto &backend_all_str = - ::nnfw::util::EnvVar{std::string("OP_BACKEND_ALLOPS")}.asString("none"); - if (backend_all_str.compare("none") != 0) - { - VERBOSE(BackendResolver) << "Use backend for all ops: " << backend_all_str << std::endl; -#define OP(InternalName, NnApiName) \ - { \ - auto backend = _backend_manager->get(backend_all_str); \ - _gen_map[typeid(graph::operation::InternalName::Node)] = backend; \ - } -#include "graph/operation/Op.lst" -#undef OP - } - else - { -#define OP(InternalName, NnApiName) \ - { \ - const auto &backend_str = \ - ::nnfw::util::EnvVar{std::string("OP_BACKEND_") + #NnApiName}.asString("acl_cl"); \ - auto backend = _backend_manager->get(backend_str); \ - VERBOSE(BackendResolver) << "backend for " << #NnApiName << ": " << backend_str << std::endl; \ - _gen_map[typeid(graph::operation::InternalName::Node)] = backend; \ - } - -#include "graph/operation/Op.lst" -#undef OP - } - } - -public: - const backend::Backend &getBackend(const std::type_index &type) { return _gen_map[type]; } - -private: - std::unordered_map<std::type_index, backend::Backend> _gen_map; - std::shared_ptr<backend::BackendManager> _backend_manager; -}; - -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_BACKEND_RESOLVER_H__ diff --git a/runtimes/neurun/src/codegen/IPlanBuilder.h b/runtimes/neurun/src/codegen/IPlanBuilder.h deleted file mode 100644 index 197681432..000000000 --- a/runtimes/neurun/src/codegen/IPlanBuilder.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_I_PLAN_BUILDER_H__ -#define __NEURUN_CODEGEN_I_PLAN_BUILDER_H__ - -#include "arm_compute/core/TensorInfo.h" -#include "backend/IStageGenerator.h" -#include "backend/IInitializerGenerator.h" - -namespace neurun -{ -namespace codegen -{ - -struct IPlanBuilder -{ - virtual ~IPlanBuilder() = default; - - virtual void addShapeConstr(const ::neurun::graph::operand::Index &ind, - const ::arm_compute::TensorInfo &info) = 0; - virtual void addInitializer(const ::neurun::graph::operand::Index &ind, - const Initializer &initializer) = 0; - virtual void addStage(const Stage &) = 0; -}; - -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_I_PLAN_BUILDER_H__ diff --git a/runtimes/neurun/src/codegen/Plan.h b/runtimes/neurun/src/codegen/Plan.h deleted file mode 100644 index 47624ed35..000000000 --- a/runtimes/neurun/src/codegen/Plan.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_PLAN_H__ -#define __NEURUN_CODEGEN_PLAN_H__ - -#include "graph/Graph.h" -#include "codegen/operand/Context.h" -#include "codegen/operation/Sequence.h" - -namespace neurun -{ -namespace codegen -{ - -class Plan -{ -public: - Plan(const std::shared_ptr<neurun::graph::Graph> &model) : _model(model) - { - // DO NOTHING - } - -public: - neurun::graph::Graph &model(void) { return *_model; } - const neurun::graph::Graph &model(void) const { return *_model; } - -public: - operand::Context &operands(void) { return _operands; } - const operand::Context &operands(void) const { return _operands; } - -public: - operation::Sequence &operations(void) { return _ops; } - const operation::Sequence &operations(void) const { return _ops; } - -private: - std::shared_ptr<neurun::graph::Graph> _model; - operand::Context _operands; - operation::Sequence _ops; -}; - -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_PLAN_H__ diff --git a/runtimes/neurun/src/codegen/PlanBuilder.cc b/runtimes/neurun/src/codegen/PlanBuilder.cc deleted file mode 100644 index 5d4d6f9c9..000000000 --- a/runtimes/neurun/src/codegen/PlanBuilder.cc +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PlanBuilder.h" - -namespace neurun -{ -namespace codegen -{ - -void PlanBuilder::addShapeConstr(const ::neurun::graph::operand::Index &ind, - const ::arm_compute::TensorInfo &info) -{ - _tensor_info_ctx[ind.asInt()] = info; -} - -void PlanBuilder::addInitializer(const ::neurun::graph::operand::Index &ind, - const Initializer &initializer) -{ - _initializer_ctx[ind.asInt()] = initializer; -} - -void PlanBuilder::addStage(const Stage &stage) { _stages.emplace_back(stage); } - -void PlanBuilder::finalize(const backend::TensorBuilderSet &tensor_builders) -{ - // Prepare tensors - for (auto &tensor_builder : tensor_builders) - { - tensor_builder->prepare(_plan, _tensor_info_ctx); - } - - // Process Stage - ExecutionBuilder execution_builder{_plan}; - - for (const auto &stage : _stages) - { - stage(execution_builder); - } - - // TODO Add code for CPU/ACL tensor allocation - // Allocate Tensor Memory for cl_tensors - for (auto &tensor_builder : tensor_builders) - { - tensor_builder->allocate(); - } - - // Fill weight/bias - for (auto it = _initializer_ctx.begin(); it != _initializer_ctx.end(); ++it) - { - const ::neurun::graph::operand::Index operand_index{it->first}; - auto objects = _plan.operands().at(operand_index); - - for (auto object : objects) - { - object->access(it->second); - } - } -} - -} // namepsace codegen -} // namespace neurun diff --git a/runtimes/neurun/src/codegen/PlanBuilder.h b/runtimes/neurun/src/codegen/PlanBuilder.h deleted file mode 100644 index 4323143b3..000000000 --- a/runtimes/neurun/src/codegen/PlanBuilder.h +++ /dev/null @@ -1,86 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_PLAN_BUILDER_H__ -#define __NEURUN_CODEGEN_PLAN_BUILDER_H__ - -#include "IPlanBuilder.h" -#include "codegen/Plan.h" -#include "backend/IStageGenerator.h" -#include "backend/ITensorBuilder.h" - -namespace neurun -{ -namespace codegen -{ - -class ExecutionBuilder final : public IExecutionBuilder -{ -public: - ExecutionBuilder(codegen::Plan &plan) : _plan{plan} - { - // DO NOTHING - } - -public: - void append(std::unique_ptr<::arm_compute::IFunction> &&f) override - { - _plan.operations().append(std::move(f)); - } - -private: - codegen::Plan &_plan; -}; - -class PlanBuilder final : public IPlanBuilder -{ -public: - PlanBuilder(codegen::Plan &plan) : _plan{plan} - { - // DO NOTHING - } - -public: - void addShapeConstr(const ::neurun::graph::operand::Index &ind, - const ::arm_compute::TensorInfo &info) override; - -public: - void addInitializer(const ::neurun::graph::operand::Index &ind, - const Initializer &initializer) override; - -public: - void addStage(const Stage &stage) override; - -public: - // TODO Remove the argument `tensor_builders` - void finalize(const backend::TensorBuilderSet &tensor_builders); - -public: - const std::map<int, ::arm_compute::TensorInfo> &tensor_info_ctx() { return _tensor_info_ctx; } - -private: - codegen::Plan &_plan; - -private: - std::map<int, ::arm_compute::TensorInfo> _tensor_info_ctx; - std::map<int, Initializer> _initializer_ctx; - std::vector<Stage> _stages; -}; - -} // namepsace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_PLAN_BUILDER_H__ diff --git a/runtimes/neurun/src/codegen/Planner.cc b/runtimes/neurun/src/codegen/Planner.cc deleted file mode 100644 index 1add3acf5..000000000 --- a/runtimes/neurun/src/codegen/Planner.cc +++ /dev/null @@ -1,253 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Planner.h" - -#include <typeinfo> - -#include "internal/Convert.h" -#include "graph/operand/Set.h" -#include "codegen/IPlanBuilder.h" -#include "graph/operation/LowerInfo.h" - -#include "logging.h" - -namespace neurun -{ -namespace codegen -{ - -void Planner::visit(const graph::operation::Conv2D::Implicit::Node &node) -{ - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_index = node.getInputs().at(0); - const auto ker_index = node.getInputs().at(1); - const auto bias_index = node.getInputs().at(2); - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - const auto ker_shape = _ctx.at(ker_index).shape().asKernel(); - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - // Set Shape Constraints - _builder.addShapeConstr(ofm_index, ::internal::asTensorInfo(ofm_shape)); - _builder.addShapeConstr(ifm_index, ::internal::asTensorInfo(ifm_shape)); - _builder.addShapeConstr(ker_index, ::internal::asTensorInfo(ker_shape)); - _builder.addShapeConstr(bias_index, ::internal::asTensorInfo(bias_size)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Initializers - auto init_gen = backend.initializer_gen(); - _builder.addInitializer(ker_index, init_gen->generateWeight(node)); - _builder.addInitializer(bias_index, init_gen->generateBias(node)); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::MaxPool2D::Implicit::Node &node) -{ - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - - // Set Shape Constraints - _builder.addShapeConstr(ofm_index, ::internal::asTensorInfo(ofm_shape)); - _builder.addShapeConstr(ifm_index, ::internal::asTensorInfo(ifm_shape)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::AvgPool2D::Implicit::Node &node) -{ - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index ifm_index{node.getInputs().at(0)}; - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - - // Set Shape Constraints - _builder.addShapeConstr(ofm_index, ::internal::asTensorInfo(ofm_shape)); - _builder.addShapeConstr(ifm_index, ::internal::asTensorInfo(ifm_shape)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::Concat::Node &node) -{ - const ::neurun::graph::operand::Index ofm_index{node.getOutputs().at(0)}; - - // NOTE This implementation assumes that input and output are a feature - // TODO Remove this assumption - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - - // NOTE This implementation assumes concat over feature depth - // TODO Remove this assumption - assert(_ctx.at(::neurun::graph::operand::Index{node.param().axis_index}).asScalar<int32_t>() == - 3); - - // Set Shape Constraints (for output) - _builder.addShapeConstr(ofm_index, ::internal::asTensorInfo(ofm_shape)); - - // Set Shape Constraints (for input) - for (const auto &index : node.getInputs()) - { - const ::neurun::graph::operand::Index ifm_index{index}; - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - _builder.addShapeConstr(ifm_index, ::internal::asTensorInfo(ifm_shape)); - } - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::FullyConnected::Node &node) -{ - VERBOSE(FullyConnected) << "Configure FULLY_CONNECTED operation" << std::endl; - - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - const ::neurun::graph::operand::Index weight_index{node.getInputs().at(1)}; - const ::neurun::graph::operand::Index bias_index{node.getInputs().at(2)}; - - const ::neurun::graph::operand::Index activation_index{node.param().activation_index}; - - assert(_ctx.at(output_index).shape().rank() == 2); - const auto output_size = _ctx.at(output_index).shape().dim(1); - - // NOTE We assume that input is a feature map - // TODO Remove this restriction! - const auto ifm_shape = _ctx.at(input_index).shape().asFeature(); - - assert(_ctx.at(weight_index).shape().rank() == 2); - const auto num_output = _ctx.at(weight_index).shape().dim(0); - const auto input_size = _ctx.at(weight_index).shape().dim(1); - assert(ifm_shape.C * ifm_shape.H * ifm_shape.W == input_size); - - const auto bias_size = _ctx.at(bias_index).shape().asVector(); - - // Set Shape Constraints - _builder.addShapeConstr(output_index, ::internal::asTensorInfo(output_size)); - _builder.addShapeConstr(input_index, ::internal::asTensorInfo(ifm_shape)); - _builder.addShapeConstr(weight_index, - ::internal::asTensorInfo(num_output /*H*/, input_size /*W*/)); - _builder.addShapeConstr(bias_index, ::internal::asTensorInfo(bias_size)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Initializers - auto init_gen = backend.initializer_gen(); - _builder.addInitializer(weight_index, init_gen->generateWeight(node)); - _builder.addInitializer(bias_index, init_gen->generateBias(node)); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::Reshape::Node &node) -{ - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - - // NOTE The content of a tensor specified by shape_index should be aligned with - // output tensor shape - // TODO Check consistency of ouput shape - - // 'Feature Map' to 'Vector' reshape - assert(_ctx.at(input_index).shape().rank() == 4); - assert(_ctx.at(output_index).shape().rank() == 2); - assert(_ctx.at(output_index).shape().dim(0) == 1); - - const auto ifm_shape = _ctx.at(input_index).shape().asFeature(); - const auto out_size = _ctx.at(output_index).shape().dim(1); - - // NOTE Vector element ordering issue arises when H or W is not 1 - assert(ifm_shape.H == 1); - assert(ifm_shape.W == 1); - assert((ifm_shape.C * ifm_shape.H * ifm_shape.W) == out_size); - - _builder.addShapeConstr(output_index, ::internal::asTensorInfo(out_size)); - _builder.addShapeConstr(input_index, ::internal::asTensorInfo(ifm_shape)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::Softmax::Node &node) -{ - VERBOSE(Softmax) << "Configure SOFTMAX operation" << std::endl; - - const ::neurun::graph::operand::Index output_index{node.getOutputs().at(0)}; - const ::neurun::graph::operand::Index input_index{node.getInputs().at(0)}; - - assert(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); - - // TODO Support 'feature map' input - assert(_ctx.at(input_index).shape().rank() == 2); - assert(_ctx.at(input_index).shape().dim(0) == 1); - assert(_ctx.at(input_index).shape().dim(0) == _ctx.at(output_index).shape().dim(0)); - assert(_ctx.at(input_index).shape().dim(1) == _ctx.at(output_index).shape().dim(1)); - - const uint32_t len = _ctx.at(output_index).shape().dim(1); - - _builder.addShapeConstr(output_index, ::internal::asTensorInfo(len)); - _builder.addShapeConstr(input_index, ::internal::asTensorInfo(len)); - - // backend - auto backend = node.lower_info()->backend(); - - // Generate Stage - auto stage_gen = backend.stage_gen(); - _builder.addStage(stage_gen->generate(node)); -} - -void Planner::visit(const graph::operation::NOP::Node & /* node */) -{ - // DO NOTHING - // TODO : It's just for graph manipulation test now, it should be added tensor copy stage later. -} - -void Planner::visit(const graph::operation::Permute::Node & /* node */) { throw "NYI"; } - -} // namespace codegen -} // namespace neurun diff --git a/runtimes/neurun/src/codegen/Planner.h b/runtimes/neurun/src/codegen/Planner.h deleted file mode 100644 index d725567b5..000000000 --- a/runtimes/neurun/src/codegen/Planner.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_PLANNER_H__ -#define __NEURUN_CODEGEN_PLANNER_H__ - -#include "graph/operation/NodeVisitor.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ -class Set; -} // namespace operand -} // namespace graph -} // namespace neurun - -namespace neurun -{ -namespace codegen -{ - -class IPlanBuilder; - -class Planner : public graph::operation::NodeVisitor -{ -public: - Planner(const neurun::graph::operand::Set &ctx, neurun::codegen::IPlanBuilder &builder) - : _ctx{ctx}, _builder{builder} - { - } - -public: - virtual void visit(const graph::operation::Conv2D::Implicit::Node &) override; - virtual void visit(const graph::operation::MaxPool2D::Implicit::Node &) override; - virtual void visit(const graph::operation::AvgPool2D::Implicit::Node &) override; - virtual void visit(const graph::operation::Concat::Node &) override; - virtual void visit(const graph::operation::Reshape::Node &) override; - virtual void visit(const graph::operation::FullyConnected::Node &) override; - virtual void visit(const graph::operation::Softmax::Node &) override; - virtual void visit(const graph::operation::NOP::Node &) override; - virtual void visit(const graph::operation::Permute::Node &) override; - -private: - const neurun::graph::operand::Set &_ctx; - neurun::codegen::IPlanBuilder &_builder; -}; - -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_PLANNER_H__ diff --git a/runtimes/neurun/src/codegen/operand/Context.cc b/runtimes/neurun/src/codegen/operand/Context.cc deleted file mode 100644 index 7e5cdeccf..000000000 --- a/runtimes/neurun/src/codegen/operand/Context.cc +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Context.h" - -namespace neurun -{ -namespace codegen -{ -namespace operand -{ - -Context &Context::set(const graph::operand::Index &id, - const std::shared_ptr<backend::operand::IObject> &object) -{ - _objects[id.value()].emplace_back(object); - return (*this); -} - -} // namespace operand -} // namespace codegen -} // namespace neurun diff --git a/runtimes/neurun/src/codegen/operand/Context.h b/runtimes/neurun/src/codegen/operand/Context.h deleted file mode 100644 index 386c253c6..000000000 --- a/runtimes/neurun/src/codegen/operand/Context.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_OPERAND_CONTEXT_H__ -#define __NEURUN_CODEGEN_OPERAND_CONTEXT_H__ - -#include "backend/IObject.h" -#include "graph/operand/Index.h" - -#include <map> - -namespace neurun -{ -namespace codegen -{ -namespace operand -{ - -class Context -{ -public: - Context &set(const graph::operand::Index &ind, - const std::shared_ptr<backend::operand::IObject> &object); - -public: - bool exist(const ::neurun::graph::operand::Index &ind) const - { - return _objects.find(ind.asInt()) != _objects.end(); - } - -public: - const std::vector<std::shared_ptr<backend::operand::IObject>> & - at(const graph::operand::Index &ind) const - { - return _objects.at(ind.asInt()); - } - - std::vector<std::shared_ptr<backend::operand::IObject>> &at(const graph::operand::Index &ind) - { - return _objects.at(ind.asInt()); - } - -private: - std::map<int, std::vector<std::shared_ptr<backend::operand::IObject>>> _objects; -}; - -} // namespace operand -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_OPERAND_CONTEXT_H__ diff --git a/runtimes/neurun/src/codegen/operation/Sequence.cc b/runtimes/neurun/src/codegen/operation/Sequence.cc deleted file mode 100644 index 908e84a5c..000000000 --- a/runtimes/neurun/src/codegen/operation/Sequence.cc +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Sequence.h" - -namespace neurun -{ -namespace codegen -{ -namespace operation -{ - -// NO IMPLEMENTATION YET - -} // namespace operation -} // namespace codegen -} // namespace neurun diff --git a/runtimes/neurun/src/codegen/operation/Sequence.h b/runtimes/neurun/src/codegen/operation/Sequence.h deleted file mode 100644 index 83403feae..000000000 --- a/runtimes/neurun/src/codegen/operation/Sequence.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_CODEGEN_OPERATION_SEQUENCE_H__ -#define __NEURUN_CODEGEN_OPERATION_SEQUENCE_H__ - -#include <stdint.h> -#include <arm_compute/runtime/IFunction.h> -#include <memory> -#include <vector> - -namespace neurun -{ -namespace codegen -{ -namespace operation -{ - -class Sequence -{ -public: - uint32_t size(void) const { return _functions.size(); } - -public: - Sequence &append(std::unique_ptr<::arm_compute::IFunction> &&func) - { - _functions.emplace_back(std::move(func)); - return (*this); - } - -public: - ::arm_compute::IFunction &at(uint32_t n) const { return *(_functions.at(n)); } - -private: - std::vector<std::unique_ptr<::arm_compute::IFunction>> _functions; -}; - -} // namespace operation -} // namespace codegen -} // namespace neurun - -#endif // __NEURUN_CODEGEN_OPERATION_SEQUENCE_H__ diff --git a/runtimes/neurun/src/compiler/BackendResolver.cc b/runtimes/neurun/src/compiler/BackendResolver.cc new file mode 100644 index 000000000..6c1f32603 --- /dev/null +++ b/runtimes/neurun/src/compiler/BackendResolver.cc @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BackendResolver.h" + +namespace neurun +{ +namespace compiler +{ + +// NOT IMPLEMENTED + +} // namespace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/BackendResolver.h b/runtimes/neurun/src/compiler/BackendResolver.h new file mode 100644 index 000000000..4742b2d94 --- /dev/null +++ b/runtimes/neurun/src/compiler/BackendResolver.h @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_BACKEND_RESOLVER_H__ +#define __NEURUN_COMPILER_BACKEND_RESOLVER_H__ + +#include <set> +#include <unordered_map> +#include <typeindex> + +#include "util/logging.h" +#include "util/config/ConfigManager.h" +#include "backend/BackendManager.h" +#include "backend/interface/IStageGenerator.h" + +namespace neurun +{ +namespace compiler +{ + +class BackendResolver +{ +public: + BackendResolver(const neurun::model::operand::Set &operands) + { + _backend_manager = std::make_shared<backend::BackendManager>(operands); + + const auto backend_all_str = + config::ConfigManager::instance().get<std::string>("OP_BACKEND_ALLOPS"); + if (backend_all_str.compare("none") != 0) + { + VERBOSE(BackendResolver) << "Use backend for all ops: " << backend_all_str << std::endl; +#define OP(InternalName, IsNnApi, NnApiName) \ + if (IsNnApi) \ + { \ + auto backend = _backend_manager->get(backend_all_str); \ + _gen_map[typeid(model::operation::InternalName)] = backend; \ + } +#include "model/operation/Op.lst" +#undef OP + } + else + { +#define OP(InternalName, IsNnApi, NnApiName) \ + if (IsNnApi) \ + { \ + const auto &backend_str = \ + config::ConfigManager::instance().get<std::string>("OP_BACKEND_" #NnApiName); \ + auto backend = _backend_manager->get(backend_str); \ + VERBOSE(BackendResolver) << "backend for " << #NnApiName << ": " << backend_str << std::endl; \ + _gen_map[typeid(model::operation::InternalName)] = backend; \ + } + +#include "model/operation/Op.lst" +#undef OP + } + } + +public: + const backend::Backend *getBackend(const std::type_index &type) { return _gen_map[type]; } + const backend::Backend *getDefaultBackend() const + { + backend::Backend *default_backend = _backend_manager->get("cpu"); + return default_backend; + } + +private: + std::unordered_map<std::type_index, backend::Backend *> _gen_map; + std::shared_ptr<backend::BackendManager> _backend_manager; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_BACKEND_RESOLVER_H__ diff --git a/runtimes/neurun/src/compiler/Compiler.cc b/runtimes/neurun/src/compiler/Compiler.cc new file mode 100644 index 000000000..92ec69afb --- /dev/null +++ b/runtimes/neurun/src/compiler/Compiler.cc @@ -0,0 +1,124 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Compiler.h" + +#include "OperationValidator.h" +#include "SubTensorAnalyzer.h" +#include "PlanBuilder.h" +#include "ConstantInitializer.h" + +#include "graph/dumper/Dumper.h" +#include "graph/operation/LowerInfo.h" +#include "dumper/dot/DotDumper.h" +#include "linear/Linear.h" + +namespace neurun +{ + +namespace compiler +{ + +void Compiler::compile(void) +{ + auto &plan = this->plan(); + auto &graph = plan.model(); + const auto &operands = graph.operands(); + + // Disable compile phase + // When ready to use interpreter backend, remove this config and use backend setting + const auto env_disable_compile = config::ConfigManager::instance().get<bool>("DISABLE_COMPILE"); + if (env_disable_compile) + { + plan.state(State::NOT_COMPILED); + return; + } + + /*************************************************** + * Backend independent analysis & optimization phase + ***************************************************/ + + /************************************************************* + * Backend independent analysis & optimization phase finished + *************************************************************/ + + // dump graph to .dot + neurun::dumper::dot::DotDumper dot_dumper(graph); + dot_dumper.dumpIfNeeded("before_lower"); + + // Lower: decide backend + graph.lower(); + plan.state(State::LOWERED); + + dot_dumper.dumpIfNeeded("after_lower"); + + auto linear = graph.linearize(); + plan.state(State::LINEARIZED); + + // Dump ops + linear->accept(neurun::graph::dumper::Dumper{}); + + linear->accept(OperationValidator{operands}); + + /************************************************* + * Backend dependent analysis & optimization phase + *************************************************/ + + // SubTensorInfo should be generated after lower, before stage generation and finalize + // because SubTensorAnalyzer assume that insert permutation is already finished + // lower: decide backend and insert permutation + // stage generation: prepare codegen to optimization + // finalize: generate tensor using subtensor info, then execute stage + // Generated SubTensorInfo is in operand(Object) + // for easy pass SubTensorInfo to plan builder and tensor builder + linear->accept(SubTensorAnalyzer{graph.operands()}); + + /********************************************************** + * Backend dependent analysis & optimization phase finished + **********************************************************/ + + /*********************** + * Code generation phase + ***********************/ + + PlanBuilder plan_builder{plan}; + + // Plan building + linear->iterate([&](const linear::Element &element) { + auto backend = element.lower_info->backend(); + + // Generate Stage + auto stage_gen = backend->stage_gen(); + plan_builder.addStage(stage_gen->generate(*element.node)); + }); + + auto tensor_builders = linear->planTensors(); + + // TODO Add optimization passes + plan_builder.finalize(tensor_builders); + + ConstantInitializer{graph, plan}(); + + /******************************** + * Code generation phase finished + ********************************/ + + plan.state(State::COMPILED); +} + +} // namespace compiler + +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/Compiler.h b/runtimes/neurun/src/compiler/Compiler.h new file mode 100644 index 000000000..d8f620a10 --- /dev/null +++ b/runtimes/neurun/src/compiler/Compiler.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Compiler.h + * @brief This file contains Compiler class to define and run compilation phase + */ + +#ifndef __NEURUN_COMPILER_COMPILE_H_ +#define __NEURUN_COMPILER_COMPILE_H_ + +#include "graph/Graph.h" +#include "Plan.h" + +namespace neurun +{ + +namespace compiler +{ + +/** + * @brief Class to compile graph model + */ +class Compiler +{ +public: + /** + * @brief Construct a new Compiler object + * @param[in] model Graph model + */ + Compiler(const std::shared_ptr<graph::Graph> &model) : _plan{new Plan{model}} + { + // DO NOTHING + } + +public: + /** + * @brief Return plan + * @return Plan + */ + Plan &plan(void) { return *_plan; } + /** + * @brief Run compilation + */ + void compile(void); + /** + * @brief Pass plan reference + * @param[out] plan Plan reference to return + */ + void release(std::shared_ptr<const Plan> &plan) { plan = _plan; } + +private: + std::shared_ptr<Plan> _plan; +}; + +} // namespace compiler + +} // namespace neurun + +#endif // __NEURUN_COMPILER_COMPILE_H_ diff --git a/runtimes/neurun/src/compiler/ConstantInitializer.cc b/runtimes/neurun/src/compiler/ConstantInitializer.cc new file mode 100644 index 000000000..d6d58e273 --- /dev/null +++ b/runtimes/neurun/src/compiler/ConstantInitializer.cc @@ -0,0 +1,188 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConstantInitializer.h" + +#include "backend/interface/operand/IObject.h" +#include "backend/interface/IConfig.h" +#include "backend/BackendManager.h" +#include "model/operation/FullyConnectedNode.h" +#include "util/feature/nhwc/Reader.h" +#include "util/feature/nhwc/View.h" +#include "util/feature/nchw/View.h" +#include "misc/feature/IndexIterator.h" +#include "util/logging.h" + +namespace neurun +{ +namespace compiler +{ + +ConstantInitializer::ConstantInitializer(const graph::Graph &graph, Plan &plan) + : _graph{graph}, _plan{plan} +{ +} + +void ConstantInitializer::operator()() +{ + // Fill operand data + _plan.operands().iterate([&](int ind, neurun::backend::operand::IObject &obj) { + neurun::model::operand::Index index(ind); + const auto &model_obj = _graph.operands().at(index); + + // For only CONSTANTS + if (model_obj.getUsage() != neurun::model::operand::OperandUsage::CONSTANT) + return; + + // Only float32 is supported + auto type = model_obj.typeInfo().type(); + if (type != ::neurun::model::operand::DataType::TENSOR_FLOAT32) + throw std::runtime_error{"Unsupported data type. Only TENSOR_FLOAT32 is supported."}; + + VERBOSE(FillOperandData) << "Fill data for operand " << ind << std::endl; + + auto layout = + model_obj.lower_info()->def_backends().getOnlyElement()->config()->getOperandLayout(); + const auto shape = model_obj.shape(); + auto base = reinterpret_cast<const float *>(model_obj.data().base()); + auto size = model_obj.data().size(); + + obj.access([&](::neurun::backend::operand::ITensor &tensor) { + switch (shape.rank()) + { + case 1: + { + auto vec_size = shape.asVector(); + for (int32_t n = 0; n < vec_size; ++n) + { + const float *from = reinterpret_cast<const float *>(base) + n; + const auto value = *from; + + float *into = reinterpret_cast<float *>(tensor.buffer()) + n; + + *into = value; + } + break; + } + case 2: + { + // NOTE This is a WORKAROUND which supports FullyConnected weight only + // For FullyConnected, we must know the IFM shape to deduce 2D weight shape from 4D + // IFM. + // This is because of NHWC/NCHW layout, the order of mapping will be different. + // TODO Support general case - explicitly insert Reshape op for IFM as 2D + + // Find corresponding FullyConnected IFM + auto operation_index = _graph.operands().at(index).getUses().list().front(); + auto operation = &_graph.operations().at(operation_index); + auto fc_operation = + dynamic_cast<const neurun::model::operation::FullyConnectedNode *>(operation); + + if (fc_operation == nullptr) + break; + + auto ifm_index = fc_operation->getInputs().at( + neurun::model::operation::FullyConnectedNode::Input::INPUT); + const auto &ifm = _graph.operands().at(ifm_index); + const auto ifm_shape = ifm.shape().asFeature(); + const auto num_output = shape.dim(0); + + const ::nnfw::misc::feature::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, + ifm_shape.W}; + const util::feature::nhwc::Reader<float> from{ker_shape, base, size}; + + if (layout == neurun::graph::operand::Layout::NHWC) + { + ::nnfw::misc::feature::iterate(ker_shape) + << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(nth, ch, row, col); + + uint32_t offset = 0; + + // NNAPI uses NHWC ordering + offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C; + offset += row * ifm_shape.W * ifm_shape.C; + offset += col * ifm_shape.C; + offset += ch; + + float *into = reinterpret_cast<float *>(tensor.buffer()) + offset; + + *into = value; + }; + } + else + { + assert(layout == neurun::graph::operand::Layout::NCHW); + + ::nnfw::misc::feature::iterate(ker_shape) + << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(nth, ch, row, col); + + uint32_t offset = 0; + + // 'NCHW' ordering + offset += nth * ifm_shape.C * ifm_shape.H * ifm_shape.W; + offset += ch * ifm_shape.H * ifm_shape.W; + offset += row * ifm_shape.W; + offset += col; + + float *into = reinterpret_cast<float *>(tensor.buffer()) + offset; + + *into = value; + }; + } + + break; + } + case 4: + { + auto ker_shape = shape.asFeature(); + auto from = util::feature::nhwc::Reader<float>{ker_shape, base, size}; + + if (layout == neurun::graph::operand::Layout::NHWC) + { + auto into = util::feature::nhwc::View<float>{ + ker_shape, reinterpret_cast<float *>(tensor.buffer()), size}; + + ::nnfw::misc::feature::iterate(ker_shape) + << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(nth, ch, row, col); + into.at(nth, ch, row, col) = value; + }; + } + else + { + assert(layout == neurun::graph::operand::Layout::NCHW); + + auto into = util::feature::nchw::View<float>{&tensor}; + + ::nnfw::misc::feature::iterate(ker_shape) + << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(nth, ch, row, col); + into.at(nth, ch, row, col) = value; + }; + } + break; + } + default: + throw std::runtime_error{"Not yet supported"}; + } + }); + }); +} + +} // namespace codegen +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/ConstantInitializer.h b/runtimes/neurun/src/compiler/ConstantInitializer.h new file mode 100644 index 000000000..7d9231908 --- /dev/null +++ b/runtimes/neurun/src/compiler/ConstantInitializer.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_CONSTANT_INITIALIZER_H__ +#define __NEURUN_COMPILER_CONSTANT_INITIALIZER_H__ + +#include "graph/Graph.h" +#include "Plan.h" + +namespace neurun +{ +namespace compiler +{ + +class ConstantInitializer +{ +public: + ConstantInitializer(const graph::Graph &graph, Plan &plan); + + void operator()(); + +private: + const graph::Graph &_graph; + Plan &_plan; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_CONSTANT_INITIALIZER_H__ diff --git a/runtimes/neurun/src/compiler/OperationValidator.cc b/runtimes/neurun/src/compiler/OperationValidator.cc new file mode 100644 index 000000000..0110eccb8 --- /dev/null +++ b/runtimes/neurun/src/compiler/OperationValidator.cc @@ -0,0 +1,121 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OperationValidator.h" + +#include <typeinfo> + +#include "model/operand/Set.h" +#include "graph/operation/LowerInfo.h" + +#include "util/logging.h" + +namespace neurun +{ +namespace compiler +{ + +void OperationValidator::visit(const model::operation::Conv2DNode &) +{ + // DO NOTHING +} + +void OperationValidator::visit(const model::operation::MaxPool2DNode &) +{ + // DO NOTHING +} + +void OperationValidator::visit(const model::operation::AvgPool2DNode &) +{ + // DO NOTHING +} + +void OperationValidator::visit(const model::operation::ConcatNode &node) +{ + (void)node; // NOTE To prevent from unused variable warning + + // NOTE This implementation assumes concat over feature depth + // TODO Remove this assumption + assert(_ctx.at(::neurun::model::operand::Index{node.param().axis_index}).asScalar<int32_t>() == + 3); +} + +void OperationValidator::visit(const model::operation::FullyConnectedNode &) +{ + // DO NOTHING +} + +void OperationValidator::visit(const model::operation::ReshapeNode &node) +{ + (void)node; // NOTE To prevent from unused variable warning + + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(0)}; + + // NOTE The content of a tensor specified by shape_index should be aligned with + // output tensor shape + // TODO Check consistency of ouput shape + + // 'Feature Map' to 'Vector' reshape + assert(_ctx.at(input_index).shape().rank() == 4); + assert(_ctx.at(output_index).shape().rank() == 2); + assert(_ctx.at(output_index).shape().dim(0) == 1); + + // NOTE Vector element ordering issue arises when H or W is not 1 + assert(_ctx.at(input_index).shape().dim(1) == 1); // H + assert(_ctx.at(input_index).shape().dim(2) == 1); // W + // input(4D)'s C * H * W == output(2D)'s W + assert((_ctx.at(input_index).shape().dim(3) * _ctx.at(input_index).shape().dim(1) * + _ctx.at(input_index).shape().dim(2)) == _ctx.at(output_index).shape().dim(1)); +} + +void OperationValidator::visit(const model::operation::SoftmaxNode &node) +{ + (void)node; // NOTE To prevent from unused variable warning + + VERBOSE(Softmax) << "Configure SOFTMAX operation" << std::endl; + + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(0)}; + + assert(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); + + // TODO Support 'feature map' input + assert(_ctx.at(input_index).shape().rank() == 2); + assert(_ctx.at(input_index).shape().dim(0) == 1); + assert(_ctx.at(input_index).shape().dim(0) == _ctx.at(output_index).shape().dim(0)); + assert(_ctx.at(input_index).shape().dim(1) == _ctx.at(output_index).shape().dim(1)); +} + +void OperationValidator::visit(const model::operation::PermuteNode &node) +{ + (void)node; // NOTE To prevent from unused variable warning + + VERBOSE(Permute) << "Configure Permute operation" << std::endl; + + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(0)}; + + assert(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); +} + +void OperationValidator::visit(const model::operation::AddNode &) +{ + // DO NOTHING +} + +} // namespace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/OperationValidator.h b/runtimes/neurun/src/compiler/OperationValidator.h new file mode 100644 index 000000000..f4ed533ed --- /dev/null +++ b/runtimes/neurun/src/compiler/OperationValidator.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_OPERATION_VALIDATOR_H__ +#define __NEURUN_COMPILER_OPERATION_VALIDATOR_H__ + +#include "model/operation/NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ +class Set; +} // namespace operand +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace compiler +{ + +class OperationValidator : public model::operation::NodeVisitor +{ +public: + OperationValidator(const neurun::model::operand::Set &ctx) : _ctx{ctx} {} + +public: +#define OP(InternalName, IsNnApi, NnApiName) \ + virtual void visit(const model::operation::InternalName &) override; +#include "model/operation/Op.lst" +#undef OP + +private: + const neurun::model::operand::Set &_ctx; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_OPERATION_VALIDATOR_H__ diff --git a/runtimes/neurun/src/codegen/Plan.cc b/runtimes/neurun/src/compiler/Plan.cc index b7637b189..b7637b189 100644 --- a/runtimes/neurun/src/codegen/Plan.cc +++ b/runtimes/neurun/src/compiler/Plan.cc diff --git a/runtimes/neurun/src/compiler/Plan.h b/runtimes/neurun/src/compiler/Plan.h new file mode 100644 index 000000000..f2a526e0e --- /dev/null +++ b/runtimes/neurun/src/compiler/Plan.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_CODEGEN_PLAN_H__ +#define __NEURUN_CODEGEN_PLAN_H__ + +#include "graph/Graph.h" +#include "compiler/operand/Context.h" +#include "compiler/operation/Sequence.h" + +namespace neurun +{ +namespace compiler +{ + +enum class State +{ + NONE, // Initial state + LOWERED, // Backend is decided + LINEARIZED, // Everything is moved to Linear object so this Graph object is no longer effective + COMPILED, // Success compilation + NOT_COMPILED // Not compiled by environment or graph status +}; + +class Plan +{ +public: + Plan(const std::shared_ptr<neurun::graph::Graph> &model) : _model(model), _state(State::NONE) + { + // DO NOTHING + } + +public: + neurun::graph::Graph &model(void) { return *_model; } + const neurun::graph::Graph &model(void) const { return *_model; } + + void state(State state) { _state = state; } + State state(void) const { return _state; } + +public: + operand::Context &operands(void) { return _operands; } + const operand::Context &operands(void) const { return _operands; } + +public: + operation::Sequence &operations(void) { return _ops; } + const operation::Sequence &operations(void) const { return _ops; } + +private: + std::shared_ptr<neurun::graph::Graph> _model; + operand::Context _operands; + operation::Sequence _ops; + State _state; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_CODEGEN_PLAN_H__ diff --git a/runtimes/neurun/src/compiler/PlanBuilder.cc b/runtimes/neurun/src/compiler/PlanBuilder.cc new file mode 100644 index 000000000..8ef3fedbf --- /dev/null +++ b/runtimes/neurun/src/compiler/PlanBuilder.cc @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PlanBuilder.h" + +#include "backend/interface/operand/IObject.h" + +namespace neurun +{ +namespace compiler +{ + +void PlanBuilder::addStage(const Stage &stage) { _stages.emplace_back(stage); } + +void PlanBuilder::finalize(const backend::TensorBuilderSet &tensor_builders) +{ + auto &operands = _plan.operands(); + + // Prepare tensors + for (auto &tensor_builder : tensor_builders) + { + tensor_builder->prepare(); + + // Wrap tensors as Object and store them to plan + tensor_builder->iterate([&](const model::operand::Index &index) { + auto object = tensor_builder->wrapTensor(index); + operands.set(index, object); + }); + } + + // Process Stage + ExecutionBuilder execution_builder{_plan}; + + for (const auto &stage : _stages) + { + stage(execution_builder); + } + + // Allocate Tensor Memory for cl_tensors + for (auto &tensor_builder : tensor_builders) + { + tensor_builder->allocate(); + } +} + +} // namepsace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/PlanBuilder.h b/runtimes/neurun/src/compiler/PlanBuilder.h new file mode 100644 index 000000000..3231906d2 --- /dev/null +++ b/runtimes/neurun/src/compiler/PlanBuilder.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_PLAN_BUILDER_H__ +#define __NEURUN_COMPILER_PLAN_BUILDER_H__ + +#include "Plan.h" +#include "backend/interface/IStageGenerator.h" +#include "backend/interface/ITensorBuilder.h" + +namespace neurun +{ +namespace compiler +{ + +class ExecutionBuilder final : public IExecutionBuilder +{ +public: + ExecutionBuilder(Plan &plan) : _plan{plan} + { + // DO NOTHING + } + +public: + void append(std::unique_ptr<::neurun::exec::IFunction> &&f) override + { + _plan.operations().append(std::move(f)); + } + +private: + Plan &_plan; +}; + +class PlanBuilder +{ +public: + PlanBuilder(Plan &plan) : _plan{plan} + { + // DO NOTHING + } + +public: + void addStage(const Stage &stage); + +public: + // TODO Remove the argument `tensor_builders` + void finalize(const backend::TensorBuilderSet &tensor_builders); + +private: + Plan &_plan; + +private: + std::vector<Stage> _stages; +}; + +} // namepsace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_PLAN_BUILDER_H__ diff --git a/runtimes/neurun/src/compiler/SubTensorAnalyzer.cc b/runtimes/neurun/src/compiler/SubTensorAnalyzer.cc new file mode 100644 index 000000000..0851b7991 --- /dev/null +++ b/runtimes/neurun/src/compiler/SubTensorAnalyzer.cc @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SubTensorAnalyzer.h" + +#include <typeinfo> + +#include "cpp14/memory.h" +#include "model/operand/Set.h" +#include "graph/operation/LowerInfo.h" +#include "util/logging.h" + +namespace neurun +{ +namespace compiler +{ + +void SubTensorAnalyzer::visit(const model::operation::ConcatNode &node) +{ + // If operator is concat (or other operators related with subsumption), fill subsumption info + // TODO: if one tensor is subset of many parents or model input + // Solution 1. Handle 1st parent only, ignore others (need to invert for other childrun) + // Solution 2. Insert copy operation for other parents + auto axis_index = node.param().axis_index; + + // To prepare concat elimination, axis should be constant + if (_ctx.at(axis_index).getUsage() != model::operand::OperandUsage::CONSTANT) + { + VERBOSE(SUBTENSOR) << "Cannot handle non-constant axis" << std::endl; + return; + } + + // NOTE This implementation assumes concat over feature depth + // TODO Remove this assumption + int32_t axis = _ctx.at(axis_index).asScalar<int32_t>(); + if (axis != 3) + { + VERBOSE(SUBTENSOR) << "Cannot handle axis is not channel" << std::endl; + return; + } + + auto &output_index = node.getOutputs().at(0); + auto &inputs = node.getInputs(); + + int32_t axis_point = 0; + for (auto &input_index : inputs) + { + auto input_shape_4D = _ctx.at(input_index).lower_info()->shape(); + std::vector<int32_t> offset = {0, 0, 0, 0}; + offset[axis] = axis_point; + neurun::util::feature::Coordinate4D coordinate_info(offset[0], offset[1], offset[2], offset[3]); + std::unique_ptr<graph::operand::ParentInfo> parentInfo = + nnfw::cpp14::make_unique<graph::operand::ParentInfo>(output_index, coordinate_info); + + // NOTD Not support multiple parent tensor yet + assert(_ctx.at(input_index).parent_info() == nullptr); + _ctx.at(input_index).parent_info(std::move(parentInfo)); + + // NOTE Only support when axis is 3(channel) + axis_point += input_shape_4D.c(); + } +} + +} // namespace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/SubTensorAnalyzer.h b/runtimes/neurun/src/compiler/SubTensorAnalyzer.h new file mode 100644 index 000000000..ddfd10263 --- /dev/null +++ b/runtimes/neurun/src/compiler/SubTensorAnalyzer.h @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file SubTensorAnalyzer.h + * @brief This file contains SubTensorAnalyzer to analyze tensor subsumption + * using operation visitor + */ + +#ifndef __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__ +#define __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__ + +#include "model/operation/NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ +class Set; +} // namespace operation +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace compiler +{ + +/** + * @brief Class to analyze tensor subsumption + */ +class SubTensorAnalyzer : public model::operation::NodeVisitor +{ +public: + /** + * @brief Construct a new SubTensorAnalyzer object + * @param[in] ctx Graph operand set + */ + SubTensorAnalyzer(neurun::model::operand::Set &ctx) : _ctx{ctx} {} + +public: + virtual void visit(const model::operation::ConcatNode &) override; + +private: + neurun::model::operand::Set &_ctx; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_SUBTENSOR_ANALYZER_H__ diff --git a/runtimes/neurun/src/compiler/SubTensorInfo.h b/runtimes/neurun/src/compiler/SubTensorInfo.h new file mode 100644 index 000000000..c0fb857d0 --- /dev/null +++ b/runtimes/neurun/src/compiler/SubTensorInfo.h @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file SubTensorInfo.h + * @brief This file contains SubTensorInfo to represent subsumption between tensors + * for backend tensor allocation + */ +#ifndef __NEURUN_COMPILER_SUBTENSOR_INFO_H__ +#define __NEURUN_COMPILER_SUBTENSOR_INFO_H__ + +#include "model/operand/Object.h" +#include "util/feature/Coordinate4D.h" + +namespace neurun +{ +namespace compiler +{ + +/** + * @brief Class to represent information of subtensor + */ +class SubTensorInfo +{ +public: + SubTensorInfo() = delete; + + /** + * @brief Construct a new SubTensorInfo object + * @param[in] obj SubTensor object + */ + SubTensorInfo(const model::operand::Object &obj) + : _parent{obj.parent_info()->parent()}, _shape{obj.shape()}, _type{obj.typeInfo()}, + _offset{obj.parent_info()->offset()} + { + // DO NOTHING + } + +public: + /** + * @brief Return parent tensor index + * @return Parent tensor index + */ + const model::operand::Index parent(void) const { return _parent; } + /** + * @brief Return tensor shape + * @return Tensor shape + */ + const model::operand::Shape shape(void) const { return _shape; } + /** + * @brief Return tensor type + * @return Tensor type + */ + const model::operand::TypeInfo type(void) const { return _type; } + /** + * @brief Return tensor's offset in parent tensor + * @return Tensor offset + */ + const neurun::util::feature::Coordinate4D offset(void) const { return _offset; } + +private: + const model::operand::Index _parent; + const model::operand::Shape _shape; + const model::operand::TypeInfo _type; + const neurun::util::feature::Coordinate4D _offset; +}; + +} // compiler +} // neurun + +#endif // __NEURUN_COMPILER_SUBTENSOR_INFO_H__ diff --git a/runtimes/neurun/src/compiler/TensorInfo.h b/runtimes/neurun/src/compiler/TensorInfo.h new file mode 100644 index 000000000..787c433e5 --- /dev/null +++ b/runtimes/neurun/src/compiler/TensorInfo.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_TENSOR_INFO_H__ +#define __NEURUN_COMPILER_TENSOR_INFO_H__ + +#include "model/operand/Shape.h" +#include "model/operand/TypeInfo.h" + +#include <numeric> + +namespace neurun +{ +namespace compiler +{ + +class TensorInfo +{ +public: + TensorInfo() = default; + +public: + TensorInfo(const ::neurun::model::operand::Shape &shape, + const ::neurun::model::operand::TypeInfo &typeInfo) + : _shape(shape), _typeInfo(typeInfo) + { + // DO NOTHING + } + +public: + const ::neurun::model::operand::Shape &shape() const { return _shape; } + const ::neurun::model::operand::TypeInfo &typeInfo() const { return _typeInfo; } + size_t total_size() const + { + const auto &dims = _shape.dims(); + return std::accumulate(dims.begin(), dims.end(), 4, std::multiplies<size_t>()); + } + +private: + ::neurun::model::operand::Shape _shape; + ::neurun::model::operand::TypeInfo _typeInfo; +}; + +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_TENSOR_INFO_H__ diff --git a/runtimes/neurun/src/compiler/operand/Context.cc b/runtimes/neurun/src/compiler/operand/Context.cc new file mode 100644 index 000000000..3fa529995 --- /dev/null +++ b/runtimes/neurun/src/compiler/operand/Context.cc @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Context.h" + +#include <cassert> + +namespace neurun +{ +namespace compiler +{ +namespace operand +{ + +Context &Context::set(const model::operand::Index &id, + const std::shared_ptr<backend::operand::IObject> &object) +{ + // Only one object for an id + assert(_objects.find(id.value()) == _objects.end()); + _objects[id.value()] = object; + return (*this); +} + +void Context::iterate(const std::function<void(int, backend::operand::IObject &)> &fn) +{ + for (auto &e : _objects) + { + fn(e.first, *e.second); + } +} + +} // namespace operand +} // namespace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/operand/Context.h b/runtimes/neurun/src/compiler/operand/Context.h new file mode 100644 index 000000000..bc558404e --- /dev/null +++ b/runtimes/neurun/src/compiler/operand/Context.h @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_OPERAND_CONTEXT_H__ +#define __NEURUN_COMPILER_OPERAND_CONTEXT_H__ + +#include "backend/interface/operand/IObject.h" +#include "model/operand/Index.h" + +#include <map> +#include <memory> + +namespace neurun +{ +namespace compiler +{ +namespace operand +{ + +class Context +{ +public: + Context &set(const model::operand::Index &ind, + const std::shared_ptr<backend::operand::IObject> &object); + +public: + bool exist(const ::neurun::model::operand::Index &ind) const + { + return _objects.find(ind.asInt()) != _objects.end(); + } + +public: + std::shared_ptr<backend::operand::IObject> at(const model::operand::Index &ind) const + { + return _objects.at(ind.asInt()); + } + + std::shared_ptr<backend::operand::IObject> &at(const model::operand::Index &ind) + { + return _objects.at(ind.asInt()); + } + + void iterate(const std::function<void(int, backend::operand::IObject &)> &fn); + +private: + std::map<int, std::shared_ptr<backend::operand::IObject>> _objects; +}; + +} // namespace operand +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_OPERAND_CONTEXT_H__ diff --git a/runtimes/neurun/src/compiler/operation/Sequence.cc b/runtimes/neurun/src/compiler/operation/Sequence.cc new file mode 100644 index 000000000..3160e04b6 --- /dev/null +++ b/runtimes/neurun/src/compiler/operation/Sequence.cc @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Sequence.h" + +namespace neurun +{ +namespace compiler +{ +namespace operation +{ + +// NO IMPLEMENTATION YET + +} // namespace operation +} // namespace compiler +} // namespace neurun diff --git a/runtimes/neurun/src/compiler/operation/Sequence.h b/runtimes/neurun/src/compiler/operation/Sequence.h new file mode 100644 index 000000000..d69cfcfe3 --- /dev/null +++ b/runtimes/neurun/src/compiler/operation/Sequence.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_COMPILER_OPERATION_SEQUENCE_H__ +#define __NEURUN_COMPILER_OPERATION_SEQUENCE_H__ +#include <stdint.h> +#include "exec/interface/IFunction.h" +#include <memory> +#include <vector> + +namespace neurun +{ +namespace compiler +{ +namespace operation +{ + +class Sequence +{ +public: + uint32_t size(void) const { return _functions.size(); } + +public: + Sequence &append(std::unique_ptr<::neurun::exec::IFunction> &&func) + { + _functions.emplace_back(std::move(func)); + return (*this); + } + +public: + ::neurun::exec::IFunction &at(uint32_t n) const { return *(_functions.at(n)); } + +private: + std::vector<std::unique_ptr<::neurun::exec::IFunction>> _functions; +}; + +} // namespace operation +} // namespace compiler +} // namespace neurun + +#endif // __NEURUN_COMPILER_OPERATION_SEQUENCE_H__ diff --git a/runtimes/neurun/src/dumper/dot/DotBuilder.cc b/runtimes/neurun/src/dumper/dot/DotBuilder.cc new file mode 100644 index 000000000..d694323b4 --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotBuilder.cc @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DotBuilder.h" + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +// NodeAttr +NodeAttr &NodeAttr::addAttr(const std::string &name, const std::string &attr) +{ + _attrs.emplace_back(name, attr); + + return *this; +} + +void NodeAttr::finish() +{ + _attr_stream << "["; + for (auto attr : _attrs) + { + _attr_stream << attr.first << "=" + << "\"" << attr.second << "\" "; + } + _attr_stream << "];\n"; +} + +// DotDumper +DotBuilder::DotBuilder() {} + +void DotBuilder::update(const IDotInfo &node_info) +{ + addNode(node_info); + for (auto child : node_info.children()) + { + addEdge(node_info, *child); + } +} + +void DotBuilder::writeDot(std::ostream &os) +{ + os << "digraph D {\n" + << _dot.str() << "\n" + << "}\n"; +} + +void DotBuilder::addNode(const IDotInfo &dotinfo) +{ + NodeAttr attr; + attr.addAttr("shape", dotinfo.dot_shape()) + .addAttr("label", dotinfo.label()) + .addAttr("style", "filled") + .addAttr("colorscheme", dotinfo.bg_color_scheme()) + .addAttr("fillcolor", dotinfo.bg_color()); + + attr.finish(); + + _dot << dotinfo.index_str() << attr.attr_stream(); +} + +void DotBuilder::addEdge(const IDotInfo &dotinfo1, const IDotInfo &dotinfo2) +{ + _dot << dotinfo1.index_str() << " -> " << dotinfo2.index_str() << ";\n"; +} + +} // namespace dot +} // namespace dumper +} // namespace neurun diff --git a/runtimes/neurun/src/dumper/dot/DotBuilder.h b/runtimes/neurun/src/dumper/dot/DotBuilder.h new file mode 100644 index 000000000..783e92b80 --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotBuilder.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_DUMPER_DOT_DOT_BUILDER_H__ +#define __NEURUN_DUMPER_DOT_DOT_BUILDER_H__ + +#include <sstream> + +#include "model/operation/Index.h" +#include "model/operand/Index.h" + +#include "model/operation/Node.h" +#include "model/operand/Object.h" + +#include "DotNodeInfo.h" +#include "DotOperandInfo.h" + +using Node = neurun::model::operation::Node; +using Object = neurun::model::operand::Object; + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +class NodeAttr +{ +public: + NodeAttr() = default; + +public: + void finish(); + NodeAttr &addAttr(const std::string &name, const std::string &attr); + +public: + std::stringbuf *attr_stream() { return _attr_stream.rdbuf(); } + +private: + std::vector<std::pair<std::string, std::string>> _attrs; + std::stringstream _attr_stream; +}; + +class DotBuilder +{ +public: + DotBuilder(); + +public: + void update(const IDotInfo &dotinfo); + + void writeDot(std::ostream &os); + +private: + void addNode(const IDotInfo &dotinfo); + void addEdge(const IDotInfo &dotinfo1, const IDotInfo &dotinfo2); + + std::stringstream _dot; +}; + +} // namespace dot +} // namespace dumper +} // namespace neurun + +#endif // __NEURUN_DUMPER_DOT_DOT_BUILDER_H__ diff --git a/runtimes/neurun/src/dumper/dot/DotDumper.cc b/runtimes/neurun/src/dumper/dot/DotDumper.cc new file mode 100644 index 000000000..1e53ece19 --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotDumper.cc @@ -0,0 +1,115 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <fstream> + +#include "DotDumper.h" +#include "DotBuilder.h" + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +using namespace neurun::graph; + +void DotDumper::dumpIfNeeded(const std::string &tag) +{ + if (_option == OPTIONS::DUMP_OFF) + { + return; + } + neurun::dumper::dot::DotBuilder dot_builder; + + auto &operations = _graph.operations(); + auto &operands = _graph.operands(); + + operations.iterate([&](const model::operation::Index &index, const model::operation::Node &node) { + neurun::dumper::dot::DotNodeInfo node_info(_graph, index, node); + + for (auto output : node.getOutputs()) + { + using neurun::dumper::dot::DotOperandInfo; + auto child = std::make_shared<DotOperandInfo>(output, operands.at(output), + DotOperandInfo::Type::MODEL_OUTPUT); + node_info.appendChild(child); + } + + dot_builder.update(node_info); + }); + + operands.iterate([&](const model::operand::Index &index, const model::operand::Object &object) { + bool showing_cond = false; + auto usage = object.getUsage(); + if (_option == OPTIONS::SHOW_CONSTANTS) + { + showing_cond = object.usageIsDefined(); + } + else + { + showing_cond = (usage == model::operand::OperandUsage::MODEL_INPUT) || + (usage == model::operand::OperandUsage::OPERATION_OUTPUT); + } + if (usage != model::operand::OperandUsage::OPERATION_OUTPUT) + { + showing_cond = showing_cond && (object.getUses().size() > 0); + } + if (showing_cond) + { + auto type = [&]() { + using neurun::dumper::dot::DotOperandInfo; + if (_graph.getInputs().contains(index)) + return DotOperandInfo::Type::MODEL_INPUT; + if (_graph.getOutputs().contains(index)) + return DotOperandInfo::Type::MODEL_OUTPUT; + return DotOperandInfo::Type::INTERNAL; + }(); + + neurun::dumper::dot::DotOperandInfo operand_info(index, object, type); + + for (auto operation_index : object.getUses().list()) + { + auto &node = operations.at(operation_index); + auto child = + std::make_shared<neurun::dumper::dot::DotNodeInfo>(_graph, operation_index, node); + operand_info.appendChild(child); + } + + dot_builder.update(operand_info); + } + }); + + // Dump to file + { + std::string file_name; + file_name += tag; + file_name += ".dot"; + std::filebuf fb; + + fb.open(file_name, std::ios::out); + std::ostream os(&fb); + + dot_builder.writeDot(os); + + fb.close(); + } +} + +} // namespace dot +} // namespace dumper +} // namespace neurun diff --git a/runtimes/neurun/src/dumper/dot/DotDumper.h b/runtimes/neurun/src/dumper/dot/DotDumper.h new file mode 100644 index 000000000..0c0a9b8df --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotDumper.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "graph/Graph.h" +#include "util/config/ConfigManager.h" + +#ifndef __NEURUN_DUMPER_DOT_DOT_DUMPER_H__ +#define __NEURUN_DUMPER_DOT_DOT_DUMPER_H__ + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +enum OPTIONS +{ + DUMP_OFF = 0, // Don't dump + DEFAULT = 1, // Show default dot graph + SHOW_CONSTANTS // Show dot graph with input constants +}; + +class DotDumper +{ +public: + DotDumper(const neurun::graph::Graph &graph) : _graph(graph) + { + _option = config::ConfigManager::instance().get<int>("GRAPH_DOT_DUMP"); + } + +public: + /** + * @brief Dump to dot file as tag name if "GRAPH_DOT_DUMP" is set + * + * @param[in] tag The name of dot file that would be created + * @return N/A + */ + void dumpIfNeeded(const std::string &tag); + +private: + const neurun::graph::Graph &_graph; + uint32_t _option; +}; + +} // namespace dot +} // namespace dumper +} // namespace neurun + +#endif // __NEURUN_DUMPER_DOT_DOT_DUMPER_H__ diff --git a/runtimes/neurun/src/dumper/dot/DotNodeInfo.cc b/runtimes/neurun/src/dumper/dot/DotNodeInfo.cc new file mode 100644 index 000000000..aefe12e2a --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotNodeInfo.cc @@ -0,0 +1,108 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <sstream> + +#include "DotNodeInfo.h" +#include "graph/Graph.h" +#include "graph/operation/LowerInfo.h" +#include "backend/interface/IConfig.h" + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +const std::string DotNodeInfo::NODE_SHAPE = "rect"; +const std::string DotNodeInfo::BG_COLOR_SCHEME = "pastel18"; +// RED BLUE ORANGE YELLOW GREEN PUPLE CYAN PINK +const std::string DotNodeInfo::BG_COLORS[8] = {"1", "2", "5", "6", "3", "4", "7", "8"}; + +DotNodeInfo::DotNodeInfo(const neurun::graph::Graph &graph, + const neurun::model::operation::Index &index, + const neurun::model::operation::Node &node) + : _index(index), _node(node), _lower_info(graph.getLowerInfo(index)) +{ + addBackendLabel(); +} + +std::string DotNodeInfo::index_str() const +{ + std::stringstream ss; + ss << "node" << _index.value(); + + return ss.str(); +} + +std::string DotNodeInfo::label() const +{ + std::stringstream ss; + ss << _index.value() << " : " << _node.getName() << std::endl; + for (auto label : _labels) + { + ss << label << std::endl; + } + + return ss.str(); +} + +std::string DotNodeInfo::dot_shape() const { return NODE_SHAPE; } + +std::string DotNodeInfo::bg_color_scheme() const { return BG_COLOR_SCHEME; } + +std::string DotNodeInfo::bg_color() const +{ + if (!_lower_info) + return DEFAULT_BG_COLOR; + assert(_lower_info != nullptr); + const auto &backend = _lower_info->backend(); + assert(backend != nullptr); + + std::string backend_id = backend->config()->id(); + // TODO : This is just workaround it can be made more efficient. + if (backend_id == "acl_cl") + { + return BG_COLORS[RED]; + } + else if (backend_id == "cpu") + { + return BG_COLORS[BLUE]; + } + else + { + return DEFAULT_BG_COLOR; + } +} + +void DotNodeInfo::addBackendLabel() +{ + if (!_lower_info) + return; + + std::string label; + const auto &backend = _lower_info->backend(); + assert(backend != nullptr); + + label += "[Backend] : "; + label += backend->config()->id(); + _labels.emplace_back(label); +} + +} // namespace dot +} // namespace dumper +} // namespace neurun diff --git a/runtimes/neurun/src/dumper/dot/DotNodeInfo.h b/runtimes/neurun/src/dumper/dot/DotNodeInfo.h new file mode 100644 index 000000000..656a05af6 --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotNodeInfo.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__ +#define __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__ + +#include "IDotInfo.h" +#include "model/operation/Node.h" +#include "model/operation/Index.h" + +namespace neurun +{ +namespace graph +{ +class Graph; +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +class DotNodeInfo : public IDotInfo +{ +public: + static const std::string NODE_SHAPE; + static const std::string BG_COLOR_SCHEME; + static const std::string BG_COLORS[8]; + +public: + DotNodeInfo(const neurun::graph::Graph &graph, const neurun::model::operation::Index &index, + const neurun::model::operation::Node &node); + +public: + virtual std::string index_str() const override; + virtual std::string label() const override; + virtual std::string dot_shape() const override; + virtual std::string bg_color_scheme() const override; + virtual std::string bg_color() const override; + +private: + void addBackendLabel(); + +private: + neurun::model::operation::Index _index; + const neurun::model::operation::Node &_node; + const neurun::graph::operation::LowerInfo *_lower_info; + std::vector<std::string> _labels; +}; + +} // namespace dot +} // namespace dumper +} // namespace neurun + +#endif // __NEURUN_DUMPER_DOT_DOT_NODE_INFO_H__ diff --git a/runtimes/neurun/src/dumper/dot/DotOperandInfo.cc b/runtimes/neurun/src/dumper/dot/DotOperandInfo.cc new file mode 100644 index 000000000..8f5905020 --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotOperandInfo.cc @@ -0,0 +1,129 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <sstream> + +#include "DotOperandInfo.h" +#include "graph/operand/LowerInfo.h" +#include "backend/interface/IConfig.h" +#include "backend/BackendManager.h" + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +const std::string DotOperandInfo::INPUT_SHAPE = "doublecircle"; +const std::string DotOperandInfo::OUTPUT_SHAPE = "doublecircle"; +const std::string DotOperandInfo::OPERAND_SHAPE = "ellipse"; +const std::string DotOperandInfo::BG_COLOR_SCHEME = "set38"; +// RED BLUE ORANGE YELLOW GREEN PUPLE CYAN PINK +const std::string DotOperandInfo::BG_COLORS[8] = {"4", "5", "6", "2", "7", "3", "1", "8"}; + +DotOperandInfo::DotOperandInfo(const neurun::model::operand::Index &index, + const neurun::model::operand::Object &object, Type type) + : _index(index), _object(object), _type(type) +{ + const auto &lower_info = object.lower_info(); + if (lower_info) + { + addBackendLabel(); + } +} + +std::string DotOperandInfo::index_str() const +{ + std::stringstream ss; + ss << "obj" << _index.value(); + + return ss.str(); +} + +std::string DotOperandInfo::label() const +{ + std::stringstream ss; + ss << _index.value() << std::endl; + for (auto label : _labels) + { + ss << label << std::endl; + } + + return ss.str(); +} + +std::string DotOperandInfo::dot_shape() const +{ + switch (_type) + { + case Type::MODEL_INPUT: + return INPUT_SHAPE; + + case Type::MODEL_OUTPUT: + return OUTPUT_SHAPE; + + case Type::UNDEFINED: + case Type::INTERNAL: + default: + return OPERAND_SHAPE; + } +} + +std::string DotOperandInfo::bg_color_scheme() const { return BG_COLOR_SCHEME; } + +std::string DotOperandInfo::bg_color() const +{ + const auto &lower_info = _object.lower_info(); + if (!lower_info) + return DEFAULT_BG_COLOR; + assert(lower_info != nullptr); + const auto &def_backends = lower_info->def_backends(); + assert(def_backends.size() == 1); + + std::string backend_id = def_backends.getOnlyElement()->config()->id(); + // TODO : This is just workaround it can be made more efficient. + if (backend_id == "acl_cl") + { + return BG_COLORS[RED]; + } + else if (backend_id == "cpu") + { + return BG_COLORS[BLUE]; + } + else + { + return DEFAULT_BG_COLOR; + } +} + +void DotOperandInfo::addBackendLabel() +{ + std::string label; + const auto &lower_info = _object.lower_info(); + assert(lower_info != nullptr); + const auto &def_backends = lower_info->def_backends(); + assert(def_backends.size() == 1); + + label += "["; + label += def_backends.getOnlyElement()->config()->id(); + label += "]"; + _labels.emplace_back(label); +} + +} // namespace dot +} // namespace dumper +} // namespace neurun diff --git a/runtimes/neurun/src/dumper/dot/DotOperandInfo.h b/runtimes/neurun/src/dumper/dot/DotOperandInfo.h new file mode 100644 index 000000000..c54da444d --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/DotOperandInfo.h @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__ +#define __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__ + +#include <vector> + +#include "IDotInfo.h" +#include "model/operand/Object.h" +#include "model/operand/Index.h" + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +class DotOperandInfo : public IDotInfo +{ +public: + enum class Type + { + UNDEFINED, + MODEL_INPUT, + MODEL_OUTPUT, + INTERNAL + }; + +public: + static const std::string INPUT_SHAPE; + static const std::string OUTPUT_SHAPE; + static const std::string OPERAND_SHAPE; + static const std::string BG_COLOR_SCHEME; + static const std::string BG_COLORS[8]; + +public: + DotOperandInfo(const neurun::model::operand::Index &index, + const neurun::model::operand::Object &object, Type type); + +public: + virtual std::string index_str() const override; + virtual std::string label() const override; + virtual std::string dot_shape() const override; + virtual std::string bg_color_scheme() const override; + virtual std::string bg_color() const override; + +private: + void addBackendLabel(); + +private: + const neurun::model::operand::Index &_index; + const neurun::model::operand::Object &_object; + Type _type; + + std::vector<std::string> _labels; +}; + +} // namespace dot +} // namespace dumper +} // namespace neurun + +#endif // __NEURUN_DUMPER_DOT_DOT_OPERAND_INFO_H__ diff --git a/runtimes/neurun/src/dumper/dot/IDotInfo.h b/runtimes/neurun/src/dumper/dot/IDotInfo.h new file mode 100644 index 000000000..d507e724a --- /dev/null +++ b/runtimes/neurun/src/dumper/dot/IDotInfo.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_DUMPER_DOT_IDOTINFO_H__ +#define __NEURUN_DUMPER_DOT_IDOTINFO_H__ + +#include <string> +#include <memory> +#include <vector> + +namespace neurun +{ +namespace dumper +{ +namespace dot +{ + +#define DEFAULT_BG_COLOR_SCHEME "x11" +#define DEFAULT_BG_COLOR "white" + +enum BGCOLORS : int +{ + RED, + BLUE, + ORANGE, + YELLOW, + GREEN, + PUPLE, + CYAN, + PINK +}; + +struct IDotInfo +{ + virtual ~IDotInfo() = default; + + virtual std::string index_str() const = 0; + virtual std::string label() const = 0; + virtual std::string dot_shape() const = 0; + virtual std::string bg_color_scheme() const { return DEFAULT_BG_COLOR_SCHEME; } + virtual std::string bg_color() const { return DEFAULT_BG_COLOR; } + + void appendChild(std::shared_ptr<IDotInfo> dotinfo) { _children.emplace_back(dotinfo); } + const std::vector<std::shared_ptr<IDotInfo>> &children() const { return _children; } + +private: + std::vector<std::shared_ptr<IDotInfo>> _children; +}; + +} // namespace dot +} // namespace dumper +} // namespace neurun + +#endif // __NEURUN_DUMPER_DOT_IDOTINFO_H__ diff --git a/runtimes/neurun/src/exec/Sink.h b/runtimes/neurun/src/exec/Sink.h index a96f08320..fe23e8ac3 100644 --- a/runtimes/neurun/src/exec/Sink.h +++ b/runtimes/neurun/src/exec/Sink.h @@ -19,105 +19,119 @@ #include <cassert> -#include <arm_compute/core/ITensor.h> - -#include <util/feature/Shape.h> -#include <util/feature/IndexIterator.h> - -#include "backend/cpu/operand/Tensor.h" // TODO Remove this dependency to backend -#include "internal/nnapi/feature/View.h" -#include "internal/nnapi/feature/Reader.h" +#include "cpp14/memory.h" +#include "util/feature/nhwc/View.h" +#include "util/feature/nchw/View.h" +#include <misc/feature/IndexIterator.h> namespace neurun { namespace exec { -struct Sink +struct ISink { - virtual ~Sink() = default; + virtual ~ISink() = default; - virtual void pull(::arm_compute::ITensor &tensor) const = 0; + virtual void pull(::neurun::backend::operand::ITensor &tensor) const = 0; }; -// -// VectorSink -// -class VectorSink final : public Sink +template <typename T> class Sink final : public ISink { public: - VectorSink(const int32_t vlen, uint8_t *base, const size_t size) : _vlen{vlen}, _base{base} - { - (void)size; // Workaround for unused variable in release mode - assert(size >= _vlen * sizeof(float)); - } + Sink(T *base, const size_t size) : _base{base}, _size{size} {} public: - void pull(::arm_compute::ITensor &tensor) const override + void pull(::neurun::backend::operand::ITensor &tensor) const override { - float *base = reinterpret_cast<float *>(_base); - - for (int32_t n = 0; n < _vlen; ++n) - { - auto from = reinterpret_cast<float *>(tensor.ptr_to_element(::arm_compute::Coordinates{n})); - auto into = base + n; - - *into = *from; - } + memcpy(_base, tensor.buffer(), _size); } private: - const int32_t _vlen; - uint8_t *const _base; + T *const _base; + const size_t _size; }; -// -// FeatureSink -// -class FeatureSink final : public Sink +class PermutateSink final : public ISink { public: - FeatureSink(const nnfw::util::feature::Shape &shape, uint8_t *base, const size_t size) - : _shape{shape}, _base{base}, _size{size} + PermutateSink(void *output_buffer, const size_t &output_size, const model::operand::Shape &shape) + : _output_buffer{(uint8_t *)output_buffer}, _output_size{output_size}, _shape{shape} { - // DO NOTHING } public: - void pull(::arm_compute::ITensor &tensor) const override + void pull(neurun::backend::operand::ITensor &tensor) const override { - // TODO: This is just workaround codes, It needs to refactor. - if (typeid(tensor) == typeid(neurun::backend::cpu::operand::Tensor)) - { - const ::internal::nnapi::feature::Reader<float> from{_shape, tensor.buffer(), _size}; - ::internal::nnapi::feature::View<float> into{_shape, _base, _size}; - - ::nnfw::util::feature::iterate(_shape) - << [&](uint32_t bat, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(bat, ch, row, col); - into.at(bat, ch, row, col) = value; - }; - } - else if (typeid(tensor) == typeid(::arm_compute::CLTensor)) + // do NCHW_TO_NHWC permutation + auto input_buffer = tensor.buffer(); + auto rank = _shape.rank(); + + switch (rank) { - const ::internal::arm_compute::feature::View<float> from{&tensor}; - ::internal::nnapi::feature::View<float> into{_shape, _base, _size}; - - ::nnfw::util::feature::iterate(_shape) - << [&](uint32_t bat, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(bat, ch, row, col); - into.at(bat, ch, row, col) = value; - }; + case 0: + case 1: + { + memcpy(_output_buffer, input_buffer, _output_size); + break; + } + case 2: + { + auto matrix_shape = _shape.asMatrix(); + + for (auto h = 0; h < matrix_shape.H; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, 0}; + memcpy(_output_buffer + h * matrix_shape.W, input_buffer + tensor.calcOffset(coord), + matrix_shape.W * sizeof(float)); + } + break; + } + case 3: + { + const int32_t depth = _shape.dim(0); + const int32_t height = _shape.dim(1); + const int32_t width = _shape.dim(2); + + for (auto c = 0; c < depth; ++c) + { + for (auto h = 0; h < height; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, c}; + memcpy(_output_buffer + c * height * width + h * width, + input_buffer + tensor.calcOffset(coord), width * sizeof(float)); + } + } + break; + } + case 4: + { + auto feature = _shape.asFeature(); + + const util::feature::nchw::View<float> from{&tensor}; + util::feature::nhwc::View<float> into{feature, reinterpret_cast<float *>(_output_buffer), + _output_size}; + + ::nnfw::misc::feature::iterate(feature) + << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(batch, ch, row, col); + into.at(batch, ch, row, col) = value; + }; + break; + } + default: + throw "NYI"; + break; } } private: - const nnfw::util::feature::Shape _shape; - uint8_t *const _base; - const size_t _size; + uint8_t *_output_buffer; + const size_t _output_size; + const model::operand::Shape _shape; }; } // namespace exec } // namespace neurun -#endif // __INTERNAL_SINK_H__ +#endif // __NEURUN_EXEC_SINK_H__ diff --git a/runtimes/neurun/src/exec/Source.h b/runtimes/neurun/src/exec/Source.h index e7c2e80c4..169f8b386 100644 --- a/runtimes/neurun/src/exec/Source.h +++ b/runtimes/neurun/src/exec/Source.h @@ -19,105 +19,118 @@ #include <cassert> -#include <arm_compute/runtime/CL/CLTensor.h> - -#include <util/feature/Shape.h> -#include <util/feature/IndexIterator.h> - -#include "backend/cpu/operand/Tensor.h" // TODO Remove this dependency to backend -#include "internal/nnapi/feature/Reader.h" -#include "internal/nnapi/feature/View.h" - -#include "backend/acl_cl/feature/View.h" +#include "cpp14/memory.h" +#include "util/feature/nchw/View.h" +#include "util/feature/nhwc/Reader.h" +#include "util/feature/Coordinate4D.h" +#include <misc/feature/IndexIterator.h> namespace neurun { namespace exec { -struct Source +struct ISource { - virtual ~Source() = default; + virtual ~ISource() = default; - virtual void push(::arm_compute::ITensor &tensor) const = 0; + virtual void push(::neurun::backend::operand::ITensor &tensor) const = 0; }; -// -// VectorSource -// -class VectorSource final : public Source +template <typename T> class Source final : public ISource { public: - VectorSource(const int32_t vlen, const uint8_t *base, const size_t size) - : _vlen{vlen}, _base{base} - { - (void)size; // Workaround for unused variable in release mode - assert(size >= _vlen * sizeof(float)); - } + Source(const T *base, const size_t size) : _base{base}, _size{size} {} public: - void push(::arm_compute::ITensor &tensor) const override + void push(::neurun::backend::operand::ITensor &tensor) const override { - auto base = reinterpret_cast<const float *>(_base); - - for (int32_t n = 0; n < _vlen; ++n) - { - auto from = base + n; - auto into = reinterpret_cast<float *>(tensor.ptr_to_element(::arm_compute::Coordinates{n})); - - *into = *from; - } + memcpy(tensor.buffer(), _base, _size); } private: - const int32_t _vlen; - const uint8_t *const _base; + const T *const _base; + const size_t _size; }; -// -// FeatureSource -// -class FeatureSource final : public Source +class PermutateSource final : public ISource { public: - FeatureSource(const nnfw::util::feature::Shape &shape, const uint8_t *base, const size_t size) - : _shape{shape}, _base{base}, _size{size} + PermutateSource(const void *input_buffer, const size_t &input_size, + const model::operand::Shape &shape) + : _input_buffer{(uint8_t *)input_buffer}, _input_size{input_size}, _shape{shape} { - // DO NOTHING } public: - void push(::arm_compute::ITensor &tensor) const override + void push(neurun::backend::operand::ITensor &tensor) const override { - // TODO: This is just workaround codes, It needs to refactor. - if (typeid(tensor) == typeid(neurun::backend::cpu::operand::Tensor)) - { - const ::internal::nnapi::feature::Reader<float> from{_shape, _base, _size}; - ::internal::nnapi::feature::View<float> into{_shape, tensor.buffer(), _size}; - - ::nnfw::util::feature::iterate(_shape) - << [&](uint32_t bat, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(bat, ch, row, col); - into.at(bat, ch, row, col) = value; - }; - } - else if (typeid(tensor) == typeid(::arm_compute::CLTensor)) + // do NHWC_TO_NCHW permutation + auto output_buffer = tensor.buffer(); + auto rank = _shape.rank(); + + switch (rank) { - const ::internal::nnapi::feature::Reader<float> from{_shape, _base, _size}; - ::internal::arm_compute::feature::View<float> into{&tensor}; - - ::nnfw::util::feature::iterate(_shape) - << [&](uint32_t bat, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(bat, ch, row, col); - into.at(bat, ch, row, col) = value; - }; + case 0: + case 1: + { + memcpy(output_buffer, _input_buffer, _input_size); + break; + } + case 2: + { + auto matrix_shape = _shape.asMatrix(); + + for (auto h = 0; h < matrix_shape.H; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, 0}; + memcpy(output_buffer + tensor.calcOffset(coord), _input_buffer + h * matrix_shape.W, + matrix_shape.W * sizeof(float)); + } + break; + } + case 3: + { + const int32_t depth = _shape.dim(0); + const int32_t height = _shape.dim(1); + const int32_t width = _shape.dim(2); + + for (auto c = 0; c < depth; ++c) + { + for (auto h = 0; h < height; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, c}; + memcpy(output_buffer + tensor.calcOffset(coord), + _input_buffer + c * height * width + h * width, width * sizeof(float)); + } + } + break; + } + case 4: + { + auto feature = _shape.asFeature(); + + const util::feature::nhwc::Reader<float> from{ + feature, reinterpret_cast<const float *>(_input_buffer), _input_size}; + util::feature::nchw::View<float> into{&tensor}; + + ::nnfw::misc::feature::iterate(feature) + << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(batch, ch, row, col); + into.at(batch, ch, row, col) = value; + }; + break; + } + default: + throw "NYI"; + break; } } private: - const nnfw::util::feature::Shape _shape; - const uint8_t *const _base; - const size_t _size; + const uint8_t *_input_buffer; + const size_t _input_size; + const model::operand::Shape _shape; }; } // namespace exec diff --git a/runtimes/neurun/src/exec/interface/IFunction.h b/runtimes/neurun/src/exec/interface/IFunction.h new file mode 100644 index 000000000..b7a721d1d --- /dev/null +++ b/runtimes/neurun/src/exec/interface/IFunction.h @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_EXEC_I_FUNCTION_H__ +#define __NEURUN_EXEC_I_FUNCTION_H__ + +namespace neurun +{ +namespace exec +{ + +class IFunction +{ +public: + virtual ~IFunction() = default; + virtual void run() = 0; + virtual void prepare() {} +}; + +} // namespace exec +} // namespace neurun + +#endif // __NEURUN_EXEC_I_FUNCTION_H__ diff --git a/runtimes/neurun/src/frontend/compilation.cc b/runtimes/neurun/src/frontend/compilation.cc index a135edac5..9b0719f46 100644 --- a/runtimes/neurun/src/frontend/compilation.cc +++ b/runtimes/neurun/src/frontend/compilation.cc @@ -32,6 +32,11 @@ int ANeuralNetworksCompilation_create(ANeuralNetworksModel *model, return ANEURALNETWORKS_UNEXPECTED_NULL; } + if (!model->isFinished()) + { + return ANEURALNETWORKS_BAD_STATE; + } + std::shared_ptr<neurun::graph::Graph> internal; model->release(internal); diff --git a/runtimes/neurun/src/frontend/execution.cc b/runtimes/neurun/src/frontend/execution.cc index ff34921b7..5f1729b30 100644 --- a/runtimes/neurun/src/frontend/execution.cc +++ b/runtimes/neurun/src/frontend/execution.cc @@ -22,7 +22,122 @@ #include "frontend/wrapper/execution.h" #include "frontend/wrapper/event.h" -#include "graph/operand/Index.h" +#include "model/operand/DataType.h" +#include "model/operand/Index.h" +#include "graph/operand/Layout.h" +#include "backend/BackendManager.h" +#include "backend/interface/IConfig.h" +#include "compiler/BackendResolver.h" +#include "compiler/TensorInfo.h" +#include "backend/interface/operand/ITensor.h" + +inline void source(ANeuralNetworksExecution *execution, + const ::neurun::model::operand::DataType &type, int32_t index, + const void *buffer, size_t length) +{ + const auto &operands = execution->plan().model().operands(); + neurun::model::operand::IO::Index input_index{index}; + + const auto operand_index = execution->plan().model().getInputs().at(input_index); + auto operand = &operands.at(operand_index); + auto operand_li = operand->lower_info(); + const auto output_backend = operand_li->def_backends().getOnlyElement(); + const auto output_layout = output_backend->config()->getOperandLayout(); + auto input_layout = execution->plan() + .model() + .backend_resolver() + ->getDefaultBackend() + ->config() + ->getOperandLayout(); + if (input_layout == neurun::graph::operand::Layout::NHWC && + output_layout == neurun::graph::operand::Layout::NCHW) + { + const auto tensor_info = neurun::compiler::TensorInfo(operand->shape(), operand->typeInfo()); + + execution->source<::neurun::exec::PermutateSource>(index, buffer, tensor_info.total_size(), + operand->shape()); + return; + } + using ::neurun::model::operand::DataType; + switch (type) + { + case DataType::SCALAR_FLOAT32: + case DataType::TENSOR_FLOAT32: + execution->source<::neurun::exec::Source<float>>( + index, reinterpret_cast<const float *>(buffer), length); + break; + case DataType::SCALAR_INT32: + case DataType::TENSOR_INT32: + execution->source<::neurun::exec::Source<int32_t>>( + index, reinterpret_cast<const int32_t *>(buffer), length); + break; + case DataType::SCALAR_UINT32: + execution->source<::neurun::exec::Source<uint32_t>>( + index, reinterpret_cast<const uint32_t *>(buffer), length); + break; + case DataType::TENSOR_QUANT8_ASYMM: + execution->source<::neurun::exec::Source<uint8_t>>( + index, reinterpret_cast<const uint8_t *>(buffer), length); + break; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +inline void sink(ANeuralNetworksExecution *execution, + const ::neurun::model::operand::DataType &type, int32_t index, void *buffer, + size_t length) +{ + const auto &operands = execution->plan().model().operands(); + neurun::model::operand::IO::Index input_index{index}; + + const auto operand_index = execution->plan().model().getOutputs().at(input_index); + auto operand = &operands.at(operand_index); + auto operand_li = operand->lower_info(); + const auto input_backend = operand_li->def_backends().getOnlyElement(); + const auto input_layout = input_backend->config()->getOperandLayout(); + auto output_layout = execution->plan() + .model() + .backend_resolver() + ->getDefaultBackend() + ->config() + ->getOperandLayout(); + if (input_layout == neurun::graph::operand::Layout::NCHW && + output_layout == neurun::graph::operand::Layout::NHWC) + { + const auto tensor_info = neurun::compiler::TensorInfo(operand->shape(), operand->typeInfo()); + + execution->sink<::neurun::exec::PermutateSink>(index, buffer, tensor_info.total_size(), + operand->shape()); + return; + } + using ::neurun::model::operand::DataType; + switch (type) + { + case DataType::SCALAR_FLOAT32: + case DataType::TENSOR_FLOAT32: + execution->sink<::neurun::exec::Sink<float>>(index, reinterpret_cast<float *>(buffer), + length); + break; + case DataType::SCALAR_INT32: + case DataType::TENSOR_INT32: + execution->sink<::neurun::exec::Sink<int32_t>>(index, reinterpret_cast<int32_t *>(buffer), + length); + break; + case DataType::SCALAR_UINT32: + execution->sink<::neurun::exec::Sink<uint32_t>>(index, reinterpret_cast<uint32_t *>(buffer), + length); + break; + case DataType::TENSOR_QUANT8_ASYMM: + execution->sink<::neurun::exec::Sink<uint8_t>>(index, reinterpret_cast<uint8_t *>(buffer), + length); + break; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} // // NNAPI Implementation @@ -35,7 +150,13 @@ int ANeuralNetworksExecution_create(ANeuralNetworksCompilation *compilation, return ANEURALNETWORKS_UNEXPECTED_NULL; } - std::shared_ptr<const neurun::codegen::Plan> plan; + // Can handle compiled state only + if (compilation->plan().state() != neurun::compiler::State::COMPILED) + { + return ANEURALNETWORKS_BAD_STATE; + } + + std::shared_ptr<const neurun::compiler::Plan> plan; compilation->publish(plan); @@ -61,36 +182,23 @@ int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution *execution, int32 return ANEURALNETWORKS_UNEXPECTED_NULL; } + // TODO Handle optional input + if (buffer == nullptr) + { + throw std::runtime_error("Not supported optional input, yet"); + } + const auto &operands = execution->plan().model().operands(); // TODO Check type conflicts - // NOTE The current implemenation assumes that every input is a feature map. - // TODO Remove this assumption - neurun::graph::operand::IO::Index input_index{index}; + neurun::model::operand::IO::Index input_index{index}; const auto operand_index = execution->plan().model().getInputs().at(input_index); + const auto data_type = operands.at(operand_index).typeInfo().type(); + const auto operand_shape = operands.at(operand_index).shape(); - if (operands.at(operand_index).shape().rank() == 2) - { - assert(operands.at(operand_index).shape().dim(0) == 1); - - const auto len = operands.at(operand_index).shape().dim(1); - - execution->source<neurun::exec::VectorSource>( - index, len, reinterpret_cast<const uint8_t *>(buffer), length); - } - else if (operands.at(operand_index).shape().rank() == 4) - { - const auto &operand_shape = operands.at(operand_index).shape().asFeature(); - - execution->source<neurun::exec::FeatureSource>( - index, operand_shape, reinterpret_cast<const uint8_t *>(buffer), length); - } - else - { - throw std::runtime_error{"Not supported, yet"}; - } + source(execution, data_type, index, buffer, length); return ANEURALNETWORKS_NO_ERROR; } @@ -108,36 +216,23 @@ int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution *execution, int3 return ANEURALNETWORKS_UNEXPECTED_NULL; } + // Handle optional output + if (buffer == nullptr) + { + return ANEURALNETWORKS_NO_ERROR; + } + const auto &operands = execution->plan().model().operands(); // TODO Check type conflicts - // NOTE The current implemenation assumes that every output is a feature map. - // TODO Remove this assumption - neurun::graph::operand::IO::Index output_index{index}; + neurun::model::operand::IO::Index output_index{index}; const auto operand_index = execution->plan().model().getOutputs().at(output_index); + const auto data_type = operands.at(operand_index).typeInfo().type(); + const auto operand_shape = operands.at(operand_index).shape(); - if (operands.at(operand_index).shape().rank() == 2) - { - assert(operands.at(operand_index).shape().dim(0) == 1); - - const auto len = operands.at(operand_index).shape().dim(1); - - execution->sink<neurun::exec::VectorSink>(index, len, reinterpret_cast<uint8_t *>(buffer), - length); - } - else if (operands.at(operand_index).shape().rank() == 4) - { - const auto &operand_shape = operands.at(operand_index).shape().asFeature(); - - execution->sink<neurun::exec::FeatureSink>(index, operand_shape, - reinterpret_cast<uint8_t *>(buffer), length); - } - else - { - throw std::runtime_error{"Not supported, yet"}; - } + sink(execution, data_type, index, buffer, length); return ANEURALNETWORKS_NO_ERROR; } @@ -163,17 +258,16 @@ int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution, // Set input(s) for (uint32_t n = 0; n < model.getInputs().size(); ++n) { - auto setter = [&](::arm_compute::ITensor &tensor) { execution->source(n).push(tensor); }; + auto setter = [&](::neurun::backend::operand::ITensor &tensor) { + execution->source(n).push(tensor); + }; - neurun::graph::operand::IO::Index input_index{n}; + neurun::model::operand::IO::Index input_index{n}; - ::neurun::graph::operand::Index index{model.getInputs().at(input_index)}; - auto objects = plan.operands().at(index); + ::neurun::model::operand::Index index{model.getInputs().at(input_index)}; + auto object = plan.operands().at(index); - for (auto object : objects) - { - object->access(setter); - } + object->access(setter); } const auto &operations = execution->plan().operations(); @@ -186,17 +280,16 @@ int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution, // Get output(s) for (uint32_t n = 0; n < model.getOutputs().size(); ++n) { - auto getter = [&](::arm_compute::ITensor &tensor) { execution->sink(n).pull(tensor); }; + auto getter = [&](::neurun::backend::operand::ITensor &tensor) { + execution->sink(n).pull(tensor); + }; - neurun::graph::operand::IO::Index output_index{n}; + neurun::model::operand::IO::Index output_index{n}; - ::neurun::graph::operand::Index index{model.getOutputs().at(output_index)}; - auto objects = plan.operands().at(index); + ::neurun::model::operand::Index index{model.getOutputs().at(output_index)}; + auto object = plan.operands().at(index); - for (auto object : objects) - { - object->access(getter); - } + object->access(getter); } return ANEURALNETWORKS_NO_ERROR; diff --git a/runtimes/neurun/src/frontend/memory.cc b/runtimes/neurun/src/frontend/memory.cc index cc891feef..b2f6ab2d0 100644 --- a/runtimes/neurun/src/frontend/memory.cc +++ b/runtimes/neurun/src/frontend/memory.cc @@ -19,7 +19,7 @@ #include <new> #include <memory> -#include "nnfw/std/memory.h" +#include "cpp14/memory.h" #include "frontend/wrapper/memory.h" int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, @@ -32,7 +32,7 @@ int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t // Use unique pointer to avoid memory leak std::unique_ptr<ANeuralNetworksMemory> memory_ptr = - nnfw::make_unique<ANeuralNetworksMemory>(size, protect, fd, offset); + nnfw::cpp14::make_unique<ANeuralNetworksMemory>(size, protect, fd, offset); if (memory_ptr == nullptr) { return ANEURALNETWORKS_OUT_OF_MEMORY; diff --git a/runtimes/neurun/src/frontend/model.cc b/runtimes/neurun/src/frontend/model.cc index 28a9b2515..3aa2aa2ff 100644 --- a/runtimes/neurun/src/frontend/model.cc +++ b/runtimes/neurun/src/frontend/model.cc @@ -21,18 +21,12 @@ #include <stdexcept> #include <new> -#include "nnfw/std/memory.h" +#include "cpp14/memory.h" #include "graph/Graph.h" #include "frontend/wrapper/model.h" #include "frontend/wrapper/memory.h" -#include "graph/operation/AvgPool2D.h" -#include "graph/operation/Concat.h" -#include "graph/operation/Conv2D.h" -#include "graph/operation/FullyConnected.h" -#include "graph/operation/MaxPool2D.h" -#include "graph/operation/Reshape.h" -#include "graph/operation/Softmax.h" +#include "model/operation/Node.Include.h" int ANeuralNetworksModel_create(ANeuralNetworksModel **model) { @@ -94,8 +88,8 @@ int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model, return ANEURALNETWORKS_BAD_DATA; } - ::neurun::graph::operand::Shape shape(type->dimensionCount); - ::neurun::graph::operand::TypeInfo typeInfo((OperandCode)(type->type), type->scale, + ::neurun::model::operand::Shape shape(type->dimensionCount); + ::neurun::model::operand::TypeInfo typeInfo((OperandCode)(type->type), type->scale, type->zeroPoint); for (uint32_t axis = 0; axis < type->dimensionCount; ++axis) @@ -115,6 +109,8 @@ int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model, int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t index, const void *buffer, size_t length) { + const bool isOptional = ((buffer == nullptr) && (length == 0)); + if ((model == nullptr) || ((buffer == nullptr) && (length != 0))) { return ANEURALNETWORKS_UNEXPECTED_NULL; @@ -130,7 +126,7 @@ int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t in { return ANEURALNETWORKS_BAD_DATA; } - const neurun::graph::operand::Index ind{static_cast<uint32_t>(index)}; + const neurun::model::operand::Index ind{static_cast<uint32_t>(index)}; if (!model->deref().operands().exist(ind)) { @@ -138,7 +134,7 @@ int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t in } auto &obj = model->deref().operands().at(ind); - if (obj.operandSize() != length) + if ((obj.operandSize() != length) && !isOptional) { return ANEURALNETWORKS_BAD_DATA; } @@ -147,10 +143,30 @@ int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t in return ANEURALNETWORKS_BAD_DATA; } - using ::neurun::graph::operand::CachedData; + using ::neurun::model::operand::CachedData; + using ::neurun::model::operand::ExternalData; - model->deref().setOperandValue( - ind, nnfw::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(buffer), length)); + // Remain operands.at(ind).data()->base() as nullptr for optional operand + // This will be filled when model finished + if (isOptional) + { + model->setOptionalOperand(ind); + } + + // NNAPI spec in NeuralNetworks.h + // For values of length greater than ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES, + // the application is responsible for not changing the content of this region + // until all executions using this model have completed + if (length <= ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES) + { + model->deref().setOperandValue(ind, nnfw::cpp14::make_unique<CachedData>( + reinterpret_cast<const uint8_t *>(buffer), length)); + } + else + { + model->deref().setOperandValue(ind, nnfw::cpp14::make_unique<ExternalData>( + reinterpret_cast<const uint8_t *>(buffer), length)); + } return ANEURALNETWORKS_NO_ERROR; } @@ -174,7 +190,7 @@ int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel *model, { return ANEURALNETWORKS_BAD_DATA; } - const neurun::graph::operand::Index ind{static_cast<uint32_t>(index)}; + const neurun::model::operand::Index ind{static_cast<uint32_t>(index)}; if (!model->deref().operands().exist(ind)) { @@ -191,10 +207,10 @@ int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel *model, return ANEURALNETWORKS_BAD_DATA; } - using ::neurun::graph::operand::ExternalData; + using ::neurun::model::operand::ExternalData; model->deref().setOperandValue( - ind, nnfw::make_unique<ExternalData>( + ind, nnfw::cpp14::make_unique<ExternalData>( reinterpret_cast<const uint8_t *>(memory->base() + offset), length)); return ANEURALNETWORKS_NO_ERROR; @@ -210,6 +226,13 @@ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, return ANEURALNETWORKS_UNEXPECTED_NULL; } + const ANeuralNetworksOperationType FIRST_OPERATION = ANEURALNETWORKS_ADD; + const ANeuralNetworksOperationType LAST_OPERATION = ANEURALNETWORKS_TRANSPOSE; + if ((type < FIRST_OPERATION) || (type > LAST_OPERATION)) + { + return ANEURALNETWORKS_BAD_DATA; + } + if (model->isFinished()) { return ANEURALNETWORKS_BAD_STATE; @@ -217,7 +240,7 @@ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, for (uint32_t i = 0; i < outputCount; i++) { - const ::neurun::graph::operand::Index ind{outputs[i]}; + const ::neurun::model::operand::Index ind{outputs[i]}; auto &obj = model->deref().operands().at(ind); if (!obj.setAsOperationOutput()) @@ -229,108 +252,115 @@ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, auto &graph = model->deref(); auto node_param = - neurun::graph::operation::Node::InitParam{inputCount, inputs, outputCount, outputs}; + neurun::model::operation::Node::InitParam{inputCount, inputs, outputCount, outputs}; - switch (type) + try { - case ANEURALNETWORKS_CONV_2D: + switch (type) { - // inputCount is either 7 or 10 acccording to NN API specification. - // - Padding is implicit when inputCount is 7 - // - Padding is explicit when inputCount is 10 - assert(inputCount == 7 || inputCount == 10); - assert(outputCount == 1); - - if (inputCount == 7) + case ANEURALNETWORKS_CONV_2D: { - using GraphNode = neurun::graph::operation::Conv2D::Implicit::Node; - - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + // inputCount is either 7 or 10 acccording to NN API specification. + // - Padding is implicit when inputCount is 7 + // - Padding is explicit when inputCount is 10 + assert(inputCount == 7 || inputCount == 10); + assert(outputCount == 1); + + if (inputCount == 7) + { + using GraphNode = neurun::model::operation::Conv2DNode; + + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); + } + else + { + throw std::runtime_error{"Explicit padding in Conv2D is not supported, yet"}; + } + + break; } - else + case ANEURALNETWORKS_MAX_POOL_2D: { - throw std::runtime_error{"Explicit padding in Conv2D is not supported, yet"}; + // inputCount is either 7 or 10 acccording to NN API specification. + // - Padding is implicit when inputCount is 7 + // - Padding is explicit when inputCount is 10 + assert(inputCount == 7 || inputCount == 10); + assert(outputCount == 1); + + if (inputCount == 7) + { + using GraphNode = neurun::model::operation::MaxPool2DNode; + + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); + } + else + { + throw std::runtime_error{"Explicit padding in MaxPool2D is not supported, yet"}; + } + + break; } - - break; - } - case ANEURALNETWORKS_MAX_POOL_2D: - { - // inputCount is either 7 or 10 acccording to NN API specification. - // - Padding is implicit when inputCount is 7 - // - Padding is explicit when inputCount is 10 - assert(inputCount == 7 || inputCount == 10); - assert(outputCount == 1); - - if (inputCount == 7) + case ANEURALNETWORKS_AVERAGE_POOL_2D: { - using GraphNode = neurun::graph::operation::MaxPool2D::Implicit::Node; - - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + // inputCount is either 7 or 10 acccording to NN API specification. + // - Padding is implicit when inputCount is 7 + // - Padding is explicit when inputCount is 10 + assert(inputCount == 7 || inputCount == 10); + assert(outputCount == 1); + + if (inputCount == 7) + { + using GraphNode = neurun::model::operation::AvgPool2DNode; + + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); + } + else + { + throw std::runtime_error{"Explicit padding in AvgPool2D is not supported, yet"}; + } + + break; } - else + case ANEURALNETWORKS_CONCATENATION: { - throw std::runtime_error{"Explicit padding in MaxPool2D is not supported, yet"}; - } - - break; - } - case ANEURALNETWORKS_AVERAGE_POOL_2D: - { - // inputCount is either 7 or 10 acccording to NN API specification. - // - Padding is implicit when inputCount is 7 - // - Padding is explicit when inputCount is 10 - assert(inputCount == 7 || inputCount == 10); - assert(outputCount == 1); + using GraphNode = neurun::model::operation::ConcatNode; - if (inputCount == 7) - { - using GraphNode = neurun::graph::operation::AvgPool2D::Implicit::Node; + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + break; } - else + case ANEURALNETWORKS_RESHAPE: { - throw std::runtime_error{"Explicit padding in AvgPool2D is not supported, yet"}; - } - - break; - } - case ANEURALNETWORKS_CONCATENATION: - { - using GraphNode = neurun::graph::operation::Concat::Node; + using GraphNode = neurun::model::operation::ReshapeNode; - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); - break; - } - case ANEURALNETWORKS_RESHAPE: - { - using GraphNode = neurun::graph::operation::Reshape::Node; - - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); - - break; - } - case ANEURALNETWORKS_FULLY_CONNECTED: - { - using GraphNode = neurun::graph::operation::FullyConnected::Node; + break; + } + case ANEURALNETWORKS_FULLY_CONNECTED: + { + using GraphNode = neurun::model::operation::FullyConnectedNode; - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); - break; - } - case ANEURALNETWORKS_SOFTMAX: - { - using GraphNode = neurun::graph::operation::Softmax::Node; + break; + } + case ANEURALNETWORKS_SOFTMAX: + { + using GraphNode = neurun::model::operation::SoftmaxNode; - graph.addOperation(nnfw::make_unique<GraphNode>(node_param)); + graph.addOperation(nnfw::cpp14::make_unique<GraphNode>(node_param)); - break; - } - default: - throw std::runtime_error{"Not supported operation"}; - }; + break; + } + default: + throw std::runtime_error{"Not supported operation"}; + }; + } + catch (const std::exception &e) + { + return ANEURALNETWORKS_BAD_STATE; + } return ANEURALNETWORKS_NO_ERROR; } @@ -350,9 +380,16 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, return ANEURALNETWORKS_BAD_STATE; } + const ANeuralNetworksOperationTypeEx FIRST_OPERATION = ANEURALNETWORKS_GATHER_EX; + const ANeuralNetworksOperationTypeEx LAST_OPERATION = ANEURALNETWORKS_PRELU_EX; + if ((type < FIRST_OPERATION) || (type > LAST_OPERATION)) + { + return ANEURALNETWORKS_BAD_DATA; + } + for (uint32_t i = 0; i < outputCount; i++) { - const ::neurun::graph::operand::Index ind{outputs[i]}; + const ::neurun::model::operand::Index ind{outputs[i]}; auto &obj = model->deref().operands().at(ind); if (!obj.setAsOperationOutput()) @@ -367,11 +404,20 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, return ANEURALNETWORKS_BAD_DATA; } - switch (type) + try { - default: - throw std::runtime_error{"Not supported operation"}; + switch (type) + { + default: + throw std::runtime_error{"Not supported operation"}; + } } + catch (const std::exception &e) + { + return ANEURALNETWORKS_BAD_STATE; + } + + return ANEURALNETWORKS_NO_ERROR; } int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, uint32_t inputCount, @@ -388,7 +434,7 @@ int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, u return ANEURALNETWORKS_BAD_STATE; } - // NOTE ::neurun::graph::operand::Index uses int as its underlying type as various NNAPI + // NOTE ::neurun::model::operand::Index uses int as its underlying type as various NNAPI // functions such as ANeuralNetworksModel_setOperandValue use int to represent operand index // // ANeuralNetworksModel_identifyInputsAndOutputs, however, uses uint32_t to represent operand @@ -397,7 +443,7 @@ int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, u // Below, static_cast<int>(...) is introduced to eliminate compiler warning. for (uint32_t n = 0; n < inputCount; ++n) { - const neurun::graph::operand::Index ind{static_cast<uint32_t>(inputs[n])}; + const neurun::model::operand::Index ind{static_cast<uint32_t>(inputs[n])}; model->deref().addInput(ind); auto &obj = model->deref().operands().at(ind); @@ -409,7 +455,7 @@ int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, u for (uint32_t n = 0; n < outputCount; ++n) { - const neurun::graph::operand::Index ind{static_cast<uint32_t>(outputs[n])}; + const neurun::model::operand::Index ind{static_cast<uint32_t>(outputs[n])}; model->deref().addOutput(ind); auto &obj = model->deref().operands().at(ind); diff --git a/runtimes/neurun/src/frontend/wrapper/compilation.cc b/runtimes/neurun/src/frontend/wrapper/compilation.cc index 4ff33faa5..e4aa99f7a 100644 --- a/runtimes/neurun/src/frontend/wrapper/compilation.cc +++ b/runtimes/neurun/src/frontend/wrapper/compilation.cc @@ -14,53 +14,18 @@ * limitations under the License. */ -#include <NeuralNetworks.h> - -#include <algorithm> - -#include <arm_compute/core/CL/ICLTensor.h> - -#include <arm_compute/runtime/IFunction.h> -#include <arm_compute/runtime/CL/CLScheduler.h> - -#include "internal/Convert.h" -#include "backend/acl_cl/kernel/View.h" -#include "backend/acl_cl/TensorBuilder.h" -#include "internal/nnapi/kernel/Reader.h" -#include "internal/Padding.h" -#include "backend/IInitializerGenerator.h" -#include "backend/IStageGenerator.h" - #include "compilation.h" -#include "model.h" -#include "logging.h" - -#include "graph/dumper/Dumper.h" -#include "codegen/IPlanBuilder.h" -#include "codegen/Planner.h" -#include "codegen/PlanBuilder.h" - -#include "linear/Linear.h" int ANeuralNetworksCompilation::finish() { - auto &plan = this->plan(); - const auto &operands = plan.model().operands(); - - plan.model().lower(); - auto linear = plan.model().linearize(); - - // Dump ops - linear->accept(neurun::graph::dumper::Dumper{}); - - neurun::codegen::PlanBuilder plan_builder{plan}; - - auto tensor_builders = linear->markTensors(); - - linear->accept(neurun::codegen::Planner{operands, plan_builder}); - - // TODO Add optimization passes - plan_builder.finalize(tensor_builders); + try + { + _compiler->compile(); + } + catch (const std::exception &e) + { + return ANEURALNETWORKS_BAD_STATE; + } return ANEURALNETWORKS_NO_ERROR; } diff --git a/runtimes/neurun/src/frontend/wrapper/compilation.h b/runtimes/neurun/src/frontend/wrapper/compilation.h index 20d5a3d98..d4ba32ea5 100644 --- a/runtimes/neurun/src/frontend/wrapper/compilation.h +++ b/runtimes/neurun/src/frontend/wrapper/compilation.h @@ -17,27 +17,27 @@ #ifndef __COMPILATION_H__ #define __COMPILATION_H__ -#include "codegen/Plan.h" +#include "compiler/Compiler.h" #include "graph/Graph.h" struct ANeuralNetworksCompilation { public: ANeuralNetworksCompilation(const std::shared_ptr<neurun::graph::Graph> &model) - : _plan{new neurun::codegen::Plan{model}} + : _compiler{new neurun::compiler::Compiler{model}} { // DO NOTHING } public: - neurun::codegen::Plan &plan(void) { return *_plan; } + neurun::compiler::Plan &plan(void) { return _compiler->plan(); } public: - void publish(std::shared_ptr<const neurun::codegen::Plan> &plan) { plan = _plan; } + void publish(std::shared_ptr<const neurun::compiler::Plan> &plan) { _compiler->release(plan); } int finish(); private: - std::shared_ptr<neurun::codegen::Plan> _plan; + std::shared_ptr<neurun::compiler::Compiler> _compiler; }; #endif diff --git a/runtimes/neurun/src/frontend/wrapper/execution.h b/runtimes/neurun/src/frontend/wrapper/execution.h index 374201eb2..b68a7b967 100644 --- a/runtimes/neurun/src/frontend/wrapper/execution.h +++ b/runtimes/neurun/src/frontend/wrapper/execution.h @@ -17,28 +17,28 @@ #ifndef __EXECUTION_H__ #define __EXECUTION_H__ -#include "codegen/Plan.h" +#include "compiler/Plan.h" #include "exec/Source.h" #include "exec/Sink.h" struct ANeuralNetworksExecution { public: - ANeuralNetworksExecution(const std::shared_ptr<const neurun::codegen::Plan> &plan) : _plan{plan} + ANeuralNetworksExecution(const std::shared_ptr<const neurun::compiler::Plan> &plan) : _plan{plan} { _sources.resize(_plan->model().getInputs().size()); _sinks.resize(_plan->model().getOutputs().size()); } public: - const neurun::codegen::Plan &plan(void) const { return *_plan; } + const neurun::compiler::Plan &plan(void) const { return *_plan; } private: - std::shared_ptr<const neurun::codegen::Plan> _plan; + std::shared_ptr<const neurun::compiler::Plan> _plan; public: // TODO Use InputIndex instead of int - void source(int n, std::unique_ptr<neurun::exec::Source> &&source) + void source(int n, std::unique_ptr<neurun::exec::ISource> &&source) { _sources.at(n) = std::move(source); } @@ -48,22 +48,22 @@ public: } public: - const neurun::exec::Source &source(int n) const { return *(_sources.at(n)); } + const neurun::exec::ISource &source(int n) const { return *(_sources.at(n)); } public: // TODO Use OutputIndex instead of int - void sink(int n, std::unique_ptr<neurun::exec::Sink> &&sink) { _sinks.at(n) = std::move(sink); } + void sink(int n, std::unique_ptr<neurun::exec::ISink> &&sink) { _sinks.at(n) = std::move(sink); } template <typename T, typename... Args> void sink(int n, Args &&... args) { sink(n, std::unique_ptr<T>{new T{std::forward<Args>(args)...}}); } public: - const neurun::exec::Sink &sink(int n) const { return *(_sinks.at(n)); } + const neurun::exec::ISink &sink(int n) const { return *(_sinks.at(n)); } private: - std::vector<std::unique_ptr<neurun::exec::Source>> _sources; - std::vector<std::unique_ptr<neurun::exec::Sink>> _sinks; + std::vector<std::unique_ptr<neurun::exec::ISource>> _sources; + std::vector<std::unique_ptr<neurun::exec::ISink>> _sinks; }; #endif diff --git a/runtimes/neurun/src/frontend/wrapper/model.cc b/runtimes/neurun/src/frontend/wrapper/model.cc index c7ccbc60a..a7a9275fc 100644 --- a/runtimes/neurun/src/frontend/wrapper/model.cc +++ b/runtimes/neurun/src/frontend/wrapper/model.cc @@ -21,7 +21,8 @@ // // ANeuralNetworksModel // -ANeuralNetworksModel::ANeuralNetworksModel() : _model{new neurun::graph::Graph} +ANeuralNetworksModel::ANeuralNetworksModel() + : _model{new neurun::graph::Graph}, _optional_operands{} { // DO NOTHING } @@ -34,7 +35,24 @@ ResultCode ANeuralNetworksModel::finish() return ANEURALNETWORKS_BAD_STATE; } + fillOptionalOperand(); + _model->finishBuilding(); return ANEURALNETWORKS_NO_ERROR; } + +void ANeuralNetworksModel::fillOptionalOperand(void) +{ + _model->operations().iterate( + [&](const ::neurun::model::operation::Index &, ::neurun::model::operation::Node &node) { + for (auto input : node.getInputs()) + { + // TODO fill default value for optional operands + if (_optional_operands.find(input) != _optional_operands.end()) + { + throw std::runtime_error{"Optional operand is not supported yet"}; + } + } + }); +} diff --git a/runtimes/neurun/src/frontend/wrapper/model.h b/runtimes/neurun/src/frontend/wrapper/model.h index 3c7b027dc..2386a648d 100644 --- a/runtimes/neurun/src/frontend/wrapper/model.h +++ b/runtimes/neurun/src/frontend/wrapper/model.h @@ -30,12 +30,18 @@ public: neurun::graph::Graph &deref(void) { return *_model; } ResultCode finish(); bool isFinished() { return !_model->isBuildingPhase(); } - -public: void release(std::shared_ptr<neurun::graph::Graph> &model) { model = _model; } + void setOptionalOperand(const neurun::model::operand::Index idx) + { + _optional_operands.insert(idx); + } + +private: + void fillOptionalOperand(void); private: std::shared_ptr<neurun::graph::Graph> _model; + std::unordered_set<neurun::model::operand::Index> _optional_operands; }; #endif // __MODEL_H__ diff --git a/runtimes/neurun/src/graph/Graph.cc b/runtimes/neurun/src/graph/Graph.cc index 07194ff7e..832e2b887 100644 --- a/runtimes/neurun/src/graph/Graph.cc +++ b/runtimes/neurun/src/graph/Graph.cc @@ -19,116 +19,62 @@ #include <algorithm> #include <bitset> -#include "logging.h" -#include "verifier/IVerifier.h" -#include "nnfw/std/memory.h" +#include "util/logging.h" +#include "verifier/Verifier.h" +#include "cpp14/memory.h" #include "linear/Linear.h" #include "operation/LowerInfo.h" #include "operand/LowerInfo.h" #include "operand/Shape4DConvert.h" -#include "codegen/BackendResolver.h" -#include "backend/IBackendConfig.h" +#include "compiler/BackendResolver.h" +#include "backend/interface/IConfig.h" +#include "pass/PermutationInsertionPass.h" +#include "pass/PermutationEliminationPass.h" namespace neurun { namespace graph { -operand::Index Graph::addOperand(const operand::Shape &shape, const operand::TypeInfo &type) -{ - return _operands.append(shape, type); -} +Graph::Graph(void) = default; -operation::Index Graph::addOperation(std::unique_ptr<operation::Node> &&node) +Graph::~Graph(void) = default; + +model::operand::Index Graph::addOperand(const model::operand::Shape &shape, + const model::operand::TypeInfo &type) { - assert(_phase == Phase::BUILDING); - return _operations.append(std::move(node)); + return _model->operands.append(shape, type); } -// TODO : If operand's use-def information is introduced, -// Following API and implements would be refactored. -/** - * @brief Insert operation into between an operand and next operation. - * - * @param prev_operand_index is an previous operand index of insertion. - * @param next_operation_index is an next operation index of insertion. - * @param node is an operation::Node to insert. - * - * @return operation::Index - */ -operation::Index Graph::insertOperation(const operand::Index &prev_operand_index, - const operation::Index &next_operation_index, - std::unique_ptr<operation::Node> &&node) +model::operation::Index Graph::addOperation(std::unique_ptr<model::operation::Node> &&node) { - assert(_phase != Phase::BUILDING); - auto &next_operation = _operations.at(next_operation_index); - auto next_input_indexes = next_operation.getInputs(); - - assert(next_input_indexes.contains(prev_operand_index)); - assert(node->getInputs().size() == 0); // node to be inserted must not have any inputs - - node->setInputs({prev_operand_index}); - - // For multi input operation (ex. concat) - operand::IndexSet index_set; - auto cur_output_indexes = node->getOutputs(); - assert(cur_output_indexes.size() == 1); // Assume output of inserted node size always 1 - auto cur_output_index = cur_output_indexes.at(operand::IO::Index{0}); - // TODO : If the API for setting input one by one is introduced, it would be changed to simple. - for (auto next_input_index : next_input_indexes) - { - if (prev_operand_index == next_input_index) - { - index_set.append(cur_output_index); - } - else - { - index_set.append(next_input_index); - } - } - - next_operation.setInputs(index_set); - - operation::Index node_index = _operations.append(std::move(node)); - - // Update Use/Def info - { - _operands.at(prev_operand_index).removeUse(next_operation_index); - _operands.at(cur_output_index).appendUse(next_operation_index); - - _operands.at(prev_operand_index).appendUse(node_index); - - auto node_output_indexes = _operations.at(node_index).getOutputs(); - assert(node_output_indexes.size() == 1); - auto node_output_index = node_output_indexes.at(operand::IO::Index{0}); - _operands.at(node_output_index).appendDef(node_index); - } - - return node_index; + assert(isBuildingPhase()); + return _model->operations.append(std::move(node)); } -void Graph::setOperandValue(const operand::Index &ind, std::unique_ptr<operand::Data> &&data) +void Graph::setOperandValue(const model::operand::Index &ind, + std::unique_ptr<model::operand::Data> &&data) { - assert(_phase == Phase::BUILDING); - assert(_operands.exist(ind)); - _operands.at(ind).data(std::move(data)); + assert(isBuildingPhase()); + assert(_model->operands.exist(ind)); + _model->operands.at(ind).data(std::move(data)); } -void Graph::addInput(const operand::Index &ind) +void Graph::addInput(const model::operand::Index &ind) { - assert(_phase == Phase::BUILDING); - _inputs.append(ind); + assert(isBuildingPhase()); + _model->inputs.append(ind); } -void Graph::addOutput(const operand::Index &ind) +void Graph::addOutput(const model::operand::Index &ind) { - assert(_phase == Phase::BUILDING); - _outputs.append(ind); + assert(isBuildingPhase()); + _model->outputs.append(ind); } void Graph::finishBuilding(void) { - assert(_phase == Phase::BUILDING); + assert(isBuildingPhase()); _phase = Phase::MODEL; // Initialize operand use-def @@ -136,8 +82,8 @@ void Graph::finishBuilding(void) // Call graph verifications for the MODEL phase { - verifier::DAGChecker dag_checker; - dag_checker.verify(*this); + assert(verifier::DAGChecker().verify(*this)); + assert(verifier::EdgeConsistencyChecker().verify(*this)); } } @@ -148,106 +94,174 @@ void Graph::lower(void) // Lower { // operand::LowerInfo holder - std::unordered_map<operand::Index, std::unique_ptr<operand::LowerInfo>> operands_lower_info; + std::unordered_map<model::operand::Index, std::unique_ptr<operand::LowerInfo>> + operands_lower_info; - _operands.iterate([&](const operand::Index &index, const operand::Object &object) { + _model->operands.iterate([&](const model::operand::Index &index, + const model::operand::Object &object) { operands_lower_info[index] = - nnfw::make_unique<operand::LowerInfo>(operand::asShape4D(object.shape())); + nnfw::cpp14::make_unique<operand::LowerInfo>(graph::operand::asShape4D(object.shape())); }); - auto _backend_resolver = codegen::BackendResolver(_operands); + _backend_resolver = nnfw::cpp14::make_unique<compiler::BackendResolver>(_model->operands); + + _model->operations.iterate( + [&](const model::operation::Index &index, model::operation::Node &node) { + auto backend = _backend_resolver->getBackend(typeid(node)); + + // Operation LowerInfo + setLowerInfo(index, nnfw::cpp14::make_unique<graph::operation::LowerInfo>(backend)); - _operations.iterate([&](const operation::Index &, operation::Node &node) { - auto backend = _backend_resolver.getBackend(typeid(node)); + // LowerInfo for in/output operands + for (auto operand : node.getInputs()) + { + auto &&lower_info = operands_lower_info.at(operand); + lower_info->addUseBackend(backend); + } + for (auto operand : node.getOutputs()) + { + auto &&lower_info = operands_lower_info.at(operand); + lower_info->addDefBackend(backend); + } + }); + + // Add def backend to model input/output operand as default backend + for (auto index : getInputs()) + { + auto &&lower_info = operands_lower_info.at(index); + lower_info->addDefBackend(_backend_resolver->getDefaultBackend()); + } - // Operation LowerInfo - node.lower_info(nnfw::make_unique<operation::LowerInfo>(backend)); + for (auto index : getOutputs()) + { + auto &&lower_info = operands_lower_info.at(index); + lower_info->addUseBackend(_backend_resolver->getDefaultBackend()); + } - // LowerInfo for in/output operands + // Add DefBackend constants same as UseBackend + // NOTE This assumes a constant operand is used by only one operation + _model->operations.iterate([&](const model::operation::Index &, model::operation::Node &node) { + // LowerInfo for input operands for (auto operand : node.getInputs()) { auto &&lower_info = operands_lower_info.at(operand); - lower_info->addUseLayout(backend.config()->getOperandLayout()); - } - for (auto operand : node.getOutputs()) - { - auto &&lower_info = operands_lower_info.at(operand); - lower_info->addDefLayout(backend.config()->getOperandLayout()); + if (lower_info->def_backends().empty()) + { + lower_info->addDefBackend(lower_info->use_backends().getOnlyElement()); + } } }); // Set LowerInfo for each operand from the operand::LowerInfo holder - _operands.iterate([&](const operand::Index &index, operand::Object &object) { + _model->operands.iterate([&](const model::operand::Index &index, + model::operand::Object &object) { object.lower_info(std::move(operands_lower_info[index])); // Dump operand LowerInfo + // TODO Extract this dumping procedure to be reusable + if (!object.lower_info()->def_backends().empty() || + !object.lower_info()->use_backends().empty()) { - auto layouts_to_string = [](const operand::LayoutSet &layouts) { + auto backends_to_string = [](const operand::BackendSet &backends) { std::string str; - for (auto layout : layouts) + for (auto backend : backends) { - const char *name = ""; - if (layout == operand::Layout::NHWC) - name = "NHWC"; - if (layout == operand::Layout::NCHW) - name = "NCHW"; - str += name; + str += backend->config()->id(); + str += " "; + } + return "{ " + str + "}"; + }; + + auto operation_index_to_string = [](const model::operation::IndexList &operations) { + std::string str; + for (auto op : operations.list()) + { + str += std::to_string(op.value()); str += " "; } return "{ " + str + "}"; }; const auto &lower_info = object.lower_info(); - const auto &shape = lower_info->shape(); - std::string def_layouts = layouts_to_string(lower_info->def_layouts()); - std::string use_layouts = layouts_to_string(lower_info->use_layouts()); + const auto &shape = object.shape(); + const auto &lower_shape = lower_info->shape(); + std::string def_ops = operation_index_to_string(object.getDef()); + std::string use_ops = operation_index_to_string(object.getUses()); + std::string def_layouts = backends_to_string(lower_info->def_backends()); + std::string use_layouts = backends_to_string(lower_info->use_backends()); VERBOSE(Lower) << "* Operand #" << index.value() << " LowerInfo" << std::endl; - VERBOSE(Lower) << " - 4D Shape (NHWC) : { " << shape.n() << " " << shape.h() << " " - << shape.w() << " " << shape.c() << " " + VERBOSE(Lower) << " - Shape : { " << shape.dim(0) << " " + << (shape.rank() > 1 ? shape.dim(1) : 0) << " " + << (shape.rank() > 2 ? shape.dim(2) : 0) << " " + << (shape.rank() > 3 ? shape.dim(3) : 0) << " " << "}" << std::endl; - VERBOSE(Lower) << " - Def Layout : " << def_layouts << std::endl; - VERBOSE(Lower) << " - Use Layout : " << use_layouts << std::endl; + VERBOSE(Lower) << " - Def Operations : " << def_ops << std::endl; + VERBOSE(Lower) << " - Use Operations : " << use_ops << std::endl; + VERBOSE(Lower) << " - Lower Info" << std::endl; + VERBOSE(Lower) << " - 4D Shape (NHWC) : { " << lower_shape.n() << " " << lower_shape.h() + << " " << lower_shape.w() << " " << lower_shape.c() << " " + << "}" << std::endl; + VERBOSE(Lower) << " - Def Backends : " << def_layouts << std::endl; + VERBOSE(Lower) << " - Use Backends : " << use_layouts << std::endl; } }); } - // Graph verifications for the LOWERED phase + // Run PermutationInsertionPass { - verifier::DAGChecker dag_checker; - dag_checker.verify(*this); + pass::PermutationInsertionPass pi_pass(*this); + pi_pass.run(); + pass::PermutationEliminationPass pe_pass(*this); + pe_pass.run(); } - _phase = Phase::LOWERED; + // Graph verifications for the LOWERED phase + { + assert(verifier::DAGChecker().verify(*this)); + assert(verifier::EdgeConsistencyChecker().verify(*this)); + } } std::unique_ptr<linear::Linear> Graph::linearize(void) { - assert(_phase == Phase::LOWERED); + assert(_phase == Phase::MODEL); - auto linear = nnfw::make_unique<linear::Linear>(*this); + auto linear = nnfw::cpp14::make_unique<linear::Linear>(*this); // TODO Move the operations and operands to linear object - - _phase = Phase::LINEARIZED; - return std::move(linear); } void Graph::initializeUseDef() { - operations().iterate([&](const operation::Index &index, const operation::Node &node) -> void { - auto outputs = node.getOutputs(); - for (auto output : outputs) - { - operands().at(output).appendDef(index); - } + operations().iterate( + [&](const model::operation::Index &index, const model::operation::Node &node) -> void { + auto outputs = node.getOutputs(); + for (auto output : outputs) + { + operands().at(output).appendDef(index); + } + + auto inputs = node.getInputs(); + for (auto input : inputs) + { + operands().at(input).appendUse(index); + } + }); +} - auto inputs = node.getInputs(); - for (auto input : inputs) - { - operands().at(input).appendUse(index); - } - }); +const operation::LowerInfo *Graph::getLowerInfo(const model::operation::Index &index) const +{ + auto itr = _operation_lower_info.find(index); + if (itr == _operation_lower_info.end()) + return nullptr; + return itr->second.get(); +} + +void Graph::setLowerInfo(const model::operation::Index &index, + std::unique_ptr<operation::LowerInfo> &&lower_info) +{ + _operation_lower_info.insert(std::make_pair(index, std::move(lower_info))); } } // namespace graph @@ -273,7 +287,8 @@ template class Graph::PostDfsIterator<false>; template <bool is_const> void Graph::DefaultIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) const { - graph._operations.iterate([&](const operation::Index &, NodeRef node) -> void { fn(node); }); + graph.operations().iterate( + [&](const model::operation::Index &index, NodeRef node) -> void { fn(index, node); }); } // @@ -285,30 +300,34 @@ void Graph::PostDfsIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) { assert(!graph.isBuildingPhase()); // Restrict iteration condition - std::vector<bool> visited(graph._operations.size(), false); + std::unordered_map<model::operation::Index, bool> visited; + graph.operations().iterate( + [&](const model::operation::Index &index, NodeRef) { visited[index] = false; }); - std::function<void(const operation::Index &, NodeRef)> dfs_recursive = - [&](const operation::Index &index, NodeRef node) -> void { - if (visited[index.asInt()]) + std::function<void(const model::operation::Index &, NodeRef)> dfs_recursive = + [&](const model::operation::Index &index, NodeRef node) -> void { + if (visited[index]) return; - visited[index.asInt()] = true; + visited[index] = true; for (auto output : node.getOutputs()) { - const auto &operand = graph._operands.at(output); + const auto &operand = graph.operands().at(output); for (const auto &use : operand.getUses().list()) { - dfs_recursive(use, graph._operations.at(use)); + dfs_recursive(use, graph.operations().at(use)); } } - fn(node); + fn(index, node); }; - graph._operations.iterate(dfs_recursive); + graph.operations().iterate(dfs_recursive); // All of the operations(nodes) must have been visited. - assert(std::all_of(visited.begin(), visited.end(), [](bool v) { return v; })); + assert(std::all_of( + visited.begin(), visited.end(), + [](const std::pair<const model::operation::Index, bool> &v) { return v.second; })); } } // namespace graph diff --git a/runtimes/neurun/src/graph/Graph.h b/runtimes/neurun/src/graph/Graph.h index dd1489a93..afcfdce12 100644 --- a/runtimes/neurun/src/graph/Graph.h +++ b/runtimes/neurun/src/graph/Graph.h @@ -19,10 +19,8 @@ #include <functional> -#include "graph/operation/Node.h" -#include "graph/operation/Set.h" -#include "graph/operand/IndexSet.h" -#include "graph/operand/Set.h" +#include "model/operation/Node.h" +#include "graph/Model.h" namespace neurun { @@ -34,6 +32,14 @@ class Linear; namespace neurun { +namespace compiler +{ +class BackendResolver; +} // namespace compiler +} // namespace neurun + +namespace neurun +{ namespace graph { @@ -43,9 +49,7 @@ private: enum class Phase { BUILDING, - MODEL, - LOWERED, - LINEARIZED // Everything is moved to Linear object so this Graph object is no longer effective + MODEL }; public: @@ -53,9 +57,10 @@ public: { public: using GraphRef = typename std::conditional<is_const, const Graph &, Graph &>::type; - using NodeRef = - typename std::conditional<is_const, const operation::Node &, operation::Node &>::type; - using IterFn = std::function<void(NodeRef)>; + using IndexRef = const model::operation::Index &; + using NodeRef = typename std::conditional<is_const, const model::operation::Node &, + model::operation::Node &>::type; + using IterFn = std::function<void(IndexRef, NodeRef)>; public: virtual ~Iterator() = default; @@ -66,6 +71,7 @@ public: { public: using GraphRef = typename Iterator<is_const>::GraphRef; + using IndexRef = typename Iterator<is_const>::IndexRef; using NodeRef = typename Iterator<is_const>::NodeRef; using IterFn = typename Iterator<is_const>::IterFn; @@ -78,6 +84,7 @@ public: { public: using GraphRef = typename Iterator<is_const>::GraphRef; + using IndexRef = typename Iterator<is_const>::IndexRef; using NodeRef = typename Iterator<is_const>::NodeRef; using IterFn = typename Iterator<is_const>::IterFn; @@ -87,20 +94,21 @@ public: using PostDfsConstIterator = PostDfsIterator<true>; public: - Graph(void) = default; + Graph(void); + ~Graph(void); // Graph Building public: - operand::Index addOperand(const operand::Shape &shape, const operand::TypeInfo &type); - operation::Index addOperation(std::unique_ptr<operation::Node> &&node); - operation::Index insertOperation(const operand::Index &prev_operand_index, - const operation::Index &next_operation_index, - std::unique_ptr<operation::Node> &&node); - void setOperandValue(const operand::Index &ind, std::unique_ptr<operand::Data> &&data); - void addInput(const operand::Index &ind); - void addOutput(const operand::Index &ind); + model::operand::Index addOperand(const model::operand::Shape &shape, + const model::operand::TypeInfo &type); + model::operation::Index addOperation(std::unique_ptr<model::operation::Node> &&node); + void setOperandValue(const model::operand::Index &ind, + std::unique_ptr<model::operand::Data> &&data); + void addInput(const model::operand::Index &ind); + void addOutput(const model::operand::Index &ind); void finishBuilding(void); void lower(void); + void removeOperand(const model::operand::Index &ind) { _model->operands.remove(ind); } std::unique_ptr<linear::Linear> linearize(void); bool isBuildingPhase(void) const { return _phase == Phase::BUILDING; } @@ -109,18 +117,33 @@ private: // Accessors public: - const operand::IndexSet &getInputs() const { return _inputs; } - const operand::IndexSet &getOutputs() const { return _outputs; } - const operand::Set &operands() const { return _operands; } - operand::Set &operands() { return _operands; } // TODO Remove this non-const accessor - const operation::Set &operations() const { return _operations; } + const model::operand::IndexSet &getInputs() const { return _model->inputs; } + model::operand::IndexSet &getInputs() { return _model->inputs; } + const model::operand::IndexSet &getOutputs() const { return _model->outputs; } + model::operand::IndexSet &getOutputs() { return _model->outputs; } + const model::operand::Set &operands() const { return _model->operands; } + model::operand::Set &operands() + { + return _model->operands; + } // TODO Remove this non-const accessor + const model::operation::Set &operations() const { return _model->operations; } + model::operation::Set &operations() { return _model->operations; } + const compiler::BackendResolver *backend_resolver() const { return _backend_resolver.get(); } private: Phase _phase{Phase::BUILDING}; - operation::Set _operations; - operand::Set _operands; - operand::IndexSet _inputs; - operand::IndexSet _outputs; + std::unique_ptr<Model> _model{new Model}; + + // For LOWERED phase +public: + const operation::LowerInfo *getLowerInfo(const model::operation::Index &index) const; + void setLowerInfo(const model::operation::Index &index, + std::unique_ptr<operation::LowerInfo> &&lower_info); + +private: + std::unique_ptr<compiler::BackendResolver> _backend_resolver; + std::unordered_map<model::operation::Index, std::unique_ptr<operation::LowerInfo>> + _operation_lower_info; }; } // namespace graph diff --git a/runtimes/neurun/src/graph/Index.h b/runtimes/neurun/src/graph/Index.h index 864aaffd0..3263d12ad 100644 --- a/runtimes/neurun/src/graph/Index.h +++ b/runtimes/neurun/src/graph/Index.h @@ -18,6 +18,7 @@ #define __NEURUN_GRAPH_INDEX_H__ #include <functional> +#include <limits> #include <stdint.h> namespace neurun @@ -27,7 +28,11 @@ namespace graph template <typename T, typename DummyTag> class Index { +private: + static const T UNDEFINED = std::numeric_limits<T>::max(); + public: + explicit Index(void) : _index{UNDEFINED} {} explicit Index(T o) : _index{o} {} explicit Index(int32_t o) : _index{static_cast<T>(o)} {} // For legacy code compatibility Index(const Index &o) : _index{o._index} {} diff --git a/runtimes/neurun/src/graph/Model.h b/runtimes/neurun/src/graph/Model.h new file mode 100644 index 000000000..20bb713af --- /dev/null +++ b/runtimes/neurun/src/graph/Model.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_MODEL_H__ +#define __NEURUN_GRAPH_MODEL_H__ + +#include "model/operation/Set.h" +#include "model/operand/IndexSet.h" +#include "model/operand/Set.h" + +namespace neurun +{ +namespace graph +{ + +struct Model +{ + model::operation::Set operations; + model::operand::Set operands; + model::operand::IndexSet inputs; + model::operand::IndexSet outputs; +}; + +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_MODEL_H__ diff --git a/runtimes/neurun/src/graph/dumper/Dumper.cc b/runtimes/neurun/src/graph/dumper/Dumper.cc index 3788317ce..efffc5849 100644 --- a/runtimes/neurun/src/graph/dumper/Dumper.cc +++ b/runtimes/neurun/src/graph/dumper/Dumper.cc @@ -18,7 +18,7 @@ #include <string> -#include "logging.h" +#include "util/logging.h" namespace neurun { @@ -27,9 +27,9 @@ namespace graph namespace dumper { -using namespace neurun::graph::operation; +using namespace neurun::model::operation; -void Dumper::visit(const Conv2D::Implicit::Node &node) +void Dumper::visit(const Conv2DNode &node) { VERBOSE(LIR) << "* Conv2D(Implicit)" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ") Kernel(" @@ -38,21 +38,21 @@ void Dumper::visit(const Conv2D::Implicit::Node &node) VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const MaxPool2D::Implicit::Node &node) +void Dumper::visit(const MaxPool2DNode &node) { VERBOSE(LIR) << "* MaxPool2D(Implicit)" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ")" << std::endl; VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const AvgPool2D::Implicit::Node &node) +void Dumper::visit(const AvgPool2DNode &node) { VERBOSE(LIR) << "* AvgPool2D(Implicit)" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ")" << std::endl; VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const Concat::Node &node) +void Dumper::visit(const ConcatNode &node) { VERBOSE(LIR) << "* Concat" << std::endl; std::string inputs; @@ -64,7 +64,7 @@ void Dumper::visit(const Concat::Node &node) VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const FullyConnected::Node &node) +void Dumper::visit(const FullyConnectedNode &node) { VERBOSE(LIR) << "* FullyConnected" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ") Weight(" @@ -73,7 +73,7 @@ void Dumper::visit(const FullyConnected::Node &node) VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const Reshape::Node &node) +void Dumper::visit(const ReshapeNode &node) { VERBOSE(LIR) << "* Reshape" << std::endl; // TODO The shape index should be "node.getInputs().at(1).value()" but not valid for now @@ -83,36 +83,28 @@ void Dumper::visit(const Reshape::Node &node) VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const Softmax::Node &node) +void Dumper::visit(const SoftmaxNode &node) { VERBOSE(LIR) << "* Softmax" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ")" << std::endl; VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } -void Dumper::visit(const NOP::Node &node) -{ - VERBOSE(LIR) << "* NOP" << std::endl; - std::string inputs, outputs; - for (auto i : node.getInputs()) - { - inputs += std::to_string(i.value()) + ","; - } - VERBOSE(LIR) << " - Inputs : IFM(" << inputs << ")" << std::endl; - for (auto i : node.getOutputs()) - { - outputs += std::to_string(i.value()) + ","; - } - VERBOSE(LIR) << " - Outputs : OFM(" << outputs << ")" << std::endl; -} - -void Dumper::visit(const Permute::Node &node) +void Dumper::visit(const PermuteNode &node) { VERBOSE(LIR) << "* Permute" << std::endl; VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(0).value() << ")" << std::endl; VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0).value() << ")" << std::endl; } +void Dumper::visit(const AddNode &node) +{ + VERBOSE(LIR) << "* Add" << std::endl; + VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(0).value() << ", " + << node.getInputs().at(1).value() << ")" << std::endl; + VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0).value() << ")" << std::endl; +} + } // namespace dumper } // namespace graph } // namespace neurun diff --git a/runtimes/neurun/src/graph/dumper/Dumper.h b/runtimes/neurun/src/graph/dumper/Dumper.h index dee490cbd..8c079a11d 100644 --- a/runtimes/neurun/src/graph/dumper/Dumper.h +++ b/runtimes/neurun/src/graph/dumper/Dumper.h @@ -17,7 +17,7 @@ #ifndef __NEURUN_GRAPH_DUMPER_H__ #define __NEURUN_GRAPH_DUMPER_H__ -#include "graph/operation/NodeVisitor.h" +#include "model/operation/NodeVisitor.h" namespace neurun { @@ -26,21 +26,21 @@ namespace graph namespace dumper { -class Dumper : public graph::operation::NodeVisitor +class Dumper : public model::operation::NodeVisitor { public: Dumper() = default; public: - void visit(const graph::operation::Conv2D::Implicit::Node &node) override; - void visit(const graph::operation::MaxPool2D::Implicit::Node &node) override; - void visit(const graph::operation::AvgPool2D::Implicit::Node &node) override; - void visit(const graph::operation::Concat::Node &node) override; - void visit(const graph::operation::FullyConnected::Node &node) override; - void visit(const graph::operation::Reshape::Node &node) override; - void visit(const graph::operation::Softmax::Node &node) override; - void visit(const graph::operation::NOP::Node &node) override; - void visit(const graph::operation::Permute::Node &node) override; + void visit(const model::operation::Conv2DNode &node) override; + void visit(const model::operation::MaxPool2DNode &node) override; + void visit(const model::operation::AvgPool2DNode &node) override; + void visit(const model::operation::ConcatNode &node) override; + void visit(const model::operation::FullyConnectedNode &node) override; + void visit(const model::operation::ReshapeNode &node) override; + void visit(const model::operation::SoftmaxNode &node) override; + void visit(const model::operation::PermuteNode &node) override; + void visit(const model::operation::AddNode &node) override; }; } // namespace dumper diff --git a/runtimes/neurun/src/graph/operand/BackendSet.cc b/runtimes/neurun/src/graph/operand/BackendSet.cc new file mode 100644 index 000000000..9a284d722 --- /dev/null +++ b/runtimes/neurun/src/graph/operand/BackendSet.cc @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BackendSet.h" + +#include <cassert> + +namespace neurun +{ +namespace graph +{ +namespace operand +{ + +BackendSet::BackendSet(std::initializer_list<const backend::Backend *> backends) +{ + for (auto backend : backends) + { + _set.insert(backend); + } +} + +const backend::Backend *BackendSet::getOnlyElement() const +{ + assert(_set.size() == 1u); + return *_set.begin(); +} + +BackendSet BackendSet::operator|(const BackendSet &other) const +{ + auto ret = *this; + for (auto backend : other) + { + ret.add(backend); + } + return ret; +} + +BackendSet BackendSet::operator&(const BackendSet &other) const +{ + BackendSet ret; + for (auto backend : other) + { + if (contains(backend)) + { + ret.add(backend); + } + } + return ret; +} + +BackendSet BackendSet::operator-(const BackendSet &other) const +{ + auto ret = *this; + for (auto backend : other) + { + ret.remove(backend); + } + return ret; +} + +} // namespace operand +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/BackendSet.h b/runtimes/neurun/src/graph/operand/BackendSet.h new file mode 100644 index 000000000..8b457a084 --- /dev/null +++ b/runtimes/neurun/src/graph/operand/BackendSet.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_OPERAND_BACKEND_SET_H__ +#define __NEURUN_GRAPH_OPERAND_BACKEND_SET_H__ + +#include <initializer_list> +#include <unordered_set> + +namespace neurun +{ +namespace backend +{ +class Backend; +} // namespace backend +} // namespace neurun + +namespace neurun +{ +namespace graph +{ +namespace operand +{ + +class BackendSet +{ +public: + BackendSet() = default; + BackendSet(std::initializer_list<const backend::Backend *> backends); + +public: + void add(const backend::Backend *backend) { _set.insert(backend); } + void remove(const backend::Backend *backend) { _set.erase(backend); } + uint32_t size() const { return static_cast<uint32_t>(_set.size()); } + bool empty() const { return _set.empty(); } + bool contains(const backend::Backend *backend) const { return _set.find(backend) != _set.end(); } + const backend::Backend *getOnlyElement() const; + +public: + BackendSet operator|(const BackendSet &other) const; // Union + BackendSet operator&(const BackendSet &other) const; // Intersect + BackendSet operator-(const BackendSet &other) const; // Minus + +public: + std::unordered_set<const backend::Backend *>::const_iterator begin() const + { + return _set.begin(); + } + std::unordered_set<const backend::Backend *>::const_iterator end() const { return _set.end(); } + +private: + std::unordered_set<const backend::Backend *> _set; +}; + +} // namespace operand +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_OPERAND_BACKEND_SET_H__ diff --git a/runtimes/neurun/src/graph/operand/Data.h b/runtimes/neurun/src/graph/operand/Data.h deleted file mode 100644 index e36a9a2ae..000000000 --- a/runtimes/neurun/src/graph/operand/Data.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_DATA_H__ -#define __NEURUN_GRAPH_OPERAND_DATA_H__ - -#include <algorithm> - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -struct Data -{ - virtual ~Data() = default; - - virtual size_t size(void) const = 0; - virtual const uint8_t *base(void) const = 0; -}; - -class CachedData final : public Data -{ -public: - CachedData(const uint8_t *base, size_t size) : _base{new uint8_t[size]}, _size{size} - { - std::copy(base, base + size, _base); - } - -public: - ~CachedData() { delete[] _base; } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base; } - -private: - uint8_t *_base; - size_t _size; -}; - -class ExternalData final : public Data -{ -public: - ExternalData(const uint8_t *base, size_t size) : _base{base}, _size{size} - { - // DO NOTHING - } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base; } - -private: - const uint8_t *_base; - const size_t _size; -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_DATA_H__ diff --git a/runtimes/neurun/src/graph/operand/DataType.h b/runtimes/neurun/src/graph/operand/DataType.h deleted file mode 100644 index 8878901fd..000000000 --- a/runtimes/neurun/src/graph/operand/DataType.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_DATATYPE_H__ -#define __NEURUN_GRAPH_OPERAND_DATATYPE_H__ - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -enum class DataType -{ - SCALAR_FLOAT32 = 0, - SCALAR_INT32 = 1, - SCALAR_UINT32 = 2, - - TENSOR_FLOAT32 = 3, - TENSOR_INT32 = 4, - - TENSOR_QUANT8_ASYMM = 5, -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_DATATYPE_H__ diff --git a/runtimes/neurun/src/graph/operand/Index.h b/runtimes/neurun/src/graph/operand/Index.h deleted file mode 100644 index a6850d061..000000000 --- a/runtimes/neurun/src/graph/operand/Index.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_INDEX_H__ -#define __NEURUN_GRAPH_OPERAND_INDEX_H__ - -#include "graph/Index.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; - -} // namespace operand -} // namespace graph -} // namespace neurun - -namespace neurun -{ -namespace graph -{ -namespace operand -{ -namespace IO -{ - -using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; - -} // namespace IO -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_INDEX_H__ diff --git a/runtimes/neurun/src/graph/operand/IndexSet.cc b/runtimes/neurun/src/graph/operand/IndexSet.cc deleted file mode 100644 index 037965a6d..000000000 --- a/runtimes/neurun/src/graph/operand/IndexSet.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "IndexSet.h" - -#include <algorithm> - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -IndexSet::IndexSet(std::initializer_list<Index> list) : _set(list) -{ - // DO NOTHING -} - -IndexSet::IndexSet(std::initializer_list<int32_t> list) -{ - for (auto val : list) - { - _set.emplace_back(static_cast<uint32_t>(val)); - } -} - -IndexSet::IndexSet(std::initializer_list<uint32_t> list) -{ - for (auto val : list) - { - _set.emplace_back(val); - } -} - -bool IndexSet::contains(const Index &index) const -{ - return std::find(_set.begin(), _set.end(), index) != _set.end(); -} - -} // namespace operand -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/IndexSet.h b/runtimes/neurun/src/graph/operand/IndexSet.h deleted file mode 100644 index 2d37de788..000000000 --- a/runtimes/neurun/src/graph/operand/IndexSet.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_INDEX_SET_H__ -#define __NEURUN_GRAPH_OPERAND_INDEX_SET_H__ - -#include <initializer_list> -#include <vector> - -#include "Index.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -class IndexSet -{ -public: - IndexSet(void) = default; - IndexSet(std::initializer_list<Index> list); - IndexSet(std::initializer_list<int32_t> list); - IndexSet(std::initializer_list<uint32_t> list); - -public: - void append(const Index &index) { _set.emplace_back(index); } - -public: - uint32_t size() const { return static_cast<uint32_t>(_set.size()); } - const Index &at(IO::Index set_index) const { return _set.at(set_index.asInt()); } - const Index &at(uint32_t index) const { return _set.at(index); } - bool contains(const Index &index) const; - -public: - std::vector<Index>::const_iterator begin(void) const { return _set.begin(); } - std::vector<Index>::const_iterator end(void) const { return _set.end(); } - -private: - std::vector<Index> _set; -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_INDEX_SET_H__ diff --git a/runtimes/neurun/src/graph/operand/LowerInfo.h b/runtimes/neurun/src/graph/operand/LowerInfo.h index d91c29fb7..7900e54d9 100644 --- a/runtimes/neurun/src/graph/operand/LowerInfo.h +++ b/runtimes/neurun/src/graph/operand/LowerInfo.h @@ -19,7 +19,7 @@ #include <stdint.h> -#include "LayoutSet.h" +#include "BackendSet.h" namespace neurun { @@ -60,17 +60,19 @@ public: public: const Shape4D &shape(void) const { return _shape; } - const LayoutSet &def_layouts(void) const { return _def_layouts; } - const LayoutSet &use_layouts(void) const { return _use_layouts; } + const BackendSet &def_backends(void) const { return _def_backends; } + const BackendSet &use_backends(void) const { return _use_backends; } public: - void addDefLayout(const Layout &layout) { _def_layouts.add(layout); } - void addUseLayout(const Layout &layout) { _use_layouts.add(layout); } + void addDefBackend(const backend::Backend *backend) { _def_backends.add(backend); } + void addUseBackend(const backend::Backend *backend) { _use_backends.add(backend); } + void removeDefBackend(const backend::Backend *backend) { _def_backends.remove(backend); } + void removeUseBackend(const backend::Backend *backend) { _use_backends.remove(backend); } private: Shape4D _shape; - LayoutSet _def_layouts; - LayoutSet _use_layouts; + BackendSet _def_backends; + BackendSet _use_backends; }; } // namespace operand diff --git a/runtimes/neurun/src/graph/operand/Object.cc b/runtimes/neurun/src/graph/operand/Object.cc deleted file mode 100644 index 7b95cea97..000000000 --- a/runtimes/neurun/src/graph/operand/Object.cc +++ /dev/null @@ -1,117 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Object.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -size_t Object::operandSize(void) const -{ - const uint32_t ranks = _shape.rank(); - int32_t elements = 1; - - for (uint32_t rank = 0; rank < ranks; rank++) - { - elements *= _shape.dim(rank); - } - - DataType type = _type.type(); - size_t element_size = 0; - - // Value of type is matched with OperandCode enum in NeuralNetworks.h - switch (type) - { - case DataType::SCALAR_FLOAT32: - case DataType::TENSOR_FLOAT32: - element_size = sizeof(float); - break; - case DataType::SCALAR_INT32: - case DataType::TENSOR_INT32: - element_size = sizeof(int32_t); - break; - case DataType::SCALAR_UINT32: - element_size = sizeof(uint32_t); - break; - case DataType::TENSOR_QUANT8_ASYMM: - element_size = sizeof(uint8_t); - break; - default: - throw std::runtime_error{"Unsuppported type size"}; - } - - return element_size * elements; -} - -bool Object::setUsage(const OperandUsage usage) -{ - if (usageIsDefined() && (_usage != usage)) - { - // Already set as different type - return false; - } - - _usage = usage; - - return true; -} - -void Object::appendUse(const ::neurun::graph::operation::Index &idx) -{ - assert(_usage != OperandUsage::NOT_DEFINED); - assert(!_uses.contains(idx)); - - _uses.append(idx); -} - -void Object::removeUse(const ::neurun::graph::operation::Index &idx) -{ - assert(_usage != OperandUsage::NOT_DEFINED); - assert(_uses.contains(idx)); - - _uses.remove(idx); -} - -void Object::appendDef(const ::neurun::graph::operation::Index &idx) -{ - assert(_usage != OperandUsage::NOT_DEFINED && _usage != OperandUsage::CONSTANT); - assert(_def.size() == 0); - - _def.append(idx); -} - -void Object::removeDef(const ::neurun::graph::operation::Index &idx) -{ - assert(_usage != OperandUsage::NOT_DEFINED); - assert(_def.contains(idx)); - - _def.remove(idx); -} - -void Object::lower_info(std::unique_ptr<LowerInfo> &&lower_info) -{ - _lower_info = std::move(lower_info); -} - -const LowerInfo *Object::lower_info() const { return _lower_info.get(); } - -} // namespace operand -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/Object.h b/runtimes/neurun/src/graph/operand/Object.h deleted file mode 100644 index 17e46ab28..000000000 --- a/runtimes/neurun/src/graph/operand/Object.h +++ /dev/null @@ -1,116 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_OBJECT_H__ -#define __NEURUN_GRAPH_OPERAND_OBJECT_H__ - -#include <cassert> -#include <cstdint> -#include <memory> -#include <algorithm> - -#include "Shape.h" -#include "Data.h" -#include "TypeInfo.h" -#include "LowerInfo.h" -#include "graph/operation/IndexList.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -// Operand usage should be exact one of these -enum class OperandUsage -{ - NOT_DEFINED, - MODEL_INPUT, - CONSTANT, - OPERATION_OUTPUT, -}; - -class Object -{ -public: - explicit Object(const Shape &shape, const TypeInfo &type) - : _shape{shape}, _type{type}, _usage{OperandUsage::NOT_DEFINED} - { - // DO NOTHING - } - -public: - const Shape &shape(void) const { return _shape; } - const TypeInfo &typeInfo(void) const { return _type; } - size_t operandSize(void) const; - bool setAsConstant() { return setUsage(OperandUsage::CONSTANT); } - bool setAsModelInput() { return setUsage(OperandUsage::MODEL_INPUT); } - bool setAsOperationOutput() { return setUsage(OperandUsage::OPERATION_OUTPUT); } - bool usageIsDefined(void) const { return _usage != OperandUsage::NOT_DEFINED; } - bool isModelInput(void) const { return _usage == OperandUsage::MODEL_INPUT; } - - const operation::IndexList &getUses() const { return _uses; } - const operation::IndexList &getDef() const { return _def; } - void appendUse(const operation::Index &idx); - void removeUse(const operation::Index &idx); - void appendDef(const operation::Index &idx); - void removeDef(const operation::Index &idx); - -private: - bool setUsage(OperandUsage usage); - -public: - void data(std::unique_ptr<Data> &&data) { _data = std::move(data); } - const Data &data(void) const { return *_data; } - -public: - template <typename T, typename... Args> void data(Args &&... args) - { - data(std::unique_ptr<T>(new T{std::forward<Args>(args)...})); - } - -public: - template <typename T> T asScalar(void) const - { - assert((_shape.rank() == 0) || ((_shape.rank() == 1) && (_shape.dim(0) == 1))); - assert(_data != nullptr); - assert((_data->base() != nullptr) && (_data->size() == sizeof(T))); - - return *(reinterpret_cast<const T *>(_data->base())); - } - -public: - void lower_info(std::unique_ptr<LowerInfo> &&lower_info); - const LowerInfo *lower_info() const; - -private: - const Shape _shape; - const TypeInfo _type; - std::unique_ptr<Data> _data; - OperandUsage _usage; - - operation::IndexList _uses; - operation::IndexList _def; // size is 0 (constant) or 1 (from def operation) - - std::unique_ptr<LowerInfo> _lower_info; -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_OBJECT_H__ diff --git a/runtimes/neurun/src/graph/operand/ParentInfo.h b/runtimes/neurun/src/graph/operand/ParentInfo.h new file mode 100644 index 000000000..5e6f56237 --- /dev/null +++ b/runtimes/neurun/src/graph/operand/ParentInfo.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file ParentInfo.h + * @brief This file contains ParentInfo class and internal Coordinate4D class + * to represent subsumption between operand + */ + +#ifndef __NEURUN_GRAPH_OPERAND_PARENT_INFO_H__ +#define __NEURUN_GRAPH_OPERAND_PARENT_INFO_H__ + +#include <stdint.h> + +#include "model/operand/Index.h" +#include "util/feature/Coordinate4D.h" + +namespace neurun +{ +namespace graph +{ +namespace operand +{ + +using neurun::util::feature::Coordinate4D; + +/** + * @brief Class to represent parent operand in child operand + */ +class ParentInfo +{ +public: + /** + * @brief Construct a new ParentInfo object + * @param[in] parent Index of parent operand + * @param[in] coordinate Offset of child operand in parent operand + * @return + */ + ParentInfo(const model::operand::Index parent, const Coordinate4D &coordinate) + : _parent{parent}, _coordinate{coordinate} + { + // DO NOTHING + } + +public: + /** + * @brief Return parent index + * @return Parent index + */ + model::operand::Index parent(void) const { return _parent; } + /** + * @brief Retern offset in parent + * @return Offset + */ + Coordinate4D offset(void) const { return _coordinate; } + +private: + model::operand::Index _parent; + Coordinate4D _coordinate; +}; + +} // namespace operand +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_OPERAND_PARENT_INFO_H__ diff --git a/runtimes/neurun/src/graph/operand/Set.cc b/runtimes/neurun/src/graph/operand/Set.cc deleted file mode 100644 index 60dad2336..000000000 --- a/runtimes/neurun/src/graph/operand/Set.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Set.h" - -#include "nnfw/std/memory.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -const Index Set::generateIndex() -{ - assert((_index_count) <= 0x7fffffff); - - return Index{_index_count++}; -} - -Index Set::append(const Shape &shape, const TypeInfo &type) -{ - auto index = generateIndex(); - - _objects[index] = nnfw::make_unique<Object>(shape, type); - - return index; -} - -const Object &Set::at(const Index &index) const { return *(_objects.at(index)); } - -Object &Set::at(const Index &index) { return *(_objects.at(index)); } - -bool Set::exist(const Index &index) const { return index.value() < _objects.size(); } - -void Set::iterate(const std::function<void(const Index &, const Object &)> &fn) const -{ - for (const auto &e : _objects) - { - fn(e.first, *e.second); - } -} - -void Set::iterate(const std::function<void(const Index &, Object &)> &fn) -{ - for (auto &e : _objects) - { - fn(e.first, *e.second); - } -} - -} // namespace operand -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/Set.h b/runtimes/neurun/src/graph/operand/Set.h deleted file mode 100644 index c8266fed0..000000000 --- a/runtimes/neurun/src/graph/operand/Set.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_SET_H__ -#define __NEURUN_GRAPH_OPERAND_SET_H__ - -#include <memory> -#include <unordered_map> - -#include "Object.h" -#include "Index.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -class Set -{ -public: - Set() : _index_count(0) {} - -public: - Index append(const Shape &, const TypeInfo &); - -public: - const Object &at(const Index &) const; - Object &at(const Index &); - bool exist(const Index &) const; - void iterate(const std::function<void(const Index &, const Object &)> &fn) const; - void iterate(const std::function<void(const Index &, Object &)> &fn); - -private: - const Index generateIndex(); - -private: - std::unordered_map<Index, std::unique_ptr<Object>> _objects; - uint32_t _index_count; -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_SET_H__ diff --git a/runtimes/neurun/src/graph/operand/Shape.cc b/runtimes/neurun/src/graph/operand/Shape.cc deleted file mode 100644 index f6d7a6999..000000000 --- a/runtimes/neurun/src/graph/operand/Shape.cc +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cassert> - -#include "Shape.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -Shape::Shape(uint32_t rank) { _dims.resize(rank); } - -int32_t Shape::asVector(void) const -{ - assert(rank() == 1); - - return dim(0); -} - -nnfw::util::feature::Shape Shape::asFeature(void) const -{ - assert(rank() == 4); - - // Feature Map in NNAPI - // - Dimension(0) -> Batch - // - Dimension(1) -> Height - // - Dimension(2) -> Width - // - Dimension(3) -> Depth - const auto batch = dim(0); - const auto depth = dim(3); - const auto height = dim(1); - const auto width = dim(2); - - return nnfw::util::feature::Shape(batch, depth, height, width); -} - -nnfw::util::kernel::Shape Shape::asKernel(void) const -{ - assert(rank() == 4); - - // Convolution Kernel in NNAPI - // - Dimension(0) -> Count - // - Dimension(1) -> Height - // - Dimension(2) -> Width - // - Dimension(3) -> Depth - const auto count = dim(0); - const auto depth = dim(3); - const auto height = dim(1); - const auto width = dim(2); - - return nnfw::util::kernel::Shape(count, depth, height, width); -} - -} // namespace operand -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/Shape.h b/runtimes/neurun/src/graph/operand/Shape.h deleted file mode 100644 index 3ae970e85..000000000 --- a/runtimes/neurun/src/graph/operand/Shape.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_SHAPE_H__ -#define __NEURUN_GRAPH_OPERAND_SHAPE_H__ - -#include <vector> -#include <cstdint> - -#include "util/feature/Shape.h" -#include "util/kernel/Shape.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -struct Shape -{ -public: - Shape(uint32_t rank); - -public: - uint32_t rank(void) const { return _dims.size(); } - -public: - int32_t dim(uint32_t n) const { return _dims.at(n); } - int32_t &dim(uint32_t n) { return _dims.at(n); } - const std::vector<int32_t> &dims() const { return _dims; } - -public: - int32_t asVector(void) const; - nnfw::util::feature::Shape asFeature(void) const; - nnfw::util::kernel::Shape asKernel(void) const; - -private: - std::vector<int32_t> _dims; -}; - -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_SHAPE_H__ diff --git a/runtimes/neurun/src/graph/operand/Shape4DConvert.h b/runtimes/neurun/src/graph/operand/Shape4DConvert.h index b840d19b8..73cf0903a 100644 --- a/runtimes/neurun/src/graph/operand/Shape4DConvert.h +++ b/runtimes/neurun/src/graph/operand/Shape4DConvert.h @@ -26,7 +26,7 @@ namespace graph namespace operand { -inline LowerInfo::Shape4D asShape4D(const Shape &shape) +inline LowerInfo::Shape4D asShape4D(const model::operand::Shape &shape) { switch (shape.rank()) { @@ -34,16 +34,16 @@ inline LowerInfo::Shape4D asShape4D(const Shape &shape) return LowerInfo::Shape4D(1, 1, 1, 1); case 1u: - return LowerInfo::Shape4D(1, 1, 1, shape.dim(0)); + return LowerInfo::Shape4D(shape.dim(0), 1, 1, 1); case 2u: - return LowerInfo::Shape4D(1, 1, shape.dim(1), shape.dim(0)); + return LowerInfo::Shape4D(shape.dim(0), shape.dim(1), 1, 1); case 3u: - return LowerInfo::Shape4D(1, shape.dim(2), shape.dim(1), shape.dim(0)); + return LowerInfo::Shape4D(shape.dim(0), shape.dim(1), shape.dim(2), 1); case 4u: - return LowerInfo::Shape4D(shape.dim(3), shape.dim(2), shape.dim(1), shape.dim(0)); + return LowerInfo::Shape4D(shape.dim(0), shape.dim(1), shape.dim(2), shape.dim(3)); default: throw "Unsupported rank > 4"; diff --git a/runtimes/neurun/src/graph/operand/TypeInfo.cc b/runtimes/neurun/src/graph/operand/TypeInfo.cc deleted file mode 100644 index 5642b1e8f..000000000 --- a/runtimes/neurun/src/graph/operand/TypeInfo.cc +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "TypeInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -DataType TypeInfo::typeFromOperandCode(OperandCode type) -{ - // Now neurun::graph::operand::DataType share same enum value with OperandCode - // in NeuralNetworks.h. - return static_cast<DataType>(static_cast<uint32_t>(type)); -} - -} // namespace operand -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operand/TypeInfo.h b/runtimes/neurun/src/graph/operand/TypeInfo.h deleted file mode 100644 index 41f4453e5..000000000 --- a/runtimes/neurun/src/graph/operand/TypeInfo.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERAND_TYPEINFO_H__ -#define __NEURUN_GRAPH_OPERAND_TYPEINFO_H__ - -#include <cstdint> - -#include <NeuralNetworks.h> - -#include "DataType.h" - -namespace neurun -{ -namespace graph -{ -namespace operand -{ - -class TypeInfo -{ -public: - TypeInfo(OperandCode type, float scale, int32_t offset) - : _type(typeFromOperandCode(type)), _scale(scale), _offset(offset) - { - // DO NOTHING - } - -public: - DataType type() const { return _type; } - float scale() const { return _scale; } - int32_t offset() const { return _offset; } - -private: - // Now neurun::graph::operand::DataType share same enum value with OperandCode - // in NeuralNetworks.h. - // If we don't share same value, we must fix this mapping function. - DataType typeFromOperandCode(OperandCode type); - -private: - DataType _type; - float _scale; - int32_t _offset; -}; -} // namespace operand -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERAND_TYPEINFO_H__ diff --git a/runtimes/neurun/src/graph/operation/AvgPool2D.cc b/runtimes/neurun/src/graph/operation/AvgPool2D.cc deleted file mode 100644 index b2612c6b5..000000000 --- a/runtimes/neurun/src/graph/operation/AvgPool2D.cc +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "AvgPool2D.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace AvgPool2D -{ -namespace Implicit -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 7); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> IFM Tensor Index - // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 2 -> Horizontal (over width) Stride Index - // 3 -> Vertial (over height) Stride Index - // 4 -> Filter Width Index - // 5 -> Filter Height Index - // 6 -> FuseCode (activation) Index - - setInputs({init_param.inputs[0]}); - setOutputs({init_param.outputs[0]}); - - _param.padding_index = init_param.inputs[1]; - _param.hstride_index = init_param.inputs[2]; - _param.vstride_index = init_param.inputs[3]; - - _param.kw_index = init_param.inputs[4]; - _param.kh_index = init_param.inputs[5]; - _param.activation_index = init_param.inputs[6]; -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Implicit -} // namespace AvgPool2D -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/AvgPool2D.h b/runtimes/neurun/src/graph/operation/AvgPool2D.h deleted file mode 100644 index a856b9cd4..000000000 --- a/runtimes/neurun/src/graph/operation/AvgPool2D.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_AVGPOOL2D_H__ -#define __NEURUN_GRAPH_OPERATION_AVGPOOL2D_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace AvgPool2D -{ -namespace Implicit -{ - -struct Param -{ - int32_t kw_index; - int32_t kh_index; - - int32_t hstride_index; - int32_t vstride_index; - - int32_t padding_index; - int32_t activation_index; -}; - -class Node : public graph::operation::Node -{ -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void accept(NodeVisitor &&) const override; - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace Implicit -} // namespace AvgPool2D -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_AVGPOOL2D_H__ diff --git a/runtimes/neurun/src/graph/operation/Concat.cc b/runtimes/neurun/src/graph/operation/Concat.cc deleted file mode 100644 index 952cf687c..000000000 --- a/runtimes/neurun/src/graph/operation/Concat.cc +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Concat.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Concat -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count > 2); // At least one one input tensor and axis - assert(init_param.output_count == 1); - - // When there are N + 1 inputs, each input should be interpreted as follows: - // - // [0, N) -> Input tensors - // N -> Axis - // - - { - operand::IndexSet inds; - for (uint32_t n = 0; n < init_param.input_count - 1; ++n) - { - inds.append(operand::Index{init_param.inputs[n]}); - } - setInputs(inds); - } - setOutputs({init_param.outputs[0]}); - - _param.axis_index = init_param.inputs[init_param.input_count - 1]; -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Concat -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Concat.h b/runtimes/neurun/src/graph/operation/Concat.h deleted file mode 100644 index dab17d031..000000000 --- a/runtimes/neurun/src/graph/operation/Concat.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_CONCAT_H__ -#define __NEURUN_GRAPH_OPERATION_CONCAT_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Concat -{ - -struct Param -{ - int32_t axis_index; -}; - -class Node : public graph::operation::Node -{ -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void accept(NodeVisitor &&) const override; - -public: - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace Concat -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_CONCAT_H__ diff --git a/runtimes/neurun/src/graph/operation/Conv2D.cc b/runtimes/neurun/src/graph/operation/Conv2D.cc deleted file mode 100644 index f88955db1..000000000 --- a/runtimes/neurun/src/graph/operation/Conv2D.cc +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Conv2D.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Conv2D -{ -namespace Implicit -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 7 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // - // 0 -> IFM Tensor Index - // 1 -> Kernel Tensor Index - // 2 -> Bias Tensor Index - // 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 4 -> Stride (width) Index - // 5 -> Stride (height) INdex - // 6 -> Activation Index - - setInputs({init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}); - setOutputs({init_param.outputs[0]}); - - _param.padding_index = init_param.inputs[3]; - _param.hstride_index = init_param.inputs[4]; - _param.vstride_index = init_param.inputs[5]; - _param.activation_index = init_param.inputs[6]; -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 3); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Implicit -} // namespace Conv2D -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Conv2D.h b/runtimes/neurun/src/graph/operation/Conv2D.h deleted file mode 100644 index f75058a30..000000000 --- a/runtimes/neurun/src/graph/operation/Conv2D.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_CONV2D_H__ -#define __NEURUN_GRAPH_OPERATION_CONV2D_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Conv2D -{ -namespace Implicit -{ - -struct Param -{ - int32_t hstride_index; - int32_t vstride_index; - - int32_t padding_index; - int32_t activation_index; -}; - -class Node : public graph::operation::Node -{ -public: - Node(const graph::operation::Node::InitParam &); - -public: - virtual void accept(NodeVisitor &&) const override; - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace Implicit -} // namespace Conv2D -} // namespace coperation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_CONV2D_H__ diff --git a/runtimes/neurun/src/graph/operation/FullyConnected.cc b/runtimes/neurun/src/graph/operation/FullyConnected.cc deleted file mode 100644 index 0a6553d1e..000000000 --- a/runtimes/neurun/src/graph/operation/FullyConnected.cc +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "FullyConnected.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace FullyConnected -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 4 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A tensor, specifying the input. - // 1 -> A 2-D tensor, specifying the weights - // 2 -> A 1-D tensor, specifying the bias - // 3 -> An INT32 value, and has to be one of the FuseCode values - - setInputs({init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}); - setOutputs({init_param.outputs[0]}); - - _param.activation_index = init_param.inputs[3]; -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 3); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace FullyConnected -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/FullyConnected.h b/runtimes/neurun/src/graph/operation/FullyConnected.h deleted file mode 100644 index a1f920e4b..000000000 --- a/runtimes/neurun/src/graph/operation/FullyConnected.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_FULLYCONNECTED_H__ -#define __NEURUN_GRAPH_OPERATION_FULLYCONNECTED_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace FullyConnected -{ - -struct Param -{ - int32_t activation_index; -}; - -class Node : public graph::operation::Node -{ -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void accept(NodeVisitor &&) const override; - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace FullyConnected -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_FULLYCONNECTED_H__ diff --git a/runtimes/neurun/src/graph/operation/Index.h b/runtimes/neurun/src/graph/operation/Index.h deleted file mode 100644 index 3902d039b..000000000 --- a/runtimes/neurun/src/graph/operation/Index.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_INDEX_H__ -#define __NEURUN_GRAPH_OPERATION_INDEX_H__ - -#include "graph/Index.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; - -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_INDEX_H__ diff --git a/runtimes/neurun/src/graph/operation/IndexList.cc b/runtimes/neurun/src/graph/operation/IndexList.cc deleted file mode 100644 index cdc5997ea..000000000 --- a/runtimes/neurun/src/graph/operation/IndexList.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "IndexList.h" - -#include <algorithm> - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -IndexList::IndexList(std::initializer_list<Index> list) : _list(list) -{ - // DO NOTHING -} - -bool IndexList::contains(const ::neurun::graph::operation::Index &index) const -{ - return std::find(_list.begin(), _list.end(), index) != _list.end(); -} - -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/IndexList.h b/runtimes/neurun/src/graph/operation/IndexList.h deleted file mode 100644 index cfac46abc..000000000 --- a/runtimes/neurun/src/graph/operation/IndexList.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_INDEX_LIST_H__ -#define __NEURUN_GRAPH_OPERATION_INDEX_LIST_H__ - -#include <initializer_list> -#include <list> - -#include "Index.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -class IndexList -{ -public: - IndexList(void) = default; - IndexList(std::initializer_list<Index> list); - -public: - void append(const Index &index) { _list.push_back(index); } - void remove(const Index &index) { _list.remove(index); } - -public: - uint32_t size() const { return static_cast<uint32_t>(_list.size()); } - const std::list<Index> &list() const { return _list; } - bool contains(const Index &index) const; - -private: - std::list<Index> _list; -}; - -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_INDEX_LIST_H__ diff --git a/runtimes/neurun/src/graph/operation/LowerInfo.cc b/runtimes/neurun/src/graph/operation/LowerInfo.cc index 2998b1922..7862fd0c9 100644 --- a/runtimes/neurun/src/graph/operation/LowerInfo.cc +++ b/runtimes/neurun/src/graph/operation/LowerInfo.cc @@ -23,7 +23,7 @@ namespace graph namespace operation { -LowerInfo::LowerInfo(const backend::Backend &backend) : _backend(backend) +LowerInfo::LowerInfo(const backend::Backend *backend) : _backend(backend) { // DO NOTHING } diff --git a/runtimes/neurun/src/graph/operation/LowerInfo.h b/runtimes/neurun/src/graph/operation/LowerInfo.h index f3fbbf178..e920b0eb9 100644 --- a/runtimes/neurun/src/graph/operation/LowerInfo.h +++ b/runtimes/neurun/src/graph/operation/LowerInfo.h @@ -31,11 +31,11 @@ namespace operation class LowerInfo { public: - LowerInfo(const backend::Backend &backend); - const backend::Backend &backend() const { return _backend; } + LowerInfo(const backend::Backend *backend); + const backend::Backend *backend() const { return _backend; } private: - backend::Backend _backend; + const backend::Backend *_backend; }; } // namespace operation diff --git a/runtimes/neurun/src/graph/operation/MaxPool2D.cc b/runtimes/neurun/src/graph/operation/MaxPool2D.cc deleted file mode 100644 index 76648baf6..000000000 --- a/runtimes/neurun/src/graph/operation/MaxPool2D.cc +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MaxPool2D.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace MaxPool2D -{ -namespace Implicit -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 7); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> IFM Tensor Index - // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 2 -> Horizontal (over width) Stride Index - // 3 -> Vertial (over height) Stride Index - // 4 -> Filter Width Index - // 5 -> Filter Height Index - // 6 -> FuseCode (activation) Index - - setInputs({init_param.inputs[0]}); - setOutputs({init_param.outputs[0]}); - - _param.padding_index = init_param.inputs[1]; - _param.hstride_index = init_param.inputs[2]; - _param.vstride_index = init_param.inputs[3]; - - _param.kw_index = init_param.inputs[4]; - _param.kh_index = init_param.inputs[5]; - _param.activation_index = init_param.inputs[6]; -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Implicit -} // namespace MaxPool2D -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/MaxPool2D.h b/runtimes/neurun/src/graph/operation/MaxPool2D.h deleted file mode 100644 index 30f9b0b50..000000000 --- a/runtimes/neurun/src/graph/operation/MaxPool2D.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_MAXPOOL2D_H__ -#define __NEURUN_GRAPH_OPERATION_MAXPOOL2D_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace MaxPool2D -{ -namespace Implicit -{ - -struct Param -{ - int32_t kw_index; - int32_t kh_index; - - int32_t hstride_index; - int32_t vstride_index; - - int32_t padding_index; - int32_t activation_index; -}; - -class Node : public graph::operation::Node -{ -public: - virtual void accept(NodeVisitor &&) const override; - -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace Implicit -} // namespace MaxPool2D -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_MAXPOOL2D_H__ diff --git a/runtimes/neurun/src/graph/operation/NOP.cc b/runtimes/neurun/src/graph/operation/NOP.cc deleted file mode 100644 index 18c3246ce..000000000 --- a/runtimes/neurun/src/graph/operation/NOP.cc +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "NOP.h" - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace NOP -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -} // namespace NOP -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/NOP.h b/runtimes/neurun/src/graph/operation/NOP.h deleted file mode 100644 index 51b0f6f71..000000000 --- a/runtimes/neurun/src/graph/operation/NOP.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_NOP_H__ -#define __NEURUN_GRAPH_OPERATION_NOP_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace NOP -{ - -class Node : public graph::operation::Node -{ -public: - Node(const graph::operation::Node::InitParam &) {} - -public: - virtual void accept(NodeVisitor &&) const override; -}; - -} // namespace NOP -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_NOP_H__ diff --git a/runtimes/neurun/src/graph/operation/Node.cc b/runtimes/neurun/src/graph/operation/Node.cc deleted file mode 100644 index f472bc08c..000000000 --- a/runtimes/neurun/src/graph/operation/Node.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Node.h" - -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -Node::Node() = default; - -Node::~Node() = default; - -void Node::lower_info(std::unique_ptr<LowerInfo> &&lower_info) -{ - _lower_info = std::move(lower_info); -} - -const LowerInfo *Node::lower_info() const { return _lower_info.get(); } - -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Node.h b/runtimes/neurun/src/graph/operation/Node.h deleted file mode 100644 index 9e98184e3..000000000 --- a/runtimes/neurun/src/graph/operation/Node.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_NODE_H__ -#define __NEURUN_GRAPH_OPERATION_NODE_H__ - -#include <memory> - -#include "graph/operand/IndexSet.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -class LowerInfo; -struct NodeVisitor; - -class Node -{ -public: - struct InitParam - { - uint32_t input_count; - const uint32_t *inputs; - uint32_t output_count; - const uint32_t *outputs; - }; - -public: - Node(); - virtual ~Node(); - -public: - virtual void accept(NodeVisitor &&) const = 0; - -public: - virtual const operand::IndexSet &getInputs() const { return _inputs; } - virtual const operand::IndexSet &getOutputs() const { return _outputs; } - // It's for only input/output tensors but const data. - virtual void setInputs(const operand::IndexSet &indexes) { _inputs = indexes; } - virtual void setOutputs(const operand::IndexSet &indexes) { _outputs = indexes; } - -public: - void lower_info(std::unique_ptr<LowerInfo> &&lower_info); - const LowerInfo *lower_info() const; - -private: - operand::IndexSet _inputs; - operand::IndexSet _outputs; - std::unique_ptr<LowerInfo> _lower_info; -}; - -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_NODE_H__ diff --git a/runtimes/neurun/src/graph/operation/NodeVisitor.h b/runtimes/neurun/src/graph/operation/NodeVisitor.h deleted file mode 100644 index 28d19b0af..000000000 --- a/runtimes/neurun/src/graph/operation/NodeVisitor.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_NODE_VISITOR_H__ -#define __NEURUN_GRAPH_OPERATION_NODE_VISITOR_H__ - -#include "Conv2D.h" -#include "MaxPool2D.h" -#include "AvgPool2D.h" -#include "Concat.h" -#include "Reshape.h" -#include "FullyConnected.h" -#include "Softmax.h" -#include "NOP.h" -#include "Permute.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -struct NodeVisitor -{ - virtual ~NodeVisitor() = default; - - virtual void visit(const Conv2D::Implicit::Node &) = 0; - virtual void visit(const MaxPool2D::Implicit::Node &) = 0; - virtual void visit(const AvgPool2D::Implicit::Node &) = 0; - virtual void visit(const Concat::Node &) = 0; - virtual void visit(const Reshape::Node &) = 0; - virtual void visit(const FullyConnected::Node &) = 0; - virtual void visit(const Softmax::Node &) = 0; - virtual void visit(const NOP::Node &) = 0; - virtual void visit(const Permute::Node &) = 0; -}; - -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_NODE_VISITOR_H__ diff --git a/runtimes/neurun/src/graph/operation/Op.lst b/runtimes/neurun/src/graph/operation/Op.lst deleted file mode 100644 index 23b4123cb..000000000 --- a/runtimes/neurun/src/graph/operation/Op.lst +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef OP -#error Define OP before including this file -#endif - -// NOTE The relation between "Internal Name" and "NN API Name" is "1 : N". - -// Internal Name | NN API Name -OP(Conv2D::Implicit , CONV_2D) -OP(AvgPool2D::Implicit , AVERAGE_POOL_2D) -OP(MaxPool2D::Implicit , MAX_POOL_2D) -OP(Concat , CONCATENATION) -OP(FullyConnected , FULLY_CONNECTED) -OP(Reshape , RESHAPE) -OP(Softmax , SOFTMAX) diff --git a/runtimes/neurun/src/graph/operation/Permute.cc b/runtimes/neurun/src/graph/operation/Permute.cc deleted file mode 100644 index 2688e5e5f..000000000 --- a/runtimes/neurun/src/graph/operation/Permute.cc +++ /dev/null @@ -1,41 +0,0 @@ -#include "Permute.h" - -#include <cassert> - -#include "NodeVisitor.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Permute -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const operand::Index &input, const operand::Index &output) -{ - setInputs({input}); - setOutputs({output}); -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Permute -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Permute.h b/runtimes/neurun/src/graph/operation/Permute.h deleted file mode 100644 index 540f869b1..000000000 --- a/runtimes/neurun/src/graph/operation/Permute.h +++ /dev/null @@ -1,33 +0,0 @@ -#ifndef __NEURUN_GRAPH_OPERATION_PERMUTE_PERMUTE_H__ -#define __NEURUN_GRAPH_OPERATION_PERMUTE_PERMUTE_H__ - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Permute -{ - -class Node : public graph::operation::Node -{ -public: - virtual void accept(NodeVisitor &&) const override; - -public: - Node(const operand::Index &input, const operand::Index &output); - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; -}; - -} // namespace Permute -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_PERMUTE_PERMUTE_H__ diff --git a/runtimes/neurun/src/graph/operation/Reshape.cc b/runtimes/neurun/src/graph/operation/Reshape.cc deleted file mode 100644 index e6bc2117f..000000000 --- a/runtimes/neurun/src/graph/operation/Reshape.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Reshape.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Reshape -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 2 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A tensor, specifying the tensor to be reshaped. - // 1 -> A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output - // tensor - - // TODO Second input should be shape tensor (init_param.inputs[1]) - setInputs({init_param.inputs[0] /* , init_param.inputs[1] */}); - setOutputs({init_param.outputs[0]}); -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); // TODO Should be 2 (See also the constructor) - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Reshape -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Reshape.h b/runtimes/neurun/src/graph/operation/Reshape.h deleted file mode 100644 index 168719b46..000000000 --- a/runtimes/neurun/src/graph/operation/Reshape.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_RESHAPE_H__ -#define __NEURUN_GRAPH_OPERATION_RESHAPE_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Reshape -{ - -class Node : public graph::operation::Node -{ -public: - virtual void accept(NodeVisitor &&) const override; - -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; -}; - -} // namespace Reshape -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_RESHAPE_H__ diff --git a/runtimes/neurun/src/graph/operation/Set.cc b/runtimes/neurun/src/graph/operation/Set.cc deleted file mode 100644 index a1ddfa6d4..000000000 --- a/runtimes/neurun/src/graph/operation/Set.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Set.h" - -#include <cassert> - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -const Index Set::generateIndex() -{ - assert((_index_count) <= 0x7fffffff); - - return Index{_index_count++}; -} - -Index Set::append(std::unique_ptr<Node> &&node) -{ - auto index = generateIndex(); - - _nodes[index] = std::move(node); - return index; -} - -const Node &Set::at(const Index &index) const { return *(_nodes.at(index)); } - -Node &Set::at(const Index &index) { return *(_nodes.at(index)); } - -bool Set::exist(const Index &index) const { return _nodes.find(index) != _nodes.end(); } - -void Set::iterate(const std::function<void(const Index &, const Node &)> &fn) const -{ - for (auto it = _nodes.begin(); it != _nodes.end(); ++it) - { - fn(it->first, *it->second); - } -} - -void Set::iterate(const std::function<void(const Index &, Node &)> &fn) -{ - for (auto it = _nodes.begin(); it != _nodes.end(); ++it) - { - fn(it->first, *it->second); - } -} - -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Set.h b/runtimes/neurun/src/graph/operation/Set.h deleted file mode 100644 index bc6913ff4..000000000 --- a/runtimes/neurun/src/graph/operation/Set.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_SET_H__ -#define __NEURUN_GRAPH_OPERATION_SET_H__ - -#include <memory> - -#include "graph/operation/Index.h" -#include "Node.h" - -#include <unordered_map> - -namespace neurun -{ -namespace graph -{ -namespace operation -{ - -class Set -{ -public: - Set() : _index_count(0) {} - -public: - Index append(std::unique_ptr<Node> &&node); - -public: - const Node &at(const Index &) const; - Node &at(const Index &); - bool exist(const Index &) const; - uint32_t size() const { return _nodes.size(); } - void iterate(const std::function<void(const Index &, const Node &)> &fn) const; - void iterate(const std::function<void(const Index &, Node &)> &fn); - -private: - const Index generateIndex(); - -private: - std::unordered_map<Index, std::unique_ptr<Node>> _nodes; - uint32_t _index_count; -}; - -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_SET_H__ diff --git a/runtimes/neurun/src/graph/operation/Softmax.cc b/runtimes/neurun/src/graph/operation/Softmax.cc deleted file mode 100644 index 3b3c8661f..000000000 --- a/runtimes/neurun/src/graph/operation/Softmax.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Softmax.h" - -#include <cassert> - -#include "NodeVisitor.h" -#include "LowerInfo.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Softmax -{ - -void Node::accept(NodeVisitor &&v) const { v.visit(*this); } - -Node::Node(const graph::operation::Node::InitParam &init_param) -{ - assert(init_param.input_count == 2 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A 2-D or 4-D tensor, specifying the tensor to be reshaped. - // 1 -> FLOAT32 value, specifying the positive scaling factor for the exponent, beta. - - setInputs({init_param.inputs[0]}); - setOutputs({init_param.outputs[0]}); - - _param.scale_index = init_param.inputs[1]; -} - -void Node::setInputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setInputs(indexes); -} - -void Node::setOutputs(const operand::IndexSet &indexes) -{ - assert(indexes.size() == 1); - - graph::operation::Node::setOutputs(indexes); -} - -} // namespace Softmax -} // namespace operation -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/operation/Softmax.h b/runtimes/neurun/src/graph/operation/Softmax.h deleted file mode 100644 index e87a27518..000000000 --- a/runtimes/neurun/src/graph/operation/Softmax.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_OPERATION_SOFTMAX_H__ -#define __NEURUN_GRAPH_OPERATION_SOFTMAX_H__ - -#include <memory> - -#include "graph/operation/Node.h" - -namespace neurun -{ -namespace graph -{ -namespace operation -{ -namespace Softmax -{ - -struct Param -{ - int32_t scale_index; -}; - -class Node : public graph::operation::Node -{ -public: - virtual void accept(NodeVisitor &&) const override; - -public: - Node(const graph::operation::Node::InitParam &init_param); - -public: - virtual void setInputs(const operand::IndexSet &indexes) override; - virtual void setOutputs(const operand::IndexSet &indexes) override; - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace Softmax -} // namespace operation -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_OPERATION_SOFTMAX_H__ diff --git a/runtimes/neurun/src/graph/pass/OperandPass.cc b/runtimes/neurun/src/graph/pass/OperandPass.cc new file mode 100644 index 000000000..3c24d3830 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/OperandPass.cc @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OperandPass.h" + +#include "graph/Graph.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +void OperandPass::run() +{ + _graph.operands().iterate([&](const model::operand::Index &index, + model::operand::Object &object) { callback(index, object); }); +} + +} // namespace pass +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/pass/OperandPass.h b/runtimes/neurun/src/graph/pass/OperandPass.h new file mode 100644 index 000000000..b84391082 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/OperandPass.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_PASS_OPERAND_PASS_H__ +#define __NEURUN_GRAPH_PASS_OPERAND_PASS_H__ + +#include "Pass.h" +#include "model/operand/Index.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ +class Object; +} // namespace operand +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +class OperandPass : public Pass +{ +public: + using Pass::Pass; + +public: + virtual std::string id() = 0; + virtual void run() override final; + virtual void callback(const model::operand::Index &i, model::operand::Object &o) = 0; +}; + +} // namespace pass +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_PASS_OPERAND_PASS_H__ diff --git a/runtimes/neurun/src/graph/pass/OperationPass.cc b/runtimes/neurun/src/graph/pass/OperationPass.cc new file mode 100644 index 000000000..e71f79188 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/OperationPass.cc @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OperationPass.h" + +#include "graph/Graph.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +void OperationPass::run() +{ + _graph.operations().iterate([&](const model::operation::Index &index, + model::operation::Node &node) { callback(index, node); }); +} + +} // namespace pass +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/pass/OperationPass.h b/runtimes/neurun/src/graph/pass/OperationPass.h new file mode 100644 index 000000000..e86f1aa57 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/OperationPass.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file OperationPass.h + * @brief This file contains OperationPass class + */ + +#ifndef __NEURUN_GRAPH_PASS_OPERATION_PASS_H__ +#define __NEURUN_GRAPH_PASS_OPERATION_PASS_H__ + +#include "Pass.h" + +#include "model/operation/Index.h" +#include "model/operation/Node.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +/** + * @brief Class to iterate over operations and calls callback() method + */ +class OperationPass : public Pass +{ +public: + using Pass::Pass; + +public: + /** + * @brief Returns string id for this pass. Same with class name. + * + * @return string id + */ + virtual std::string id() = 0; + + /** + * @brief Run the pass + */ + virtual void run() override final; + + /** + * @brief The function that will be executed for each operations + * + * @param i[in] Index of the operation node + * @param n[in] The operation node + */ + virtual void callback(const model::operation::Index &i, model::operation::Node &n) = 0; +}; + +} // namespace pass +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_PASS_OPERATION_PASS_H__ diff --git a/runtimes/neurun/src/graph/pass/Pass.cc b/runtimes/neurun/src/graph/pass/Pass.cc new file mode 100644 index 000000000..4c3436961 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/Pass.cc @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Pass.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +} // namespace pass +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/pass/Pass.h b/runtimes/neurun/src/graph/pass/Pass.h new file mode 100644 index 000000000..4200936d1 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/Pass.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_PASS_PASS_H__ +#define __NEURUN_GRAPH_PASS_PASS_H__ + +#include <string> + +namespace neurun +{ +namespace graph +{ +class Graph; +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +class Pass +{ +public: + Pass(Graph &graph) : _graph{graph} {} + virtual ~Pass() = default; + +public: + virtual std::string id() = 0; + virtual void run() = 0; + +protected: + Graph &_graph; +}; + +} // namespace pass +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_PASS_PASS_H__ diff --git a/runtimes/neurun/src/graph/pass/PermutationEliminationPass.cc b/runtimes/neurun/src/graph/pass/PermutationEliminationPass.cc new file mode 100644 index 000000000..848f6b574 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/PermutationEliminationPass.cc @@ -0,0 +1,192 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PermutationEliminationPass.h" + +#include "model/operand/Object.h" +#include "graph/Graph.h" +#include "backend/interface/IConfig.h" +#include "util/logging.h" +#include "compiler/BackendResolver.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ +void PermutationEliminationPass::callback(const model::operand::Index &inp_index, + model::operand::Object &object) +{ + if (_graph.getInputs().contains(inp_index)) + { + eliminateInput(inp_index, object); + } + else if (_graph.getOutputs().contains(inp_index)) + { + eliminateOutput(inp_index, object); + } +} + +void PermutationEliminationPass::eliminateInput(const model::operand::Index &inp_index, + model::operand::Object &object) +{ + auto &model_inputs = _graph.getInputs(); + + // get uses of the model's given input + auto uses = object.getUses(); + + // input must be used just by permutation + if (uses.size() != 1) + { + return; + } + + for (auto input_use : uses.list()) + { + auto &perm_operation = _graph.operations().at(input_use); + auto perm_inputs = perm_operation.getInputs(); + + auto perm_outputs = perm_operation.getOutputs(); + + if (!isPermuteLayerToEliminate(perm_inputs, perm_outputs, true)) + { + return; + } + + assert(perm_inputs.at(0) == inp_index); + + VERBOSE(PermutationEliminationPass::EliminateInput) << "remove NHWC_TO_NCHW permutation\n"; + + // set model's new input, which was output of permutation + model_inputs.replace(inp_index, perm_outputs.at(0)); + + // remove model's input, which is also input of permutation + _graph.removeOperand(inp_index); + + // remove permutation operation + _graph.operations().remove(input_use); + + VERBOSE(PermutationEliminationPass::EliminateInput) + << inp_index.value() << " is model's input and is removed. New input is " + << perm_outputs.at(0).value() << "\n" + << input_use.value() << " is removed permutation operation\n"; + } +} + +void PermutationEliminationPass::eliminateOutput(const model::operand::Index &out_index, + model::operand::Object &object) +{ + auto &model_outputs = _graph.getOutputs(); + + // get defs of the model's given output + auto defs = object.getDef(); + + // output must use just permutation + if (defs.size() != 1) + { + return; + } + + for (auto output_def : defs.list()) + { + auto &perm_operation = _graph.operations().at(output_def); + auto perm_outputs = perm_operation.getOutputs(); + + auto perm_inputs = perm_operation.getInputs(); + if (!isPermuteLayerToEliminate(perm_inputs, perm_outputs, false)) + { + return; + } + + assert(perm_outputs.at(0) == out_index); + + VERBOSE(PermutationEliminationPass::EliminateOutput) << "remove NCHW_TO_NHWC permutation\n"; + + // Update operations' output that is used by permute operand + for (auto perm_input_index : perm_inputs) + { + auto &perm_input_operand = _graph.operands().at(perm_input_index); + perm_input_operand.removeUse(output_def); + } + + // set model's new output, which was input of permutation + model_outputs.replace(out_index, perm_inputs.at(0)); + + // remove model's output, which is also output of permutation + _graph.removeOperand(out_index); + + // remove permutation operation + _graph.operations().remove(output_def); + + VERBOSE(PermutationEliminationPass::EliminateOutput) + << out_index.value() << " is model's output and is removed. New output is " + << perm_inputs.at(0).value() << "\n" + << output_def.value() << " is removed permutation operation\n"; + } +} + +bool PermutationEliminationPass::isPermuteLayerToEliminate( + const model::operand::IndexSet &inp_indexes, const model::operand::IndexSet &out_indexes, + bool is_for_model_input) +{ + auto input_def_backends = _graph.operands().at(inp_indexes.at(0)).lower_info()->def_backends(); + auto output_def_backends = _graph.operands().at(out_indexes.at(0)).lower_info()->def_backends(); + + auto input_layout = input_def_backends.getOnlyElement()->config()->getOperandLayout(); + auto output_layout = output_def_backends.getOnlyElement()->config()->getOperandLayout(); + + if (input_def_backends.size() != 1 || output_def_backends.size() != 1) + { + return false; + } + + // all operands' backend must be the same + for (auto index : inp_indexes) + { + auto op_backend_set = _graph.operands().at(index).lower_info()->def_backends(); + if (op_backend_set.size() != 1 || + input_layout != op_backend_set.getOnlyElement()->config()->getOperandLayout()) + { + return false; + } + } + // all operands' backend must be the same + for (auto index : out_indexes) + { + auto op_backend_set = _graph.operands().at(index).lower_info()->def_backends(); + if (op_backend_set.size() != 1 || + output_layout != op_backend_set.getOnlyElement()->config()->getOperandLayout()) + { + return false; + } + } + + if (is_for_model_input) + { + // check if this is NHWC_TO_NCHW permutation: must have single input, which is model's input + return (inp_indexes.size() == 1 && input_layout == graph::operand::Layout::NHWC && + output_layout == graph::operand::Layout::NCHW); + } + + // check if this is NCHW_TO_NHWC permutation: must have single output, which is model's output + return (out_indexes.size() == 1 && input_layout == graph::operand::Layout::NCHW && + output_layout == graph::operand::Layout::NHWC); +} + +} // namespace pass +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/pass/PermutationEliminationPass.h b/runtimes/neurun/src/graph/pass/PermutationEliminationPass.h new file mode 100644 index 000000000..2b528c479 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/PermutationEliminationPass.h @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__ +#define __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__ + +#include "OperandPass.h" +#include "model/operand/Object.h" +#include "model/operand/IndexSet.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +class PermutationEliminationPass : public OperandPass +{ +public: + using OperandPass::OperandPass; + +public: + virtual std::string id() override { return "PermutationEliminationPass"; } + + virtual void callback(const model::operand::Index &index, model::operand::Object &object); + +private: + /** + * @brief Remove Permute operation that permutates input + * + * Note: This function aslo removes model's input and + * sets output of permutation as model's new input + * + * @param inp_index is the target operand index for the elimination + * @param object is the target operand object for the elimination + * + * @return + */ + void eliminateInput(const model::operand::Index &inp_index, model::operand::Object &object); + + /** + * @brief Remove Permute operation that permutates output of a model + * + * Note: This function aslo removes model's output and + * sets input of permutation as model's new output + * + * @param out_index is the target operand index for the elimination + * @param object is the target operand object for the elimination + * + * @return + */ + void eliminateOutput(const model::operand::Index &out_index, model::operand::Object &object); + + /** + * @brief Determine if passed operands are permute layer's input and output, that must be + * eliminated + * + * @param inp_index indexes of the input operand to operation + * @param out_index indexes of the output operand to operation + * @param is_for_model_input checking for model's input or output + * + * @return if it is permutation layer + */ + bool isPermuteLayerToEliminate(const model::operand::IndexSet &inp_indexes, + const model::operand::IndexSet &out_indexes, + bool is_for_model_input); +}; + +} // namespace pass +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_PASS_PERMUTATION_ELIMINATION_PASS_H__ diff --git a/runtimes/neurun/src/graph/pass/PermutationInsertionPass.cc b/runtimes/neurun/src/graph/pass/PermutationInsertionPass.cc new file mode 100644 index 000000000..9b833b8c5 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/PermutationInsertionPass.cc @@ -0,0 +1,191 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PermutationInsertionPass.h" + +#include <cassert> +#include <utility> +#include <unordered_map> + +#include "model/operand/Object.h" +#include "graph/operation/LowerInfo.h" +#include "graph/Graph.h" +#include "backend/interface/IConfig.h" +#include "util/logging.h" +#include "cpp14/memory.h" +#include "model/operation/PermuteNode.h" +#include "graph/operand/Shape4DConvert.h" +#include "compiler/BackendResolver.h" + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +void PermutationInsertionPass::callback(const model::operand::Index &index, + model::operand::Object &object) +{ + auto &&operand_li = object.lower_info(); + assert(operand_li); + + // NOTE Later, constants also will have Def + // Ignore constants + if (operand_li->def_backends().size() == 0) + { + return; + } + + std::list<model::operation::Index> permute_indexes; + + // Build a map for all necessary type of operands + std::unordered_map<const backend::Backend *, model::operand::Index> backend_to_index; + { + assert(operand_li->def_backends().size() == 1); + for (auto backend : operand_li->def_backends()) + { + backend_to_index.insert({backend, index}); + } + + auto insert_set = operand_li->use_backends() - operand_li->def_backends(); + for (auto backend : insert_set) + { + const auto permute_operation_index = insertPermute(index, backend); + permute_indexes.push_back(permute_operation_index); + VERBOSE(PermutationInsertionPass) << "Insert 'Permute' operation for operand " + << index.value() << std::endl; + const auto &permute_operation = _graph.operations().at(permute_operation_index); + const auto permuted_operand_index = permute_operation.getOutputs().at(0); + backend_to_index.insert({backend, permuted_operand_index}); + } + } + + // Update operations' input that uses this operand + { + std::list<model::operation::Index> remove_list; + + auto uses = object.getUses(); + for (auto use : uses.list()) + { + // If permute operation, ignore it + if (std::find(permute_indexes.begin(), permute_indexes.end(), use) != permute_indexes.end()) + continue; + + auto &operation = _graph.operations().at(use); + auto operation_li = _graph.getLowerInfo(use); + assert(operation_li); + auto backend = operation_li->backend(); + + auto use_node_inputs = operation.getInputs(); + assert(use_node_inputs.contains(index)); + + auto new_index = backend_to_index.at(backend); + if (index != new_index) + { + // Update from operation + operation.replaceInput(index, new_index); + + // Update from operand + remove_list.push_back( + use); // Removal should be done in another loop since we are in the loop + _graph.operands().at(new_index).appendUse(use); + } + } + + for (auto &operation : remove_list) + { + object.removeUse(operation); + } + } +} + +model::operation::Index +PermutationInsertionPass::insertPermute(const model::operand::Index &operand_index, + const backend::Backend *backend) +{ + assert(!_graph.isBuildingPhase()); + + auto &operand = _graph.operands().at(operand_index); + + // Generate output operand and permute operation + auto out_operand_index = _graph.addOperand(operand.shape(), operand.typeInfo()); + auto &out_operand = _graph.operands().at(out_operand_index); + out_operand.setAsOperationOutput(); + // change model output if operand_index is model output index + auto &model_outputs = _graph.getOutputs(); + if (model_outputs.contains(operand_index)) + { + model_outputs.replace(operand_index, out_operand_index); + } + out_operand.setAsOperationOutput(); + auto out_operand_li = + nnfw::cpp14::make_unique<operand::LowerInfo>(operand::asShape4D(operand.shape())); + out_operand_li->addDefBackend(backend); + out_operand_li->addUseBackend(backend); + out_operand.lower_info(std::move(out_operand_li)); + + // Update LowerInfo of input operand + operand.lower_info()->removeUseBackend(backend); + operand.lower_info()->addUseBackend(operand.lower_info()->def_backends().getOnlyElement()); + + using PermuteNode = model::operation::PermuteNode; + + // Find Permutation Type + auto type = [&]() { + auto input_layout = + operand.lower_info()->def_backends().getOnlyElement()->config()->getOperandLayout(); + auto output_layout = + out_operand.lower_info()->def_backends().getOnlyElement()->config()->getOperandLayout(); + + if (input_layout == graph::operand::Layout::NHWC && + output_layout == graph::operand::Layout::NCHW) + { + return PermuteNode::Type::NHWC_TO_NCHW; + } + else if (input_layout == graph::operand::Layout::NCHW && + output_layout == graph::operand::Layout::NHWC) + { + return PermuteNode::Type::NCHW_TO_NHWC; + } + else + { + return PermuteNode::Type::COPY; + } + }(); + + // Insert permute operation to the graph + auto insert_node = nnfw::cpp14::make_unique<PermuteNode>(operand_index, out_operand_index, type); + + auto node_index = _graph.operations().append(std::move(insert_node)); + const auto &node = _graph.operations().at(node_index); + + _graph.setLowerInfo(node_index, nnfw::cpp14::make_unique<graph::operation::LowerInfo>( + _graph.backend_resolver()->getDefaultBackend())); + + // Update Use/Def info + { + _graph.operands().at(operand_index).appendUse(node_index); + + auto node_out_indexes = node.getOutputs(); + auto node_out_index = node_out_indexes.at(model::operand::IO::Index{0}); + _graph.operands().at(node_out_index).appendDef(node_index); + } + return node_index; +} +} // namespace pass +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/pass/PermutationInsertionPass.h b/runtimes/neurun/src/graph/pass/PermutationInsertionPass.h new file mode 100644 index 000000000..b2d417e82 --- /dev/null +++ b/runtimes/neurun/src/graph/pass/PermutationInsertionPass.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__ +#define __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__ + +#include "OperandPass.h" +#include "model/operand/Object.h" //for model::operation::Index + +namespace neurun +{ +namespace graph +{ +namespace pass +{ + +class PermutationInsertionPass : public OperandPass +{ +public: + using OperandPass::OperandPass; + +public: + virtual std::string id() override { return "PermutationInsertionPass"; } + virtual void callback(const model::operand::Index &index, model::operand::Object &object); + + /** + * @brief Insert Permute operation that has given operand as input + * + * @param operand_index is the target operand index for the insertion + * @param backend is the output operand's backend type + * + * @return model::operation::Index + */ + model::operation::Index insertPermute(const model::operand::Index &operand_index, + const backend::Backend *backend); + +private: +}; + +} // namespace pass +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_PASS_PERMUTATION_INSERTION_PASS_H__ diff --git a/runtimes/neurun/src/graph/verifier/IVerifier.cc b/runtimes/neurun/src/graph/verifier/IVerifier.cc deleted file mode 100644 index f8402695a..000000000 --- a/runtimes/neurun/src/graph/verifier/IVerifier.cc +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "IVerifier.h" - -#include "graph/Graph.h" - -namespace neurun -{ -namespace graph -{ -namespace verifier -{ - -bool DAGChecker::verify(const Graph &graph) const -{ - auto &operations = graph.operations(); - bool cyclic = false; - std::vector<bool> visited(operations.size(), false); - std::vector<bool> on_stack(operations.size(), false); - - std::function<void(const operation::Index &index, const operation::Node &)> dfs_recursive = - [&](const operation::Index &index, const operation::Node &node) -> void { - if (on_stack[index.value()]) - cyclic = true; - if (visited[index.value()]) - return; - visited[index.value()] = true; - on_stack[index.value()] = true; - - auto outputs = node.getOutputs(); - for (auto output : outputs) - { - // TODO Fix traversing algorithm - // Every time need to search for operations that has `outgoing` as incoming from all - // operations but we can hold that info cached - operations.iterate([&](const operation::Index &cand_index, const operation::Node &cand_node) { - auto inputs = cand_node.getInputs(); - for (auto input : inputs) - { - if (output == input) - { - dfs_recursive(cand_index, cand_node); - } - } - }); - } - - on_stack[index.value()] = false; - }; - - operations.iterate(dfs_recursive); - - return !cyclic; -} - -} // namespace verifier -} // namespace graph -} // namespace neurun diff --git a/runtimes/neurun/src/graph/verifier/IVerifier.h b/runtimes/neurun/src/graph/verifier/IVerifier.h deleted file mode 100644 index 17fe03f24..000000000 --- a/runtimes/neurun/src/graph/verifier/IVerifier.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_GRAPH_VERIFIER_I_VERIFIER_H__ -#define __NEURUN_GRAPH_VERIFIER_I_VERIFIER_H__ - -namespace neurun -{ -namespace graph -{ -class Graph; -} // namespace graph -} // namespace neurun - -namespace neurun -{ -namespace graph -{ -namespace verifier -{ - -struct IVerifier -{ - virtual ~IVerifier() = default; - virtual bool verify(const Graph &graph) const = 0; -}; - -} // namespace verifier -} // namespace graph -} // namespace neurun - -namespace neurun -{ -namespace graph -{ -namespace verifier -{ - -class DAGChecker : public IVerifier -{ -public: - virtual bool verify(const Graph &graph) const override; -}; - -} // namespace verifier -} // namespace graph -} // namespace neurun - -#endif // __NEURUN_GRAPH_VERIFIER_I_VERIFIER_H__ diff --git a/runtimes/neurun/src/graph/verifier/Verifier.cc b/runtimes/neurun/src/graph/verifier/Verifier.cc new file mode 100644 index 000000000..a5b53af85 --- /dev/null +++ b/runtimes/neurun/src/graph/verifier/Verifier.cc @@ -0,0 +1,97 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Verifier.h" + +#include "graph/Graph.h" + +#include "util/logging.h" + +namespace neurun +{ +namespace graph +{ +namespace verifier +{ + +// +// DAGChecker +// + +bool DAGChecker::verify(const Graph &graph) const +{ + auto &operations = graph.operations(); + bool cyclic = false; + + std::unordered_map<model::operation::Index, bool> visited; + operations.iterate([&](const model::operation::Index &index, const model::operation::Node &) { + visited[index] = false; + }); + std::unordered_map<model::operation::Index, bool> on_stack = visited; // Copy from visited + + std::function<void(const model::operation::Index &index, const model::operation::Node &)> + dfs_recursive = + [&](const model::operation::Index &index, const model::operation::Node &node) -> void { + if (on_stack[index]) + cyclic = true; + if (visited[index]) + return; + visited[index] = true; + on_stack[index] = true; + + for (auto output : node.getOutputs()) + { + const auto &operand = graph.operands().at(output); + for (const auto &use : operand.getUses().list()) + { + dfs_recursive(use, graph.operations().at(use)); + } + } + + on_stack[index] = false; + }; + + operations.iterate(dfs_recursive); + + return !cyclic; +} + +// +// EdgeConsistencyVerifier +// + +bool EdgeConsistencyChecker::verify(const Graph &graph) const +{ + auto &operations = graph.operations(); + uint32_t mismatches = 0; + operations.iterate([&](const model::operation::Index &index, const model::operation::Node &node) { + for (auto operand_index : node.getInputs()) + { + auto &operand = graph.operands().at(operand_index); + mismatches += (operand.getUses().contains(index) ? 0 : 1); + } + for (auto operand_index : node.getOutputs()) + { + auto &operand = graph.operands().at(operand_index); + mismatches += (operand.getDef().contains(index) ? 0 : 1); + } + }); + return mismatches == 0; +} + +} // namespace verifier +} // namespace graph +} // namespace neurun diff --git a/runtimes/neurun/src/graph/verifier/Verifier.h b/runtimes/neurun/src/graph/verifier/Verifier.h new file mode 100644 index 000000000..5f1f79ee6 --- /dev/null +++ b/runtimes/neurun/src/graph/verifier/Verifier.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_GRAPH_VERIFIER_VERIFIER_H__ +#define __NEURUN_GRAPH_VERIFIER_VERIFIER_H__ + +namespace neurun +{ +namespace graph +{ +class Graph; +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace graph +{ +namespace verifier +{ + +struct IVerifier +{ + virtual ~IVerifier() = default; + virtual bool verify(const Graph &graph) const = 0; +}; + +} // namespace verifier +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace graph +{ +namespace verifier +{ + +class DAGChecker : public IVerifier +{ +public: + virtual bool verify(const Graph &graph) const override; +}; + +class EdgeConsistencyChecker : public IVerifier +{ +public: + virtual bool verify(const Graph &graph) const override; +}; + +} // namespace verifier +} // namespace graph +} // namespace neurun + +#endif // __NEURUN_GRAPH_VERIFIER_VERIFIER_H__ diff --git a/runtimes/neurun/src/internal/Convert.cc b/runtimes/neurun/src/internal/Convert.cc deleted file mode 100644 index c0260b04e..000000000 --- a/runtimes/neurun/src/internal/Convert.cc +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Convert.h" - -namespace internal -{ - -::arm_compute::TensorShape asTensorShape(int32_t h, int32_t w) -{ - return ::arm_compute::TensorShape(w, h); -} - -::arm_compute::TensorShape asTensorShape(const nnfw::util::feature::Shape &shape) -{ - return ::arm_compute::TensorShape(shape.W, shape.H, shape.C, shape.N); -} - -::arm_compute::TensorShape asTensorShape(const nnfw::util::kernel::Shape &shape) -{ - return ::arm_compute::TensorShape(shape.W, shape.H, shape.C, shape.N); -} - -::arm_compute::TensorInfo asTensorInfo(const nnfw::util::feature::Shape &shape) -{ - return ::arm_compute::TensorInfo(asTensorShape(shape), 1, ::arm_compute::DataType::F32); -} - -::arm_compute::TensorInfo asTensorInfo(const nnfw::util::kernel::Shape &shape) -{ - return ::arm_compute::TensorInfo(asTensorShape(shape), 1, ::arm_compute::DataType::F32); -} - -::arm_compute::TensorInfo asTensorInfo(int32_t size) -{ - return ::arm_compute::TensorInfo(::arm_compute::TensorShape(size), 1, - ::arm_compute::DataType::F32); -} - -::arm_compute::TensorInfo asTensorInfo(int32_t h, int32_t w) -{ - return ::arm_compute::TensorInfo(::arm_compute::TensorShape(w, h), 1, - ::arm_compute::DataType::F32); -} - -} // namespace internal diff --git a/runtimes/neurun/src/internal/Convert.h b/runtimes/neurun/src/internal/Convert.h deleted file mode 100644 index f279133aa..000000000 --- a/runtimes/neurun/src/internal/Convert.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_CONVERT_H__ -#define __INTERNAL_CONVERT_H__ - -#include <arm_compute/core/TensorInfo.h> -#include <arm_compute/core/TensorShape.h> - -#include "util/feature/Shape.h" -#include "util/kernel/Shape.h" - -namespace internal -{ - -::arm_compute::TensorShape asTensorShape(int32_t h, int32_t w); -::arm_compute::TensorShape asTensorShape(const nnfw::util::feature::Shape &shape); -::arm_compute::TensorShape asTensorShape(const nnfw::util::kernel::Shape &shape); - -::arm_compute::TensorInfo asTensorInfo(const nnfw::util::feature::Shape &shape); -::arm_compute::TensorInfo asTensorInfo(const nnfw::util::kernel::Shape &shape); -::arm_compute::TensorInfo asTensorInfo(int32_t size); -::arm_compute::TensorInfo asTensorInfo(int32_t h, int32_t w); - -} // namespace internal - -#endif // __INTERNAL_CONVERT_H__ diff --git a/runtimes/neurun/src/internal/Padding.cc b/runtimes/neurun/src/internal/Padding.cc deleted file mode 100644 index 200fa1a02..000000000 --- a/runtimes/neurun/src/internal/Padding.cc +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "internal/Padding.h" - -#include <algorithm> - -namespace internal -{ - -Padding valid_padding(void) -{ - // - // ANEURALNETWORKS_PADDING_VALID - // - // VALID padding. No padding. - // - // When the input size is not evenly divisible by the filter size, - // the input at the end that could not fill the whole filter tile - // will simply be ignored. - // - Padding padding; - - padding.top = 0; - padding.bottom = 0; - padding.left = 0; - padding.right = 0; - - return padding; -} - -Padding same_padding(const nnfw::util::feature::Shape &ifm_shape, - const nnfw::util::feature::Shape &ofm_shape, const Stride &stride, uint32_t kw, - uint32_t kh) -{ - Padding padding; - - // ANEURALNETWORKS_PADDING_SAME (from NNAPI spec) - // - // SAME padding. Padding on both ends are the "same": - // - // padding_to_beginning = total_padding / 2 - // padding_to_end = (total_padding + 1)/2. - // - const int32_t vertical_needed_input = (ofm_shape.H - 1) * stride.vertical + kh; - const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H); - - const int32_t horizontal_needed_input = (ofm_shape.W - 1) * stride.horizontal + kw; - const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W); - - padding.top = vertical_total_padding / 2; - padding.bottom = (vertical_total_padding + 1) / 2; - padding.left = horizontal_total_padding / 2; - padding.right = (horizontal_total_padding + 1) / 2; - - return padding; -} - -} // namespace internal diff --git a/runtimes/neurun/src/internal/Padding.h b/runtimes/neurun/src/internal/Padding.h deleted file mode 100644 index 84e081a78..000000000 --- a/runtimes/neurun/src/internal/Padding.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_PADDING_H__ -#define __INTERNAL_PADDING_H__ - -#include <stdint.h> - -#include <util/feature/Shape.h> - -namespace internal -{ - -struct Padding -{ - uint32_t top; - uint32_t bottom; - uint32_t left; - uint32_t right; -}; - -struct Stride -{ - uint32_t vertical; - uint32_t horizontal; -}; - -Padding valid_padding(void); -Padding same_padding(const nnfw::util::feature::Shape &ifm_shape, - const nnfw::util::feature::Shape &ofm_shape, const Stride &stride, uint32_t kw, - uint32_t kh); - -} // namespace internal - -#endif // __INTERNAL_PADDING_H__ diff --git a/runtimes/neurun/src/internal/nnapi/feature/Reader.h b/runtimes/neurun/src/internal/nnapi/feature/Reader.h deleted file mode 100644 index eb513512d..000000000 --- a/runtimes/neurun/src/internal/nnapi/feature/Reader.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_NNAPI_FEATURE_READER_H__ -#define __INTERNAL_NNAPI_FEATURE_READER_H__ - -#include "internal/nnapi/feature/Utils.h" - -#include "util/feature/Reader.h" - -namespace internal -{ -namespace nnapi -{ -namespace feature -{ - -template <typename T> class Reader; - -template <> class Reader<float> final : public nnfw::util::feature::Reader<float> -{ -public: - Reader(const ::nnfw::util::feature::Shape &shape, const uint8_t *ptr, size_t len) - : _shape{shape}, _ptr{ptr}, _len{len} - { - // DO NOTHING - } - -public: - const nnfw::util::feature::Shape &shape(void) const { return _shape; } - -public: - float at(uint32_t ch, uint32_t row, uint32_t col) const override - { - uint32_t index = index_of(_shape, ch, row, col); - - const auto arr = reinterpret_cast<const float *>(_ptr); - - return arr[index]; - } - float at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override - { - uint32_t index = index_of(_shape, batch, ch, row, col); - - const auto arr = reinterpret_cast<const float *>(_ptr); - - return arr[index]; - } - -private: - nnfw::util::feature::Shape _shape; - -private: - const uint8_t *_ptr; - const size_t _len; -}; - -} // namespace feature -} // namespace nnapi -} // namespace internal - -#endif // __INTERNAL_NNAPI_FEATURE_READER_H__ diff --git a/runtimes/neurun/src/internal/nnapi/feature/Utils.h b/runtimes/neurun/src/internal/nnapi/feature/Utils.h deleted file mode 100644 index e6e1e71bd..000000000 --- a/runtimes/neurun/src/internal/nnapi/feature/Utils.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_NNAPI_FEATURE_UTILS_H__ -#define __INTERNAL_NNAPI_FEATURE_UTILS_H__ - -#include "util/feature/Shape.h" - -namespace internal -{ -namespace nnapi -{ -namespace feature -{ - -inline uint32_t index_of(const ::nnfw::util::feature::Shape &shape, uint32_t ch, uint32_t row, - uint32_t col) -{ - uint32_t res = 0; - - // NNAPI uses NHWC ordering - res += row * shape.W * shape.C; - res += col * shape.C; - res += ch; - - return res; -} - -inline uint32_t index_of(const ::nnfw::util::feature::Shape &shape, uint32_t batch, uint32_t ch, - uint32_t row, uint32_t col) -{ - uint32_t res = 0; - - // NNAPI uses NHWC ordering - res += batch * shape.H * shape.W * shape.C; - res += row * shape.W * shape.C; - res += col * shape.C; - res += ch; - - return res; -} - -} // namespace feature -} // namespace nnapi -} // namespace internal - -#endif // __INTERNAL_NNAPI_FEATURE_UTILS_H__ diff --git a/runtimes/neurun/src/internal/nnapi/feature/View.h b/runtimes/neurun/src/internal/nnapi/feature/View.h deleted file mode 100644 index 60335dbf1..000000000 --- a/runtimes/neurun/src/internal/nnapi/feature/View.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_NNAPI_FEATURE_VIEW_H__ -#define __INTERNAL_NNAPI_FEATURE_VIEW_H__ - -#include "internal/nnapi/feature/Utils.h" - -#include "util/feature/Reader.h" - -namespace internal -{ -namespace nnapi -{ -namespace feature -{ - -template <typename T> class View final : public nnfw::util::feature::Reader<float> -{ -public: - View(const ::nnfw::util::feature::Shape &shape, uint8_t *ptr, size_t len) - : _shape{shape}, _ptr{ptr}, _len{len} - { - // DO NOTHING - } - -public: - const nnfw::util::feature::Shape &shape(void) const { return _shape; } - -public: - T at(uint32_t ch, uint32_t row, uint32_t col) const override - { - uint32_t index = index_of(_shape, ch, row, col); - - T *arr = reinterpret_cast<T *>(_ptr); - - return arr[index]; - } - - T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override - { - uint32_t index = index_of(_shape, batch, ch, row, col); - - T *arr = reinterpret_cast<T *>(_ptr); - - return arr[index]; - } - - T &at(uint32_t ch, uint32_t row, uint32_t col) - { - uint32_t index = index_of(_shape, ch, row, col); - - T *arr = reinterpret_cast<T *>(_ptr); - - return arr[index]; - } - - T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) - { - uint32_t index = index_of(_shape, batch, ch, row, col); - - T *arr = reinterpret_cast<T *>(_ptr); - - return arr[index]; - } - -private: - nnfw::util::feature::Shape _shape; - -private: - uint8_t *_ptr; - const size_t _len; -}; - -} // namespace feature -} // namespace nnapi -} // namespace internal - -#endif // __INTERNAL_NNAPI_FEATURE_VIEW_H__ diff --git a/runtimes/neurun/src/internal/nnapi/kernel/Reader.h b/runtimes/neurun/src/internal/nnapi/kernel/Reader.h deleted file mode 100644 index 9d93800bf..000000000 --- a/runtimes/neurun/src/internal/nnapi/kernel/Reader.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_NNAPI_KERNEL_READER_H__ -#define __INTERNAL_NNAPI_KERNEL_READER_H__ - -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" - -namespace internal -{ -namespace nnapi -{ -namespace kernel -{ - -template <typename T> class Reader final : public nnfw::util::kernel::Reader<T> -{ -public: - Reader(const ::nnfw::util::kernel::Shape &shape, const uint8_t *base, size_t size) - : _shape{shape}, _base{base}, _size{size} - { - // DO NOTHING - } - -public: - const nnfw::util::kernel::Shape &shape(void) const { return _shape; } - -public: - T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override - { - // NNAPI uses NHWC ordering - uint32_t index = 0; - - index += nth * _shape.H * _shape.W * _shape.C; - index += row * _shape.W * _shape.C; - index += col * _shape.C; - index += ch; - - const T *ptr = reinterpret_cast<const T *>(_base); - - return ptr[index]; - } - -private: - nnfw::util::kernel::Shape _shape; - -private: - const uint8_t *_base; - const size_t _size; -}; - -} // namespace kernel -} // namespace nnapi -} // namespace internal - -#endif // __INTERNAL_NNAPI_KERNEL_READER_H__ diff --git a/runtimes/neurun/src/internal/nnapi/kernel/View.h b/runtimes/neurun/src/internal/nnapi/kernel/View.h deleted file mode 100644 index 86d19b87f..000000000 --- a/runtimes/neurun/src/internal/nnapi/kernel/View.h +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __INTERNAL_NNAPI_KERNEL_VIEW_H__ -#define __INTERNAL_NNAPI_KERNEL_VIEW_H__ - -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" - -#include <arm_compute/core/ITensor.h> - -namespace internal -{ -namespace nnapi -{ -namespace kernel -{ - -template <typename T> class View final : public nnfw::util::kernel::Reader<float> -{ -public: - View(::arm_compute::ITensor *tensor) : _tensor{tensor} - { - assert(tensor->info()->data_type() == ::arm_compute::DataType::F32); - - _shape.N = tensor->info()->dimension(3); - _shape.C = tensor->info()->dimension(2); - _shape.H = tensor->info()->dimension(1); - _shape.W = tensor->info()->dimension(0); - } - -public: - const nnfw::util::kernel::Shape &shape(void) const { return _shape; } - -public: - float at(uint32_t nth, uint32_t row, uint32_t col, uint32_t ch) const override - { - // NNAPI uses NHWC ordering - uint32_t index = 0; - - index += nth * _shape.H * _shape.W * _shape.C; - index += row * _shape.W * _shape.C; - index += col * _shape.C; - index += ch; - - float *ptr = reinterpret_cast<float *>(_tensor->buffer()); - - return ptr[index]; - } - - float &at(uint32_t nth, uint32_t row, uint32_t col, uint32_t ch) - { - // NNAPI uses NHWC ordering - uint32_t index = 0; - - index += nth * _shape.H * _shape.W * _shape.C; - index += row * _shape.W * _shape.C; - index += col * _shape.C; - index += ch; - - float *ptr = reinterpret_cast<float *>(_tensor->buffer()); - - return ptr[index]; - } - -private: - nnfw::util::kernel::Shape _shape; - ::arm_compute::ITensor *_tensor; -}; - -} // namespace kernel -} // namespace nnapi -} // namespace internal - -#endif // __INTERNAL_NNAPI_KERNEL_VIEW_H__ diff --git a/runtimes/neurun/src/kernel/acl_cl/CLFunction.h b/runtimes/neurun/src/kernel/acl_cl/CLFunction.h new file mode 100644 index 000000000..f34210c8a --- /dev/null +++ b/runtimes/neurun/src/kernel/acl_cl/CLFunction.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_KERNEL_ACL_CL_CL_FUNCTION_H__ +#define __NEURUN_KERNEL_ACL_CL_CL_FUNCTION_H__ + +#include "exec/interface/IFunction.h" +#include <arm_compute/runtime/IFunction.h> +#include <memory> + +namespace neurun +{ +namespace kernel +{ +namespace acl_cl +{ + +class CLFunction : public ::neurun::exec::IFunction +{ +public: + CLFunction() = delete; + +public: + CLFunction(std::unique_ptr<::arm_compute::IFunction> &&func) + : _func(std::forward<std::unique_ptr<::arm_compute::IFunction>>(func)) + { + // DO NOTHING + } + +public: + void run() override { _func->run(); } + void prepare() override { _func->prepare(); } + +private: + std::unique_ptr<::arm_compute::IFunction> _func; +}; + +} // namespace acl_cl +} // namespace kernel +} // namespace neurun + +#endif // __NEURUN_KERNEL_ACL_CL_CL_FUNCTION_H__ diff --git a/runtimes/neurun/src/kernel/acl_cl/CMakeLists.txt b/runtimes/neurun/src/kernel/acl_cl/CMakeLists.txt index 857fe6fe6..0658effea 100644 --- a/runtimes/neurun/src/kernel/acl_cl/CMakeLists.txt +++ b/runtimes/neurun/src/kernel/acl_cl/CMakeLists.txt @@ -4,11 +4,9 @@ add_library(${LIB_NEURUN_KERNEL_ACL_CL} STATIC ${SOURCES}) target_include_directories(${LIB_NEURUN_KERNEL_ACL_CL} PUBLIC ${NNFW_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_KERNEL_ACL_CL} PUBLIC ${NEURUN_INCLUDE_DIR}) -target_include_directories(${LIB_NEURUN_KERNEL_ACL_CL} PUBLIC ${CMAKE_SOURCE_DIR}/externals/tensorflow) # TODO We should not need this target_link_libraries(${LIB_NEURUN_KERNEL_ACL_CL} arm_compute) -target_link_libraries(${LIB_NEURUN_KERNEL_ACL_CL} tensorflow-lite) # TODO We should not need this -target_link_libraries(${LIB_NEURUN_KERNEL_ACL_CL} ${LIB_NEURUN_KERNEL_CPU}) # TODO We should not need this +target_link_libraries(${LIB_NEURUN_KERNEL_ACL_CL} nnfw_lib_misc) set_target_properties(${LIB_NEURUN_KERNEL_ACL_CL} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${LIB_NEURUN_KERNEL_ACL_CL} PROPERTIES OUTPUT_NAME kernel_acl_cl) diff --git a/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.cc b/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.cc index b75ac90f0..3844317ab 100644 --- a/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.cc +++ b/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.cc @@ -18,23 +18,23 @@ #include <arm_compute/runtime/CL/CLScheduler.h> -#include "backend/acl_cl/kernel/View.h" -#include "logging.h" +#include "util/feature/nchw/View.h" +#include "util/logging.h" namespace { -bool matchSizeExceptAxis(const ::arm_compute::ICLTensor *t1, const ::arm_compute::ICLTensor *t2, - uint32_t axis) +bool matchSizeExceptAxis(const ::neurun::backend::acl_cl::operand::ICLTensor *t1, + const ::neurun::backend::acl_cl::operand::ICLTensor *t2, uint32_t axis) { - assert(t1->info()->num_dimensions() <= 4); - assert(t2->info()->num_dimensions() <= 4); + assert(t1->num_dimensions() <= 4); + assert(t2->num_dimensions() <= 4); for (uint32_t i = 0; i < 4; i++) { if (axis == i) continue; - if (t1->info()->dimension(i) != t2->info()->dimension(i)) + if (t1->dimension(i) != t2->dimension(i)) return false; } return true; @@ -66,10 +66,10 @@ bool ConcatLayer::concatenationFloat32() for (auto input : _input_allocs) { assert(matchSizeExceptAxis(_output_alloc, input, _axis)); - axis_sum += input->info()->dimension(_axis); + axis_sum += input->dimension(_axis); } - assert(_output_alloc->info()->dimension(_axis) == axis_sum); + assert(_output_alloc->dimension(_axis) == axis_sum); } VERBOSE(Concat_RUN) << "START Concat" << std::endl; @@ -81,12 +81,12 @@ bool ConcatLayer::concatenationFloat32() auto &queue = ::arm_compute::CLScheduler::get().queue(); _output_alloc->map(queue); - ::internal::arm_compute::kernel::View<float> output_view{_output_alloc}; + util::feature::nchw::View<float> output_view{_output_alloc}; for (auto input : _input_allocs) { input->map(queue); - const ::internal::arm_compute::kernel::View<float> input_reader{input}; + const util::feature::nchw::View<float> input_reader{input}; for (uint32_t n = 0; n < input_reader.shape().N; n++) { @@ -124,8 +124,9 @@ bool ConcatLayer::concatenationFloat32() return true; } -void ConcatLayer::configure(const std::vector<::arm_compute::ICLTensor *> &input_allocs, - int32_t axis, ::arm_compute::ICLTensor *output_alloc) +void ConcatLayer::configure( + const std::vector<::neurun::backend::acl_cl::operand::ICLTensor *> &input_allocs, int32_t axis, + ::neurun::backend::acl_cl::operand::ICLTensor *output_alloc) { _input_allocs = input_allocs; _output_alloc = output_alloc; diff --git a/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.h b/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.h index 4767721fa..d468a6dfb 100644 --- a/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.h +++ b/runtimes/neurun/src/kernel/acl_cl/ConcatLayer.h @@ -14,17 +14,17 @@ * limitations under the License. */ -#ifndef __INTERNAL_KERNEL_ACL_CL_CONCAT_LAYER_H__ -#define __INTERNAL_KERNEL_ACL_CL_CONCAT_LAYER_H__ +#ifndef __NEURUN_KERNEL_ACL_CL_CONCAT_LAYER_H__ +#define __NEURUN_KERNEL_ACL_CL_CONCAT_LAYER_H__ #include <NeuralNetworks.h> -#include <arm_compute/core/CL/ICLTensor.h> #include <arm_compute/runtime/IFunction.h> -#include "graph/operand/DataType.h" +#include "model/operand/DataType.h" +#include "backend/acl_cl/operand/ICLTensor.h" -using OperandType = neurun::graph::operand::DataType; +using OperandType = neurun::model::operand::DataType; namespace neurun { @@ -44,9 +44,9 @@ public: ConcatLayer(); public: - void configure(const std::vector<::arm_compute::ICLTensor *> &input_allocs, + void configure(const std::vector<::neurun::backend::acl_cl::operand::ICLTensor *> &input_allocs, int32_t axis /* NNAPI tensor axis from NHWC order */, - ::arm_compute::ICLTensor *output_alloc); + ::neurun::backend::acl_cl::operand::ICLTensor *output_alloc); void run(); @@ -54,8 +54,8 @@ private: bool concatenationFloat32(); private: - std::vector<::arm_compute::ICLTensor *> _input_allocs; - ::arm_compute::ICLTensor *_output_alloc; + std::vector<::neurun::backend::acl_cl::operand::ICLTensor *> _input_allocs; + ::neurun::backend::acl_cl::operand::ICLTensor *_output_alloc; int32_t _axis; OperandType _input_type; }; @@ -64,4 +64,4 @@ private: } // namespace kernel } // namespace neurun -#endif // __INTERNAL_KERNEL_ACL_CL_CONCAT_LAYER_H__ +#endif // __NEURUN_KERNEL_ACL_CL_CONCAT_LAYER_H__ diff --git a/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.cc b/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.cc deleted file mode 100644 index fa1d77579..000000000 --- a/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#include "TensorConvertFromCommonLayer.h" - -#include "internal/nnapi/feature/Reader.h" -#include "backend/acl_cl/feature/View.h" - -#include <util/feature/IndexIterator.h> -#include <arm_compute/runtime/CL/CLScheduler.h> - -namespace neurun -{ -namespace kernel -{ -namespace acl_cl -{ - -bool TensorConvertFromCommonLayer::convert() -{ - auto inputBuffer = _inputTensor->buffer(); - auto inputSize = _inputTensor->info()->total_size(); - - auto &queue = ::arm_compute::CLScheduler::get().queue(); - - _outputTensor->map(queue); - - if (_tensorShape.rank() == 2) - { - const auto len = _tensorShape.dim(1); - - auto base = reinterpret_cast<const float *>(inputBuffer); - - for (int32_t n = 0; n < len; ++n) - { - auto from = base + n; - auto into = - reinterpret_cast<float *>(_outputTensor->ptr_to_element(::arm_compute::Coordinates{n})); - - *into = *from; - } - } - else if (_tensorShape.rank() == 4) - { - auto featureShape = _tensorShape.asFeature(); - - const ::internal::nnapi::feature::Reader<float> from{featureShape, inputBuffer, inputSize}; - ::internal::arm_compute::feature::View<float> into{_outputTensor}; - - ::nnfw::util::feature::iterate(featureShape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, ch, row, col); - into.at(batch, ch, row, col) = value; - }; - } - - _outputTensor->unmap(queue); -} - -void TensorConvertFromCommonLayer::configure(::internal::common::Tensor *inputTensor, - ::arm_compute::ICLTensor *outputTensor, - const ::neurun::graph::operand::Shape &tensorShape) -{ - _inputTensor = inputTensor; - _outputTensor = outputTensor; - _tensorShape = tensorShape; -} - -void TensorConvertFromCommonLayer::run() { convert(); } - -} // namespace acl_cl -} // namespace kernel -} // namespace neurun - -#endif diff --git a/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.h b/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.h deleted file mode 100644 index bd031a106..000000000 --- a/runtimes/neurun/src/kernel/acl_cl/TensorConvertFromCommonLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#ifndef __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ -#define __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ - -#include <NeuralNetworks.h> - -#include <arm_compute/runtime/IFunction.h> -#include <arm_compute/core/CL/ICLTensor.h> - -#include "internal/Model.h" -#include "internal/common/Tensor.h" - -namespace neurun -{ -namespace kernel -{ -namespace acl_cl -{ - -class TensorConvertFromCommonLayer : public ::arm_compute::IFunction -{ -public: - TensorConvertFromCommonLayer() {} - -public: - bool convert(); - - void configure(::internal::common::Tensor *inputTensor, ::arm_compute::ICLTensor *outputTensor, - const ::neurun::graph::operand::Shape &tensorShape); - - void run(); - -private: - ::internal::common::Tensor *_inputTensor; - ::arm_compute::ICLTensor *_outputTensor; - - ::neurun::graph::operand::Shape _tensorShape{1}; -}; - -} // namespace acl_cl -} // namespace kernel -} // namespace neurun - -#endif // __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ - -#endif diff --git a/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.cc b/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.cc deleted file mode 100644 index 985524bc3..000000000 --- a/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#include "TensorConvertToCommonLayer.h" - -#include "backend/acl_cl/feature/View.h" -#include "internal/nnapi/feature/View.h" - -#include <util/feature/IndexIterator.h> -#include <arm_compute/runtime/CL/CLScheduler.h> - -namespace neurun -{ -namespace kernel -{ -namespace acl_cl -{ - -bool TensorConvertToCommonLayer::convert() -{ - auto outputBuffer = _outputTensor->buffer(); - auto outputSize = _outputTensor->info()->total_size(); - - auto &queue = ::arm_compute::CLScheduler::get().queue(); - - _inputTensor->map(queue); - - if (_tensorShape.rank() == 2) - { - const auto len = _tensorShape.dim(1); - - auto base = reinterpret_cast<float *>(outputBuffer); - - for (int32_t n = 0; n < len; ++n) - { - auto from = reinterpret_cast<const float *>( - _inputTensor->ptr_to_element(::arm_compute::Coordinates{n})); - auto into = base + n; - - *into = *from; - } - } - else if (_tensorShape.rank() == 4) - { - auto featureShape = _tensorShape.asFeature(); - - const ::internal::arm_compute::feature::View<float> from{_inputTensor}; - ::internal::nnapi::feature::View<float> into{featureShape, outputBuffer, outputSize}; - - ::nnfw::util::feature::iterate(featureShape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, ch, row, col); - into.at(batch, ch, row, col) = value; - }; - } - - _inputTensor->unmap(queue); -} - -void TensorConvertToCommonLayer::configure(::arm_compute::ICLTensor *inputTensor, - ::internal::common::Tensor *outputTensor, - const ::neurun::graph::operand::Shape &tensorShape) -{ - _inputTensor = inputTensor; - _outputTensor = outputTensor; - _tensorShape = tensorShape; -} - -void TensorConvertToCommonLayer::run() { convert(); } - -} // namespace acl_cl -} // namespace kernel -} // namespace neurun - -#endif diff --git a/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.h b/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.h deleted file mode 100644 index 576f1ee71..000000000 --- a/runtimes/neurun/src/kernel/acl_cl/TensorConvertToCommonLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#ifndef __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_TO_COMMON_LAYER_H__ -#define __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_TO_COMMON_LAYER_H__ - -#include <NeuralNetworks.h> - -#include <arm_compute/runtime/IFunction.h> -#include <arm_compute/core/CL/ICLTensor.h> - -#include "internal/Model.h" -#include "internal/common/Tensor.h" - -namespace neurun -{ -namespace kernel -{ -namespace acl_cl -{ - -class TensorConvertToCommonLayer : public ::arm_compute::IFunction -{ -public: - TensorConvertToCommonLayer() {} - -public: - bool convert(); - - void configure(::arm_compute::ICLTensor *inputTensor, ::internal::common::Tensor *outputTensor, - const ::neurun::graph::operand::Shape &tensorShape); - - void run(); - -private: - ::arm_compute::ICLTensor *_inputTensor; - ::internal::common::Tensor *_outputTensor; - - ::neurun::graph::operand::Shape _tensorShape{1}; -}; - -} // namespace acl_cl -} // namespace kernel -} // namespace neurun - -#endif // __INTERNAL_KERNELS_ACL_CL_TENSOR_CONVERT_TO_COMMON_LAYER_H__ - -#endif diff --git a/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.cc b/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.cc index 2a6a84e10..f434a6dec 100644 --- a/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.cc @@ -27,14 +27,14 @@ namespace kernel namespace cpu { -#define AVGPOOLING_PARAMETERS \ - uint32_t height = getSizeOfDimension(_inputShape, 1); \ - uint32_t width = getSizeOfDimension(_inputShape, 2); \ - uint32_t outHeight = getSizeOfDimension(_outputShape, 1); \ - uint32_t outWidth = getSizeOfDimension(_outputShape, 2); \ - \ - uint32_t paddingHeight = (uint32_t)_paddingTop; \ - uint32_t paddingWidth = (uint32_t)_paddingLeft; +#define AVGPOOLING_PARAMETERS \ + tflite::PoolParams op_params; \ + op_params.stride_height = _strideHeight; \ + op_params.stride_width = _strideWidth; \ + op_params.filter_height = _kernelHeight; \ + op_params.filter_width = _kernelWidth; \ + op_params.padding_values.height = (int8_t)_paddingTop; \ + op_params.padding_values.width = (int8_t)_paddingLeft; AvgPoolLayer::AvgPoolLayer() : _inputData(nullptr), _outputData(nullptr), _inputShape(), _outputShape(), _paddingLeft(0), @@ -47,31 +47,31 @@ AvgPoolLayer::AvgPoolLayer() bool AvgPoolLayer::averagePoolFloat32() { - AVGPOOLING_PARAMETERS float output_activation_min, output_activation_max; CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max); + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; - ::tflite::optimized_ops::AveragePool( - reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape), _strideWidth, - _strideHeight, paddingWidth, paddingHeight, _kernelWidth, _kernelHeight, - output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData), - convertShapeToDims(_outputShape)); + ::tflite::optimized_ops::AveragePool(op_params, convertShapeToTFLiteShape(_inputShape), + reinterpret_cast<const float *>(_inputData), + convertShapeToTFLiteShape(_outputShape), + reinterpret_cast<float *>(_outputData)); return true; } bool AvgPoolLayer::averagePoolQuant8() { - AVGPOOLING_PARAMETERS int32_t output_activation_min = 0; int32_t output_activation_max = 0; CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min, &output_activation_max); + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; - ::tflite::optimized_ops::AveragePool(_inputData, convertShapeToDims(_inputShape), _strideWidth, - _strideHeight, paddingWidth, paddingHeight, _kernelWidth, - _kernelHeight, output_activation_min, output_activation_max, - _outputData, convertShapeToDims(_outputShape)); + ::tflite::optimized_ops::AveragePool(op_params, convertShapeToTFLiteShape(_inputShape), + _inputData, convertShapeToTFLiteShape(_outputShape), + _outputData); return true; } diff --git a/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.h b/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.h index 9f390a9e1..280f7ae5f 100644 --- a/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.h +++ b/runtimes/neurun/src/kernel/cpu/AvgPoolLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class AvgPoolLayer : public ::arm_compute::IFunction +class AvgPoolLayer : public ::neurun::exec::IFunction { public: AvgPoolLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/CMakeLists.txt b/runtimes/neurun/src/kernel/cpu/CMakeLists.txt index dddf154c3..436cb898c 100644 --- a/runtimes/neurun/src/kernel/cpu/CMakeLists.txt +++ b/runtimes/neurun/src/kernel/cpu/CMakeLists.txt @@ -6,8 +6,8 @@ target_include_directories(${LIB_NEURUN_KERNEL_CPU} PUBLIC ${NNFW_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_KERNEL_CPU} PUBLIC ${NEURUN_INCLUDE_DIR}) target_include_directories(${LIB_NEURUN_KERNEL_CPU} PUBLIC ${CMAKE_SOURCE_DIR}/externals/tensorflow) -target_link_libraries(${LIB_NEURUN_KERNEL_CPU} arm_compute) # TODO We should not need this target_link_libraries(${LIB_NEURUN_KERNEL_CPU} tensorflow-lite) +target_link_libraries(${LIB_NEURUN_KERNEL_CPU} nnfw_lib_misc) set_target_properties(${LIB_NEURUN_KERNEL_CPU} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${LIB_NEURUN_KERNEL_CPU} PROPERTIES OUTPUT_NAME kernel_cpu) diff --git a/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc b/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc index 5fe5e3993..be093b437 100644 --- a/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/ConcatLayer.cc @@ -24,6 +24,7 @@ namespace neurun { namespace kernel { + namespace cpu { @@ -36,13 +37,21 @@ ConcatLayer::ConcatLayer() bool ConcatLayer::concatenationFloat32() { - int num_inputs = _inputShapes.size(); - std::vector<::tflite::Dims<4> *> inputDimsPtr(num_inputs); - std::vector<::tflite::Dims<4>> inputDims(num_inputs); - for (int i = 0; i < num_inputs; i++) + uint32_t num_inputs = _inputShapes.size(); + + tflite::ConcatenationParams op_params; + op_params.axis = _axis; + op_params.inputs_count = num_inputs; + + std::vector<::tflite::RuntimeShape *> inputDimsPtr; + std::vector<::tflite::RuntimeShape> inputDims; + inputDimsPtr.reserve(num_inputs); + inputDims.reserve(num_inputs); + + for (uint32_t i = 0; i < num_inputs; i++) { - inputDims[i] = convertShapeToDims(_inputShapes[i]); - inputDimsPtr[i] = &inputDims[i]; + inputDims.push_back(convertShapeToTFLiteShape(_inputShapes[i])); + inputDimsPtr.push_back(&inputDims[i]); } std::vector<const float *> inputFloatPtrs; @@ -52,24 +61,44 @@ bool ConcatLayer::concatenationFloat32() inputFloatPtrs.emplace_back(reinterpret_cast<const float *>(ptr)); } - ::tflite::optimized_ops::Concatenation<::tflite::FusedActivationFunctionType::kNone, float>( - getNumberOfDimensions(_outputShape) - _axis - 1, inputFloatPtrs.data(), inputDimsPtr.data(), - num_inputs, reinterpret_cast<float *>(_outputData), convertShapeToDims(_outputShape)); + ::tflite::optimized_ops::Concatenation<float>( + op_params, inputDimsPtr.data(), inputFloatPtrs.data(), + convertShapeToTFLiteShape(_outputShape), reinterpret_cast<float *>(_outputData)); return true; } bool ConcatLayer::concatenationQuant8() { int num_inputs = _inputShapes.size(); - std::vector<::tflite::Dims<4> *> inputDimsPtr(num_inputs); - std::vector<::tflite::Dims<4>> inputDims(num_inputs); - for (int i = 0; i < num_inputs; i++) + + std::vector<int32_t> input_zeropoints(num_inputs); + std::vector<float> input_scales(num_inputs); + for (uint32_t i = 0; i < num_inputs; i++) { - inputDims[i] = convertShapeToDims(_inputShapes[i]); - inputDimsPtr[i] = &inputDims[i]; + input_zeropoints[i] = _inputShapes[i].offset; + input_scales[i] = _inputShapes[i].scale; } - ::tflite::optimized_ops::Concatenation<::tflite::FusedActivationFunctionType::kNone, uint8_t>( - getNumberOfDimensions(_outputShape) - _axis - 1, _inputDataPtrs.data(), inputDimsPtr.data(), - num_inputs, _outputData, convertShapeToDims(_outputShape)); + + tflite::ConcatenationParams op_params; + op_params.axis = _axis; + op_params.inputs_count = num_inputs; + op_params.input_zeropoint = input_zeropoints.data(); + op_params.input_scale = input_scales.data(); + op_params.output_zeropoint = _outputShape.offset; + op_params.output_scale = _outputShape.scale; + + std::vector<::tflite::RuntimeShape *> inputDimsPtr; + std::vector<::tflite::RuntimeShape> inputDims; + inputDimsPtr.reserve(num_inputs); + inputDims.reserve(num_inputs); + for (uint32_t i = 0; i < num_inputs; i++) + { + inputDims.push_back(convertShapeToTFLiteShape(_inputShapes[i])); + inputDimsPtr.push_back(&inputDims[i]); + } + + ::tflite::optimized_ops::Concatenation<uint8_t>( + op_params, inputDimsPtr.data(), _inputDataPtrs.data(), + convertShapeToTFLiteShape(_outputShape), _outputData); return true; } diff --git a/runtimes/neurun/src/kernel/cpu/ConcatLayer.h b/runtimes/neurun/src/kernel/cpu/ConcatLayer.h index 9aacab5e8..64f813508 100644 --- a/runtimes/neurun/src/kernel/cpu/ConcatLayer.h +++ b/runtimes/neurun/src/kernel/cpu/ConcatLayer.h @@ -20,7 +20,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -31,7 +31,7 @@ namespace kernel namespace cpu { -class ConcatLayer : public ::arm_compute::IFunction +class ConcatLayer : public ::neurun::exec::IFunction { public: ConcatLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc b/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc index 81e88e0f0..c694fa75f 100644 --- a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.cc @@ -33,55 +33,51 @@ static constexpr int kStaticBufferSize = 1605632; static char static_scratch_buffer[kStaticBufferSize]; static std::mutex executionMutex; -#define ANDROID_NN_CONV_PARAMETERS(Type) \ - uint32_t height = getSizeOfDimension(_inputShape, 1); \ - uint32_t width = getSizeOfDimension(_inputShape, 2); \ - uint32_t kernelHeight = getSizeOfDimension(_kernelShape, 1); \ - uint32_t kernelWidth = getSizeOfDimension(_kernelShape, 2); \ - uint32_t outHeight = getSizeOfDimension(_outputShape, 1); \ - uint32_t outWidth = getSizeOfDimension(_outputShape, 2); \ - uint32_t inDepth = getSizeOfDimension(_inputShape, 3); \ - \ - uint32_t paddingHeight = (uint32_t)_paddingTop; \ - uint32_t paddingWidth = (uint32_t)_paddingLeft; \ - \ - ::tflite::Dims<4> im2colDim; \ - im2colDim.sizes[3] = (int)getSizeOfDimension(_outputShape, 0); \ - im2colDim.sizes[2] = (int)getSizeOfDimension(_outputShape, 1); \ - im2colDim.sizes[1] = (int)getSizeOfDimension(_outputShape, 2); \ - im2colDim.sizes[0] = (int)inDepth * kernelHeight * kernelWidth; \ - \ - im2colDim.strides[0] = 1; \ - for (int i = 1; i < 4; i++) \ - { \ - im2colDim.strides[i] = im2colDim.strides[i - 1] * im2colDim.sizes[i - 1]; \ - } \ - Type *im2colData = nullptr; \ - uint64_t im2colByteSize = sizeof(Type); \ - std::unique_ptr<Type[]> im2colGuard; \ - for (int i = 0; i < 4; i++) \ - { \ - im2colByteSize *= im2colDim.sizes[i]; \ - } \ - /* http://b/77982879, tflite::optimized_ops::Conv uses int for offsets */ \ - if (im2colByteSize >= 0x7fffffff) \ - { \ - std::cout << "Conv size is too large, not enough memory" << std::endl; \ - return false; \ - } \ - if (im2colByteSize <= kStaticBufferSize) \ - { \ - im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ - } \ - else \ - { \ - im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ - if (im2colData == nullptr) \ - { \ - std::cout << "Conv size is too large, not enough memory" << std::endl; \ - return false; \ - } \ - im2colGuard.reset(im2colData); \ +#define ANDROID_NN_CONV_PARAMETERS(Type) \ + uint32_t height = getSizeOfDimension(_inputShape, 1); \ + uint32_t width = getSizeOfDimension(_inputShape, 2); \ + uint32_t kernelHeight = getSizeOfDimension(_kernelShape, 1); \ + uint32_t kernelWidth = getSizeOfDimension(_kernelShape, 2); \ + uint32_t outHeight = getSizeOfDimension(_outputShape, 1); \ + uint32_t outWidth = getSizeOfDimension(_outputShape, 2); \ + uint32_t inDepth = getSizeOfDimension(_inputShape, 3); \ + \ + uint32_t paddingHeight = (uint32_t)_paddingTop; \ + uint32_t paddingWidth = (uint32_t)_paddingLeft; \ + \ + Shape im2colShape; \ + im2colShape.dimensions.resize(4); \ + im2colShape.dimensions[0] = getSizeOfDimension(_outputShape, 0); \ + im2colShape.dimensions[1] = getSizeOfDimension(_outputShape, 1); \ + im2colShape.dimensions[2] = getSizeOfDimension(_outputShape, 2); \ + im2colShape.dimensions[3] = inDepth * kernelHeight * kernelWidth; \ + \ + Type *im2colData = nullptr; \ + uint64_t im2colByteSize = sizeof(Type); \ + std::unique_ptr<Type[]> im2colGuard; \ + for (int i = 0; i < 4; i++) \ + { \ + im2colByteSize *= im2colShape.dimensions[i]; \ + } \ + /* http://b/77982879, tflite::optimized_ops::Conv uses int for offsets */ \ + if (im2colByteSize >= 0x7fffffff) \ + { \ + std::cout << "Conv size is too large, not enough memory" << std::endl; \ + return false; \ + } \ + if (im2colByteSize <= kStaticBufferSize) \ + { \ + im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ + } \ + else \ + { \ + im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ + if (im2colData == nullptr) \ + { \ + std::cout << "Conv size is too large, not enough memory" << std::endl; \ + return false; \ + } \ + im2colGuard.reset(im2colData); \ } ConvolutionLayer::ConvolutionLayer() @@ -112,19 +108,32 @@ bool ConvolutionLayer::convFloat32() float output_activation_min, output_activation_max; CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max); int32_t dilationWidthFactor = 1, dilationHeightFactor = 1; + + ::tflite::ConvParams op_params; + op_params.padding_type = ::tflite::PaddingType::kSame; + op_params.padding_values.width = paddingWidth; + op_params.padding_values.height = paddingHeight; + op_params.stride_width = _strideWidth; + op_params.stride_height = _strideHeight; + op_params.dilation_width_factor = dilationWidthFactor; + op_params.dilation_height_factor = dilationHeightFactor; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + ::tflite::optimized_ops::Conv( - reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape), - reinterpret_cast<const float *>(_kernelData), convertShapeToDims(_kernelShape), - reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape), _strideWidth, - _strideHeight, dilationWidthFactor, dilationHeightFactor, paddingWidth, paddingHeight, - output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData), - convertShapeToDims(_outputShape), im2colDataToPass, im2colDim); + op_params, convertShapeToTFLiteShape(_inputShape), + reinterpret_cast<const float *>(_inputData), convertShapeToTFLiteShape(_kernelShape), + reinterpret_cast<const float *>(_kernelData), convertShapeToTFLiteShape(_biasShape), + reinterpret_cast<const float *>(_biasData), convertShapeToTFLiteShape(_outputShape), + reinterpret_cast<float *>(_outputData), convertShapeToTFLiteShape(im2colShape), + im2colDataToPass); return true; } bool ConvolutionLayer::convQuant8() { ANDROID_NN_CONV_PARAMETERS(uint8_t) + int32_t inputOffset = -_inputShape.offset; int32_t kernelOffset = -_kernelShape.offset; int32_t outputOffset = _outputShape.offset; @@ -141,6 +150,24 @@ bool ConvolutionLayer::convQuant8() } CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min, &output_activation_max); + int32_t dilationWidthFactor = 1, dilationHeightFactor = 1; + + ::tflite::ConvParams op_params; + op_params.padding_type = ::tflite::PaddingType::kSame; + op_params.padding_values.width = paddingWidth; + op_params.padding_values.height = paddingHeight; + op_params.stride_width = _strideWidth; + op_params.stride_height = _strideHeight; + op_params.dilation_width_factor = dilationWidthFactor; + op_params.dilation_height_factor = dilationHeightFactor; + op_params.input_offset = inputOffset; + op_params.weights_offset = kernelOffset; + op_params.output_offset = outputOffset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + static gemmlowp::GemmContext gemm_context; // Prevent concurrent executions that may access the scratch buffer and // gemm_context. @@ -148,11 +175,10 @@ bool ConvolutionLayer::convQuant8() // Alow gemmlowp automatically decide how many threads to use. gemm_context.set_max_num_threads(0); ::tflite::optimized_ops::Conv( - _inputData, convertShapeToDims(_inputShape), inputOffset, _kernelData, - convertShapeToDims(_kernelShape), kernelOffset, reinterpret_cast<const int32_t *>(_biasData), - convertShapeToDims(_biasShape), _strideWidth, _strideHeight, paddingWidth, paddingHeight, - outputOffset, output_multiplier, output_shift, output_activation_min, output_activation_max, - _outputData, convertShapeToDims(_outputShape), im2colData, im2colDim, &gemm_context); + op_params, convertShapeToTFLiteShape(_inputShape), _inputData, + convertShapeToTFLiteShape(_kernelShape), _kernelData, convertShapeToTFLiteShape(_biasShape), + reinterpret_cast<const int32_t *>(_biasData), convertShapeToTFLiteShape(_outputShape), + _outputData, convertShapeToTFLiteShape(im2colShape), im2colData, &gemm_context); return true; } diff --git a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.h b/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.h index b7afbcec6..9b7f55ff1 100644 --- a/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.h +++ b/runtimes/neurun/src/kernel/cpu/ConvolutionLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class ConvolutionLayer : public ::arm_compute::IFunction +class ConvolutionLayer : public ::neurun::exec::IFunction { public: ConvolutionLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.cc b/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.cc index 41b9afc0c..abe82db5e 100644 --- a/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.cc @@ -44,64 +44,39 @@ FullyConnectedLayer::FullyConnectedLayer() static std::mutex executionMutex; bool FullyConnectedLayer::fullyConnectedFloat32() { - float output_activation_min, output_activation_max; - CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max); - // b/80425683, optimized implementation produces incorrect results when the - // number of input elements is the squre of batch_size. - uint32_t batch_size = getSizeOfDimension(_outputShape, 0); - uint32_t input_n_elements = getNumberOfElements(_inputShape); - if (batch_size * batch_size == input_n_elements) + int total_input_size = 1; + for (int i = 0; i < _inputShape.dimensions.size(); i++) { - ::tflite::reference_ops::FullyConnected( - reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape), - reinterpret_cast<const float *>(_weightsData), convertShapeToDims(_weightsShape), - reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape), - output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData), - convertShapeToDims(_outputShape)); - } - else - { - ::tflite::optimized_ops::FullyConnected( - reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape), - reinterpret_cast<const float *>(_weightsData), convertShapeToDims(_weightsShape), - reinterpret_cast<const float *>(_biasData), convertShapeToDims(_biasShape), - output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData), - convertShapeToDims(_outputShape)); + total_input_size *= _inputShape.dimensions[i]; } + + int input_size = _weightsShape.dimensions[1]; + const int batch_size = total_input_size / input_size; + const int num_units = _weightsShape.dimensions[0]; + + TfLiteFusedActivation act = convertFusedActivation(_activation); + + ::tflite::tensor_utils::VectorBatchVectorAssign(reinterpret_cast<const float *>(_biasData), + num_units, batch_size, + reinterpret_cast<float *>(_outputData)); + + // Compute output += weight * input + ::tflite::tensor_utils::MatrixBatchVectorMultiplyAccumulate( + reinterpret_cast<const float *>(_weightsData), num_units, input_size, + reinterpret_cast<const float *>(_inputData), batch_size, + reinterpret_cast<float *>(_outputData), /*result_stride=*/1); + + // Apply activation function + ::tflite::tensor_utils::ApplyActivationToVector(reinterpret_cast<float *>(_outputData), + batch_size * num_units, act, + reinterpret_cast<float *>(_outputData)); + return true; } bool FullyConnectedLayer::fullyConnectedQuant8() { - int32_t inputOffset = -_inputShape.offset; - int32_t weightsOffset = -_weightsShape.offset; - int32_t outputOffset = _outputShape.offset; - float real_multiplier = 0.0; - int32_t output_multiplier = 0; - int32_t output_shift = 0; - int32_t output_activation_min = 0; - int32_t output_activation_max = 0; - // Caution : 'Convolution' can make misleading. It seems it is just math term. - if (!GetQuantizedConvolutionMultipler(_inputShape, _weightsShape, _biasShape, _outputShape, - &real_multiplier) || - !QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier, &output_shift)) - { - return false; - } - CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min, - &output_activation_max); - static gemmlowp::GemmContext gemm_context; - // Prevent concurrent executions that access gemm_context. - std::unique_lock<std::mutex> lock(executionMutex); - // Alow gemmlowp automatically decide how many threads to use. - gemm_context.set_max_num_threads(0); - ::tflite::optimized_ops::FullyConnected( - _inputData, convertShapeToDims(_inputShape), inputOffset, _weightsData, - convertShapeToDims(_weightsShape), weightsOffset, - reinterpret_cast<const int32_t *>(_biasData), convertShapeToDims(_biasShape), outputOffset, - output_multiplier, output_shift, output_activation_min, output_activation_max, _outputData, - convertShapeToDims(_outputShape), &gemm_context); - return true; + throw std::runtime_error{"FullyConnectedLayer : Not tested for TENSOR_QUANT8_ASYMM"}; } void FullyConnectedLayer::configure(uint8_t *inputData, const Shape inputShape, diff --git a/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.h b/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.h index b1ba172b0..20a388349 100644 --- a/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.h +++ b/runtimes/neurun/src/kernel/cpu/FullyConnectedLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class FullyConnectedLayer : public ::arm_compute::IFunction +class FullyConnectedLayer : public ::neurun::exec::IFunction { public: FullyConnectedLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.cc b/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.cc index 3d96bb401..c4a288b07 100644 --- a/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.cc @@ -26,14 +26,14 @@ namespace kernel namespace cpu { -#define MAXPOOLING_PARAMETERS \ - uint32_t height = getSizeOfDimension(_inputShape, 1); \ - uint32_t width = getSizeOfDimension(_inputShape, 2); \ - uint32_t outHeight = getSizeOfDimension(_outputShape, 1); \ - uint32_t outWidth = getSizeOfDimension(_outputShape, 2); \ - \ - uint32_t paddingHeight = (uint32_t)_paddingTop; \ - uint32_t paddingWidth = (uint32_t)_paddingLeft; +#define MAXPOOLING_PARAMETERS \ + tflite::PoolParams op_params; \ + op_params.stride_height = _strideHeight; \ + op_params.stride_width = _strideWidth; \ + op_params.filter_height = _kernelHeight; \ + op_params.filter_width = _kernelWidth; \ + op_params.padding_values.height = (int8_t)_paddingTop; \ + op_params.padding_values.width = (int8_t)_paddingLeft; MaxPoolLayer::MaxPoolLayer() : _inputData(nullptr), _outputData(nullptr), _inputShape(), _outputShape(), _paddingLeft(0), @@ -46,31 +46,30 @@ MaxPoolLayer::MaxPoolLayer() bool MaxPoolLayer::maxPoolFloat32() { - MAXPOOLING_PARAMETERS float output_activation_min, output_activation_max; CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max); + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; - ::tflite::optimized_ops::MaxPool( - reinterpret_cast<const float *>(_inputData), convertShapeToDims(_inputShape), _strideWidth, - _strideHeight, paddingWidth, paddingHeight, _kernelWidth, _kernelHeight, - output_activation_min, output_activation_max, reinterpret_cast<float *>(_outputData), - convertShapeToDims(_outputShape)); + ::tflite::optimized_ops::MaxPool(op_params, convertShapeToTFLiteShape(_inputShape), + reinterpret_cast<const float *>(_inputData), + convertShapeToTFLiteShape(_outputShape), + reinterpret_cast<float *>(_outputData)); return true; } bool MaxPoolLayer::maxPoolQuant8() { - MAXPOOLING_PARAMETERS int32_t output_activation_min = 0; int32_t output_activation_max = 0; CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min, &output_activation_max); + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; - ::tflite::optimized_ops::MaxPool(_inputData, convertShapeToDims(_inputShape), _strideWidth, - _strideHeight, paddingWidth, paddingHeight, _kernelWidth, - _kernelHeight, output_activation_min, output_activation_max, - _outputData, convertShapeToDims(_outputShape)); + ::tflite::optimized_ops::MaxPool(op_params, convertShapeToTFLiteShape(_inputShape), _inputData, + convertShapeToTFLiteShape(_outputShape), _outputData); return true; } diff --git a/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.h b/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.h index b42efb9f6..2b185550b 100644 --- a/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.h +++ b/runtimes/neurun/src/kernel/cpu/MaxPoolLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class MaxPoolLayer : public ::arm_compute::IFunction +class MaxPoolLayer : public ::neurun::exec::IFunction { public: MaxPoolLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/OperationUtils.cc b/runtimes/neurun/src/kernel/cpu/OperationUtils.cc index 5ec2f8e62..b28508c27 100644 --- a/runtimes/neurun/src/kernel/cpu/OperationUtils.cc +++ b/runtimes/neurun/src/kernel/cpu/OperationUtils.cc @@ -184,7 +184,7 @@ int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) return static_cast<int32_t>(std::floor(max_input_rescaled)); } -Shape getShape(const ::neurun::graph::operand::Object &o) +Shape getShape(const ::neurun::model::operand::Object &o) { Shape shape; diff --git a/runtimes/neurun/src/kernel/cpu/OperationUtils.h b/runtimes/neurun/src/kernel/cpu/OperationUtils.h index 5914d04e3..3610990a5 100644 --- a/runtimes/neurun/src/kernel/cpu/OperationUtils.h +++ b/runtimes/neurun/src/kernel/cpu/OperationUtils.h @@ -23,11 +23,13 @@ #include <limits> #include <vector> +#include "tensorflow/contrib/lite/c/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/internal/types.h" -#include "graph/operand/Object.h" -#include "graph/operand/DataType.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "model/operand/Object.h" +#include "model/operand/DataType.h" -using OperandType = neurun::graph::operand::DataType; +using OperandType = neurun::model::operand::DataType; namespace neurun { @@ -75,6 +77,51 @@ inline ::tflite::Dims<4> convertShapeToDims(const Shape &shape) return dims; } +inline ::tflite::RuntimeShape convertShapeToTFLiteShape(const Shape &shape) +{ + std::vector<int32_t> raw_shape; + raw_shape.resize(4); + + for (uint32_t i = 0; i < 4; ++i) + { + if (i >= shape.dimensions.size()) + { + raw_shape[i] = 1; + } + else + { + raw_shape[i] = shape.dimensions[i]; + } + } + + return ::tflite::GetTensorShape(raw_shape); +} + +inline TfLiteFusedActivation convertFusedActivation(FuseCode act) +{ + if (act == ANEURALNETWORKS_FUSED_NONE) + { + return kTfLiteActNone; + } + + if (act == ANEURALNETWORKS_FUSED_RELU) + { + return kTfLiteActRelu; + } + + if (act == ANEURALNETWORKS_FUSED_RELU1) + { + return kTfLiteActRelu1; + } + + if (act == ANEURALNETWORKS_FUSED_RELU6) + { + return kTfLiteActRelu6; + } + + return kTfLiteActNone; +} + __wur bool QuantizeMultiplierSmallerThanOne(double double_multiplier, int32_t *quantized_multiplier, int32_t *right_shift); @@ -92,7 +139,7 @@ void CalculateActivationRangeUint8(int32_t activation, const Shape &outputShape, int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift); -Shape getShape(const ::neurun::graph::operand::Object &o); +Shape getShape(const ::neurun::model::operand::Object &o); uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions); diff --git a/runtimes/neurun/src/kernel/cpu/PermuteLayer.cc b/runtimes/neurun/src/kernel/cpu/PermuteLayer.cc new file mode 100644 index 000000000..ba8c5ab92 --- /dev/null +++ b/runtimes/neurun/src/kernel/cpu/PermuteLayer.cc @@ -0,0 +1,201 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PermuteLayer.h" + +#include "util/feature/nhwc/Reader.h" +#include "util/feature/nhwc/View.h" +#include "util/feature/nchw/View.h" +#include "util/feature/Coordinate4D.h" + +#include <misc/feature/IndexIterator.h> + +namespace neurun +{ +namespace kernel +{ +namespace cpu +{ + +using Type = model::operation::PermuteNode::Type; + +void PermuteLayer::configure(std::shared_ptr<::neurun::backend::operand::IObject> input, + std::shared_ptr<::neurun::backend::operand::IObject> output, + const model::operand::Shape &shape, Type type) +{ + _input = input; + _output = output; + _shape = shape; + _type = type; +} + +void PermuteLayer::run() +{ + auto rank = _shape.rank(); + + switch (_type) + { + case Type::NHWC_TO_NCHW: + { + auto fn = [&](::neurun::backend::operand::ITensor &tensor) { + auto input_tensor = _input->ptr(); + + auto input_buffer = input_tensor->buffer(); + auto input_size = input_tensor->total_size(); + + auto output_buffer = tensor.buffer(); + auto output_size = tensor.total_size(); + switch (rank) + { + case 0: + case 1: + { + memcpy(output_buffer, input_buffer, input_size); + break; + } + case 2: + { + auto matrix_shape = _shape.asMatrix(); + + for (auto h = 0; h < matrix_shape.H; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, 0}; + memcpy(output_buffer + tensor.calcOffset(coord), input_buffer + h * matrix_shape.W, + matrix_shape.W * sizeof(float)); + } + break; + } + case 3: + { + const int32_t depth = _shape.dim(0); + const int32_t height = _shape.dim(1); + const int32_t width = _shape.dim(2); + + for (auto c = 0; c < depth; ++c) + { + for (auto h = 0; h < height; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, c}; + memcpy(output_buffer + tensor.calcOffset(coord), + input_buffer + c * height * width + h * width, width * sizeof(float)); + } + } + break; + } + case 4: + { + auto feature = _shape.asFeature(); + + const util::feature::nhwc::Reader<float> from{ + feature, reinterpret_cast<const float *>(input_buffer), input_size}; + util::feature::nchw::View<float> into{&tensor}; + + ::nnfw::misc::feature::iterate(feature) + << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(batch, ch, row, col); + into.at(batch, ch, row, col) = value; + }; + break; + } + default: + throw "NYI"; + break; + } + }; + _output->access(fn); + break; + } + case Type::NCHW_TO_NHWC: + { + auto fn = [&](::neurun::backend::operand::ITensor &tensor) { + auto input_buffer = tensor.buffer(); + auto input_size = tensor.total_size(); + + auto output_tensor = _output->ptr(); + + auto output_buffer = output_tensor->buffer(); + auto output_size = output_tensor->total_size(); + + switch (rank) + { + case 0: + case 1: + { + memcpy(output_buffer, input_buffer, output_size); + break; + } + case 2: + { + auto matrix_shape = _shape.asMatrix(); + + for (auto h = 0; h < matrix_shape.H; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, 0}; + memcpy(output_buffer + h * matrix_shape.W, input_buffer + tensor.calcOffset(coord), + matrix_shape.W * sizeof(float)); + } + break; + } + case 3: + { + const int32_t depth = _shape.dim(0); + const int32_t height = _shape.dim(1); + const int32_t width = _shape.dim(2); + + for (auto c = 0; c < depth; ++c) + { + for (auto h = 0; h < height; ++h) + { + neurun::util::feature::Coordinate4D coord{0, h, 0, c}; + memcpy(output_buffer + c * height * width + h * width, + input_buffer + tensor.calcOffset(coord), width * sizeof(float)); + } + } + break; + } + case 4: + { + auto feature = _shape.asFeature(); + + const util::feature::nchw::View<float> from{&tensor}; + util::feature::nhwc::View<float> into{feature, reinterpret_cast<float *>(output_buffer), + output_size}; + + ::nnfw::misc::feature::iterate(feature) + << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { + const auto value = from.at(batch, ch, row, col); + into.at(batch, ch, row, col) = value; + }; + break; + } + default: + throw "NYI"; + break; + } + }; + _input->access(fn); + break; + } + case Type::COPY: + // If two different backends using same tensor layout, we need this. + throw "NYI"; + break; + } +} + +} // namespace cpu +} // namespace kernel +} // namespace neurun diff --git a/runtimes/neurun/src/kernel/cpu/PermuteLayer.h b/runtimes/neurun/src/kernel/cpu/PermuteLayer.h new file mode 100644 index 000000000..d9e1709bc --- /dev/null +++ b/runtimes/neurun/src/kernel/cpu/PermuteLayer.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_KERNEL_CPU_PERMUTE_LAYER_H__ +#define __NEURUN_KERNEL_CPU_PERMUTE_LAYER_H__ + +#include <NeuralNetworks.h> + +#include "exec/interface/IFunction.h" + +#include "util/feature/nhwc/View.h" +#include "OperationUtils.h" +#include "backend/interface/operand/IObject.h" +#include "model/operation/PermuteNode.h" + +namespace neurun +{ +namespace kernel +{ +namespace cpu +{ + +class PermuteLayer : public ::neurun::exec::IFunction +{ +public: + PermuteLayer() = default; + +public: + void configure(std::shared_ptr<::neurun::backend::operand::IObject> input, + std::shared_ptr<::neurun::backend::operand::IObject> output, + const model::operand::Shape &shape, model::operation::PermuteNode::Type type); + void run(); + +private: + std::shared_ptr<::neurun::backend::operand::IObject> _input; + std::shared_ptr<::neurun::backend::operand::IObject> _output; + model::operand::Shape _shape; + model::operation::PermuteNode::Type _type; +}; + +} // namespace cpu +} // namespace kernel +} // namespace neurun + +#endif // __NEURUN_KERNEL_CPU_PERMUTE_LAYER_H__ diff --git a/runtimes/neurun/src/kernel/cpu/ReshapeLayer.h b/runtimes/neurun/src/kernel/cpu/ReshapeLayer.h index 395cc1d7f..51d0bacee 100644 --- a/runtimes/neurun/src/kernel/cpu/ReshapeLayer.h +++ b/runtimes/neurun/src/kernel/cpu/ReshapeLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class ReshapeLayer : public ::arm_compute::IFunction +class ReshapeLayer : public ::neurun::exec::IFunction { public: ReshapeLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.cc b/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.cc index 4f5a69f2e..c998c65f6 100644 --- a/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.cc +++ b/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.cc @@ -33,45 +33,86 @@ SoftMaxLayer::SoftMaxLayer() // DO NOTHING } +// Performs softmax along the input of size (input_size * batch_size). +void Softmax(const float *in, const int input_size, const int batch_size, const float beta, + float *out) +{ + TF_LITE_ASSERT(input_size > 0); + + // For each batch + for (int b = 0; b < batch_size; b++) + { + // Find the max coeff. + float max_coeff = in[0]; + for (int i = 1; i < input_size; i++) + { + if (in[i] > max_coeff) + max_coeff = in[i]; + } + + // Compute the normalized sum of exps. + float exp_sum = 0.0; + for (int i = 0; i < input_size; i++) + { + out[i] = std::exp((in[i] - max_coeff) * beta); + exp_sum += out[i]; + } + + // Divide by the sum of exps. + float reciprocal_sum_exp = 1.f / exp_sum; + for (int i = 0; i < input_size; i++) + { + out[i] *= reciprocal_sum_exp; + } + + // Advance in and out pointers for the next batch. + in += input_size; + out += input_size; + } +} + bool SoftMaxLayer::softmaxFloat32() { - ::tflite::Dims<4> dim; + Shape shapeIn4D; + if (getNumberOfDimensions(_inputShape) == 2) { uint32_t batch_size = getSizeOfDimension(_inputShape, 0); uint32_t input_size = getNumberOfElements(_inputShape) / batch_size; - Shape shapeIn4D; - shapeIn4D.dimensions = {batch_size, 1, 1, input_size}; - dim = convertShapeToDims(shapeIn4D); + Softmax(reinterpret_cast<const float *>(_inputData), input_size, batch_size, _beta, + reinterpret_cast<float *>(_outputData)); } else if (getNumberOfDimensions(_inputShape) == 4) { - dim = convertShapeToDims(_inputShape); + ::tflite::SoftmaxParams op_params; + op_params.beta = _beta; + ::tflite::optimized_ops::Softmax(op_params, convertShapeToTFLiteShape(_inputShape), + reinterpret_cast<const float *>(_inputData), + convertShapeToTFLiteShape(_outputShape), + reinterpret_cast<float *>(_outputData)); } else { std::cout << "only 2D and 4D tensors supported" << std::endl; return false; } - ::tflite::optimized_ops::Softmax(reinterpret_cast<const float *>(_inputData), dim, _beta, - reinterpret_cast<float *>(_outputData), dim); + return true; } bool SoftMaxLayer::softmaxQuant8() { - ::tflite::Dims<4> dim; + Shape shapeIn4D = _inputShape; + if (getNumberOfDimensions(_inputShape) == 2) { uint32_t batch_size = getSizeOfDimension(_inputShape, 0); uint32_t input_size = getNumberOfElements(_inputShape) / batch_size; - Shape shapeIn4D; shapeIn4D.dimensions = {batch_size, 1, 1, input_size}; - dim = convertShapeToDims(shapeIn4D); } else if (getNumberOfDimensions(_inputShape) == 4) { - dim = convertShapeToDims(_inputShape); + shapeIn4D = _inputShape; } else { @@ -94,8 +135,13 @@ bool SoftMaxLayer::softmaxQuant8() return false; } float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift); - ::tflite::optimized_ops::Softmax(_inputData, dim, input_multiplier, input_left_shift, diff_min, - _outputData, dim); + + ::tflite::SoftmaxParams op_params; + op_params.input_multiplier = input_multiplier; + op_params.input_left_shift = input_left_shift; + op_params.diff_min = diff_min; + ::tflite::optimized_ops::Softmax(op_params, convertShapeToTFLiteShape(shapeIn4D), _inputData, + convertShapeToTFLiteShape(shapeIn4D), _outputData); return true; } diff --git a/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.h b/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.h index 8057be52f..df1aa4044 100644 --- a/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.h +++ b/runtimes/neurun/src/kernel/cpu/SoftMaxLayer.h @@ -19,7 +19,7 @@ #include <NeuralNetworks.h> -#include <arm_compute/runtime/IFunction.h> +#include "exec/interface/IFunction.h" #include "kernel/cpu/OperationUtils.h" @@ -30,7 +30,7 @@ namespace kernel namespace cpu { -class SoftMaxLayer : public ::arm_compute::IFunction +class SoftMaxLayer : public ::neurun::exec::IFunction { public: SoftMaxLayer(); diff --git a/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.cc b/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.cc deleted file mode 100644 index 00e914732..000000000 --- a/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.cc +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#include "TensorConvertFromCommonLayer.h" - -#include "internal/nnapi/feature/Reader.h" -#include "internal/nnapi/feature/View.h" - -#include <util/feature/IndexIterator.h> - -namespace neurun -{ -namespace kernel -{ -namespace cpu -{ - -bool TensorConvertFromCommonLayer::convert() -{ - auto inputBuffer = _inputTensor->buffer(); - auto inputSize = _inputTensor->info()->total_size(); - - auto outputBuffer = _outputTensor->buffer(); - auto outputSize = _outputTensor->info()->total_size(); - - if (_tensorShape.rank() == 2) - { - const auto len = _tensorShape.dim(1); - - auto base = reinterpret_cast<const float *>(inputBuffer); - - for (int32_t n = 0; n < len; ++n) - { - auto from = base + n; - auto into = - reinterpret_cast<float *>(_outputTensor->ptr_to_element(::arm_compute::Coordinates{n})); - - *into = *from; - } - } - else if (_tensorShape.rank() == 4) - { - auto featureShape = _tensorShape.asFeature(); - - const ::internal::nnapi::feature::Reader<float> from{featureShape, inputBuffer, inputSize}; - ::internal::nnapi::feature::View<float> into{featureShape, outputBuffer, outputSize}; - - ::nnfw::util::feature::iterate(featureShape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, ch, row, col); - into.at(batch, ch, row, col) = value; - }; - } -} - -void TensorConvertFromCommonLayer::configure(::internal::common::Tensor *inputTensor, - ::internal::cpu::Tensor *outputTensor, - const Shape &tensorShape) -{ - _inputTensor = inputTensor; - _outputTensor = outputTensor; - _tensorShape = tensorShape; -} - -void TensorConvertFromCommonLayer::run() { convert(); } - -} // namespace cpu -} // namespace kernel -} // namespace neurun - -#endif diff --git a/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.h b/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.h deleted file mode 100644 index 56f7bcf32..000000000 --- a/runtimes/neurun/src/kernel/cpu/TensorConvertFromCommonLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#ifndef __NEURUN_KERNEL_CPU_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ -#define __NEURUN_KERNEL_CPU_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ - -#include <NeuralNetworks.h> - -#include <arm_compute/runtime/IFunction.h> - -#include "internal/Model.h" -#include "internal/common/Tensor.h" -#include "internal/cpu.h" - -namespace neurun -{ -namespace kernel -{ -namespace cpu -{ - -class TensorConvertFromCommonLayer : public ::arm_compute::IFunction -{ -public: - TensorConvertFromCommonLayer() {} - -public: - bool convert(); - - void configure(::internal::common::Tensor *inputTensor, ::internal::cpu::Tensor *outputTensor, - const Shape &tensorShape); - - void run(); - -private: - ::internal::common::Tensor *_inputTensor; - ::internal::cpu::Tensor *_outputTensor; - - Shape _tensorShape{1}; -}; - -} // namespace cpu -} // namespace kernel -} // namespace neurun - -#endif // __NEURUN_KERNEL_CPU_TENSOR_CONVERT_FROM_COMMON_LAYER_H__ - -#endif diff --git a/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.cc b/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.cc deleted file mode 100644 index 7d721f494..000000000 --- a/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.cc +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#include "TensorConvertToCommonLayer.h" - -#include "internal/nnapi/feature/Reader.h" -#include "internal/nnapi/feature/View.h" - -#include <util/feature/IndexIterator.h> - -namespace neurun -{ -namespace kernel -{ -namespace cpu -{ - -bool TensorConvertToCommonLayer::convert() -{ - auto inputBuffer = _inputTensor->buffer(); - auto inputSize = _inputTensor->info()->total_size(); - - auto outputBuffer = _outputTensor->buffer(); - auto outputSize = _outputTensor->info()->total_size(); - - if (_tensorShape.rank() == 2) - { - const auto len = _tensorShape.dim(1); - - auto base = reinterpret_cast<float *>(outputBuffer); - - for (int32_t n = 0; n < len; ++n) - { - auto from = reinterpret_cast<const float *>( - _inputTensor->ptr_to_element(::arm_compute::Coordinates{n})); - auto into = base + n; - - *into = *from; - } - } - else if (_tensorShape.rank() == 4) - { - auto featureShape = _tensorShape.asFeature(); - - const ::internal::nnapi::feature::Reader<float> from{featureShape, inputBuffer, inputSize}; - ::internal::nnapi::feature::View<float> into{featureShape, outputBuffer, outputSize}; - - ::nnfw::util::feature::iterate(featureShape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, ch, row, col); - into.at(batch, ch, row, col) = value; - }; - } -} - -void TensorConvertToCommonLayer::configure(::internal::cpu::Tensor *inputTensor, - ::internal::common::Tensor *outputTensor, - const Shape &tensorShape) -{ - _inputTensor = inputTensor; - _outputTensor = outputTensor; - _tensorShape = tensorShape; -} - -void TensorConvertToCommonLayer::run() { convert(); } - -} // namespace cpu -} // namespace kernel -} // namespace neurun - -#endif diff --git a/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.h b/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.h deleted file mode 100644 index 7e96d1aff..000000000 --- a/runtimes/neurun/src/kernel/cpu/TensorConvertToCommonLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// -// THIS FILE IS UNUSED BUT LEFT FOR FUTURE REFERNCE -// - -#if 0 - -#ifndef __NEURUN_KERNEL_CPU_TENSOR_CONVERT_TO_COMMON_LAYER_H__ -#define __NEURUN_KERNEL_CPU_TENSOR_CONVERT_TO_COMMON_LAYER_H__ - -#include <NeuralNetworks.h> - -#include <arm_compute/runtime/IFunction.h> - -#include "internal/Model.h" -#include "internal/common/Tensor.h" -#include "internal/cpu.h" - -namespace neurun -{ -namespace kernel -{ -namespace cpu -{ - -class TensorConvertToCommonLayer : public ::arm_compute::IFunction -{ -public: - TensorConvertToCommonLayer() {} - -public: - bool convert(); - - void configure(::internal::cpu::Tensor *inputTensor, ::internal::common::Tensor *outputTensor, - const Shape &tensorShape); - - void run(); - -private: - ::internal::cpu::Tensor *_inputTensor; - ::internal::common::Tensor *_outputTensor; - - Shape _tensorShape{1}; -}; - -} // namespace cpu -} // namespace kernel -} // namespace neurun - -#endif // __NEURUN_KERNEL_CPU_TENSOR_CONVERT_TO_COMMON_LAYER_H__ - -#endif diff --git a/runtimes/neurun/src/linear/Linear.cc b/runtimes/neurun/src/linear/Linear.cc index 2ffcbdb93..6452bbd49 100644 --- a/runtimes/neurun/src/linear/Linear.cc +++ b/runtimes/neurun/src/linear/Linear.cc @@ -14,19 +14,26 @@ * limitations under the License. */ +#include <algorithm> + #include "Linear.h" #include "graph/Graph.h" #include "graph/operation/LowerInfo.h" -#include "backend/IStageGenerator.h" +#include "backend/interface/IStageGenerator.h" +#include "backend/interface/IConfig.h" +#include "compiler/SubTensorInfo.h" +#include "compiler/TensorInfo.h" + +#include "util/logging.h" namespace neurun { namespace linear { -Linear::Linear(const graph::Graph &graph) +Linear::Linear(const graph::Graph &graph) : _graph(graph) { // Linearize with topological sort // @@ -36,38 +43,157 @@ Linear::Linear(const graph::Graph &graph) // 3. Reverse the order of nodes graph::Graph::PostDfsConstIterator().iterate( - graph, [&](const neurun::graph::operation::Node &node) { _operations.emplace_back(&node); }); + graph, [&](const model::operation::Index &index, const model::operation::Node &node) { + const auto lower_info = graph.getLowerInfo(index); + _operations.emplace_back(&node, lower_info); + }); std::reverse(std::begin(_operations), std::end(_operations)); } -void Linear::accept(graph::operation::NodeVisitor &&visitor) const +void Linear::accept(model::operation::NodeVisitor &&visitor) const { for (const auto op : _operations) { - op->accept(std::move(visitor)); + op.node->accept(std::move(visitor)); } } -backend::TensorBuilderSet Linear::markTensors() const +backend::TensorBuilderSet Linear::planTensors() { + using ITensorBuilderPtr = std::shared_ptr<backend::ITensorBuilder>; + using FnOnTensorBuilder = + std::function<void(const model::operand::Index &ind, ITensorBuilderPtr)>; + + const auto &operands = _graph.operands(); + auto iterTensorBuilders = [&operands](const model::operand::Index &ind, FnOnTensorBuilder fn) { + const auto &obj = operands.at(ind); + for (auto backend : obj.lower_info()->def_backends()) + { + auto tensor_builder = backend->tensor_builder(); + fn(ind, tensor_builder); + } + }; + backend::TensorBuilderSet tensor_builders; + + std::unordered_map<model::operand::Index, uint32_t> uses_map; + std::vector<model::operand::Index> constants; + + _graph.operands().iterate( + [&](const model::operand::Index &ind, const model::operand::Object &obj) { + uses_map[ind] = obj.getUses().size(); + + // If a tensor is a constant, increase the use of the tensor. + // It makes the tensor not be dealloced. + if (obj.getUsage() == model::operand::OperandUsage::CONSTANT) + { + constants.push_back(ind); + uses_map[ind]++; + } + + for (auto backend : obj.lower_info()->def_backends()) + { + bool isSubTensor = false; + auto tensor_builder = backend->tensor_builder(); + + if (backend->config()->SupportSubTensorAlloc()) + { + const auto parentInfo = obj.parent_info(); + if (parentInfo != nullptr) + { + isSubTensor = true; + } + } + + if (isSubTensor) + { + const compiler::SubTensorInfo info(obj); + tensor_builder->registerSubTensorInfo(ind, info); + } + else + { + const auto info = compiler::TensorInfo(obj.shape(), obj.typeInfo()); + tensor_builder->registerTensorInfo(ind, info); + } + + // Prepare tensor builders to be returned + tensor_builders.insert(tensor_builder); + } + }); + + // If a tensor is model output, increase the use of the tensor. + // This aim is same to above one. + for (const auto &ind : _graph.getOutputs()) + { + uses_map[ind]++; + } + + // Allocate constant operands first + VERBOSE(LINEAR) << "TENSORS as CONSTANT" << std::endl; + for (const auto &ind : constants) + { + iterTensorBuilders(ind, [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { + tensor_builder->notifyFirstUse(ind); + }); + } + + // Allocate Model's inputs + VERBOSE(LINEAR) << "TENSORS as MODEL INPUT" << std::endl; + for (const auto &ind : _graph.getInputs()) + { + iterTensorBuilders(ind, [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { + tensor_builder->notifyFirstUse(ind); + }); + } + + // At each operation, + // 1. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0 + // 2. Scan DEF of outputs. If the DEF, allocate it + VERBOSE(LINEAR) << "TENSORS" << std::endl; for (const auto op : _operations) { - const auto tensor_builder = op->lower_info()->backend().stage_gen()->tensor_builder(); - for (const auto &ind : op->getInputs()) + for (const auto &ind : op.node->getOutputs()) { - tensor_builder->mark(ind); - tensor_builders.insert(tensor_builder); + const auto &obj = operands.at(ind); + if (obj.getDef().size()) + { + iterTensorBuilders(ind, + [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { + tensor_builder->notifyFirstUse(ind); + }); + } } - for (const auto &ind : op->getOutputs()) + + for (const auto &ind : op.node->getInputs()) { - tensor_builder->mark(ind); - tensor_builders.insert(tensor_builder); + uses_map[ind]--; + if (uses_map[ind] == 0) + { + iterTensorBuilders(ind, + [](const model::operand::Index &ind, ITensorBuilderPtr tensor_builder) { + tensor_builder->notifyLastUse(ind); + }); + } } } + + // Now, model outputs should be not deallocated + assert(std::all_of(_graph.getOutputs().begin(), _graph.getOutputs().end(), + [&uses_map](const model::operand::Index &ind) { return uses_map[ind] > 0; })); + + // Set subtensor information + // Todo: move this phase outside as optimization phase return tensor_builders; } +void Linear::iterate(const std::function<void(const Element &element)> &fn) const +{ + for (const auto op : _operations) + { + fn(op); + } +} + } // namespace linear } // namespace neurun diff --git a/runtimes/neurun/src/linear/Linear.h b/runtimes/neurun/src/linear/Linear.h index ffbc68ecb..fb3f539d4 100644 --- a/runtimes/neurun/src/linear/Linear.h +++ b/runtimes/neurun/src/linear/Linear.h @@ -19,8 +19,8 @@ #include <vector> -#include "graph/operation/Node.h" -#include "backend/ITensorBuilder.h" +#include "model/operation/Node.h" +#include "backend/interface/ITensorBuilder.h" namespace neurun { @@ -46,6 +46,17 @@ namespace neurun namespace linear { +struct Element +{ + const model::operation::Node *node; + const graph::operation::LowerInfo *lower_info; + + Element(const model::operation::Node *node, const graph::operation::LowerInfo *lower_info) + : node{node}, lower_info{lower_info} + { + } +}; + class Linear { public: @@ -55,14 +66,16 @@ public: Linear(const Linear &linear) = delete; public: - void accept(graph::operation::NodeVisitor &&visitor) const; + void accept(model::operation::NodeVisitor &&visitor) const; // TODO Should not return TensorBuilderSet - virtual backend::TensorBuilderSet markTensors() const; + backend::TensorBuilderSet planTensors(); + + void iterate(const std::function<void(const Element &element)> &fn) const; -public: private: - std::vector<const graph::operation::Node *> _operations; + const graph::Graph &_graph; + std::vector<Element> _operations; }; } // namespace linear diff --git a/runtimes/neurun/src/logging.h b/runtimes/neurun/src/logging.h deleted file mode 100644 index 950df7b52..000000000 --- a/runtimes/neurun/src/logging.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_LOGGING_H__ -#define __NEURUN_LOGGING_H__ - -#include <iostream> - -namespace logging -{ - -class Context -{ -public: - Context() : _enabled{false} - { - auto env = std::getenv("NEURUN_LOG_ENABLE"); - - if (env && std::atoi(env) > 0) - { - _enabled = true; - } - } - -public: - bool enabled(void) const { return _enabled; } - -private: - bool _enabled; -}; - -static Context ctx; - -} // namespace logging - -#define VERBOSE(name) \ - if (::logging::ctx.enabled()) \ - std::cout << "[" << #name << "] " - -#endif // __NEURUN_LOGGING_H__ diff --git a/runtimes/neurun/src/model/operand/Data.h b/runtimes/neurun/src/model/operand/Data.h new file mode 100644 index 000000000..506cb185a --- /dev/null +++ b/runtimes/neurun/src/model/operand/Data.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_DATA_H__ +#define __NEURUN_MODEL_OPERAND_DATA_H__ + +#include <algorithm> + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +struct Data +{ + virtual ~Data() = default; + + virtual size_t size(void) const = 0; + virtual const uint8_t *base(void) const = 0; +}; + +class CachedData final : public Data +{ +public: + CachedData(const uint8_t *base, size_t size) : _base{new uint8_t[size]}, _size{size} + { + std::copy(base, base + size, _base); + } + +public: + ~CachedData() { delete[] _base; } + +public: + size_t size(void) const override { return _size; } + const uint8_t *base(void) const override { return _base; } + +private: + uint8_t *_base; + size_t _size; +}; + +class ExternalData final : public Data +{ +public: + ExternalData(const uint8_t *base, size_t size) : _base{base}, _size{size} + { + // DO NOTHING + } + +public: + size_t size(void) const override { return _size; } + const uint8_t *base(void) const override { return _base; } + +private: + const uint8_t *_base; + const size_t _size; +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_DATA_H__ diff --git a/runtimes/neurun/src/model/operand/DataType.h b/runtimes/neurun/src/model/operand/DataType.h new file mode 100644 index 000000000..d75a0dbf1 --- /dev/null +++ b/runtimes/neurun/src/model/operand/DataType.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_DATATYPE_H__ +#define __NEURUN_MODEL_OPERAND_DATATYPE_H__ + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +enum class DataType +{ + SCALAR_FLOAT32 = 0, + SCALAR_INT32 = 1, + SCALAR_UINT32 = 2, + + TENSOR_FLOAT32 = 3, + TENSOR_INT32 = 4, + + TENSOR_QUANT8_ASYMM = 5, +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_DATATYPE_H__ diff --git a/runtimes/neurun/src/model/operand/Index.h b/runtimes/neurun/src/model/operand/Index.h new file mode 100644 index 000000000..1c84ba451 --- /dev/null +++ b/runtimes/neurun/src/model/operand/Index.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_INDEX_H__ +#define __NEURUN_MODEL_OPERAND_INDEX_H__ + +#include "graph/Index.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; + +} // namespace operand +} // namespace model +} // namespace neurun + +namespace neurun +{ +namespace model +{ +namespace operand +{ +namespace IO +{ + +using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; + +} // namespace IO +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_INDEX_H__ diff --git a/runtimes/neurun/src/model/operand/IndexSet.cc b/runtimes/neurun/src/model/operand/IndexSet.cc new file mode 100644 index 000000000..b83d314e4 --- /dev/null +++ b/runtimes/neurun/src/model/operand/IndexSet.cc @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "IndexSet.h" + +#include <algorithm> + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +IndexSet::IndexSet(std::initializer_list<Index> list) : _set(list) +{ + // DO NOTHING +} + +IndexSet::IndexSet(std::initializer_list<int32_t> list) +{ + for (auto val : list) + { + _set.emplace_back(static_cast<uint32_t>(val)); + } +} + +IndexSet::IndexSet(std::initializer_list<uint32_t> list) +{ + for (auto val : list) + { + _set.emplace_back(val); + } +} + +bool IndexSet::contains(const Index &index) const +{ + return std::find(_set.begin(), _set.end(), index) != _set.end(); +} + +void IndexSet::replace(const Index &from, const Index &to) +{ + std::replace(_set.begin(), _set.end(), from, to); +} + +} // namespace operand +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operand/IndexSet.h b/runtimes/neurun/src/model/operand/IndexSet.h new file mode 100644 index 000000000..e8827de9c --- /dev/null +++ b/runtimes/neurun/src/model/operand/IndexSet.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_INDEX_SET_H__ +#define __NEURUN_MODEL_OPERAND_INDEX_SET_H__ + +#include <initializer_list> +#include <vector> + +#include "Index.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +class IndexSet +{ +public: + IndexSet(void) = default; + IndexSet(std::initializer_list<Index> list); + IndexSet(std::initializer_list<int32_t> list); + IndexSet(std::initializer_list<uint32_t> list); + +public: + void append(const Index &index) { _set.emplace_back(index); } + +public: + uint32_t size() const { return static_cast<uint32_t>(_set.size()); } + const Index &at(IO::Index set_index) const { return _set.at(set_index.asInt()); } + const Index &at(uint32_t index) const { return _set.at(index); } + bool contains(const Index &index) const; + void replace(const Index &from, const Index &to); + +public: + std::vector<Index>::const_iterator begin(void) const { return _set.begin(); } + std::vector<Index>::const_iterator end(void) const { return _set.end(); } + +private: + std::vector<Index> _set; +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_INDEX_SET_H__ diff --git a/runtimes/neurun/src/model/operand/Object.cc b/runtimes/neurun/src/model/operand/Object.cc new file mode 100644 index 000000000..63cf29bd3 --- /dev/null +++ b/runtimes/neurun/src/model/operand/Object.cc @@ -0,0 +1,128 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Object.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +size_t Object::operandSize(void) const +{ + const uint32_t ranks = _shape.rank(); + int32_t elements = 1; + + for (uint32_t rank = 0; rank < ranks; rank++) + { + elements *= _shape.dim(rank); + } + + DataType type = _type.type(); + size_t element_size = 0; + + // Value of type is matched with OperandCode enum in NeuralNetworks.h + switch (type) + { + case DataType::SCALAR_FLOAT32: + case DataType::TENSOR_FLOAT32: + element_size = sizeof(float); + break; + case DataType::SCALAR_INT32: + case DataType::TENSOR_INT32: + element_size = sizeof(int32_t); + break; + case DataType::SCALAR_UINT32: + element_size = sizeof(uint32_t); + break; + case DataType::TENSOR_QUANT8_ASYMM: + element_size = sizeof(uint8_t); + break; + default: + throw std::runtime_error{"Unsuppported type size"}; + } + + return element_size * elements; +} + +bool Object::setUsage(const OperandUsage usage) +{ + if (usageIsDefined() && (_usage != usage)) + { + // Already set as different type + return false; + } + + _usage = usage; + + return true; +} + +void Object::appendUse(const ::neurun::model::operation::Index &idx) +{ + assert(_usage != OperandUsage::NOT_DEFINED); + assert(!_uses.contains(idx)); + + _uses.append(idx); +} + +void Object::removeUse(const ::neurun::model::operation::Index &idx) +{ + assert(_usage != OperandUsage::NOT_DEFINED); + assert(_uses.contains(idx)); + + _uses.remove(idx); +} + +void Object::appendDef(const ::neurun::model::operation::Index &idx) +{ + assert(_usage != OperandUsage::NOT_DEFINED && _usage != OperandUsage::CONSTANT); + assert(_def.size() == 0); + + _def.append(idx); +} + +void Object::removeDef(const ::neurun::model::operation::Index &idx) +{ + assert(_usage != OperandUsage::NOT_DEFINED); + assert(_def.contains(idx)); + + _def.remove(idx); +} + +void Object::lower_info(std::unique_ptr<graph::operand::LowerInfo> &&lower_info) +{ + _lower_info = std::move(lower_info); +} + +const graph::operand::LowerInfo *Object::lower_info() const { return _lower_info.get(); } + +graph::operand::LowerInfo *Object::lower_info() { return _lower_info.get(); } + +void Object::parent_info(std::unique_ptr<graph::operand::ParentInfo> &&parent_info) +{ + _parent_info = std::move(parent_info); +} + +const graph::operand::ParentInfo *Object::parent_info() const { return _parent_info.get(); } + +graph::operand::ParentInfo *Object::parent_info() { return _parent_info.get(); } + +} // namespace operand +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operand/Object.h b/runtimes/neurun/src/model/operand/Object.h new file mode 100644 index 000000000..eb5f6275e --- /dev/null +++ b/runtimes/neurun/src/model/operand/Object.h @@ -0,0 +1,135 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_OBJECT_H__ +#define __NEURUN_MODEL_OPERAND_OBJECT_H__ + +#include <cassert> +#include <cstdint> +#include <memory> +#include <algorithm> + +#include "Shape.h" +#include "Data.h" +#include "TypeInfo.h" +#include "graph/operand/LowerInfo.h" // TODO Remove this dependency +#include "graph/operand/ParentInfo.h" // TODO Remove this dependency +#include "model/operation/IndexList.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +// Operand usage should be exact one of these +enum class OperandUsage +{ + NOT_DEFINED, + MODEL_INPUT, + CONSTANT, + OPERATION_OUTPUT, +}; + +class Object +{ +public: + explicit Object(const Shape &shape, const TypeInfo &type) + : _shape{shape}, _type{type}, _usage{OperandUsage::NOT_DEFINED} + { + // DO NOTHING + } + +public: + const Shape &shape(void) const { return _shape; } + const TypeInfo &typeInfo(void) const { return _type; } + size_t operandSize(void) const; + bool setAsConstant() { return setUsage(OperandUsage::CONSTANT); } + bool setAsModelInput() { return setUsage(OperandUsage::MODEL_INPUT); } + bool setAsOperationOutput() { return setUsage(OperandUsage::OPERATION_OUTPUT); } + bool usageIsDefined(void) const { return _usage != OperandUsage::NOT_DEFINED; } + bool isModelInput(void) const { return _usage == OperandUsage::MODEL_INPUT; } + OperandUsage getUsage() const { return _usage; } + + const operation::IndexList &getUses() const { return _uses; } + const operation::IndexList &getDef() const { return _def; } + void appendUse(const operation::Index &idx); + void removeUse(const operation::Index &idx); + void appendDef(const operation::Index &idx); + void removeDef(const operation::Index &idx); + +private: + bool setUsage(OperandUsage usage); + +public: + void data(std::unique_ptr<Data> &&data) { _data = std::move(data); } + const Data &data(void) const { return *_data; } + +public: + template <typename T, typename... Args> void data(Args &&... args) + { + data(std::unique_ptr<T>(new T{std::forward<Args>(args)...})); + } + +public: + template <typename T> T asScalar(void) const + { + assert((_shape.rank() == 0) || ((_shape.rank() == 1) && (_shape.dim(0) == 1))); + assert(_data != nullptr); + assert((_data->base() != nullptr) && (_data->size() == sizeof(T))); + + return *(reinterpret_cast<const T *>(_data->base())); + } + +public: + void lower_info(std::unique_ptr<graph::operand::LowerInfo> &&lower_info); + const graph::operand::LowerInfo *lower_info() const; + graph::operand::LowerInfo *lower_info(); + /** + * @brief Set parent information + * @param[in] parent_info Parent information + */ + void parent_info(std::unique_ptr<graph::operand::ParentInfo> &&parent_info); + /** + * @brief Return parent information pointer as constant + * @return Parent information pointer + */ + const graph::operand::ParentInfo *parent_info() const; + /** + * @brief Return parent information pointer + * @return Perent information pointer + */ + graph::operand::ParentInfo *parent_info(); + +private: + const Shape _shape; + const TypeInfo _type; + std::unique_ptr<Data> _data; + OperandUsage _usage; + + operation::IndexList _uses; + operation::IndexList _def; // size is 0 (constant) or 1 (from def operation) + + std::unique_ptr<graph::operand::LowerInfo> _lower_info; + std::unique_ptr<graph::operand::ParentInfo> _parent_info; +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_OBJECT_H__ diff --git a/runtimes/neurun/src/model/operand/Set.cc b/runtimes/neurun/src/model/operand/Set.cc new file mode 100644 index 000000000..d93c21514 --- /dev/null +++ b/runtimes/neurun/src/model/operand/Set.cc @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Set.h" + +#include "cpp14/memory.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +const Index Set::generateIndex() +{ + assert((_index_count) <= 0x7fffffff); + + return Index{_index_count++}; +} + +Index Set::append(const Shape &shape, const TypeInfo &type) +{ + auto index = generateIndex(); + + _objects[index] = nnfw::cpp14::make_unique<Object>(shape, type); + + return index; +} + +const Object &Set::at(const Index &index) const { return *(_objects.at(index)); } + +Object &Set::at(const Index &index) { return *(_objects.at(index)); } + +bool Set::exist(const Index &index) const { return index.value() < _objects.size(); } + +void Set::iterate(const std::function<void(const Index &, const Object &)> &fn) const +{ + for (const auto &e : _objects) + { + fn(e.first, *e.second); + } +} + +void Set::iterate(const std::function<void(const Index &, Object &)> &fn) +{ + // TODO Remove this workaround + // This implementation is a workaround in case of adding operands while iteration + // + // // Original Implementation (We probably should be back to this) + // for (auto &e : _objects) + // { + // fn(e.first, *e.second); + // } + + std::list<Index> l; + + for (auto &e : _objects) + { + l.push_back(e.first); + } + + for (auto index : l) + { + fn(index, *_objects[index]); + } +} + +} // namespace operand +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operand/Set.h b/runtimes/neurun/src/model/operand/Set.h new file mode 100644 index 000000000..9dff7ec3c --- /dev/null +++ b/runtimes/neurun/src/model/operand/Set.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_SET_H__ +#define __NEURUN_MODEL_OPERAND_SET_H__ + +#include <memory> +#include <unordered_map> + +#include "Object.h" +#include "Index.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +class Set +{ +public: + Set() : _index_count(0) {} + +public: + Index append(const Shape &, const TypeInfo &); + void remove(const Index &index) { _objects.erase(index); }; + +public: + const Object &at(const Index &) const; + Object &at(const Index &); + bool exist(const Index &) const; + void iterate(const std::function<void(const Index &, const Object &)> &fn) const; + void iterate(const std::function<void(const Index &, Object &)> &fn); + +private: + const Index generateIndex(); + +private: + std::unordered_map<Index, std::unique_ptr<Object>> _objects; + uint32_t _index_count; +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_SET_H__ diff --git a/runtimes/neurun/src/model/operand/Shape.cc b/runtimes/neurun/src/model/operand/Shape.cc new file mode 100644 index 000000000..f74c48d88 --- /dev/null +++ b/runtimes/neurun/src/model/operand/Shape.cc @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <cassert> + +#include "Shape.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +Shape::Shape(uint32_t rank) { _dims.resize(rank); } + +int32_t Shape::asVector(void) const +{ + assert(rank() == 1); + + return dim(0); +} + +nnfw::misc::matrix::Shape Shape::asMatrix(void) const +{ + assert(rank() == 2); + + const auto height = dim(0); + const auto width = dim(1); + + return nnfw::misc::matrix::Shape(height, width); +} + +nnfw::misc::feature::Shape Shape::asFeature(void) const +{ + assert(rank() == 4); + + // Feature Map in NNAPI + // - Dimension(0) -> Batch + // - Dimension(1) -> Height + // - Dimension(2) -> Width + // - Dimension(3) -> Depth + const auto batch = dim(0); + const auto depth = dim(3); + const auto height = dim(1); + const auto width = dim(2); + + return nnfw::misc::feature::Shape(batch, depth, height, width); +} + +nnfw::misc::kernel::Shape Shape::asKernel(void) const +{ + assert(rank() == 4); + + // Convolution Kernel in NNAPI + // - Dimension(0) -> Count + // - Dimension(1) -> Height + // - Dimension(2) -> Width + // - Dimension(3) -> Depth + const auto count = dim(0); + const auto depth = dim(3); + const auto height = dim(1); + const auto width = dim(2); + + return nnfw::misc::kernel::Shape(count, depth, height, width); +} + +nnfw::misc::tensor::Shape Shape::asTensor(void) const +{ + nnfw::misc::tensor::Shape shape{}; + for (uint32_t i = 0; i < rank(); ++i) + { + shape.append(dim(i)); + } + + return shape; // this shape represents shape of NNAPI +} + +} // namespace operand +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operand/Shape.h b/runtimes/neurun/src/model/operand/Shape.h new file mode 100644 index 000000000..b80f647d5 --- /dev/null +++ b/runtimes/neurun/src/model/operand/Shape.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_SHAPE_H__ +#define __NEURUN_MODEL_OPERAND_SHAPE_H__ + +#include <vector> +#include <cstdint> + +#include "misc/feature/Shape.h" +#include "misc/kernel/Shape.h" +#include "misc/matrix/Shape.h" +#include "misc/tensor/Shape.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +struct Shape +{ +public: + Shape(uint32_t rank = 0); + +public: + uint32_t rank(void) const { return _dims.size(); } + +public: + int32_t dim(uint32_t n) const { return _dims.at(n); } + int32_t &dim(uint32_t n) { return _dims.at(n); } + const std::vector<int32_t> &dims() const { return _dims; } + +public: + int32_t asVector(void) const; + nnfw::misc::matrix::Shape asMatrix(void) const; + nnfw::misc::feature::Shape asFeature(void) const; + nnfw::misc::kernel::Shape asKernel(void) const; + nnfw::misc::tensor::Shape asTensor(void) const; + +private: + std::vector<int32_t> _dims; +}; + +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_SHAPE_H__ diff --git a/runtimes/neurun/src/model/operand/TypeInfo.cc b/runtimes/neurun/src/model/operand/TypeInfo.cc new file mode 100644 index 000000000..0b9f63c93 --- /dev/null +++ b/runtimes/neurun/src/model/operand/TypeInfo.cc @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TypeInfo.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +DataType TypeInfo::typeFromOperandCode(OperandCode type) +{ + // Now neurun::model::operand::DataType share same enum value with OperandCode + // in NeuralNetworks.h. + return static_cast<DataType>(static_cast<uint32_t>(type)); +} + +} // namespace operand +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operand/TypeInfo.h b/runtimes/neurun/src/model/operand/TypeInfo.h new file mode 100644 index 000000000..d16172a09 --- /dev/null +++ b/runtimes/neurun/src/model/operand/TypeInfo.h @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERAND_TYPEINFO_H__ +#define __NEURUN_MODEL_OPERAND_TYPEINFO_H__ + +#include <cstdint> + +#include <NeuralNetworks.h> + +#include "DataType.h" + +namespace neurun +{ +namespace model +{ +namespace operand +{ + +class TypeInfo +{ +public: + TypeInfo() = default; + + TypeInfo(OperandCode type, float scale, int32_t offset) + : _type(typeFromOperandCode(type)), _scale(scale), _offset(offset) + { + // DO NOTHING + } + +public: + DataType type() const { return _type; } + float scale() const { return _scale; } + int32_t offset() const { return _offset; } + +private: + // Now neurun::model::operand::DataType share same enum value with OperandCode + // in NeuralNetworks.h. + // If we don't share same value, we must fix this mapping function. + DataType typeFromOperandCode(OperandCode type); + +private: + DataType _type; + float _scale; + int32_t _offset; +}; +} // namespace operand +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERAND_TYPEINFO_H__ diff --git a/runtimes/neurun/src/model/operation/AddNode.cc b/runtimes/neurun/src/model/operation/AddNode.cc new file mode 100644 index 000000000..0c9d4e09b --- /dev/null +++ b/runtimes/neurun/src/model/operation/AddNode.cc @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AddNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void AddNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +AddNode::AddNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(2u)} +{ + assert(init_param.input_count == 2); + assert(init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> Lefthand side operand + // 1 -> Righthand side operand + + setInputs({init_param.inputs[0], init_param.inputs[1]}); + setOutputs({init_param.outputs[0]}); +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/AddNode.h b/runtimes/neurun/src/model/operation/AddNode.h new file mode 100644 index 000000000..533fb0ab3 --- /dev/null +++ b/runtimes/neurun/src/model/operation/AddNode.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_ADD_NODE_H__ +#define __NEURUN_MODEL_OPERATION_ADD_NODE_H__ + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class AddNode : public model::operation::Node +{ +public: + AddNode(const model::operation::Node::InitParam &init_param); + + enum Input + { + LHS = 0, + RHS + }; + + struct Param + { + operand::Index activation_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "Add"; } +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_ADD_H__ diff --git a/runtimes/neurun/src/model/operation/AvgPool2DNode.cc b/runtimes/neurun/src/model/operation/AvgPool2DNode.cc new file mode 100644 index 000000000..8c688e60a --- /dev/null +++ b/runtimes/neurun/src/model/operation/AvgPool2DNode.cc @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AvgPool2DNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void AvgPool2DNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +AvgPool2DNode::AvgPool2DNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(1u)} +{ + assert(init_param.input_count == 7); + assert(init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> IFM Tensor Index + // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index + // 2 -> Horizontal (over width) Stride Index + // 3 -> Vertial (over height) Stride Index + // 4 -> Filter Width Index + // 5 -> Filter Height Index + // 6 -> FuseCode (activation) Index + + setInputs({init_param.inputs[0]}); + setOutputs({init_param.outputs[0]}); + + _param.padding_index = operand::Index{init_param.inputs[1]}; + _param.hstride_index = operand::Index{init_param.inputs[2]}; + _param.vstride_index = operand::Index{init_param.inputs[3]}; + + _param.kw_index = operand::Index{init_param.inputs[4]}; + _param.kh_index = operand::Index{init_param.inputs[5]}; + _param.activation_index = operand::Index{init_param.inputs[6]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/AvgPool2DNode.h b/runtimes/neurun/src/model/operation/AvgPool2DNode.h new file mode 100644 index 000000000..e66e6146e --- /dev/null +++ b/runtimes/neurun/src/model/operation/AvgPool2DNode.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_AVGPOOL2D_NODE_H__ +#define __NEURUN_MODEL_OPERATION_AVGPOOL2D_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class AvgPool2DNode : public model::operation::Node +{ +public: + AvgPool2DNode(const model::operation::Node::InitParam &init_param); + + enum Input + { + INPUT = 0 + }; + + struct Param + { + operand::Index kw_index; + operand::Index kh_index; + + operand::Index hstride_index; + operand::Index vstride_index; + + operand::Index padding_index; + operand::Index activation_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "AvgPool2D"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_AVGPOOL2D_H__ diff --git a/runtimes/neurun/src/model/operation/ConcatNode.cc b/runtimes/neurun/src/model/operation/ConcatNode.cc new file mode 100644 index 000000000..23cfef294 --- /dev/null +++ b/runtimes/neurun/src/model/operation/ConcatNode.cc @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConcatNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void ConcatNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +ConcatNode::ConcatNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createAtLeast(2u)} +{ + assert(init_param.input_count >= 2); // At least one one input tensor and axis + assert(init_param.output_count == 1); + + // When there are N + 1 inputs, each input should be interpreted as follows: + // + // [0, N) -> Input tensors + // N -> Axis + // + + { + operand::IndexSet inds; + for (uint32_t n = 0; n < init_param.input_count - 1; ++n) + { + inds.append(operand::Index{init_param.inputs[n]}); + } + setInputs(inds); + } + setOutputs({init_param.outputs[0]}); + + _param.axis_index = operand::Index{init_param.inputs[init_param.input_count - 1]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/ConcatNode.h b/runtimes/neurun/src/model/operation/ConcatNode.h new file mode 100644 index 000000000..b69ee2f23 --- /dev/null +++ b/runtimes/neurun/src/model/operation/ConcatNode.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_CONCAT_NODE_H__ +#define __NEURUN_MODEL_OPERATION_CONCAT_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class ConcatNode : public model::operation::Node +{ +public: + ConcatNode(const model::operation::Node::InitParam &init_param); + + struct Param + { + operand::Index axis_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "Concat"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_CONCAT_H__ diff --git a/runtimes/neurun/src/model/operation/Conv2DNode.cc b/runtimes/neurun/src/model/operation/Conv2DNode.cc new file mode 100644 index 000000000..7eb2b183d --- /dev/null +++ b/runtimes/neurun/src/model/operation/Conv2DNode.cc @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Conv2DNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void Conv2DNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +Conv2DNode::Conv2DNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(3u)} +{ + assert(init_param.input_count == 7 && init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // + // 0 -> IFM Tensor Index + // 1 -> Kernel Tensor Index + // 2 -> Bias Tensor Index + // 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index + // 4 -> Stride (width) Index + // 5 -> Stride (height) INdex + // 6 -> Activation Index + + setInputs({init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}); + setOutputs({init_param.outputs[0]}); + + _param.padding_index = operand::Index{init_param.inputs[3]}; + _param.hstride_index = operand::Index{init_param.inputs[4]}; + _param.vstride_index = operand::Index{init_param.inputs[5]}; + _param.activation_index = operand::Index{init_param.inputs[6]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/Conv2DNode.h b/runtimes/neurun/src/model/operation/Conv2DNode.h new file mode 100644 index 000000000..34a95f0d9 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Conv2DNode.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_CONV2D_NODE_H__ +#define __NEURUN_MODEL_OPERATION_CONV2D_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class Conv2DNode : public model::operation::Node +{ +public: + Conv2DNode(const model::operation::Node::InitParam &); + + enum Input + { + INPUT = 0, + KERNEL, + BIAS + }; + + struct Param + { + operand::Index hstride_index; + operand::Index vstride_index; + + operand::Index padding_index; + operand::Index activation_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "Conv2D"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_CONV2D_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/FullyConnectedNode.cc b/runtimes/neurun/src/model/operation/FullyConnectedNode.cc new file mode 100644 index 000000000..0fde5182d --- /dev/null +++ b/runtimes/neurun/src/model/operation/FullyConnectedNode.cc @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FullyConnectedNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void FullyConnectedNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +FullyConnectedNode::FullyConnectedNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(3u)} +{ + assert(init_param.input_count == 4 && init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> A tensor, specifying the input. + // 1 -> A 2-D tensor, specifying the weights + // 2 -> A 1-D tensor, specifying the bias + // 3 -> An INT32 value, and has to be one of the FuseCode values + + setInputs({init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}); + setOutputs({init_param.outputs[0]}); + + _param.activation_index = operand::Index{init_param.inputs[3]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/FullyConnectedNode.h b/runtimes/neurun/src/model/operation/FullyConnectedNode.h new file mode 100644 index 000000000..9820ddc8c --- /dev/null +++ b/runtimes/neurun/src/model/operation/FullyConnectedNode.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_FULLYCONNECTED_NODE_H__ +#define __NEURUN_MODEL_OPERATION_FULLYCONNECTED_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class FullyConnectedNode : public model::operation::Node +{ +public: + FullyConnectedNode(const model::operation::Node::InitParam &init_param); + + enum Input + { + INPUT = 0, + WEIGHT, + BIAS + }; + + struct Param + { + operand::Index activation_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "FullyConnected"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_FULLYCONNECTED_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/Index.h b/runtimes/neurun/src/model/operation/Index.h new file mode 100644 index 000000000..e03dd74d6 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Index.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_INDEX_H__ +#define __NEURUN_MODEL_OPERATION_INDEX_H__ + +#include "graph/Index.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +using Index = ::neurun::graph::Index<uint32_t, struct IndexTag>; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_INDEX_H__ diff --git a/runtimes/neurun/src/model/operation/IndexList.cc b/runtimes/neurun/src/model/operation/IndexList.cc new file mode 100644 index 000000000..e46987036 --- /dev/null +++ b/runtimes/neurun/src/model/operation/IndexList.cc @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "IndexList.h" + +#include <algorithm> + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +IndexList::IndexList(std::initializer_list<Index> list) : _list(list) +{ + // DO NOTHING +} + +bool IndexList::contains(const ::neurun::model::operation::Index &index) const +{ + return std::find(_list.begin(), _list.end(), index) != _list.end(); +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/IndexList.h b/runtimes/neurun/src/model/operation/IndexList.h new file mode 100644 index 000000000..c0af29829 --- /dev/null +++ b/runtimes/neurun/src/model/operation/IndexList.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_INDEX_LIST_H__ +#define __NEURUN_MODEL_OPERATION_INDEX_LIST_H__ + +#include <initializer_list> +#include <list> + +#include "Index.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class IndexList +{ +public: + IndexList(void) = default; + IndexList(std::initializer_list<Index> list); + +public: + void append(const Index &index) { _list.push_back(index); } + void remove(const Index &index) { _list.remove(index); } + +public: + uint32_t size() const { return static_cast<uint32_t>(_list.size()); } + const std::list<Index> &list() const { return _list; } + bool contains(const Index &index) const; + +private: + std::list<Index> _list; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_INDEX_LIST_H__ diff --git a/runtimes/neurun/src/model/operation/MaxPool2DNode.cc b/runtimes/neurun/src/model/operation/MaxPool2DNode.cc new file mode 100644 index 000000000..3d3686b0e --- /dev/null +++ b/runtimes/neurun/src/model/operation/MaxPool2DNode.cc @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MaxPool2DNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void MaxPool2DNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +MaxPool2DNode::MaxPool2DNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(1u)} +{ + assert(init_param.input_count == 7); + assert(init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> IFM Tensor Index + // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index + // 2 -> Horizontal (over width) Stride Index + // 3 -> Vertial (over height) Stride Index + // 4 -> Filter Width Index + // 5 -> Filter Height Index + // 6 -> FuseCode (activation) Index + + setInputs({init_param.inputs[0]}); + setOutputs({init_param.outputs[0]}); + + _param.padding_index = operand::Index{init_param.inputs[1]}; + _param.hstride_index = operand::Index{init_param.inputs[2]}; + _param.vstride_index = operand::Index{init_param.inputs[3]}; + + _param.kw_index = operand::Index{init_param.inputs[4]}; + _param.kh_index = operand::Index{init_param.inputs[5]}; + _param.activation_index = operand::Index{init_param.inputs[6]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/MaxPool2DNode.h b/runtimes/neurun/src/model/operation/MaxPool2DNode.h new file mode 100644 index 000000000..96d1210a7 --- /dev/null +++ b/runtimes/neurun/src/model/operation/MaxPool2DNode.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_MAXPOOL2D_NODE_H__ +#define __NEURUN_MODEL_OPERATION_MAXPOOL2D_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class MaxPool2DNode : public model::operation::Node +{ +public: + MaxPool2DNode(const model::operation::Node::InitParam &init_param); + + enum Input + { + INPUT = 0 + }; + + struct Param + { + operand::Index kw_index; + operand::Index kh_index; + + operand::Index hstride_index; + operand::Index vstride_index; + + operand::Index padding_index; + operand::Index activation_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "MaxPool2D"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_MAXPOOL2D_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/Node.Include.h b/runtimes/neurun/src/model/operation/Node.Include.h new file mode 100644 index 000000000..95e78c7b5 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Node.Include.h @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// This file has no ifdef guard intentionally + +#include "Conv2DNode.h" +#include "MaxPool2DNode.h" +#include "AvgPool2DNode.h" +#include "ConcatNode.h" +#include "ReshapeNode.h" +#include "FullyConnectedNode.h" +#include "SoftmaxNode.h" +#include "PermuteNode.h" +#include "AddNode.h" diff --git a/runtimes/neurun/src/model/operation/Node.cc b/runtimes/neurun/src/model/operation/Node.cc new file mode 100644 index 000000000..76397afde --- /dev/null +++ b/runtimes/neurun/src/model/operation/Node.cc @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Node.h" + +#include <cassert> + +#include "graph/operation/LowerInfo.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +Node::Node(OperandConstraint input_constr) : _input_constr{input_constr} {} + +Node::~Node() = default; + +void Node::setInputs(const operand::IndexSet &indexes) +{ + assert(_input_constr.check(indexes.size())); + _inputs = indexes; +} + +void Node::setOutputs(const operand::IndexSet &indexes) { _outputs = indexes; } + +void Node::replaceInput(const operand::Index &from, const operand::Index &to) +{ + _inputs.replace(from, to); +} + +void Node::replaceOutput(const operand::Index &from, const operand::Index &to) +{ + _outputs.replace(from, to); +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/Node.h b/runtimes/neurun/src/model/operation/Node.h new file mode 100644 index 000000000..76f0d2d00 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Node.h @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_NODE_H__ +#define __NEURUN_MODEL_OPERATION_NODE_H__ + +#include <memory> + +#include "model/operand/Object.h" +#include "model/operand/IndexSet.h" +#include "OperandConstraint.h" + +namespace neurun +{ +namespace graph +{ +namespace operation +{ +class LowerInfo; +} // namespace operation +} // namespace graph +} // namespace neurun + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +struct NodeVisitor; + +class Node +{ +public: + struct InitParam + { + uint32_t input_count; + const uint32_t *inputs; + uint32_t output_count; + const uint32_t *outputs; + }; + +public: + Node(OperandConstraint input_constr); + virtual ~Node(); + +public: + virtual void accept(NodeVisitor &&) const = 0; + virtual std::string getName() const = 0; + +public: + void replaceInput(const operand::Index &from, const operand::Index &to); + void replaceOutput(const operand::Index &from, const operand::Index &to); + const operand::IndexSet &getInputs() const { return _inputs; } + const operand::IndexSet &getOutputs() const { return _outputs; } + // It's for only input/output tensors but const data. + void setInputs(const operand::IndexSet &indexes); + void setOutputs(const operand::IndexSet &indexes); + +private: + operand::IndexSet _inputs; + operand::IndexSet _outputs; + OperandConstraint _input_constr; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/NodeVisitor.h b/runtimes/neurun/src/model/operation/NodeVisitor.h new file mode 100644 index 000000000..8420de998 --- /dev/null +++ b/runtimes/neurun/src/model/operation/NodeVisitor.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_NODE_VISITOR_H__ +#define __NEURUN_MODEL_OPERATION_NODE_VISITOR_H__ + +#include "Node.Include.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +struct NodeVisitor +{ + virtual ~NodeVisitor() = default; + +#define OP(InternalName, IsNnApi, NnApiName) \ + virtual void visit(const InternalName &) {} +#include "model/operation/Op.lst" +#undef OP +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_NODE_VISITOR_H__ diff --git a/runtimes/neurun/src/model/operation/Op.lst b/runtimes/neurun/src/model/operation/Op.lst new file mode 100644 index 000000000..23f4b5118 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Op.lst @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef OP +#error Define OP before including this file +#endif + +// NOTE The relation between "Internal Name" and "NN API Name" is "1 : N". + +// Internal Name | NN API? | NN API Name +OP(AddNode , true , ADD) +OP(Conv2DNode , true , CONV_2D) +OP(AvgPool2DNode , true , AVERAGE_POOL_2D) +OP(MaxPool2DNode , true , MAX_POOL_2D) +OP(ConcatNode , true , CONCATENATION) +OP(FullyConnectedNode , true , FULLY_CONNECTED) +OP(ReshapeNode , true , RESHAPE) +OP(SoftmaxNode , true , SOFTMAX) +OP(PermuteNode , false , NOT_AVAILABLE) diff --git a/runtimes/neurun/src/model/operation/OperandConstraint.cc b/runtimes/neurun/src/model/operation/OperandConstraint.cc new file mode 100644 index 000000000..5c69de928 --- /dev/null +++ b/runtimes/neurun/src/model/operation/OperandConstraint.cc @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OperandConstraint.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/OperandConstraint.h b/runtimes/neurun/src/model/operation/OperandConstraint.h new file mode 100644 index 000000000..d1cd8aa2c --- /dev/null +++ b/runtimes/neurun/src/model/operation/OperandConstraint.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_OPERAND_CONSTRAINT_H__ +#define __NEURUN_MODEL_OPERATION_OPERAND_CONSTRAINT_H__ + +#include <stdint.h> +#include <limits> +#include <set> + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class OperandConstraint +{ +private: + static const uint32_t INF = std::numeric_limits<uint32_t>::max(); + +public: + static OperandConstraint createAny() { return OperandConstraint{0u, INF}; } + static OperandConstraint createExact(uint32_t exact) { return OperandConstraint{exact, exact}; } + static OperandConstraint createAtMost(uint32_t end) { return OperandConstraint{0u, end}; } + static OperandConstraint createAtLeast(uint32_t begin) { return OperandConstraint{begin, INF}; } + static OperandConstraint createInRange(uint32_t begin, uint32_t end) + { + return OperandConstraint{begin, end}; + } + +private: + OperandConstraint(uint32_t begin, uint32_t end) : _begin{begin}, _end{end} {} + +public: + bool check(uint32_t ind) const { return _begin <= ind && ind <= _end; } + +private: + uint32_t _begin; + uint32_t _end; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_OPERAND_CONSTRAINT_H__ diff --git a/runtimes/neurun/src/model/operation/PermuteNode.cc b/runtimes/neurun/src/model/operation/PermuteNode.cc new file mode 100644 index 000000000..174d2a86b --- /dev/null +++ b/runtimes/neurun/src/model/operation/PermuteNode.cc @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PermuteNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void PermuteNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +PermuteNode::PermuteNode(const operand::Index &input, const operand::Index &output, Type type) + : model::operation::Node{OperandConstraint::createExact(1u)}, _param{type} +{ + setInputs({input}); + setOutputs({output}); +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/PermuteNode.h b/runtimes/neurun/src/model/operation/PermuteNode.h new file mode 100644 index 000000000..b589975be --- /dev/null +++ b/runtimes/neurun/src/model/operation/PermuteNode.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_PERMUTE_NODE_H__ +#define __NEURUN_MODEL_OPERATION_PERMUTE_NODE_H__ + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class PermuteNode : public model::operation::Node +{ +public: + enum class Type + { + NHWC_TO_NCHW, + NCHW_TO_NHWC, + COPY + }; + + struct Param + { + Type type; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "Permute"; } + +public: + PermuteNode(const operand::Index &input, const operand::Index &output, Type type); + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_PERMUTE_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/ReshapeNode.cc b/runtimes/neurun/src/model/operation/ReshapeNode.cc new file mode 100644 index 000000000..616b8cd65 --- /dev/null +++ b/runtimes/neurun/src/model/operation/ReshapeNode.cc @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReshapeNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void ReshapeNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +ReshapeNode::ReshapeNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(1u)} +{ + assert(init_param.input_count == 2 && init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> A tensor, specifying the tensor to be reshaped. + // 1 -> A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output + // tensor + + // TODO Second input should be shape tensor (init_param.inputs[1]) + setInputs({init_param.inputs[0] /* , init_param.inputs[1] */}); + setOutputs({init_param.outputs[0]}); +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/ReshapeNode.h b/runtimes/neurun/src/model/operation/ReshapeNode.h new file mode 100644 index 000000000..1758e9ec8 --- /dev/null +++ b/runtimes/neurun/src/model/operation/ReshapeNode.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_RESHAPE_NODE_H__ +#define __NEURUN_MODEL_OPERATION_RESHAPE_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class ReshapeNode : public model::operation::Node +{ +public: + ReshapeNode(const model::operation::Node::InitParam &init_param); + + enum Input + { + INPUT = 0 + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "Reshape"; } +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_RESHAPE_NODE_H__ diff --git a/runtimes/neurun/src/model/operation/Set.cc b/runtimes/neurun/src/model/operation/Set.cc new file mode 100644 index 000000000..14bd4f584 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Set.cc @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Set.h" + +#include <cassert> + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +const Index Set::generateIndex() +{ + assert((_index_count) <= 0x7fffffff); + + return Index{_index_count++}; +} + +Index Set::append(std::unique_ptr<Node> &&node) +{ + auto index = generateIndex(); + + _nodes[index] = std::move(node); + return index; +} + +const Node &Set::at(const Index &index) const { return *(_nodes.at(index)); } + +Node &Set::at(const Index &index) { return *(_nodes.at(index)); } + +bool Set::exist(const Index &index) const { return _nodes.find(index) != _nodes.end(); } + +void Set::iterate(const std::function<void(const Index &, const Node &)> &fn) const +{ + for (auto it = _nodes.begin(); it != _nodes.end(); ++it) + { + fn(it->first, *it->second); + } +} + +void Set::iterate(const std::function<void(const Index &, Node &)> &fn) +{ + for (auto it = _nodes.begin(); it != _nodes.end(); ++it) + { + fn(it->first, *it->second); + } +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/Set.h b/runtimes/neurun/src/model/operation/Set.h new file mode 100644 index 000000000..eebf91e65 --- /dev/null +++ b/runtimes/neurun/src/model/operation/Set.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_SET_H__ +#define __NEURUN_MODEL_OPERATION_SET_H__ + +#include <memory> + +#include "model/operation/Index.h" +#include "Node.h" + +#include <unordered_map> + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class Set +{ +public: + Set() : _index_count(0) {} + +public: + Index append(std::unique_ptr<Node> &&node); + void remove(const Index &index) { _nodes.erase(index); }; + +public: + const Node &at(const Index &) const; + Node &at(const Index &); + bool exist(const Index &) const; + uint32_t size() const { return _nodes.size(); } + void iterate(const std::function<void(const Index &, const Node &)> &fn) const; + void iterate(const std::function<void(const Index &, Node &)> &fn); + +private: + const Index generateIndex(); + +private: + std::unordered_map<Index, std::unique_ptr<Node>> _nodes; + uint32_t _index_count; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_SET_H__ diff --git a/runtimes/neurun/src/model/operation/SoftmaxNode.cc b/runtimes/neurun/src/model/operation/SoftmaxNode.cc new file mode 100644 index 000000000..d157aa4a7 --- /dev/null +++ b/runtimes/neurun/src/model/operation/SoftmaxNode.cc @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SoftmaxNode.h" + +#include <cassert> + +#include "NodeVisitor.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +void SoftmaxNode::accept(NodeVisitor &&v) const { v.visit(*this); } + +SoftmaxNode::SoftmaxNode(const model::operation::Node::InitParam &init_param) + : model::operation::Node{OperandConstraint::createExact(1u)} +{ + assert(init_param.input_count == 2 && init_param.output_count == 1); + + // Each input should be interpreted as follows: + // + // 0 -> A 2-D or 4-D tensor, specifying the tensor to be reshaped. + // 1 -> FLOAT32 value, specifying the positive scaling factor for the exponent, beta. + + setInputs({init_param.inputs[0]}); + setOutputs({init_param.outputs[0]}); + + _param.scale_index = operand::Index{init_param.inputs[1]}; +} + +} // namespace operation +} // namespace model +} // namespace neurun diff --git a/runtimes/neurun/src/model/operation/SoftmaxNode.h b/runtimes/neurun/src/model/operation/SoftmaxNode.h new file mode 100644 index 000000000..4a5a72e5a --- /dev/null +++ b/runtimes/neurun/src/model/operation/SoftmaxNode.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_MODEL_OPERATION_SOFTMAX_NODE_H__ +#define __NEURUN_MODEL_OPERATION_SOFTMAX_NODE_H__ + +#include <memory> + +#include "model/operation/Node.h" + +namespace neurun +{ +namespace model +{ +namespace operation +{ + +class SoftmaxNode : public model::operation::Node +{ +public: + SoftmaxNode(const model::operation::Node::InitParam &init_param); + enum Input + { + INPUT = 0 + }; + + struct Param + { + operand::Index scale_index; + }; + +public: + virtual void accept(NodeVisitor &&) const override; + virtual std::string getName() const override { return "SoftMax"; } + +public: + const Param ¶m() const { return _param; } + +private: + Param _param; +}; + +} // namespace operation +} // namespace model +} // namespace neurun + +#endif // __NEURUN_MODEL_OPERATION_SOFTMAX_NODE_H__ diff --git a/runtimes/neurun/src/util/Padding.cc b/runtimes/neurun/src/util/Padding.cc new file mode 100644 index 000000000..a24c9ddf7 --- /dev/null +++ b/runtimes/neurun/src/util/Padding.cc @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "util/Padding.h" + +#include <algorithm> + +namespace neurun +{ +namespace util +{ + +Padding valid_padding(void) +{ + // + // ANEURALNETWORKS_PADDING_VALID + // + // VALID padding. No padding. + // + // When the input size is not evenly divisible by the filter size, + // the input at the end that could not fill the whole filter tile + // will simply be ignored. + // + Padding padding; + + padding.top = 0; + padding.bottom = 0; + padding.left = 0; + padding.right = 0; + + return padding; +} + +Padding same_padding(const nnfw::misc::feature::Shape &ifm_shape, + const nnfw::misc::feature::Shape &ofm_shape, const Stride &stride, uint32_t kw, + uint32_t kh) +{ + Padding padding; + + // ANEURALNETWORKS_PADDING_SAME (from NNAPI spec) + // + // SAME padding. Padding on both ends are the "same": + // + // padding_to_beginning = total_padding / 2 + // padding_to_end = (total_padding + 1)/2. + // + const int32_t vertical_needed_input = (ofm_shape.H - 1) * stride.vertical + kh; + const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H); + + const int32_t horizontal_needed_input = (ofm_shape.W - 1) * stride.horizontal + kw; + const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W); + + padding.top = vertical_total_padding / 2; + padding.bottom = (vertical_total_padding + 1) / 2; + padding.left = horizontal_total_padding / 2; + padding.right = (horizontal_total_padding + 1) / 2; + + return padding; +} + +} // namespace util +} // namespace neurun diff --git a/runtimes/neurun/src/util/Padding.h b/runtimes/neurun/src/util/Padding.h new file mode 100644 index 000000000..05a14eb31 --- /dev/null +++ b/runtimes/neurun/src/util/Padding.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_PADDING_H__ +#define __NEURUN_UTIL_PADDING_H__ + +#include <stdint.h> + +#include <misc/feature/Shape.h> + +namespace neurun +{ +namespace util +{ + +struct Padding +{ + uint32_t top; + uint32_t bottom; + uint32_t left; + uint32_t right; +}; + +struct Stride +{ + uint32_t vertical; + uint32_t horizontal; +}; + +Padding valid_padding(void); +Padding same_padding(const nnfw::misc::feature::Shape &ifm_shape, + const nnfw::misc::feature::Shape &ofm_shape, const Stride &stride, uint32_t kw, + uint32_t kh); + +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_PADDING_H__ diff --git a/runtimes/neurun/src/util/Utils.cc b/runtimes/neurun/src/util/Utils.cc new file mode 100644 index 000000000..def02db69 --- /dev/null +++ b/runtimes/neurun/src/util/Utils.cc @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Utils.h" + +#include <cassert> + +namespace neurun +{ +namespace util +{ + +const char *to_string(const PaddingCode &code) +{ + assert((ANEURALNETWORKS_PADDING_SAME == code) || (ANEURALNETWORKS_PADDING_VALID == code)); + + switch (code) + { + case ANEURALNETWORKS_PADDING_SAME: + return "ANEURALNETWORKS_PADDING_SAME"; + case ANEURALNETWORKS_PADDING_VALID: + return "ANEURALNETWORKS_PADDING_VALID"; + } + + return nullptr; +} + +} // namespace util +} // namespace neurun diff --git a/runtimes/neurun/src/util/Utils.h b/runtimes/neurun/src/util/Utils.h new file mode 100644 index 000000000..a1e5bf0ba --- /dev/null +++ b/runtimes/neurun/src/util/Utils.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Utils.h + * @brief This file contains utility functions + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __NEURUN_UTIL_UTILS_H__ +#define __NEURUN_UTIL_UTILS_H__ + +#include "NeuralNetworks.h" + +namespace neurun +{ +namespace util +{ + +/** + * @brief Converts a PaddingCode to const char* + * @param[in] code The PaddingCode to be converted + * @return A string holding the converted value + */ +const char *to_string(const PaddingCode &code); + +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_UTILS_H__ diff --git a/runtimes/neurun/src/util/config/Config.lst b/runtimes/neurun/src/util/config/Config.lst new file mode 100644 index 000000000..e029ebe37 --- /dev/null +++ b/runtimes/neurun/src/util/config/Config.lst @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef CONFIG +#error Define CONFIG before including this file +#endif + +// Name | Type | Default +CONFIG(GRAPH_DOT_DUMP , int , "0") +CONFIG(BACKENDS , std::string , "cpu;acl_cl") +CONFIG(OP_BACKEND_ALLOPS , std::string , "none") +CONFIG(DISABLE_COMPILE , bool , "0") + + +// Auto-generate all operations + +#define OP(InternalName, IsNnApi, NnApiName) \ + CONFIG(OP_BACKEND_ ## NnApiName, std::string, "acl_cl") +#include "model/operation/Op.lst" +#undef OP + diff --git a/runtimes/neurun/src/util/config/ConfigManager.cc b/runtimes/neurun/src/util/config/ConfigManager.cc new file mode 100644 index 000000000..46b80311c --- /dev/null +++ b/runtimes/neurun/src/util/config/ConfigManager.cc @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConfigManager.h" + +#include <cstdlib> + +namespace neurun +{ +namespace config +{ + +ConfigManager &ConfigManager::instance() +{ + static ConfigManager manager; + return manager; +} + +ConfigManager::ConfigManager() +{ + auto fetch_from_env_var = [&](const std::string &key) { + const char *value = std::getenv(key.c_str()); + if (value != nullptr) + { + _map[key] = value; + } + }; + +#define CONFIG(Name, Type, Default) \ + _map.insert({std::string{#Name}, std::string{Default}}); \ + fetch_from_env_var(#Name); + +#include "Config.lst" + +#undef CONFIG +} + +template <> bool ConfigManager::get<bool>(const std::string &key) const +{ + auto raw = _map.at(key); + + static const std::array<std::string, 5> false_list{"0", "OFF", "FALSE", "N", "NO"}; + auto false_found = std::find(false_list.begin(), false_list.end(), raw); + + return (false_found == false_list.end()); +} + +template <> int ConfigManager::get<int>(const std::string &key) const +{ + auto raw = _map.at(key); + return std::stoi(raw); +} + +template <> std::string ConfigManager::get<std::string>(const std::string &key) const +{ + auto raw = _map.at(key); + return raw; +} + +} // namespace config +} // namespace neurun diff --git a/runtimes/neurun/src/util/config/ConfigManager.h b/runtimes/neurun/src/util/config/ConfigManager.h new file mode 100644 index 000000000..78db03dc1 --- /dev/null +++ b/runtimes/neurun/src/util/config/ConfigManager.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_CONFIG_CONFIG_MANAGER_H__ +#define __NEURUN_CONFIG_CONFIG_MANAGER_H__ + +#include <algorithm> +#include <string> +#include <unordered_map> + +/** + * @file ConfigManager.h + * @brief This file contains neurun::config::ConfigManager class + */ + +namespace neurun +{ +namespace config +{ + +/** + * @brief Class that manages configurations + */ + +class ConfigManager +{ +public: + static ConfigManager &instance(); + +private: + /** + * @brief Construct a new ConfigManager object. Fetch variables from Environment Variables. + */ + ConfigManager(); + +public: + /** + * @brief Return the configuration value of given key + * + * @tparam T Type of the config + * @param key String key value + * + * @return The configuration value of given key value + */ + template <typename T> T get(const std::string &key) const; + +private: + std::unordered_map<std::string, std::string> _map; +}; + +template <> bool ConfigManager::get<bool>(const std::string &key) const; +template <> int ConfigManager::get<int>(const std::string &key) const; +template <> std::string ConfigManager::get<std::string>(const std::string &key) const; + +} // namespace config +} // namespace neurun + +#endif // __NEURUN_CONFIG_CONFIG_MANAGER_H__ diff --git a/runtimes/neurun/src/util/feature/Coordinate4D.h b/runtimes/neurun/src/util/feature/Coordinate4D.h new file mode 100644 index 000000000..27d6f7b9e --- /dev/null +++ b/runtimes/neurun/src/util/feature/Coordinate4D.h @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__ +#define __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__ + +#include <stdint.h> + +namespace neurun +{ +namespace util +{ +namespace feature +{ + +/** + * @brief Class to represent position(offset) of subtensor.\n + * Assume that parent and child are already lowered (can get Shape4D). + */ +class Coordinate4D +{ +public: + /** + * @brief Construct a new Coordinate4D object + */ + Coordinate4D(void) : _n{0}, _h{0}, _w{0}, _c{0} + { + // DO NOTHING + } + /** + * @brief Construct a new Coordinate4D object + * @param[in] n Batch offset + * @param[in] h Height offset + * @param[in] w Width offset + * @param[in] c Channel offset + * @return + */ + Coordinate4D(int32_t n, int32_t h, int32_t w, int32_t c) : _n{n}, _h{h}, _w{w}, _c{c} + { + // DO NOTHING + } + +public: + /** + * @brief Return batch offset + * @return Batch offset + */ + int32_t n(void) const { return _n; } + /** + * @brief Return height offset + * @return Height offset + */ + int32_t h(void) const { return _h; } + /** + * @brief Return width offset + * @return Width offset + */ + int32_t w(void) const { return _w; } + /** + * @brief Return channel offset + * @return Channel offset + */ + int32_t c(void) const { return _c; } + +private: + int32_t _n; + int32_t _h; + int32_t _w; + int32_t _c; +}; + +} // namespace feature +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_FEATURE_COORDINATE_4D_H__ diff --git a/runtimes/neurun/src/util/feature/nchw/View.h b/runtimes/neurun/src/util/feature/nchw/View.h new file mode 100644 index 000000000..048fdecd8 --- /dev/null +++ b/runtimes/neurun/src/util/feature/nchw/View.h @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__ +#define __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__ + +#include "misc/feature/Reader.h" +#include "misc/feature/Shape.h" + +#include "backend/interface/operand/ITensor.h" +#include "util/feature/Coordinate4D.h" + +#include <cassert> + +namespace neurun +{ +namespace util +{ +namespace feature +{ +namespace nchw +{ + +template <typename T> class View final : public nnfw::misc::feature::Reader<T> +{ +public: + View(::neurun::backend::operand::ITensor *tensor) : _tensor{tensor} + { + // TODO Validate whether tensor is a feature map, or not + _shape.N = tensor->dimension(3); + _shape.C = tensor->dimension(2); + _shape.H = tensor->dimension(1); + _shape.W = tensor->dimension(0); + } + +public: + const ::nnfw::misc::feature::Shape &shape(void) const { return _shape; } + +public: + T at(uint32_t ch, uint32_t row, uint32_t col) const override + { + const auto offset = feature_index_to_byte_offset(0, ch, row, col); + + T *ptr = reinterpret_cast<T *>(_tensor->buffer() + offset); + + return *ptr; + } + T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override + { + const auto offset = feature_index_to_byte_offset(batch, ch, row, col); + + T *ptr = reinterpret_cast<T *>(_tensor->buffer() + offset); + + return *ptr; + } + +public: + T &at(uint32_t ch, uint32_t row, uint32_t col) + { + const auto offset = feature_index_to_byte_offset(0, ch, row, col); + + T *ptr = reinterpret_cast<T *>(_tensor->buffer() + offset); + + return *ptr; + } + T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) + { + const auto offset = feature_index_to_byte_offset(batch, ch, row, col); + + T *ptr = reinterpret_cast<T *>(_tensor->buffer() + offset); + + return *ptr; + } + +private: + size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const + { + return _tensor->calcOffset( + neurun::util::feature::Coordinate4D{static_cast<int32_t>(batch), static_cast<int32_t>(row), + static_cast<int32_t>(col), static_cast<int32_t>(ch)}); + } + +private: + ::nnfw::misc::feature::Shape _shape; + ::neurun::backend::operand::ITensor *_tensor; +}; + +} // namespace nchw +} // namespace feature +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_FEATURE_NCHW_VIEW_H__ diff --git a/runtimes/neurun/src/util/feature/nhwc/Reader.h b/runtimes/neurun/src/util/feature/nhwc/Reader.h new file mode 100644 index 000000000..85b8cab74 --- /dev/null +++ b/runtimes/neurun/src/util/feature/nhwc/Reader.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_FEATURE_NHWC_READER_H__ +#define __NEURUN_UTIL_FEATURE_NHWC_READER_H__ + +#include "Utils.h" + +#include "misc/feature/Reader.h" + +namespace neurun +{ +namespace util +{ +namespace feature +{ +namespace nhwc +{ + +template <typename T> class Reader final : public nnfw::misc::feature::Reader<T> +{ +public: + Reader(const ::nnfw::misc::feature::Shape &shape, const T *ptr, size_t len) + : _shape{shape}, _ptr{ptr} + { + (void)len; // Workaround for unused variable in release mode + assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); + } + +public: + const nnfw::misc::feature::Shape &shape(void) const { return _shape; } + +public: + T at(uint32_t ch, uint32_t row, uint32_t col) const override + { + uint32_t index = index_of(_shape, ch, row, col); + + return _ptr[index]; + } + T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override + { + uint32_t index = index_of(_shape, batch, ch, row, col); + + return _ptr[index]; + } + +private: + nnfw::misc::feature::Shape _shape; + +private: + const T *_ptr; +}; + +} // namespace nhwc +} // namespace feature +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_FEATURE_NHWC_READER_H__ diff --git a/runtimes/neurun/src/util/feature/nhwc/Utils.h b/runtimes/neurun/src/util/feature/nhwc/Utils.h new file mode 100644 index 000000000..3dab4261c --- /dev/null +++ b/runtimes/neurun/src/util/feature/nhwc/Utils.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_FEATURE_NHWC_UTILS_H__ +#define __NEURUN_UTIL_FEATURE_NHWC_UTILS_H__ + +#include "misc/feature/Shape.h" + +namespace neurun +{ +namespace util +{ +namespace feature +{ +namespace nhwc +{ + +inline uint32_t index_of(const ::nnfw::misc::feature::Shape &shape, uint32_t ch, uint32_t row, + uint32_t col) +{ + uint32_t res = 0; + + // NNAPI uses NHWC ordering + res += row * shape.W * shape.C; + res += col * shape.C; + res += ch; + + return res; +} + +inline uint32_t index_of(const ::nnfw::misc::feature::Shape &shape, uint32_t batch, uint32_t ch, + uint32_t row, uint32_t col) +{ + uint32_t res = 0; + + // NNAPI uses NHWC ordering + res += batch * shape.H * shape.W * shape.C; + res += row * shape.W * shape.C; + res += col * shape.C; + res += ch; + + return res; +} + +} // namespace nhwc +} // namespace feature +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_FEATURE_NHWC_UTILS_H__ diff --git a/runtimes/neurun/src/util/feature/nhwc/View.h b/runtimes/neurun/src/util/feature/nhwc/View.h new file mode 100644 index 000000000..1b9be9e1d --- /dev/null +++ b/runtimes/neurun/src/util/feature/nhwc/View.h @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__ +#define __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__ + +#include <cassert> + +#include "Utils.h" + +#include "misc/feature/Reader.h" + +namespace neurun +{ +namespace util +{ +namespace feature +{ +namespace nhwc +{ + +template <typename T> class View final : public nnfw::misc::feature::Reader<T> +{ +public: + View(const ::nnfw::misc::feature::Shape &shape, T *ptr, size_t len) : _shape{shape}, _ptr{ptr} + { + (void)len; // Workaround for unused variable in release mode + assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); + } + +public: + const nnfw::misc::feature::Shape &shape(void) const { return _shape; } + +public: + T at(uint32_t ch, uint32_t row, uint32_t col) const override + { + uint32_t index = index_of(_shape, ch, row, col); + + return _ptr[index]; + } + + T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override + { + uint32_t index = index_of(_shape, batch, ch, row, col); + + return _ptr[index]; + } + + T &at(uint32_t ch, uint32_t row, uint32_t col) + { + uint32_t index = index_of(_shape, ch, row, col); + + return _ptr[index]; + } + + T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) + { + uint32_t index = index_of(_shape, batch, ch, row, col); + + return _ptr[index]; + } + +private: + nnfw::misc::feature::Shape _shape; + +private: + T *_ptr; +}; + +} // namespace nhwc +} // namespace feature +} // namespace util +} // namespace neurun + +#endif // __NEURUN_UTIL_FEATURE_NHWC_VIEW_H__ diff --git a/runtimes/neurun/src/util/logging.h b/runtimes/neurun/src/util/logging.h new file mode 100644 index 000000000..62d563967 --- /dev/null +++ b/runtimes/neurun/src/util/logging.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_UTIL_LOGGING_H__ +#define __NEURUN_UTIL_LOGGING_H__ + +#include <iostream> + +namespace neurun +{ +namespace util +{ +namespace logging +{ + +class Context +{ +public: + Context() : _enabled{false} + { + auto env = std::getenv("NEURUN_LOG_ENABLE"); + + if (env && std::atoi(env) > 0) + { + _enabled = true; + } + } + +public: + bool enabled(void) const { return _enabled; } + +private: + bool _enabled; +}; + +static Context ctx; + +} // namespace logging +} // namespace util +} // namespace neurun + +#define VERBOSE(name) \ + if (::neurun::util::logging::ctx.enabled()) \ + std::cout << "[" << #name << "] " + +#endif // __NEURUN_UTIL_LOGGING_H__ diff --git a/runtimes/neurun/test/backend/cpu/MemoryPlanner.cc b/runtimes/neurun/test/backend/cpu/MemoryPlanner.cc new file mode 100644 index 000000000..04f2e5da4 --- /dev/null +++ b/runtimes/neurun/test/backend/cpu/MemoryPlanner.cc @@ -0,0 +1,127 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include <gtest/gtest.h> + +#include "backend/cpu/MemoryPlanner.h" +#include "model/operand/Index.h" + +TEST(Allocator, allocate_test) +{ + ::neurun::backend::cpu::Allocator allocator(1024); + ASSERT_NE(allocator.base(), nullptr); +} + +TEST(BumpPlanner, claim_test) +{ + ::neurun::backend::cpu::BumpPlanner planner; + + auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) { + ::neurun::model::operand::Index mem_idx(index); + planner.claim(mem_idx, size); + auto mem_blk = planner.memory_plans()[mem_idx]; + ASSERT_EQ(mem_blk.offset, expected_offset); + ASSERT_EQ(mem_blk.size, size); + }; + + claim(0, 10, 0); + claim(1, 20, 10); + claim(2, 30, 30); +} + +TEST(FirstFitPlanner, claim_release_test) +{ + ::neurun::backend::cpu::FirstFitPlanner planner; + + auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) { + ::neurun::model::operand::Index mem_idx(index); + planner.claim(mem_idx, size); + auto mem_blk = planner.memory_plans()[mem_idx]; + ASSERT_EQ(mem_blk.offset, expected_offset); + ASSERT_EQ(mem_blk.size, size); + }; + + auto release = [&planner](uint32_t index) { + ::neurun::model::operand::Index mem_idx(index); + planner.release(mem_idx); + }; + + // 0 CLAIM - 10 + claim(0, 10, 0); + + // 1 CLAIM - 20 + claim(1, 20, 10); + + // 2 CLAIM - 30 + claim(2, 30, 30); + + // 0 RELEASE - 10 + release(0); + + // 3 CLAIM - 20 + claim(3, 20, 60); + + // 4 CLAIM - 5 + claim(4, 5, 0); + + // 5 CLAIM - 10 + claim(5, 10, 80); + + // 6 CLAIM - 5 + claim(6, 5, 5); + + // 2 RELEASE - 30 + release(2); + + // 7 CLAIM - 35 + claim(7, 35, 90); + + // 8 CLAIM - 10 + claim(8, 10, 30); + + // 4 RELEASE - 5 + release(4); + + // 9 CLAIM - 10 + claim(9, 10, 40); + + // 10 CLAIM - 10 + claim(10, 10, 50); + + // 6 RELEASE + release(6); + + // 1 RELEASE + release(1); + + // 8 RELEASE + release(8); + + // 9 RELEASE + release(9); + + // 10 RELEASE + release(10); + + // 3 RELEASE + release(3); + + // 5 RELEASE + release(5); + + // 7 RELEASE + release(7); +} diff --git a/runtimes/neurun/test/graph/Graph.cc b/runtimes/neurun/test/graph/Graph.cc index 5de3c50d0..e6db3fe49 100644 --- a/runtimes/neurun/test/graph/Graph.cc +++ b/runtimes/neurun/test/graph/Graph.cc @@ -22,15 +22,15 @@ TEST(Graph, inputs_and_outputs) { ::neurun::graph::Graph graph; - ::neurun::graph::operand::Index index0{0u}; - ::neurun::graph::operand::Index index1{1u}; + ::neurun::model::operand::Index index0{0u}; + ::neurun::model::operand::Index index1{1u}; graph.addInput({index0}); graph.addInput({index1}); - ::neurun::graph::operand::Index index10{10u}; - ::neurun::graph::operand::Index index11{11u}; - ::neurun::graph::operand::Index index12{12u}; + ::neurun::model::operand::Index index10{10u}; + ::neurun::model::operand::Index index11{11u}; + ::neurun::model::operand::Index index12{12u}; graph.addOutput({index10}); graph.addOutput({index11}); @@ -39,9 +39,9 @@ TEST(Graph, inputs_and_outputs) ASSERT_EQ(graph.getInputs().size(), 2); ASSERT_EQ(graph.getOutputs().size(), 3); - ::neurun::graph::operand::IO::Index io_index0{0}; - ::neurun::graph::operand::IO::Index io_index1{1}; - ::neurun::graph::operand::IO::Index io_index2{2}; + ::neurun::model::operand::IO::Index io_index0{0}; + ::neurun::model::operand::IO::Index io_index1{1}; + ::neurun::model::operand::IO::Index io_index2{2}; ASSERT_EQ(graph.getInputs().at(io_index0), 0); ASSERT_EQ(graph.getInputs().at(io_index1), 1); diff --git a/runtimes/neurun/test/graph/MockNode.h b/runtimes/neurun/test/graph/MockNode.h new file mode 100644 index 000000000..46a6274dd --- /dev/null +++ b/runtimes/neurun/test/graph/MockNode.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NEURUN_TEST_GRAPH_MOCK_NODE_H__ +#define __NEURUN_TEST_GRAPH_MOCK_NODE_H__ + +#include "model/operation/Node.h" +#include "model/operand/IndexSet.h" + +namespace neurun_test +{ +namespace graph +{ + +class SimpleMockNode : public neurun::model::operation::Node +{ +public: + SimpleMockNode(const neurun::model::operand::IndexSet &inputs, + const neurun::model::operand::IndexSet &outputs) + : neurun::model::operation::Node{neurun::model::operation::OperandConstraint::createAny()} + { + setInputs(inputs); + setOutputs(outputs); + } + +public: + virtual void accept(neurun::model::operation::NodeVisitor &&) const override {} + virtual std::string getName() const override { return "SimpleMockNode"; } +}; + +} // namespace graph +} // namespace neurun_test + +#endif // __NEURUN_TEST_GRAPH_MOCK_NODE_H__ diff --git a/runtimes/neurun/test/graph/operand/IndexSet.cc b/runtimes/neurun/test/graph/operand/IndexSet.cc index eeb4cef97..de4768cda 100644 --- a/runtimes/neurun/test/graph/operand/IndexSet.cc +++ b/runtimes/neurun/test/graph/operand/IndexSet.cc @@ -16,12 +16,12 @@ #include <gtest/gtest.h> -#include "graph/operand/IndexSet.h" +#include "model/operand/IndexSet.h" -using neurun::graph::operand::Index; -using neurun::graph::operand::IndexSet; +using neurun::model::operand::Index; +using neurun::model::operand::IndexSet; -TEST(graph_operand_IndexSet, index_set_test) +TEST(graph_operand_IndexSet, append) { IndexSet iset{0, 2, 4, 8}; @@ -31,8 +31,8 @@ TEST(graph_operand_IndexSet, index_set_test) ASSERT_EQ(iset.size(), 5); - neurun::graph::operand::IO::Index index1{1}; - neurun::graph::operand::IO::Index index2{4}; + neurun::model::operand::IO::Index index1{1}; + neurun::model::operand::IO::Index index2{4}; ASSERT_EQ(iset.at(index1), 2); ASSERT_EQ(iset.at(index2), 10); @@ -41,3 +41,12 @@ TEST(graph_operand_IndexSet, index_set_test) ASSERT_TRUE(iset.contains(Index{10})); ASSERT_FALSE(iset.contains(Index{11})); } + +TEST(graph_operand_IndexSet, replace) +{ + IndexSet iset{0, 1, 2, 3}; + + iset.replace(Index{1}, Index{9}); + ASSERT_FALSE(iset.contains(Index{1})); + ASSERT_TRUE(iset.contains(Index{9})); +} diff --git a/runtimes/neurun/test/graph/operand/LayoutSet.cc b/runtimes/neurun/test/graph/operand/LayoutSet.cc index 6ff21cb60..f83e76e30 100644 --- a/runtimes/neurun/test/graph/operand/LayoutSet.cc +++ b/runtimes/neurun/test/graph/operand/LayoutSet.cc @@ -1,3 +1,19 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + #include <gtest/gtest.h> #include "graph/operand/LayoutSet.h" diff --git a/runtimes/neurun/test/graph/operand/Set.cc b/runtimes/neurun/test/graph/operand/Set.cc index 176cedc38..00b6a7222 100644 --- a/runtimes/neurun/test/graph/operand/Set.cc +++ b/runtimes/neurun/test/graph/operand/Set.cc @@ -16,33 +16,33 @@ #include <gtest/gtest.h> -#include "graph/operand/Set.h" +#include "model/operand/Set.h" TEST(graph_operand_Set, set_test) { - neurun::graph::operand::Set set; + neurun::model::operand::Set set; - ::neurun::graph::operand::Shape shape0{3}; + ::neurun::model::operand::Shape shape0{3}; shape0.dim(0) = 1; shape0.dim(1) = 2; shape0.dim(2) = 3; - ::neurun::graph::operand::Shape shape1{4}; + ::neurun::model::operand::Shape shape1{4}; shape1.dim(0) = 10; shape1.dim(1) = 20; shape1.dim(2) = 30; shape1.dim(3) = 40; - ::neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; + ::neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; set.append(shape0, type); set.append(shape1, type); - ASSERT_EQ(set.exist(neurun::graph::operand::Index{0u}), true); - ASSERT_EQ(set.exist(neurun::graph::operand::Index{1u}), true); - ASSERT_EQ(set.exist(neurun::graph::operand::Index{2u}), false); + ASSERT_EQ(set.exist(neurun::model::operand::Index{0u}), true); + ASSERT_EQ(set.exist(neurun::model::operand::Index{1u}), true); + ASSERT_EQ(set.exist(neurun::model::operand::Index{2u}), false); - ASSERT_EQ(set.at(neurun::graph::operand::Index{0u}).shape().dim(0), 1); - ASSERT_EQ(set.at(neurun::graph::operand::Index{0u}).shape().dim(1), 2); - ASSERT_EQ(set.at(neurun::graph::operand::Index{0u}).shape().dim(2), 3); + ASSERT_EQ(set.at(neurun::model::operand::Index{0u}).shape().dim(0), 1); + ASSERT_EQ(set.at(neurun::model::operand::Index{0u}).shape().dim(1), 2); + ASSERT_EQ(set.at(neurun::model::operand::Index{0u}).shape().dim(2), 3); } diff --git a/runtimes/neurun/test/graph/operand/UseDef.cc b/runtimes/neurun/test/graph/operand/UseDef.cc index caaea1f6a..e3792f746 100644 --- a/runtimes/neurun/test/graph/operand/UseDef.cc +++ b/runtimes/neurun/test/graph/operand/UseDef.cc @@ -17,17 +17,17 @@ #include <gtest/gtest.h> #include "graph/Graph.h" -#include "graph/verifier/IVerifier.h" -#include "nnfw/std/memory.h" -#include "../operation/MockNode.h" +#include "graph/verifier/Verifier.h" +#include "cpp14/memory.h" +#include "../MockNode.h" #include <typeindex> namespace { -using IndexSet = neurun::graph::operand::IndexSet; -using MockNode = neurun_test::graph::operation::SimpleMockNode; +using IndexSet = neurun::model::operand::IndexSet; +using MockNode = neurun_test::graph::SimpleMockNode; } // namespace anonymous @@ -36,8 +36,8 @@ TEST(graph_operand_usedef, usedef_test) neurun::graph::Graph graph; neurun::graph::verifier::DAGChecker verifier; - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; + neurun::model::operand::Shape shape{1u}; + neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; shape.dim(0) = 3; // Model Input/Output @@ -53,16 +53,16 @@ TEST(graph_operand_usedef, usedef_test) auto operand_index1 = graph.addOperand(shape, type); graph.operands().at(operand_index1).setAsOperationOutput(); auto mocknode_index1 = graph.addOperation( - nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index1})); + nnfw::cpp14::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index1})); // MockNode2 auto operand_index2 = graph.addOperand(shape, type); graph.operands().at(operand_index2).setAsOperationOutput(); auto mocknode_index2 = graph.addOperation( - nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index2})); + nnfw::cpp14::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index2})); // MockNode3(two input) - auto multiinput_index = graph.addOperation(nnfw::make_unique<MockNode>( + auto multiinput_index = graph.addOperation(nnfw::cpp14::make_unique<MockNode>( IndexSet{operand_index1, operand_index2}, IndexSet{output_operand})); graph.finishBuilding(); @@ -89,85 +89,3 @@ TEST(graph_operand_usedef, usedef_test) ASSERT_EQ(graph.operands().at(operand_index1).getUses().size(), 1); ASSERT_EQ(graph.operands().at(output_operand).getUses().size(), 0); } - -TEST(graph_operand_usedef, usedef_test_insertion) -{ - neurun::graph::Graph graph; - neurun::graph::verifier::DAGChecker verifier; - - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; - shape.dim(0) = 3; - - // Model Input/Output - auto input_operand = graph.addOperand(shape, type); - auto output_operand = graph.addOperand(shape, type); - - graph.addInput(input_operand); - graph.operands().at(input_operand).setAsModelInput(); - graph.addOutput(output_operand); - graph.operands().at(output_operand).setAsOperationOutput(); - - // MockNode1 - auto operand_index1 = graph.addOperand(shape, type); - graph.operands().at(operand_index1).setAsOperationOutput(); - auto mocknode_index1 = graph.addOperation( - nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index1})); - - // MockNode2 - auto operand_index2 = graph.addOperand(shape, type); - graph.operands().at(operand_index2).setAsOperationOutput(); - auto mocknode_index2 = graph.addOperation( - nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand_index2})); - - // MockNode3(two input) - auto multiinput_index = graph.addOperation(nnfw::make_unique<MockNode>( - IndexSet{operand_index1, operand_index2}, IndexSet{output_operand})); - - graph.finishBuilding(); - - // Insert node1 (between 1 and 2) - auto inserted_operand1 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand1).setAsOperationOutput(); - auto inserted_index1 = - graph.insertOperation(input_operand, mocknode_index2, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand1})); - - ASSERT_EQ(inserted_index1.asInt(), 3); - - // Insert node2 (between 2 and 3) - auto inserted_operand2 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand2).setAsOperationOutput(); - auto inserted_index2 = - graph.insertOperation(operand_index2, multiinput_index, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand2})); - - ASSERT_EQ(inserted_index2.asInt(), 4); - - ASSERT_EQ(verifier.verify(graph), true); - - // Check def - ASSERT_EQ(graph.operands().at(input_operand).getDef().size(), 0); - ASSERT_EQ(graph.operands().at(operand_index1).getDef().contains(mocknode_index1), true); - ASSERT_EQ(graph.operands().at(inserted_operand1).getDef().contains(inserted_index1), true); - ASSERT_EQ(graph.operands().at(operand_index2).getDef().contains(mocknode_index2), true); - ASSERT_EQ(graph.operands().at(inserted_operand2).getDef().contains(inserted_index2), true); - ASSERT_EQ(graph.operands().at(output_operand).getDef().contains(multiinput_index), true); - - // Check use - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index1), true); - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(inserted_index1), true); - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index2), false); - ASSERT_EQ(graph.operands().at(inserted_operand1).getUses().contains(mocknode_index2), true); - ASSERT_EQ(graph.operands().at(operand_index1).getUses().contains(multiinput_index), true); - ASSERT_EQ(graph.operands().at(operand_index2).getUses().contains(inserted_index2), true); - ASSERT_EQ(graph.operands().at(operand_index2).getUses().contains(multiinput_index), false); - ASSERT_EQ(graph.operands().at(inserted_operand2).getUses().contains(multiinput_index), true); - - ASSERT_EQ(graph.operands().at(input_operand).getUses().size(), 2); - ASSERT_EQ(graph.operands().at(inserted_operand1).getUses().size(), 1); - ASSERT_EQ(graph.operands().at(operand_index1).getUses().size(), 1); - ASSERT_EQ(graph.operands().at(inserted_operand2).getUses().size(), 1); - ASSERT_EQ(graph.operands().at(operand_index2).getUses().size(), 1); - ASSERT_EQ(graph.operands().at(output_operand).getUses().size(), 0); -} diff --git a/runtimes/neurun/test/graph/operation/Insert.cc b/runtimes/neurun/test/graph/operation/Insert.cc deleted file mode 100644 index dab89c2a6..000000000 --- a/runtimes/neurun/test/graph/operation/Insert.cc +++ /dev/null @@ -1,166 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "graph/Graph.h" -#include "graph/verifier/IVerifier.h" -#include "nnfw/std/memory.h" -#include "graph/operand/Index.h" -#include "MockNode.h" - -#include <typeindex> - -using IOIndex = neurun::graph::operand::IO::Index; -using Index = neurun::graph::operand::Index; -using IndexSet = neurun::graph::operand::IndexSet; -using MockNode = neurun_test::graph::operation::SimpleMockNode; - -TEST(graph_operation_manipulation, operation_insertion) -{ - neurun::graph::Graph graph; - neurun::graph::verifier::DAGChecker verifier; - - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; - shape.dim(0) = 3; - - // Model Input/Output - auto input_operand = graph.addOperand(shape, type); - auto output_operand = graph.addOperand(shape, type); - - graph.addInput(input_operand); - graph.operands().at(input_operand).setAsModelInput(); - graph.addOutput(output_operand); - graph.operands().at(output_operand).setAsOperationOutput(); - - // MockNode1 - auto operand1 = graph.addOperand(shape, type); - graph.operands().at(operand1).setAsOperationOutput(); - auto mocknode_index1 = - graph.addOperation(nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand1})); - // MockNode2 - auto operand2 = graph.addOperand(shape, type); - graph.operands().at(operand2).setAsOperationOutput(); - auto mocknode_index2 = - graph.addOperation(nnfw::make_unique<MockNode>(IndexSet{operand1}, IndexSet{operand2})); - // MockNode3 - auto mocknode_index3 = - graph.addOperation(nnfw::make_unique<MockNode>(IndexSet{operand2}, IndexSet{output_operand})); - - graph.finishBuilding(); - - ASSERT_EQ(verifier.verify(graph), true); - - // Insert node1 (between 1 and 2) - auto inserted_operand1 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand1).setAsOperationOutput(); - auto inserted_index1 = - graph.insertOperation(operand1, mocknode_index2, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand1})); - - ASSERT_EQ(inserted_index1.asInt(), 3); - - // Insert node2 (between 2 and 3) - auto inserted_operand2 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand2).setAsOperationOutput(); - auto inserted_index2 = - graph.insertOperation(operand2, mocknode_index3, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand2})); - - ASSERT_EQ(inserted_index2.asInt(), 4); - - // Check tensor indexes - const auto &operations = graph.operations(); - ASSERT_EQ(operations.at(mocknode_index1).getOutputs().at(Index{0}), - operations.at(inserted_index1).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(inserted_index1).getOutputs().at(Index{0}), - operations.at(mocknode_index2).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(mocknode_index2).getOutputs().at(Index{0}), - operations.at(inserted_index2).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(inserted_index2).getOutputs().at(Index{0}), - operations.at(mocknode_index3).getInputs().at(Index{0})); - - ASSERT_EQ(verifier.verify(graph), true); -} - -TEST(graph_operation_manipulation, operation_insertion_multi_input) -{ - neurun::graph::Graph graph; - neurun::graph::verifier::DAGChecker verifier; - - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; - shape.dim(0) = 3; - - // Model Input/Output - auto input_operand = graph.addOperand(shape, type); - auto output_operand = graph.addOperand(shape, type); - - graph.addInput(input_operand); - graph.operands().at(input_operand).setAsModelInput(); - graph.addOutput(output_operand); - graph.operands().at(output_operand).setAsOperationOutput(); - - // MockNode1 - auto operand1 = graph.addOperand(shape, type); - graph.operands().at(operand1).setAsOperationOutput(); - auto mocknode_index1 = - graph.addOperation(nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand1})); - // MockNode2 - auto operand2 = graph.addOperand(shape, type); - graph.operands().at(operand2).setAsOperationOutput(); - auto mocknode_index2 = - graph.addOperation(nnfw::make_unique<MockNode>(IndexSet{input_operand}, IndexSet{operand2})); - // MultiInputMockNode - auto multiinput_index = graph.addOperation( - nnfw::make_unique<MockNode>(IndexSet{operand1, operand2}, IndexSet{output_operand})); - - graph.finishBuilding(); - - ASSERT_EQ(verifier.verify(graph), true); - - // Insert node1 (between 1 and multi) - auto inserted_operand1 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand1).setAsOperationOutput(); - auto inserted_index1 = - graph.insertOperation(operand1, multiinput_index, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand1})); - - ASSERT_EQ(inserted_index1.asInt(), 3); - - // Insert node2 (between 2 and multi) - auto inserted_operand2 = graph.addOperand(shape, type); - graph.operands().at(inserted_operand2).setAsOperationOutput(); - auto inserted_index2 = - graph.insertOperation(operand2, multiinput_index, - nnfw::make_unique<MockNode>(IndexSet{}, IndexSet{inserted_operand2})); - - ASSERT_EQ(inserted_index2.asInt(), 4); - - // Check tensor indexes - const auto &operations = graph.operations(); - ASSERT_EQ(operations.at(mocknode_index1).getOutputs().at(Index{0}), - operations.at(inserted_index1).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(inserted_index1).getOutputs().at(Index{0}), - operations.at(multiinput_index).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(mocknode_index2).getOutputs().at(Index{0}), - operations.at(inserted_index2).getInputs().at(Index{0})); - ASSERT_EQ(operations.at(inserted_index2).getOutputs().at(Index{0}), - operations.at(multiinput_index).getInputs().at(Index{1})); - - ASSERT_EQ(verifier.verify(graph), true); -} diff --git a/runtimes/neurun/test/graph/operation/MockNode.h b/runtimes/neurun/test/graph/operation/MockNode.h deleted file mode 100644 index 9456b97be..000000000 --- a/runtimes/neurun/test/graph/operation/MockNode.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NEURUN_TEST_GRAPH_OPERATION_MOCK_NODE_H__ -#define __NEURUN_TEST_GRAPH_OPERATION_MOCK_NODE_H__ - -#include "graph/operation/Node.h" -#include "graph/operand/IndexSet.h" - -namespace neurun_test -{ -namespace graph -{ -namespace operation -{ - -class SimpleMockNode : public neurun::graph::operation::Node -{ -public: - SimpleMockNode(const neurun::graph::operand::IndexSet &inputs, - const neurun::graph::operand::IndexSet &outputs) - { - setInputs(inputs); - setOutputs(outputs); - } - -public: - virtual void accept(neurun::graph::operation::NodeVisitor &&) const override {} -}; - -} // namespace operation -} // namespace graph -} // namespace neurun_test - -#endif // __NEURUN_TEST_GRAPH_OPERATION_MOCK_NODE_H__ diff --git a/runtimes/neurun/test/graph/operation/Set.cc b/runtimes/neurun/test/graph/operation/Set.cc index df680ff48..3560482ee 100644 --- a/runtimes/neurun/test/graph/operation/Set.cc +++ b/runtimes/neurun/test/graph/operation/Set.cc @@ -16,18 +16,18 @@ #include <gtest/gtest.h> -#include "MockNode.h" -#include "graph/operation/Set.h" +#include "../MockNode.h" +#include "model/operation/Set.h" -using neurun::graph::operation::Set; -using neurun::graph::operation::Node; -using neurun::graph::operation::Index; +using neurun::model::operation::Set; +using neurun::model::operation::Node; +using neurun::model::operation::Index; TEST(graph_operation_Set, operation_test) { Set set; - set.append(std::unique_ptr<Node>( - new neurun_test::graph::operation::SimpleMockNode({1, 2, 3, 4}, {5, 6, 7}))); + set.append( + std::unique_ptr<Node>(new neurun_test::graph::SimpleMockNode({1, 2, 3, 4}, {5, 6, 7}))); Index idx{0u}; ASSERT_EQ(set.at(idx).getInputs().size(), 4); ASSERT_EQ(set.at(idx).getOutputs().size(), 3); diff --git a/runtimes/neurun/test/graph/operation/SetIO.cc b/runtimes/neurun/test/graph/operation/SetIO.cc index c0a0c9099..a475bdcc9 100644 --- a/runtimes/neurun/test/graph/operation/SetIO.cc +++ b/runtimes/neurun/test/graph/operation/SetIO.cc @@ -17,24 +17,24 @@ #include <gtest/gtest.h> #include "graph/Graph.h" -#include "nnfw/std/memory.h" -#include "graph/operation/Conv2D.h" -#include "graph/operation/Concat.h" -#include "graph/operand/Index.h" -#include "graph/operand/IndexSet.h" +#include "cpp14/memory.h" +#include "model/operation/Conv2DNode.h" +#include "model/operation/ConcatNode.h" +#include "model/operand/Index.h" +#include "model/operand/IndexSet.h" #include <stdexcept> -using Index = neurun::graph::operand::IO::Index; -using IndexSet = neurun::graph::operand::IndexSet; -using GraphNodeInitParam = neurun::graph::operation::Node::InitParam; +using Index = neurun::model::operand::IO::Index; +using IndexSet = neurun::model::operand::IndexSet; +using GraphNodeInitParam = neurun::model::operation::Node::InitParam; TEST(graph_operation_setIO, operation_setIO_conv) { neurun::graph::Graph graph; - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; + neurun::model::operand::Shape shape{1u}; + neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; shape.dim(0) = 3; // Add Conv @@ -45,9 +45,10 @@ TEST(graph_operation_setIO, operation_setIO_conv) } uint32_t outoperand = graph.addOperand(shape, type).asInt(); - using GraphNode = neurun::graph::operation::Conv2D::Implicit::Node; + using GraphNode = neurun::model::operation::Conv2DNode; - auto conv = nnfw::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand}); + auto conv = + nnfw::cpp14::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand}); ASSERT_EQ(conv->getInputs().at(Index{0}).asInt(), params[0]); conv->setInputs({8, 9, 10}); ASSERT_NE(conv->getInputs().at(Index{0}).asInt(), params[0]); @@ -58,8 +59,8 @@ TEST(graph_operation_setIO, operation_setIO_concat) { neurun::graph::Graph graph; - neurun::graph::operand::Shape shape{1u}; - neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; + neurun::model::operand::Shape shape{1u}; + neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; shape.dim(0) = 3; // Add Concat @@ -70,9 +71,10 @@ TEST(graph_operation_setIO, operation_setIO_concat) } uint32_t outoperand = graph.addOperand(shape, type).asInt(); - using GraphNode = neurun::graph::operation::Concat::Node; + using GraphNode = neurun::model::operation::ConcatNode; - auto concat = nnfw::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand}); + auto concat = + nnfw::cpp14::make_unique<GraphNode>(GraphNodeInitParam{7, params.data(), 1, &outoperand}); ASSERT_EQ(concat->getInputs().size(), 6); ASSERT_EQ(concat->getInputs().at(Index{0}).asInt(), params[0]); diff --git a/runtimes/neurun/test/graph/verifier/Verifier.cc b/runtimes/neurun/test/graph/verifier/Verifier.cc index 68c8e8ec8..a37b0ac1f 100644 --- a/runtimes/neurun/test/graph/verifier/Verifier.cc +++ b/runtimes/neurun/test/graph/verifier/Verifier.cc @@ -16,32 +16,23 @@ #include <gtest/gtest.h> -#include "graph/operation/Node.h" +#include "model/operation/Node.h" #include "graph/Graph.h" -#include "graph/verifier/IVerifier.h" -#include "nnfw/std/memory.h" -#include "graph/operand/Object.h" +#include "graph/verifier/Verifier.h" +#include "cpp14/memory.h" +#include "model/operand/Object.h" +#include "../MockNode.h" -class MockNode : public neurun::graph::operation::Node -{ -public: - MockNode(const neurun::graph::operand::Index &input, const neurun::graph::operand::Index &output) - { - setInputs({input}); - setOutputs({output}); - } - -public: - virtual void accept(neurun::graph::operation::NodeVisitor &&) const override {} -}; +using IndexSet = neurun::model::operand::IndexSet; +using MockNode = neurun_test::graph::SimpleMockNode; TEST(Verifier, dag_checker) { neurun::graph::Graph graph; neurun::graph::verifier::DAGChecker verifier; - ::neurun::graph::operand::Shape shape{1u}; - ::neurun::graph::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; + ::neurun::model::operand::Shape shape{1u}; + ::neurun::model::operand::TypeInfo type{ANEURALNETWORKS_TENSOR_INT32, 0, 0}; shape.dim(0) = 3; auto operand1 = graph.addOperand(shape, type); @@ -52,13 +43,9 @@ TEST(Verifier, dag_checker) graph.addOutput(operand2); graph.operands().at(operand2).setAsOperationOutput(); - graph.addOperation(nnfw::make_unique<MockNode>(operand1, operand2)); + graph.addOperation(nnfw::cpp14::make_unique<MockNode>(IndexSet{operand1}, IndexSet{operand2})); - ASSERT_EQ(verifier.verify(graph), true); + graph.finishBuilding(); - // Create cycle - graph.operands().at(operand1).setAsOperationOutput(); - graph.addOperation(nnfw::make_unique<MockNode>(operand2, operand1)); - - ASSERT_EQ(verifier.verify(graph), false); + ASSERT_EQ(verifier.verify(graph), true); } diff --git a/runtimes/pure_arm_compute/CMakeLists.txt b/runtimes/pure_arm_compute/CMakeLists.txt index d85c3ec13..2a26a7bf2 100644 --- a/runtimes/pure_arm_compute/CMakeLists.txt +++ b/runtimes/pure_arm_compute/CMakeLists.txt @@ -14,9 +14,8 @@ target_include_directories(nnapi_pure_arm_compute PUBLIC src) if(BUILD_TFLITE_BENCHMARK_MODEL) target_compile_definitions(nnapi_pure_arm_compute PUBLIC "TFLITE_PROFILING_ENABLED") endif() -target_link_libraries(nnapi_pure_arm_compute arm_compute) -target_link_libraries(nnapi_pure_arm_compute arm_compute_ex) -target_link_libraries(nnapi_pure_arm_compute static_nnfw_util) +target_link_libraries(nnapi_pure_arm_compute arm_compute arm_compute_ex) +target_link_libraries(nnapi_pure_arm_compute nnfw_lib_cpp14 nnfw_lib_misc nnfw_lib_profiling) if("${TARGET_OS}" STREQUAL "android") target_link_libraries(nnapi_pure_arm_compute log) diff --git a/runtimes/pure_arm_compute/src/compilation.cc b/runtimes/pure_arm_compute/src/compilation.cc index 2488332a6..bed42529b 100644 --- a/runtimes/pure_arm_compute/src/compilation.cc +++ b/runtimes/pure_arm_compute/src/compilation.cc @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file compilation.cc + * @brief This file contains ANeuralNetworksCompilation APIs and related classes + * @ingroup COM_AI_RUNTIME + */ + #include <NeuralNetworks.h> // For CLKernelLibraryEx initialization @@ -25,28 +31,42 @@ #include <arm_compute/runtime/CL/CLScheduler.h> #include <arm_compute/runtime/CL/CLSubTensor.h> #include <arm_compute/runtime/CL/functions/CLArithmeticAddition.h> -#include <arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h> +#include <arm_compute/runtime/CL/functions/CLArithmeticSubtractionEx.h> +#include <arm_compute/runtime/CL/functions/CLPadLayerEx.h> #include <arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h> #include <arm_compute/runtime/CL/functions/CLPixelWiseDivision.h> #include <arm_compute/runtime/CL/functions/CLPoolingLayer.h> #include <arm_compute/runtime/CL/functions/CLActivationLayer.h> +#include <arm_compute/runtime/CL/functions/CLActivationLayerEx.h> #include <arm_compute/runtime/CL/functions/CLScale.h> +#include <arm_compute/runtime/CL/functions/CLSpaceToBatchND.h> +#include <arm_compute/runtime/CL/functions/CLSpaceToDepth.h> #include <arm_compute/runtime/CL/functions/CLReshapeLayer.h> -#include <arm_compute/runtime/CL/functions/CLStridedSlice.h> +#include <arm_compute/runtime/CL/functions/CLStridedSliceEx.h> #include <arm_compute/runtime/CL/functions/CLSoftmaxLayer.h> #include <arm_compute/runtime/CL/functions/CLGather.h> +#include <arm_compute/runtime/CL/functions/CLHashtableLookup.h> #include <arm_compute/runtime/CL/functions/CLTopKV2.h> -#include <arm_compute/runtime/CL/functions/CLReduceMax.h> +#include <arm_compute/runtime/CL/functions/CLArgMinMax.h> #include <arm_compute/runtime/CL/functions/CLCast.h> #include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h> #include <arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h> #include <arm_compute/runtime/CL/functions/CLDequantizationLayer.h> -#include <arm_compute/runtime/CL/functions/CLReductionMean.h> -#include <arm_compute/runtime/CL/functions/CLTranspose.h> +#include <arm_compute/runtime/CL/functions/CLDepthToSpace.h> +#include <arm_compute/runtime/CL/functions/CLPermuteEx.h> +#include <arm_compute/runtime/CL/functions/CLReduceOperation.h> #include <arm_compute/runtime/CL/functions/CLRNNLayer.h> #include <arm_compute/runtime/CL/functions/CLFloor.h> #include <arm_compute/runtime/CL/functions/CLCopy.h> -#include <arm_compute/runtime/CL/functions/CLNormalizationLayer.h> +#include <arm_compute/runtime/CL/functions/CLNormalizationLayerEx.h> +#include <arm_compute/runtime/CL/functions/CLExp.h> +#include <arm_compute/runtime/CL/functions/CLBatchToSpaceND.h> +#include <arm_compute/runtime/CL/functions/CLEmbeddingLookup.h> +#include <arm_compute/runtime/CL/functions/CLSquaredDifference.h> +#include <arm_compute/runtime/CL/functions/CLNeg.h> +#include <arm_compute/runtime/CL/functions/CLPReLU.h> +#include <arm_compute/runtime/CL/functions/CLBinaryLogicalOp.h> +#include <arm_compute/runtime/CL/functions/CLComparisonOp.h> #include <arm_compute/runtime/SubTensor.h> #include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h> @@ -58,7 +78,7 @@ #include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h> #include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h> #include <arm_compute/runtime/NEON/functions/NEFloor.h> -#include <arm_compute/runtime/NEON/functions/NENormalizationLayer.h> +#include <arm_compute/runtime/NEON/functions/NENormalizationLayerEx.h> #include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h> #include "internal/arm_compute.h" @@ -74,19 +94,28 @@ #include "internal/arm_compute/tensor/View.h" #include "internal/layers/GenericReshapeLayer.h" #include "internal/layers/SimpleArithmeticAddition.h" +#include "internal/layers/SimplePadLayer.h" #include "internal/layers/SimpleCastLayer.h" +#include "internal/layers/SimpleTransposeConv.h" #include "internal/layers/GenericFullyConnectedLayer.h" -#include "internal/layers/PadLayer.h" #include "internal/layers/SimpleSpaceToDepth.h" #include "internal/layers/SimpleEmbeddingLookup.h" -#include "internal/layers/SquaredDifferenceOperation.h" - -#include "util/matrix/IndexIterator.h" -#include "util/kernel/IndexIterator.h" -#include "util/feature/IndexIterator.h" -#include "util/tensor/IndexIterator.h" - -#include <nnfw/std/memory.h> +#include "internal/layers/SimpleDepthToSpace.h" +#include "internal/layers/SimpleBatchToSpaceNd.h" +#include "internal/layers/SimpleHashtableLookupLayer.h" +#include "internal/layers/SimplePackLayer.h" +#include "internal/layers/SimpleSpaceToBatchND.h" +#include "internal/layers/SimpleNeg.h" +#include "internal/layers/SimpleUnpackLayer.h" +#include "internal/layers/SimpleSQRT.h" +#include "internal/layers/SimpleArgMinMax.h" + +#include "misc/matrix/IndexIterator.h" +#include "misc/kernel/IndexIterator.h" +#include "misc/feature/IndexIterator.h" +#include "misc/tensor/IndexIterator.h" + +#include <cpp14/memory.h> #include "compilation.h" #include "model.h" @@ -154,8 +183,7 @@ Padding valid_padding(void) return padding; } -Padding same_padding(const nnfw::util::feature::Shape &ifm_shape, - const nnfw::util::feature::Shape &ofm_shape, const Stride &stride, uint32_t kw, +Padding same_padding(const nnfw::misc::feature::Shape &ifm_shape, const Stride &stride, uint32_t kw, uint32_t kh) { Padding padding; @@ -164,13 +192,16 @@ Padding same_padding(const nnfw::util::feature::Shape &ifm_shape, // // SAME padding. Padding on both ends are the "same": // - // padding_to_beginning = total_padding / 2 - // padding_to_end = (total_padding + 1)/2. + // padding_to_beginning = total_padding / 2 + // padding_to_end = (total_padding + 1)/2. // - const int32_t vertical_needed_input = (ofm_shape.H - 1) * stride.vertical + kh; + const int32_t out_size_height = (ifm_shape.H + stride.vertical - 1) / stride.vertical; + const int32_t out_size_width = (ifm_shape.W + stride.horizontal - 1) / stride.horizontal; + + const int32_t vertical_needed_input = (out_size_height - 1) * stride.vertical + kh; const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H); - const int32_t horizontal_needed_input = (ofm_shape.W - 1) * stride.horizontal + kw; + const int32_t horizontal_needed_input = (out_size_width - 1) * stride.horizontal + kw; const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W); padding.top = vertical_total_padding / 2; @@ -208,14 +239,14 @@ using namespace std::placeholders; template <typename T> static void initFeatureTensor(::arm_compute::ITensor &tensor, - const nnfw::util::feature::Shape &feature_shape, + const nnfw::misc::feature::Shape &feature_shape, const uint8_t *feature_base, const size_t feature_size) { const ::internal::nnapi::feature::Reader<T> from{ feature_shape, reinterpret_cast<const T *>(feature_base), feature_size}; ::internal::arm_compute::feature::View<T> into{&tensor}; - ::nnfw::util::feature::iterate(feature_shape) + ::nnfw::misc::feature::iterate(feature_shape) << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { const auto value = from.at(batch, ch, row, col); into.at(batch, ch, row, col) = value; @@ -241,29 +272,29 @@ static void initVectorTensor(::arm_compute::ITensor &tensor, const uint8_t *vec_ template <typename T> static void initTensor3D(::arm_compute::ITensor &tensor, - const nnfw::util::tensor::Shape &tensor_shape, const uint8_t *tensor_base, + const nnfw::misc::tensor::Shape &tensor_shape, const uint8_t *tensor_base, const size_t tensor_size) { const ::internal::nnapi::tensor::Reader<T> from{ tensor_shape, reinterpret_cast<const T *>(tensor_base), tensor_size}; ::internal::arm_compute::tensor::View<T> into{&tensor}; - ::nnfw::util::tensor::iterate(tensor_shape) << [&](const nnfw::util::tensor::Index &index_nnapi) { - ::nnfw::util::tensor::Index index_ACL = ::nnfw::util::tensor::copy_reverse(index_nnapi); + ::nnfw::misc::tensor::iterate(tensor_shape) << [&](const nnfw::misc::tensor::Index &index_nnapi) { + ::nnfw::misc::tensor::Index index_ACL = ::nnfw::misc::tensor::copy_reverse(index_nnapi); into.at(index_ACL) = from.at(index_nnapi); }; } template <typename T> static void initMatrixTensor(::arm_compute::ITensor &tensor, - const nnfw::util::matrix::Shape &matrix_shape, + const nnfw::misc::matrix::Shape &matrix_shape, const uint8_t *matrix_base, const size_t matrix_size) { const ::internal::nnapi::matrix::Reader<T> from{ matrix_shape, reinterpret_cast<const T *>(matrix_base), matrix_size}; ::internal::arm_compute::matrix::View<T> into{&tensor}; - ::nnfw::util::matrix::iterate(matrix_shape) << [&](uint32_t row, uint32_t col) { + ::nnfw::misc::matrix::iterate(matrix_shape) << [&](uint32_t row, uint32_t col) { const auto value = from.at(row, col); into.at(row, col) = value; }; @@ -288,34 +319,66 @@ static void initReorderVectorTensor(::arm_compute::ITensor &tensor, const uint8_ template <typename T> static void initKernelTensor(::arm_compute::ITensor &tensor, - const nnfw::util::kernel::Shape &kernel_shape, + const nnfw::misc::kernel::Shape &kernel_shape, const uint8_t *kernel_base, const size_t kernel_size) { const ::internal::nnapi::kernel::Reader<T> from{ kernel_shape, reinterpret_cast<const T *>(kernel_base), kernel_size}; ::internal::arm_compute::kernel::View<T> into{&tensor}; - ::nnfw::util::kernel::iterate(kernel_shape) + ::nnfw::misc::kernel::iterate(kernel_shape) << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { const auto value = from.at(nth, ch, row, col); into.at(nth, ch, row, col) = value; }; } +/** + * @brief Structure to provide interface methods of compilation plan builder + */ struct IPlanBuilder { + /** + * @brief Destruct IPlanBuilder object using default destructor + */ virtual ~IPlanBuilder() = default; + /** + * @brief Add TensorInfo with Shape Constraints + * @param [in] ind Index of operand + * @param [in] info TensorInfo value to set to index of operand + * @return N/A + */ virtual void addShapeConstr(const ::internal::tflite::operand::Index &ind, const ::arm_compute::TensorInfo &info) = 0; + /** + * @brief Add Subsumption constraints + * @param [in] ind Index of operand + * @param [in] base Index of base operand of Subsumption + * @param [in] offset Offset of Subsumption + * @param [in] shape Shape of Subsumption + * @param [in] extend_parent extend_parent value of Subsumption + * @return N/A + */ virtual void addSubsumptionConstr(const ::internal::tflite::operand::Index &ind, const ::internal::tflite::operand::Index &base, const ::arm_compute::Coordinates &offset, const ::arm_compute::TensorShape &shape, bool extend_parent = false) = 0; + /** + * @brief Add Initializer lambda with ITensor param + * @param [in] ind Index of operand + * @param [in] initializer Initializer to add + * @return N/A + */ virtual void addInitializer(const ::internal::tflite::operand::Index &ind, const Initializer &initializer) = 0; - virtual void addStage(const Stage &) = 0; + /** + * @brief Add Stage lambda with IAllocationContext and IExecutionBuilder params + * @param [in] stage Stage to add + * @return N/A + */ + virtual void addStage(const Stage &stage) = 0; }; // @@ -333,7 +396,6 @@ private: void appendReLU(::arm_compute::ITensor *tensor); void appendReLU6(::arm_compute::ITensor *tensor); void appendReLU1(::arm_compute::ITensor *tensor); - void appendTanh(::arm_compute::ITensor *tensor); public: void append(FuseCode code, ::arm_compute::ITensor *tensor); @@ -349,7 +411,7 @@ void ActivationBuilder::appendReLU(::arm_compute::ITensor *ifm_alloc) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), nullptr, act_info); @@ -357,7 +419,7 @@ void ActivationBuilder::appendReLU(::arm_compute::ITensor *ifm_alloc) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, nullptr, act_info); @@ -372,7 +434,7 @@ void ActivationBuilder::appendReLU1(::arm_compute::ITensor *ifm_alloc) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), nullptr, act_info); @@ -380,7 +442,7 @@ void ActivationBuilder::appendReLU1(::arm_compute::ITensor *ifm_alloc) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, nullptr, act_info); @@ -395,7 +457,7 @@ void ActivationBuilder::appendReLU6(::arm_compute::ITensor *ifm_alloc) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), nullptr, act_info); @@ -403,7 +465,7 @@ void ActivationBuilder::appendReLU6(::arm_compute::ITensor *ifm_alloc) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, nullptr, act_info); @@ -411,23 +473,6 @@ void ActivationBuilder::appendReLU6(::arm_compute::ITensor *ifm_alloc) } } -void ActivationBuilder::appendTanh(::arm_compute::ITensor *ifm_alloc) -{ - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f}; - - if (::internal::arm_compute::isGpuMode()) - { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); - - fn->configure(CAST_CL(ifm_alloc), nullptr, act_info); - - _builder.append("Tanh", std::move(fn)); - } - else - throw std::runtime_error("Not supported, yet"); -} - void ActivationBuilder::append(FuseCode code, ::arm_compute::ITensor *ifm_alloc) { switch (code) @@ -490,9 +535,11 @@ public: void visit(const ::internal::tflite::op::Softmax::Node &node) override; void visit(const ::internal::tflite::op::StridedSlice::Node &node) override; void visit(const ::internal::tflite::op::ReduceMax::Node &node) override; + void visit(const ::internal::tflite::op::ReduceMin::Node &node) override; void visit(const ::internal::tflite::op::Cast::Node &node) override; void visit(const ::internal::tflite::op::TopKV2::Node &node) override; void visit(const ::internal::tflite::op::Gather::Node &node) override; + void visit(const ::internal::tflite::op::PReLU::Node &node) override; void visit(const ::internal::tflite::op::ReLU::Node &node) override; void visit(const ::internal::tflite::op::ReLU1::Node &node) override; void visit(const ::internal::tflite::op::ReLU6::Node &node) override; @@ -504,15 +551,33 @@ public: void visit(const ::internal::tflite::op::LSTM::Node &node) override; void visit(const ::internal::tflite::op::Floor::Node &node) override; void visit(const ::internal::tflite::op::Split::Node &node) override; + void visit(const ::internal::tflite::op::ArgMax::Node &node) override; void visit(const ::internal::tflite::op::RSQRT::Node &node) override; + void visit(const ::internal::tflite::op::SQRT::Node &node) override; void visit(const ::internal::tflite::op::Pad::Node &node) override; void visit(const ::internal::tflite::op::SpaceToDepth::Node &node) override; + void visit(const ::internal::tflite::op::SpaceToBatchND::Node &node) override; + void visit(const ::internal::tflite::op::BatchToSpaceNd::Node &node) override; void visit(const ::internal::tflite::op::L2Pool2D::Implicit::Node &node) override; void visit(const ::internal::tflite::op::L2Pool2D::Explicit::Node &node) override; void visit(const ::internal::tflite::op::EmbeddingLookup::Node &node) override; void visit(const ::internal::tflite::op::HashtableLookup::Node &node) override; void visit(const ::internal::tflite::op::L2Normalization::Node &node) override; void visit(const ::internal::tflite::op::SquaredDifference::Node &node) override; + void visit(const ::internal::tflite::op::LocalResponseNormalization::Node &node) override; + void visit(const ::internal::tflite::op::DepthToSpace::Node &node) override; + void visit(const ::internal::tflite::op::Unpack::Node &node) override; + void visit(const ::internal::tflite::op::Neg::Node &node) override; + void visit(const ::internal::tflite::op::Exp::Node &node) override; + void visit(const ::internal::tflite::op::ReduceSum::Node &node) override; + void visit(const ::internal::tflite::op::Equal::Node &node) override; + void visit(const ::internal::tflite::op::TransposeConv::Node &node) override; + void visit(const ::internal::tflite::op::Pack::Node &node) override; + void visit(const ::internal::tflite::op::Abs::Node &node) override; + void visit(const ::internal::tflite::op::NotEqual::Node &node) override; + void visit(const ::internal::tflite::op::LogicalAnd::Node &node) override; + void visit(const ::internal::tflite::op::LogicalNot::Node &node) override; + void visit(const ::internal::tflite::op::LogicalOr::Node &node) override; private: const ::internal::tflite::operand::Set &_ctx; @@ -582,7 +647,7 @@ void Planner::visit(const ::internal::tflite::op::Add::Node &node) // NOTE SimpleArithmeticAddition does not support broadcasting assert(lhs_shape == rhs_shape); - auto l = nnfw::make_unique<SimpleArithmeticAddition>(); + auto l = nnfw::cpp14::make_unique<SimpleArithmeticAddition>(); l->configure(lhs_alloc, rhs_alloc, ofm_alloc); @@ -592,7 +657,7 @@ void Planner::visit(const ::internal::tflite::op::Add::Node &node) { if (::internal::arm_compute::isGpuMode()) { - auto l = nnfw::make_unique<::arm_compute::CLArithmeticAddition>(); + auto l = nnfw::cpp14::make_unique<::arm_compute::CLArithmeticAddition>(); // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification l->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc), @@ -602,7 +667,7 @@ void Planner::visit(const ::internal::tflite::op::Add::Node &node) } else // NEON { - auto l = nnfw::make_unique<::arm_compute::NEArithmeticAddition>(); + auto l = nnfw::cpp14::make_unique<::arm_compute::NEArithmeticAddition>(); // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification l->configure(lhs_alloc, rhs_alloc, ofm_alloc, ::arm_compute::ConvertPolicy::SATURATE); @@ -672,7 +737,7 @@ void Planner::visit(const ::internal::tflite::op::Sub::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLArithmeticSubtraction>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArithmeticSubtractionEx>(); // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification fn->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc), @@ -682,7 +747,7 @@ void Planner::visit(const ::internal::tflite::op::Sub::Node &node) } else // NEON { - auto fn = nnfw::make_unique<::arm_compute::NEArithmeticSubtraction>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEArithmeticSubtraction>(); // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification fn->configure(lhs_alloc, rhs_alloc, ofm_alloc, ::arm_compute::ConvertPolicy::SATURATE); @@ -696,7 +761,6 @@ void Planner::visit(const ::internal::tflite::op::Sub::Node &node) _builder.addStage(stage); } -// TODO: test with scalar*scalar, tensor bigger than 3D (e.g., 4D) void Planner::visit(const ::internal::tflite::op::Mul::Node &node) { const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; @@ -754,7 +818,7 @@ void Planner::visit(const ::internal::tflite::op::Mul::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLPixelWiseMultiplication>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPixelWiseMultiplication>(); fn->configure(CAST_CL(lhs_input_alloc), CAST_CL(rhs_input_alloc), CAST_CL(output_alloc), 1.0, // scale @@ -765,9 +829,9 @@ void Planner::visit(const ::internal::tflite::op::Mul::Node &node) } else // NEON { - auto fn = nnfw::make_unique<::arm_compute::NEPixelWiseMultiplication>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEPixelWiseMultiplication>(); - fn->configure(CAST_NE(lhs_input_alloc), CAST_NE(rhs_input_alloc), CAST_NE(output_alloc), + fn->configure(lhs_input_alloc, rhs_input_alloc, output_alloc, 1.0, // scale arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_ZERO); @@ -836,11 +900,11 @@ void Planner::visit(const ::internal::tflite::op::Div::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLPixelWiseDivision>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPixelWiseDivision>(); - // TODO Decide scale, overflow_policy, and rounding_policy. - // Currently, the default values are used. - fn->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc)); + fn->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc), + 1.0, // scale + arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_ZERO); builder.append("Div", std::move(fn)); } @@ -975,7 +1039,7 @@ void Planner::visit(const ::internal::tflite::op::Conv2D::Implicit::Node &node) param.stride = stride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? same_padding(ifm_shape, ofm_shape, stride, ker_shape.W, ker_shape.H) + ? same_padding(ifm_shape, stride, ker_shape.W, ker_shape.H) : valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); @@ -1255,7 +1319,7 @@ void Planner::visit(const ::internal::tflite::op::DepthwiseConv2D::Implicit::Nod param.stride = stride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? same_padding(ifm_shape, ofm_shape, stride, ker_shape.W, ker_shape.H) + ? same_padding(ifm_shape, stride, ker_shape.W, ker_shape.H) : valid_padding(); param.multipler = multiplier; @@ -1293,7 +1357,7 @@ void Planner::visit(const ::internal::tflite::op::DepthwiseConv2D::Implicit::Nod if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLDepthwiseConvolutionLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthwiseConvolutionLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ker_alloc), CAST_CL(bias_alloc), CAST_CL(ofm_alloc), conv_info, param.multipler); @@ -1302,7 +1366,7 @@ void Planner::visit(const ::internal::tflite::op::DepthwiseConv2D::Implicit::Nod } else { - auto fn = nnfw::make_unique<::arm_compute::NEDepthwiseConvolutionLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDepthwiseConvolutionLayer>(); fn->configure(ifm_alloc, ker_alloc, bias_alloc, ofm_alloc, conv_info, param.multipler); @@ -1436,7 +1500,7 @@ void Planner::visit(const ::internal::tflite::op::DepthwiseConv2D::Explicit::Nod if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLDepthwiseConvolutionLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthwiseConvolutionLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ker_alloc), CAST_CL(bias_alloc), CAST_CL(ofm_alloc), conv_info, param.multipler); @@ -1445,7 +1509,7 @@ void Planner::visit(const ::internal::tflite::op::DepthwiseConv2D::Explicit::Nod } else { - auto fn = nnfw::make_unique<::arm_compute::NEDepthwiseConvolutionLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEDepthwiseConvolutionLayer>(); fn->configure(ifm_alloc, ker_alloc, bias_alloc, ofm_alloc, conv_info, param.multipler); @@ -1499,7 +1563,7 @@ void Planner::visit(const ::internal::tflite::op::Dequantize::Node &node) if (from_env<bool>(std::getenv("USE_SIMPLE_CAST"))) { // Use the CPU version of CAST operation - auto l = nnfw::make_unique<SimpleCastLayer>(); + auto l = nnfw::cpp14::make_unique<SimpleCastLayer>(); l->configure(input_alloc, output_alloc); fn = std::move(l); @@ -1508,7 +1572,7 @@ void Planner::visit(const ::internal::tflite::op::Dequantize::Node &node) { if (::internal::arm_compute::isGpuMode()) { - auto l = nnfw::make_unique<::arm_compute::CLCast>(); + auto l = nnfw::cpp14::make_unique<::arm_compute::CLCast>(); l->configure(CAST_CL(input_alloc), CAST_CL(output_alloc)); fn = std::move(l); @@ -1554,10 +1618,12 @@ void Planner::visit(const ::internal::tflite::op::MaxPool2D::Implicit::Node &nod // TODO Should move to the place where the operand is handled, if it is possible. // Set Shape Constraints and TensorInfo - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); // Construct operation parameters struct Param @@ -1586,7 +1652,7 @@ void Planner::visit(const ::internal::tflite::op::MaxPool2D::Implicit::Node &nod param.stride.horizontal = hstride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) + ? same_padding(ifm_shape, param.stride, kw, kh) : valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); @@ -1652,7 +1718,6 @@ void Planner::visit(const ::internal::tflite::op::MaxPool2D::Explicit::Node &nod const ::internal::tflite::operand::Index activation_index{node.param().activation_index}; - // TODO 4D tensor (dim(0) !=1 ) const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); @@ -1669,10 +1734,12 @@ void Planner::visit(const ::internal::tflite::op::MaxPool2D::Explicit::Node &nod // TODO Should move to the place where the operand is handled, if it is possible. // Set Shape Constraints and TensorInfo - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); // Construct operation parameters struct Param @@ -1782,10 +1849,12 @@ void Planner::visit(const ::internal::tflite::op::AvgPool2D::Implicit::Node &nod // TODO Should move to the place where the operand is handled, if it is possible. // Set Shape Constraints and TensorInfo - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); // Construct operation parameters struct Param @@ -1814,7 +1883,7 @@ void Planner::visit(const ::internal::tflite::op::AvgPool2D::Implicit::Node &nod param.stride.horizontal = hstride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) + ? same_padding(ifm_shape, param.stride, kw, kh) : valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); @@ -1882,7 +1951,6 @@ void Planner::visit(const ::internal::tflite::op::AvgPool2D::Explicit::Node &nod const ::internal::tflite::operand::Index activation_index{node.param().activation_index}; - // TODO 4D tensor (dim(0) != 1) const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); @@ -1899,10 +1967,12 @@ void Planner::visit(const ::internal::tflite::op::AvgPool2D::Explicit::Node &nod // TODO Should move to the place where the operand is handled, if it is possible. // Set Shape Constraints and TensorInfo - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); // Construct operation parameters struct Param @@ -1998,8 +2068,9 @@ void Planner::visit(const ::internal::tflite::op::Concat::Node &node) } // Set Shape Constraints and TensorInfo (for output) - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); // Set Shape Constraints and TensorInfo (for input) const uint32_t coord_index = ToARMComputeAxis(input_rank, axis).value(); @@ -2060,7 +2131,7 @@ void Planner::visit(const ::internal::tflite::op::FullyConnected::Node &node) internal::tflite::operand::Shape reshape(2); if (input_rank == 4) { - nnfw::util::feature::Shape ifm_shape_feature = _ctx.at(input_index).shape().asFeature(); + nnfw::misc::feature::Shape ifm_shape_feature = _ctx.at(input_index).shape().asFeature(); auto feature_size = ifm_shape_feature.N * ifm_shape_feature.C * ifm_shape_feature.H * ifm_shape_feature.W; assert(feature_size == batch_size * input_size); @@ -2078,7 +2149,7 @@ void Planner::visit(const ::internal::tflite::op::FullyConnected::Node &node) else if (input_rank == 2) { auto ifm_shape = _ctx.at(input_index).shape(); - nnfw::util::matrix::Shape ifm_shape_matrix = ifm_shape.asMatrix(); + nnfw::misc::matrix::Shape ifm_shape_matrix = ifm_shape.asMatrix(); assert(ifm_shape.dim(0) == batch_size); assert(ifm_shape.dim(1) == input_size); @@ -2131,7 +2202,7 @@ void Planner::visit(const ::internal::tflite::op::FullyConnected::Node &node) auto weight_alloc = ctx.at(::internal::tflite::operand::Index{param.weight_index}); auto bias_alloc = ctx.at(::internal::tflite::operand::Index{param.bias_index}); - auto fn = nnfw::make_unique<GenericFullyConnectedLayer>(); + auto fn = nnfw::cpp14::make_unique<GenericFullyConnectedLayer>(); fn->configure(input_alloc, weight_alloc, bias_alloc, output_alloc, needs_reshape, asTensorShape(reshape)); @@ -2154,10 +2225,12 @@ void Planner::visit(const ::internal::tflite::op::ResizeBilinear::Node &node) // TODO Should move to the place where the operand is handled, if it is possible. // Set Shape Constraints - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); struct Param { @@ -2181,7 +2254,7 @@ void Planner::visit(const ::internal::tflite::op::ResizeBilinear::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLScale>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLScale>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), ::arm_compute::InterpolationPolicy::BILINEAR, @@ -2202,18 +2275,19 @@ void Planner::visit(const ::internal::tflite::op::Reshape::Node &node) const ::internal::tflite::operand::Index output_index{node.param().output_index}; const ::internal::tflite::operand::Index input_index{node.param().input_index}; - // NOTE The content of a tensor specified by shape_index should be aligned with - // output tensor shape - // TODO Check consistency of ouput shape + auto input_shape = asTensorShape(_ctx.at(input_index).shape()); + auto output_shape = asTensorShape(_ctx.at(output_index).shape()); - // TODO Re-enable this assert - // assert((ifm_shape.C * ifm_shape.H * ifm_shape.W) == out_size); + assert(input_shape[0] * input_shape[1] * input_shape[2] * input_shape[3] == + output_shape[0] * output_shape[1] * output_shape[2] * output_shape[3]); // TODO Should move to the place where the operand is handled, if it is possible. - _builder.addShapeConstr(output_index, asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), - _ctx.at(output_index).type())); - _builder.addShapeConstr(input_index, asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), - _ctx.at(input_index).type())); + _builder.addShapeConstr(output_index, asTensorInfo(output_shape, _ctx.at(output_index).type(), + _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, asTensorInfo(input_shape, _ctx.at(input_index).type(), + _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); struct Param { @@ -2233,7 +2307,7 @@ void Planner::visit(const ::internal::tflite::op::Reshape::Node &node) if (::internal::arm_compute::isGpuMode()) { // GenericReshape first apply NCHW->NHWC permutation, and apply reshape - auto fn = nnfw::make_unique<GenericReshapeLayer>(); + auto fn = nnfw::cpp14::make_unique<GenericReshapeLayer>(); fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc)); @@ -2241,7 +2315,7 @@ void Planner::visit(const ::internal::tflite::op::Reshape::Node &node) } else { - auto fn = nnfw::make_unique<GenericReshapeLayer>(); + auto fn = nnfw::cpp14::make_unique<GenericReshapeLayer>(); fn->configure(input_alloc, output_alloc); @@ -2259,19 +2333,15 @@ void Planner::visit(const ::internal::tflite::op::Squeeze::Node &node) const ::internal::tflite::operand::Index output_index{node.param().output_index}; const ::internal::tflite::operand::Index input_index{node.param().input_index}; - // Currently, 3D-input with dims is tested. Note that param(). dims_index_optional is optional. - // two generated test passed: - // - 3D input : squeeze_float_1 - // - 2D input : squeeze_3D_float_1 - // - 4D input fails (squeeze.mod.py) -> we need general tensor support - - // TODO Support generic tensor shape - // Set Shape Constraints - _builder.addShapeConstr(output_index, asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), - _ctx.at(output_index).type())); - _builder.addShapeConstr(input_index, asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), - _ctx.at(input_index).type())); + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); // Construct operation parameters struct Param @@ -2291,7 +2361,7 @@ void Planner::visit(const ::internal::tflite::op::Squeeze::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLReshapeLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReshapeLayer>(); fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc)); @@ -2299,7 +2369,7 @@ void Planner::visit(const ::internal::tflite::op::Squeeze::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEReshapeLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEReshapeLayer>(); fn->configure(input_alloc, output_alloc); @@ -2350,7 +2420,7 @@ void Planner::visit(const ::internal::tflite::op::Softmax::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLSoftmaxLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSoftmaxLayer>(); fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), param.scale); @@ -2358,7 +2428,7 @@ void Planner::visit(const ::internal::tflite::op::Softmax::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NESoftmaxLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NESoftmaxLayer>(); fn->configure(input_alloc, output_alloc, param.scale); @@ -2397,14 +2467,18 @@ void Planner::visit(const ::internal::tflite::op::StridedSlice::Node &node) assert(_ctx.at(startData_index).shape().rank() == 1); assert(_ctx.at(endData_index).shape().rank() == 1); assert(_ctx.at(stridesData_index).shape().rank() == 1); - _builder.addShapeConstr(startData_index, - asTensorInfo(asTensorShape(_ctx.at(startData_index).shape()), - _ctx.at(startData_index).type())); + _builder.addShapeConstr( + startData_index, + asTensorInfo(asTensorShape(_ctx.at(startData_index).shape()), _ctx.at(startData_index).type(), + _ctx.at(startData_index).scale(), _ctx.at(startData_index).zeroPoint())); _builder.addShapeConstr(endData_index, asTensorInfo(asTensorShape(_ctx.at(endData_index).shape()), - _ctx.at(endData_index).type())); - _builder.addShapeConstr(stridesData_index, - asTensorInfo(asTensorShape(_ctx.at(endData_index).shape()), - _ctx.at(stridesData_index).type())); + _ctx.at(endData_index).type(), + _ctx.at(endData_index).scale(), + _ctx.at(endData_index).zeroPoint())); + _builder.addShapeConstr( + stridesData_index, + asTensorInfo(asTensorShape(_ctx.at(endData_index).shape()), _ctx.at(stridesData_index).type(), + _ctx.at(stridesData_index).scale(), _ctx.at(stridesData_index).zeroPoint())); // Set initializers for indices data such as order of inputData { @@ -2469,7 +2543,7 @@ void Planner::visit(const ::internal::tflite::op::StridedSlice::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLStridedSlice>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLStridedSliceEx>(); fn->configure(CAST_CL(inputData_alloc), CAST_CL(outputData_alloc), CAST_CL(startData_alloc), CAST_CL(endData_alloc), CAST_CL(stridesData_alloc), param.beginMask, @@ -2484,6 +2558,133 @@ void Planner::visit(const ::internal::tflite::op::StridedSlice::Node &node) _builder.addStage(stage); } +void Planner::visit(const ::internal::tflite::op::ReduceMin::Node &node) +{ + VERBOSE(ReduceMin) << "Configure REDUCEMIN operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index axis_index{node.param().axis_index}; + + auto ifm_shape = _ctx.at(ifm_index).shape(); + auto ofm_shape = _ctx.at(ofm_index).shape(); + auto axis_shape = _ctx.at(axis_index).shape(); + assert(ifm_shape.rank() <= 4); + assert(ofm_shape.rank() <= ifm_shape.rank()); + assert(_ctx.at(axis_index).hasData()); + assert(axis_shape.rank() == 0 || axis_shape.rank() == 1); + + // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only + // supports cases reducing height and width or reducing depth. + // TODO We have to support all cases of dimensions up to 4. + // For correct permuting, we have to set output's shape to be equal in dimension position of the + // input. But the positions of the same dimensions in the input and output may be set differently. + // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original + // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to + // extend it in 4 dimensions, it should be {1,1,3,5}. + // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of + // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the + // next operation is not desired. + if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank()) + { + if (ofm_shape.rank() == 2) + { + // Reducing HW + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1)); + } + else if (ofm_shape.rank() == 3) + { + // Reducing C or + // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1) + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) && + ifm_shape.dim(2) == ofm_shape.dim(2) || + (ifm_shape.dim(0) == ofm_shape.dim(0) && + (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) && + ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1)); + } + } + + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + std::set<uint32_t> axis; + { + const auto ifm_rank = ifm_shape.rank(); + switch (axis_shape.rank()) + { + case 0: // scalar + { + int32_t axis_value = _ctx.at(axis_index).asScalar<int32_t>(); + if (axis_value < 0) + { + axis_value += ifm_rank; + } + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); + break; + } + case 1: // vector + { + const auto axis_base = _ctx.at(axis_index).data().base(); + const auto axis_size = _ctx.at(axis_index).shape().asVector(); + + // If axis's data does not exist as constant values and can be gotten as input data, we have + // to find a way to infer output shape when sinking output. + assert(axis_base != nullptr); + for (uint32_t n = 0; n < axis_size; ++n) + { + int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n); + if (axis_value < 0) + { + axis_value += ifm_rank; + } + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); + } + break; + } + default: + throw std::runtime_error("Not supported"); + break; + } + } + + // Construct operation parameters + struct Param + { + int ofm_index; + int ifm_index; + std::set<uint32_t> axis; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + param.axis = axis; + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis, + ::arm_compute::ReduceOperation::MIN); + + builder.append("ReduceMin", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + }; + + _builder.addStage(stage); +} + void Planner::visit(const ::internal::tflite::op::ReduceMax::Node &node) { VERBOSE(ReduceMax) << "Configure REDUCEMAX operation" << std::endl; @@ -2492,43 +2693,104 @@ void Planner::visit(const ::internal::tflite::op::ReduceMax::Node &node) const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; const ::internal::tflite::operand::Index axis_index{node.param().axis_index}; - // Handle special case only: - // Input: Matrix (rank 2) - // Output: Vector (rank 1) - // Axis: one element (scalar or rank 1 with 1 element), constant auto ifm_shape = _ctx.at(ifm_index).shape(); auto ofm_shape = _ctx.at(ofm_index).shape(); auto axis_shape = _ctx.at(axis_index).shape(); - assert(ofm_shape.rank() == 1); - assert(ifm_shape.rank() == 2); + assert(ifm_shape.rank() <= 4); + assert(ofm_shape.rank() <= ifm_shape.rank()); assert(_ctx.at(axis_index).hasData()); - assert(axis_shape.rank() == 0 || ((axis_shape.rank() == 1) && (axis_shape.dim(0) == 1))); + assert(axis_shape.rank() == 0 || axis_shape.rank() == 1); + + // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only + // supports cases reducing height and width or reducing depth. + // TODO We have to support all cases of dimensions up to 4. + // For correct permuting, we have to set output's shape to be equal in dimension position of the + // input. But the positions of the same dimensions in the input and output may be set differently. + // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original + // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to + // extend it in 4 dimensions, it should be {1,1,3,5}. + // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of + // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the + // next operation is not desired. + if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank()) + { + if (ofm_shape.rank() == 2) + { + // Reducing HW + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1)); + } + else if (ofm_shape.rank() == 3) + { + // Reducing C or + // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1) + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) && + ifm_shape.dim(2) == ofm_shape.dim(2) || + (ifm_shape.dim(0) == ofm_shape.dim(0) && + (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) && + ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1)); + } + } - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + std::set<uint32_t> axis; + { + const auto ifm_rank = ifm_shape.rank(); + switch (axis_shape.rank()) + { + case 0: // scalar + { + int32_t axis_value = _ctx.at(axis_index).asScalar<int32_t>(); + if (axis_value < 0) + { + axis_value += ifm_rank; + } + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); + break; + } + case 1: // vector + { + const auto axis_base = _ctx.at(axis_index).data().base(); + const auto axis_size = _ctx.at(axis_index).shape().asVector(); - // Note: Assume only one element in axis. It is checked by assertion above - // TODO: handle general case - // Axis is integer value (generally, int32) - int32_t axis_value = _ctx.at(axis_index).asScalar<int32_t>(); - assert(axis_value == 1); + // If axis's data does not exist as constant values and can be gotten as input data, we have + // to find a way to infer output shape when sinking output. + assert(axis_base != nullptr); + for (uint32_t n = 0; n < axis_size; ++n) + { + int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n); + if (axis_value < 0) + { + axis_value += ifm_rank; + } + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); + } + break; + } + default: + throw std::runtime_error("Not supported"); + break; + } + } // Construct operation parameters struct Param { int ofm_index; int ifm_index; - - int32_t axis; + std::set<uint32_t> axis; }; Param param; param.ofm_index = ofm_index.asInt(); param.ifm_index = ifm_index.asInt(); - param.axis = axis_value; + param.axis = axis; auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); @@ -2536,9 +2798,10 @@ void Planner::visit(const ::internal::tflite::op::ReduceMax::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLReduceMax>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(); - fn->configure(CAST_CL(ifm_alloc), param.axis, CAST_CL(ofm_alloc)); + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis, + ::arm_compute::ReduceOperation::MAX); builder.append("ReduceMax", std::move(fn)); } @@ -2586,7 +2849,7 @@ void Planner::visit(const ::internal::tflite::op::Cast::Node &node) if (from_env<bool>(std::getenv("USE_SIMPLE_CAST"))) { // Use the CPU version of CAST operation - auto l = nnfw::make_unique<SimpleCastLayer>(); + auto l = nnfw::cpp14::make_unique<SimpleCastLayer>(); l->configure(input_alloc, output_alloc); fn = std::move(l); @@ -2595,7 +2858,7 @@ void Planner::visit(const ::internal::tflite::op::Cast::Node &node) { if (::internal::arm_compute::isGpuMode()) { - auto l = nnfw::make_unique<::arm_compute::CLCast>(); + auto l = nnfw::cpp14::make_unique<::arm_compute::CLCast>(); l->configure(CAST_CL(input_alloc), CAST_CL(output_alloc)); fn = std::move(l); @@ -2627,13 +2890,18 @@ void Planner::visit(const ::internal::tflite::op::TopKV2::Node &node) // Set shape constraints _builder.addShapeConstr(outputValues_index, asTensorInfo(asTensorShape(_ctx.at(outputValues_index).shape()), - _ctx.at(outputValues_index).type())); + _ctx.at(outputValues_index).type(), + _ctx.at(outputValues_index).scale(), + _ctx.at(outputValues_index).zeroPoint())); _builder.addShapeConstr(outputIndices_index, asTensorInfo(asTensorShape(_ctx.at(outputIndices_index).shape()), - _ctx.at(outputIndices_index).type())); - _builder.addShapeConstr(inputData_index, - asTensorInfo(asTensorShape(_ctx.at(inputData_index).shape()), - _ctx.at(inputData_index).type())); + _ctx.at(outputIndices_index).type(), + _ctx.at(outputIndices_index).scale(), + _ctx.at(outputIndices_index).zeroPoint())); + _builder.addShapeConstr( + inputData_index, + asTensorInfo(asTensorShape(_ctx.at(inputData_index).shape()), _ctx.at(inputData_index).type(), + _ctx.at(inputData_index).scale(), _ctx.at(inputData_index).zeroPoint())); // Construct operation parameters struct Param @@ -2659,7 +2927,7 @@ void Planner::visit(const ::internal::tflite::op::TopKV2::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLTopKV2>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLTopKV2>(); fn->configure(CAST_CL(input_alloc), param.k, CAST_CL(values_alloc), CAST_CL(indices_alloc)); @@ -2686,12 +2954,15 @@ void Planner::visit(const ::internal::tflite::op::Gather::Node &node) assert(_ctx.at(rhs_index).shape().rank() == 1); // Set Shape Constraints - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(lhs_index, asTensorInfo(asTensorShape(_ctx.at(lhs_index).shape()), - _ctx.at(lhs_index).type())); - _builder.addShapeConstr(rhs_index, asTensorInfo(asTensorShape(_ctx.at(rhs_index).shape()), - _ctx.at(rhs_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + lhs_index, asTensorInfo(asTensorShape(_ctx.at(lhs_index).shape()), _ctx.at(lhs_index).type(), + _ctx.at(lhs_index).scale(), _ctx.at(lhs_index).zeroPoint())); + _builder.addShapeConstr( + rhs_index, asTensorInfo(asTensorShape(_ctx.at(rhs_index).shape()), _ctx.at(rhs_index).type(), + _ctx.at(rhs_index).scale(), _ctx.at(ofm_index).zeroPoint())); // Construct operation parameters struct Param @@ -2720,7 +2991,7 @@ void Planner::visit(const ::internal::tflite::op::Gather::Node &node) { std::unique_ptr<::arm_compute::IFunction> fn; - auto l = nnfw::make_unique<::arm_compute::CLGather>(); + auto l = nnfw::cpp14::make_unique<::arm_compute::CLGather>(); l->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc)); fn = std::move(l); builder.append("Gather", std::move(fn)); @@ -2732,6 +3003,62 @@ void Planner::visit(const ::internal::tflite::op::Gather::Node &node) _builder.addStage(stage); } +void Planner::visit(const ::internal::tflite::op::PReLU::Node &node) +{ + VERBOSE(PReLU) << "Configure PReLU operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index alpha_index{node.param().alpha_index}; + + // Set shape constraints + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + _builder.addShapeConstr(alpha_index, + asTensorInfo(asTensorShape(_ctx.at(alpha_index).shape()), + _ctx.at(alpha_index).type(), _ctx.at(alpha_index).scale(), + _ctx.at(alpha_index).zeroPoint())); + + struct Param + { + int ofm_index; + int ifm_index; + int alpha_index; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + param.alpha_index = alpha_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + auto alpha_alloc = ctx.at(::internal::tflite::operand::Index{param.alpha_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPReLU>(); + fn->configure(CAST_CL(ifm_alloc), CAST_CL(alpha_alloc), CAST_CL(ofm_alloc)); + builder.append("PReLU", std::move(fn)); + } + else + { + // TODO Add NEON support + + throw std::runtime_error("Not supported, yet"); + } + }; + + _builder.addStage(stage); +} + void Planner::visit(const ::internal::tflite::op::ReLU::Node &node) { VERBOSE(ReLU) << "Configure ReLU operation" << std::endl; @@ -2767,7 +3094,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), act_info); @@ -2775,7 +3102,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, ofm_alloc, act_info); @@ -2821,7 +3148,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU1::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), act_info); @@ -2829,7 +3156,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU1::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, ofm_alloc, act_info); @@ -2875,7 +3202,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU6::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), act_info); @@ -2883,7 +3210,7 @@ void Planner::visit(const ::internal::tflite::op::ReLU6::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, ofm_alloc, act_info); @@ -2902,10 +3229,12 @@ void Planner::visit(const ::internal::tflite::op::Tanh::Node &node) const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; // Set shape constraints - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); struct Param { @@ -2927,7 +3256,7 @@ void Planner::visit(const ::internal::tflite::op::Tanh::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), act_info); @@ -2935,7 +3264,7 @@ void Planner::visit(const ::internal::tflite::op::Tanh::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); fn->configure(ifm_alloc, ofm_alloc, act_info); @@ -2981,14 +3310,20 @@ void Planner::visit(const ::internal::tflite::op::Logistic::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLActivationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), act_info); builder.append("Logistic", std::move(fn)); } else - throw std::runtime_error("Not supported, yet"); + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); + + fn->configure(ifm_alloc, ofm_alloc, act_info); + + builder.append("Logistic", std::move(fn)); + } }; _builder.addStage(stage); @@ -3005,52 +3340,89 @@ void Planner::visit(const ::internal::tflite::op::Mean::Node &node) const ::internal::tflite::operand::Index keep_dims_index{node.param().keep_dims_index}; const int keep_dims = _ctx.at(keep_dims_index).asScalar<int>(); - // Set shape constraints - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); - _builder.addShapeConstr(axis_index, asTensorInfo(asTensorShape(_ctx.at(axis_index).shape()), - _ctx.at(axis_index).type())); + const auto ifm_shape = _ctx.at(ifm_index).shape(); + const auto ofm_shape = _ctx.at(ofm_index).shape(); - // TODO keep_dims==0 - assert(keep_dims != 0); + // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only + // supports cases reducing height and width or reducing depth. + // TODO We have to support all cases of dimensions up to 4. + // For correct permuting, we have to set output's shape to be equal in dimension position of the + // input. But the positions of the same dimensions in the input and output may be set differently. + // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original + // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to + // extend it in 4 dimensions, it should be {1,1,3,5}. + // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of + // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the + // next operation is not desired. + if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank()) + { + if (ofm_shape.rank() == 2) + { + // Reducing HW + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1)); + } + else if (ofm_shape.rank() == 3) + { + // Reducing C or + // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1) + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) && + ifm_shape.dim(2) == ofm_shape.dim(2) || + (ifm_shape.dim(0) == ofm_shape.dim(0) && + (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) && + ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1)); + } + } - // Set axis - // TODO Other axis (Axis for width and height are currently supported.) - // TODO Other ranks (Rank 4 is currently supported.) - assert(_ctx.at(ifm_index).shape().rank() == 4); + // Set shape constraints + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + _builder.addShapeConstr(axis_index, + asTensorInfo(asTensorShape(_ctx.at(axis_index).shape()), + _ctx.at(axis_index).type(), _ctx.at(axis_index).scale(), + _ctx.at(axis_index).zeroPoint())); - std::vector<uint32_t> axis; + std::set<uint32_t> axis; { - const auto axis_base = _ctx.at(axis_index).data().base(); - const auto axis_type = _ctx.at(axis_index).type(); - const auto axis_size = _ctx.at(axis_index).shape().asVector(); - - // NHWC type -> WHCN type - if (_ctx.at(ofm_index).shape().rank() == 4) + const auto ifm_rank = ifm_shape.rank(); + const auto axis_shape = _ctx.at(axis_index).shape(); + switch (axis_shape.rank()) { - for (uint32_t n = 0; n < axis_size; ++n) + case 0: // scalar { - const ::arm_compute::Coordinates coordinate{n}; - const int32_t *from = reinterpret_cast<const int32_t *>(axis_base) + n; - if (*from == 1) + int32_t axis_value = _ctx.at(axis_index).asScalar<int32_t>(); + if (axis_value < 0) { - axis.push_back(1); // h + axis_value += ifm_rank; } - else if (*from == 2) - { - axis.push_back(0); // w - } - else if (*from < 0) - { - // Nothing to do - } - else + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); + break; + } + case 1: // vector + { + const auto axis_base = _ctx.at(axis_index).data().base(); + const auto axis_size = _ctx.at(axis_index).shape().asVector(); + + // If axis's data does not exist as constant values and can be gotten as input data, we have + // to find a way to infer output shape when sinking output. + assert(axis_base != nullptr); + for (uint32_t n = 0; n < axis_size; ++n) { - throw std::runtime_error{"Not supported axis"}; + int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n); + if (axis_value < 0) + { + axis_value += ifm_rank; + } + axis.insert(ToARMComputeAxis(ifm_rank, axis_value).value()); } + break; } + default: + throw std::runtime_error("Not supported"); + break; } } @@ -3058,7 +3430,7 @@ void Planner::visit(const ::internal::tflite::op::Mean::Node &node) { int ofm_index; int ifm_index; - std::vector<uint32_t> axis; + std::set<uint32_t> axis; }; Param param; @@ -3073,9 +3445,10 @@ void Planner::visit(const ::internal::tflite::op::Mean::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLReductionMean>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(); - fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis); + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis, + ::arm_compute::ReduceOperation::MEAN); builder.append("Mean", std::move(fn)); } @@ -3125,23 +3498,37 @@ void Planner::visit(const ::internal::tflite::op::RNN::Node &node) num_units == _ctx.at(hidden_state_out_index).shape().dim(1)); // Set Shape Constraints and TensorInfo - _builder.addShapeConstr(output_index, asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), - _ctx.at(output_index).type())); + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); _builder.addShapeConstr(hidden_state_out_index, asTensorInfo(asTensorShape(_ctx.at(hidden_state_out_index).shape()), - _ctx.at(hidden_state_out_index).type())); - _builder.addShapeConstr(input_index, asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), - _ctx.at(input_index).type())); + _ctx.at(hidden_state_out_index).type(), + _ctx.at(hidden_state_out_index).scale(), + _ctx.at(hidden_state_out_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); _builder.addShapeConstr(weights_index, asTensorInfo(asTensorShape(_ctx.at(weights_index).shape()), - _ctx.at(weights_index).type())); + _ctx.at(weights_index).type(), + _ctx.at(weights_index).scale(), + _ctx.at(weights_index).zeroPoint())); _builder.addShapeConstr(recurrent_weights_index, asTensorInfo(asTensorShape(_ctx.at(recurrent_weights_index).shape()), - _ctx.at(recurrent_weights_index).type())); - _builder.addShapeConstr(bias_index, asTensorInfo(asTensorShape(_ctx.at(bias_index).shape()), - _ctx.at(bias_index).type())); + _ctx.at(recurrent_weights_index).type(), + _ctx.at(recurrent_weights_index).scale(), + _ctx.at(recurrent_weights_index).zeroPoint())); + _builder.addShapeConstr(bias_index, + asTensorInfo(asTensorShape(_ctx.at(bias_index).shape()), + _ctx.at(bias_index).type(), _ctx.at(bias_index).scale(), + _ctx.at(bias_index).zeroPoint())); _builder.addShapeConstr(hidden_state_in_index, asTensorInfo(asTensorShape(_ctx.at(hidden_state_in_index).shape()), - _ctx.at(hidden_state_in_index).type())); + _ctx.at(hidden_state_in_index).type(), + _ctx.at(hidden_state_in_index).scale(), + _ctx.at(hidden_state_in_index).zeroPoint())); // Construct operation parameters struct Param @@ -3215,13 +3602,13 @@ void Planner::visit(const ::internal::tflite::op::LSTM::Node &node) void Planner::visit(const ::internal::tflite::op::Transpose::Node &node) { VERBOSE(Transpose) << "Configure Transpose operation" << std::endl; - // Transpose supports only height-wight dimention support. - // CLPermute can be used to implement generic transpose along any axis - // But CLPermute only implements [2,0,1], [1,2,0], [3,2,0,1] - // TODO Implement other permutation CLPermute function and provide generic transpose const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index permu_index{node.param().permu_index}; + + assert(_ctx.at(ifm_index).shape().rank() == _ctx.at(ofm_index).shape().rank()); + assert(_ctx.at(permu_index).hasData() == true); // Set shape constraints _builder.addShapeConstr( @@ -3230,30 +3617,41 @@ void Planner::visit(const ::internal::tflite::op::Transpose::Node &node) _builder.addShapeConstr( ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); - // NNAPI spec provides permutation vector for generic transpose - // TODO Make the permutation vector a part of Param + struct Param { int ofm_index; int ifm_index; + const int32_t *pv; + int rank; }; Param param; param.ofm_index = ofm_index.asInt(); param.ifm_index = ifm_index.asInt(); + param.pv = reinterpret_cast<const int32_t *>(_ctx.at(permu_index).data().base()); + param.rank = _ctx.at(ifm_index).shape().rank(); auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); const auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); - // CLTranspose assumes only spatial transpose, will be replaced with CLPermute - // TODO Check the validity of permutation vector, then call CLPermute with permu vector - auto fn = nnfw::make_unique<::arm_compute::CLTranspose>(); + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPermuteEx>(); - fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc)); + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), + getARMComputePermutationVector(param.rank, param.pv)); + + builder.append("Transpose", std::move(fn)); + } + else + { + throw std::runtime_error("Not supported, yet"); + } - builder.append("Transpose", std::move(fn)); }; _builder.addStage(stage); @@ -3267,10 +3665,12 @@ void Planner::visit(const ::internal::tflite::op::Floor::Node &node) const ::internal::tflite::operand::Index ifm_index{node.param().input_index}; // Set shape constraints - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); struct Param { @@ -3289,7 +3689,7 @@ void Planner::visit(const ::internal::tflite::op::Floor::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLFloor>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLFloor>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc)); @@ -3297,7 +3697,7 @@ void Planner::visit(const ::internal::tflite::op::Floor::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NEFloor>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEFloor>(); fn->configure(ifm_alloc, ofm_alloc); @@ -3308,11 +3708,367 @@ void Planner::visit(const ::internal::tflite::op::Floor::Node &node) _builder.addStage(stage); } +void Planner::visit(const ::internal::tflite::op::ArgMax::Node &node) +{ + VERBOSE(ArgMax) << "Configure ARGMAX operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index axis_index{node.param().axis_index}; + + auto ifm_shape = _ctx.at(ifm_index).shape(); + auto ofm_shape = _ctx.at(ofm_index).shape(); + auto axis_shape = _ctx.at(axis_index).shape(); + + assert(_ctx.at(axis_index).hasData()); + // Axis dimension is always 1. + assert(axis_shape.rank() == 1); + assert(ifm_shape.rank() == ofm_shape.rank()); + + _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape(), false), + _ctx.at(ofm_index).type())); + _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape(), false), + _ctx.at(ifm_index).type())); + + std::vector<uint32_t> l_axis; + const auto axis_size = _ctx.at(axis_index).shape().asVector(); + auto axis_base = _ctx.at(axis_index).data().base(); + auto axis_type = _ctx.at(axis_index).type(); + // TODO Should support axis size > 1. + assert(axis_size == 1); + // axis is tensor with 1 dimension - always a vector. + assert(axis_base != nullptr); + for (uint32_t n = 0; n < axis_size; ++n) + { + int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n); + if (axis_value < 0) + { + axis_value += ifm_shape.rank(); + } + l_axis.push_back(ToARMComputeAxis(ifm_shape.rank(), axis_value).value()); + } + + // Construct operation parameters + struct Param + { + int ofm_index; + int ifm_index; + std::vector<uint32_t> axis; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + param.axis = l_axis; + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + if (from_env<bool>(std::getenv("USE_SIMPLE_ARGMINMAX"))) + { + // USE CPU VERSION OF ARGMAX + auto fn = nnfw::cpp14::make_unique<SimpleArgMinMax>(); + + fn->configure(ifm_alloc, ofm_alloc, param.axis, ::arm_compute::ArgOperation::MAX); + + builder.append("ArgMax", std::move(fn)); + } + else + { + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLArgMinMax>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis, + ::arm_compute::ArgOperation::MAX); + + builder.append("ArgMax", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::SQRT::Node &node) +{ + VERBOSE(SQRT) << "Configure SQRT operation" << std::endl; + + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input_index{node.param().input_index}; + + // Set shape constraints + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); + + struct Param + { + int output_index; + int input_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input_index = input_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); + + const ::arm_compute::ActivationLayerInfo act_info{ + ::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT}; + + if (from_env<bool>(std::getenv("USE_SIMPLE_SQRT"))) + { + // USE CPU VERSION OF SQRT + auto fn = nnfw::cpp14::make_unique<SimpleSQRT>(); + + fn->configure(input_alloc, output_alloc); + + builder.append("SQRT", std::move(fn)); + } + else + { + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayer>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), act_info); + + builder.append("SQRT", std::move(fn)); + } + else + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::NEActivationLayer>(); + + fn->configure(input_alloc, output_alloc, act_info); + + builder.append("SQRT", std::move(fn)); + } + } + }; + + _builder.addStage(stage); +} + void Planner::visit(const ::internal::tflite::op::RSQRT::Node &node) { VERBOSE(RSQRT) << "Configure Rsqrt operation" << std::endl; - throw std::runtime_error("Not supported, yet"); + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input_index{node.param().input_index}; + + // Set shape constraints + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape()), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); + + struct Param + { + int output_index; + int input_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input_index = input_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); + + const ::arm_compute::ActivationLayerInfoEx act_info{ + ::arm_compute::ActivationLayerInfoEx::ActivationFunction::RSQRT}; + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLActivationLayerEx>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), act_info); + + builder.append("RSQRT", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::Equal::Node &node) +{ + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input1_index{node.param().input1_index}; + const ::internal::tflite::operand::Index input2_index{node.param().input2_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + + if (!(_ctx.at(input1_index).shape() == _ctx.at(input2_index).shape())) + { + const auto broadcast_rank = + std::max(_ctx.at(input1_index).shape().rank(), _ctx.at(input2_index).shape().rank()); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input1_index).shape()) + .extendRank(broadcast_rank); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input2_index).shape()) + .extendRank(broadcast_rank); + } + _builder.addShapeConstr(input1_index, + asTensorInfo(asTensorShape(_ctx.at(input1_index).shape()), + _ctx.at(input1_index).type(), _ctx.at(input1_index).scale(), + _ctx.at(input1_index).zeroPoint())); + _builder.addShapeConstr(input2_index, + asTensorInfo(asTensorShape(_ctx.at(input2_index).shape()), + _ctx.at(input2_index).type(), _ctx.at(input2_index).scale(), + _ctx.at(input2_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input1_index; + int input2_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input1_index = input1_index.asInt(); + param.input2_index = input2_index.asInt(); + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input1_alloc = ctx.at(::internal::tflite::operand::Index{param.input1_index}); + auto input2_alloc = ctx.at(::internal::tflite::operand::Index{param.input2_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLComparisonOp>(); + + fn->configure(CAST_CL(input1_alloc), CAST_CL(input2_alloc), CAST_CL(output_alloc), + ::arm_compute::ComparisonOperation::EQUAL); + + builder.append("Equal", std::move(fn)); + } + else + { + // TODO Add NEON support + + throw std::runtime_error("Not supported, yet"); + } + }; + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::TransposeConv::Node &node) +{ + VERBOSE(TransposeConv) << "Configure TransposeConv operation" << std::endl; + + const ::internal::tflite::operand::Index op_shape_index{node.param().op_shape_index}; + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index ker_index{node.param().ker_index}; + + const ::internal::tflite::operand::Index padding_index{node.param().padding_index}; + const ::internal::tflite::operand::Index hstride_index{node.param().hstride_index}; + const ::internal::tflite::operand::Index vstride_index{node.param().vstride_index}; + + // Only 4D tensors are supported + assert(_ctx.at(ofm_index).shape().rank() == 4); + assert(_ctx.at(ofm_index).shape().rank() == _ctx.at(ifm_index).shape().rank()); + assert(_ctx.at(ofm_index).shape().rank() == _ctx.at(ker_index).shape().rank()); + + const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); + const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); + const auto ker_shape = _ctx.at(ker_index).shape().asFeature(); + + assert(_ctx.at(padding_index).hasData() == true); + + const int32_t vstride = _ctx.at(vstride_index).asScalar<int32_t>(); + const int32_t hstride = _ctx.at(hstride_index).asScalar<int32_t>(); + + const PaddingCode padding_type = + static_cast<PaddingCode>(_ctx.at(padding_index).asScalar<int32_t>()); + + assert(vstride > 0); + assert(hstride > 0); + assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || + (ANEURALNETWORKS_PADDING_VALID == padding_type)); + assert(ifm_shape.N == ofm_shape.N); + assert(ifm_shape.C == ker_shape.C); + assert(ker_shape.N == ofm_shape.C); + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ker_index, asTensorInfo(asTensorShape(_ctx.at(ker_index).shape()), _ctx.at(ker_index).type(), + _ctx.at(ker_index).scale(), _ctx.at(ker_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int ofm_index; + int ifm_index; + int ker_index; + Padding padding; + Stride stride; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + param.ker_index = ker_index.asInt(); + + param.stride.horizontal = hstride; + param.stride.vertical = vstride; + + param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) + ? same_padding(ifm_shape, param.stride, ker_shape.W, ker_shape.H) + : valid_padding(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + auto ker_alloc = ctx.at(::internal::tflite::operand::Index{param.ker_index}); + + auto fn = nnfw::cpp14::make_unique<SimpleTransposeConv>(); + + // Only rank 4 is supported + const int rank = 4; + + auto tconv_info = asPadStringInfo(param.padding, param.stride); + + fn->configure(ifm_alloc, ker_alloc, ofm_alloc, tconv_info, getARMComputeAxises(rank)); + + builder.append("TransposeConv", std::move(fn)); + }; + _builder.addStage(stage); } void Planner::visit(const ::internal::tflite::op::SquaredDifference::Node &node) @@ -3320,7 +4076,6 @@ void Planner::visit(const ::internal::tflite::op::SquaredDifference::Node &node) const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; const ::internal::tflite::operand::Index lhs_index{node.param().lhs_index}; const ::internal::tflite::operand::Index rhs_index{node.param().rhs_index}; - const ::internal::tflite::operand::Index activation_index{node.param().activation_index}; // Set Shape Constraints and TensorInfo _builder.addShapeConstr( @@ -3349,8 +4104,6 @@ void Planner::visit(const ::internal::tflite::op::SquaredDifference::Node &node) int ofm_index; int lhs_index; int rhs_index; - - FuseCode activation; }; Param param; @@ -3359,8 +4112,6 @@ void Planner::visit(const ::internal::tflite::op::SquaredDifference::Node &node) param.lhs_index = lhs_index.asInt(); param.rhs_index = rhs_index.asInt(); - param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); - auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); auto lhs_alloc = ctx.at(::internal::tflite::operand::Index{param.lhs_index}); @@ -3368,26 +4119,17 @@ void Planner::visit(const ::internal::tflite::op::SquaredDifference::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<SquaredDifferenceOperation>(); - - // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification - fn->configure(lhs_alloc, rhs_alloc, ofm_alloc, ::arm_compute::ConvertPolicy::SATURATE, 1.0, - ::arm_compute::RoundingPolicy::TO_NEAREST_EVEN); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSquaredDifference>(); + fn->configure(CAST_CL(lhs_alloc), CAST_CL(rhs_alloc), CAST_CL(ofm_alloc)); builder.append("SquaredDifference", std::move(fn)); } - else // NEON + else { - auto fn = nnfw::make_unique<SquaredDifferenceOperation>(); - - // TODO Decide ConvertPolicy (WARP? SATURATE?) according to NN API specification - fn->configure(lhs_alloc, rhs_alloc, ofm_alloc, ::arm_compute::ConvertPolicy::SATURATE, 1.0, - ::arm_compute::RoundingPolicy::TO_ZERO); - - builder.append("SquaredDifference", std::move(fn)); + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); } - ActivationBuilder{builder}.append(param.activation, ofm_alloc); }; _builder.addStage(stage); @@ -3446,55 +4188,87 @@ void Planner::visit(const ::internal::tflite::op::Pad::Node &node) const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; const ::internal::tflite::operand::Index paddings_index{node.param().paddings_index}; - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - const auto paddings_shape = _ctx.at(paddings_index).shape().asTensor(); + assert(_ctx.at(paddings_index).hasData() == true); // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(ifm_index, + asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape(), false), + _ctx.at(ifm_index).type(), _ctx.at(ifm_index).scale(), + _ctx.at(ifm_index).zeroPoint())); + _builder.addShapeConstr(ofm_index, + asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape(), false), + _ctx.at(ofm_index).type(), _ctx.at(ofm_index).scale(), + _ctx.at(ofm_index).zeroPoint())); _builder.addShapeConstr( - ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), - _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); - _builder.addShapeConstr( - ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), - _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); - _builder.addShapeConstr( - paddings_index, - asTensorInfo(asTensorShape(_ctx.at(paddings_index).shape()), _ctx.at(paddings_index).type(), - _ctx.at(paddings_index).scale(), _ctx.at(paddings_index).zeroPoint())); + paddings_index, asTensorInfo(asTensorShape(_ctx.at(paddings_index).shape(), false), + _ctx.at(paddings_index).type(), _ctx.at(paddings_index).scale(), + _ctx.at(paddings_index).zeroPoint())); + + // initializer for padding + { + auto pad_type = _ctx.at(paddings_index).type(); + + if (pad_type == ANEURALNETWORKS_TENSOR_INT32) + { + auto pad_base = _ctx.at(paddings_index).data().base(); + auto pad_size = _ctx.at(paddings_index).data().size(); + auto pad_shape = _ctx.at(paddings_index).shape().asMatrix(); + + // Supported padding for height and width only. + auto initializer = std::bind(initMatrixTensor<int32_t>, _1, pad_shape, pad_base, pad_size); + _builder.addInitializer(paddings_index, initializer); + } + else + { + throw std::runtime_error("Only Int32 datatype is supported for Pad values"); + } + } // Construct operation parameters struct Param { int ofm_index; int ifm_index; - int32_t padding_size; + int padding_index; }; Param param; param.ofm_index = ofm_index.asInt(); param.ifm_index = ifm_index.asInt(); - - assert(_ctx.at(paddings_index).hasData() == true); - - // TODO: Currently we are supporting uniform padding for the tensor, so only a single - // value is being read. (TOP = BOTTOM = LEFT = RIGHT). - // Need to read padding values for all the sides (TOP, BOTTOM, LEFT & RIGHT) - - const auto &padding_data = _ctx.at(paddings_index).data(); - auto base = padding_data.base(); - auto padsize = reinterpret_cast<const int *>(base) + 3; - param.padding_size = *padsize; + param.padding_index = paddings_index.asInt(); auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + auto pad_alloc = ctx.at(::internal::tflite::operand::Index{param.padding_index}); - auto fn = nnfw::make_unique<PadLayer>(); + if (from_env<bool>(std::getenv("USE_SIMPLE_PAD"))) + { + // USE CPU VERSION OF PADLAYER + auto rank = 4; + auto fn = nnfw::cpp14::make_unique<SimplePadLayer>(); - fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.padding_size); - builder.append("Pad", std::move(fn)); + fn->configure(ifm_alloc, ofm_alloc, pad_alloc, getARMComputeAxises(rank)); + builder.append("PAD", std::move(fn)); + } + else + { + if (::internal::arm_compute::isGpuMode()) // GPU + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLPadLayerEx>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), CAST_CL(pad_alloc)); + + builder.append("PAD", std::move(fn)); + } + else // NEON + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + } }; _builder.addStage(stage); @@ -3506,6 +4280,21 @@ void Planner::visit(const ::internal::tflite::op::SpaceToDepth::Node &node) const ::internal::tflite::operand::Index input_index{node.param().input_index}; const ::internal::tflite::operand::Index block_size_index{node.param().block_size_index}; + const auto input_batch = _ctx.at(input_index).shape().dim(0); + const auto output_batch = _ctx.at(output_index).shape().dim(0); + const auto input_depth = _ctx.at(input_index).shape().dim(3); + const auto output_depth = _ctx.at(output_index).shape().dim(3); + const auto block_size = _ctx.at(block_size_index).asScalar<int32_t>(); + const auto input_height = _ctx.at(input_index).shape().dim(1); + const auto input_width = _ctx.at(input_index).shape().dim(2); + + // All assertions as per NNAPI specification. + assert(_ctx.at(input_index).shape().rank() == 4); + assert(_ctx.at(output_index).shape().rank() == 4); + assert((block_size >= 1) && (input_height % block_size == 0) && (input_width % block_size == 0)); + assert(input_batch == output_batch); + assert(input_depth * block_size * block_size == output_depth); + // Set Shape Constraints and TensorInfo _builder.addShapeConstr(output_index, asTensorInfo(asTensorShape(_ctx.at(output_index).shape(), false), @@ -3528,17 +4317,284 @@ void Planner::visit(const ::internal::tflite::op::SpaceToDepth::Node &node) param.output_index = output_index.asInt(); param.input_index = input_index.asInt(); - param.block_size = _ctx.at(block_size_index).asScalar<int32_t>(); + param.block_size = block_size; auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); - auto rank = 4; - auto fn = nnfw::make_unique<SimpleSpaceToDepth>(); + if (from_env<bool>(std::getenv("USE_SIMPLE_SPACETODEPTH"))) + { + // USE CPU VERSION OF SPACETODEPTH + auto rank = 4; + auto fn = nnfw::cpp14::make_unique<SimpleSpaceToDepth>(); + + fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(rank)); - fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(rank)); - builder.append("SpaceToDepth", std::move(fn)); + builder.append("SpaceToDepth", std::move(fn)); + } + else + { + if (::internal::arm_compute::isGpuMode()) // GPU + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSpaceToDepth>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), param.block_size); + + builder.append("SpaceToDepth", std::move(fn)); + } + else // NEON + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::SpaceToBatchND::Node &node) +{ + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input_index{node.param().input_index}; + const ::internal::tflite::operand::Index block_size_index{node.param().block_size_index}; + const ::internal::tflite::operand::Index padding_size_index{node.param().padding_size_index}; + + { // New block for assertions + + // Currently, only 4D NHWC input/output op_context are supported. + // The 4D array need to have exactly 2 spatial dimensions. + // TODO: Support arbitrary dimension in SpaceToBatchND. + assert(_ctx.at(input_index).shape().rank() == 4); + assert(_ctx.at(output_index).shape().rank() == 4); + assert(_ctx.at(block_size_index).shape().rank() == 1); + assert(_ctx.at(padding_size_index).shape().rank() == 2); + + const auto &output_shape = _ctx.at(output_index).shape(); + const auto &input_shape = _ctx.at(input_index).shape(); + const auto &block_size_shape = _ctx.at(block_size_index).shape(); + const auto &padding_size_shape = _ctx.at(padding_size_index).shape(); + + assert(output_shape.dim(3) == input_shape.dim(3)); + assert(block_size_shape.dim(0) == 2); + assert(padding_size_shape.dim(0) == 2); + assert(padding_size_shape.dim(1) == 2); + } + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape(), false), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape(), false), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); + + _builder.addShapeConstr(block_size_index, + asTensorInfo(asTensorShape(_ctx.at(block_size_index).shape()), + _ctx.at(block_size_index).type(), + _ctx.at(block_size_index).scale(), + _ctx.at(block_size_index).zeroPoint())); + + _builder.addShapeConstr(padding_size_index, + asTensorInfo(asTensorShape(_ctx.at(padding_size_index).shape()), + _ctx.at(padding_size_index).type(), + _ctx.at(padding_size_index).scale(), + _ctx.at(padding_size_index).zeroPoint())); + + if (_ctx.at(block_size_index).hasData()) + { + const auto rank = _ctx.at(input_index).shape().rank(); + const auto num_of_block_size = _ctx.at(block_size_index).shape().asVector(); + auto block_size_base = _ctx.at(block_size_index).data().base(); + auto block_size_type = _ctx.at(block_size_index).type(); + + switch (block_size_type) + { + case ANEURALNETWORKS_TENSOR_INT32: + { + auto initializer = [block_size_base, num_of_block_size, + rank](::arm_compute::ITensor &tensor) { + assert(num_of_block_size < 4); + for (size_t n = 0; n < num_of_block_size; ++n) + { + const int32_t *from = reinterpret_cast<const int32_t *>(block_size_base) + n; + int32_t *into = reinterpret_cast<int32_t *>( + tensor.ptr_to_element({ToARMComputeAxis(rank, n + 1).value()})); + *into = *from; + } + }; + _builder.addInitializer(block_size_index, initializer); + + break; + } + default: + { + throw std::runtime_error("Not supported"); + } + } + } + + if (_ctx.at(padding_size_index).hasData()) + { + const auto padding_size_shape = _ctx.at(padding_size_index).shape(); + const auto rank = _ctx.at(input_index).shape().rank(); + auto padding_size_base = _ctx.at(padding_size_index).data().base(); + auto padding_size_type = _ctx.at(padding_size_index).type(); + + switch (padding_size_type) + { + case ANEURALNETWORKS_TENSOR_INT32: + { + auto initializer = [padding_size_base, padding_size_shape, + rank](::arm_compute::ITensor &tensor) { + assert(padding_size_shape.dim(1) == 2); + assert(padding_size_shape.dim(0) < 4); + for (size_t n = 0; n < padding_size_shape.dim(0); ++n) + { + const int32_t *from = reinterpret_cast<const int32_t *>(padding_size_base) + + (n * padding_size_shape.dim(1)); + int32_t *into = reinterpret_cast<int32_t *>( + tensor.ptr_to_element({0, ToARMComputeAxis(rank, n + 1).value()})); + into[0] = from[0]; + into[1] = from[1]; + } + }; + _builder.addInitializer(padding_size_index, initializer); + break; + } + default: + { + throw std::runtime_error("Not supported"); + } + } + } + + // Construct operation parameters + struct Param + { + int output_index; + int input_index; + int block_size_index; + int padding_size_index; + int32_t rank; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input_index = input_index.asInt(); + param.block_size_index = block_size_index.asInt(); + param.padding_size_index = padding_size_index.asInt(); + param.rank = _ctx.at(input_index).shape().rank(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); + auto block_size_alloc = ctx.at(::internal::tflite::operand::Index{param.block_size_index}); + auto padding_size_alloc = ctx.at(::internal::tflite::operand::Index{param.padding_size_index}); + + // NOTE SimpleSpaceToBatchND is quite slow + if (from_env<bool>(std::getenv("USE_SIMPLE_SPACE_TO_BATCH_ND"))) + { + auto fn = nnfw::cpp14::make_unique<SimpleSpaceToBatchND>(); + + fn->configure(input_alloc, block_size_alloc, padding_size_alloc, output_alloc); + builder.append("SpaceToBatchND", std::move(fn)); + } + else if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLSpaceToBatchND>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(block_size_alloc), CAST_CL(padding_size_alloc), + CAST_CL(output_alloc)); + builder.append("SpaceToBatchND", std::move(fn)); + } + else + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::BatchToSpaceNd::Node &node) +{ + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input_index{node.param().input_index}; + const ::internal::tflite::operand::Index block_size_index{node.param().block_size_index}; + + assert(_ctx.at(input_index).shape().rank() == 4); + assert(_ctx.at(output_index).shape().rank() == 4); + assert(_ctx.at(block_size_index).hasData() == true); + + const int32_t *block_size = + reinterpret_cast<const int32_t *>(_ctx.at(block_size_index).data().base()); + + const auto &output_shape = _ctx.at(output_index).shape(); + const auto &input_shape = _ctx.at(input_index).shape(); + + assert((_ctx.at(block_size_index).data().size() / sizeof(int32_t)) == 2 && block_size[0] > 0 && + block_size[1] > 0); + { + assert(output_shape.dim(3) == input_shape.dim(3)); + assert(output_shape.dim(1) == input_shape.dim(1) * block_size[0]); + assert(output_shape.dim(2) == input_shape.dim(2) * block_size[1]); + assert(output_shape.dim(0) == input_shape.dim(0) / (block_size[0] * block_size[1])); + } + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr( + output_index, asTensorInfo(asTensorShape(output_shape, false), _ctx.at(output_index).type(), + _ctx.at(output_index).scale(), _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr( + input_index, asTensorInfo(asTensorShape(input_shape, false), _ctx.at(input_index).type(), + _ctx.at(input_index).scale(), _ctx.at(input_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input_index; + const int32_t *block_size; + int32_t rank; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input_index = input_index.asInt(); + param.block_size = block_size; + param.rank = _ctx.at(input_index).shape().rank(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); + + // NOTE SimpleBatchToSpaceND is quite slow, but may be useful for debugging + if (from_env<bool>(std::getenv("USE_SIMPLE_BATCH_TO_SPACE_ND"))) + { + auto fn = nnfw::cpp14::make_unique<SimpleBatchToSpaceND>(); + + fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(param.rank)); + builder.append("BatchToSpaceND", std::move(fn)); + } + else if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBatchToSpaceND>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), param.block_size); + builder.append("BatchToSpaceND", std::move(fn)); + } + else + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } }; @@ -3550,9 +4606,6 @@ void Planner::visit(const ::internal::tflite::op::L2Normalization::Node &node) const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; - const auto ofm_shape = _ctx.at(ofm_index).shape(); - const auto ifm_shape = _ctx.at(ifm_index).shape(); - // Set Shape Constraints and TensorInfo _builder.addShapeConstr( ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), @@ -3583,10 +4636,10 @@ void Planner::visit(const ::internal::tflite::op::L2Normalization::Node &node) param.ofm_index = ofm_index.asInt(); param.ifm_index = ifm_index.asInt(); - param.radius = 2 * ifm_shape.dim(3) + 1; // normSize = depth * 2 + 1 - param.alpha = 1.0f; // In the implementation to make alpha_ become 1 - param.beta = 0.5f; // pow(reduction, -0.5) = 1 / sqrt(reduction) - param.bias = 0.0f; // Don't offset the reduction. + param.radius = 2 * _ctx.at(ifm_index).shape().dim(3) + 1; // normSize = depth * 2 + 1 + param.alpha = 1.0f; // In the implementation to make alpha_ become 1 + param.beta = 0.5f; // pow(reduction, -0.5) = 1 / sqrt(reduction) + param.bias = 0.0f; // Don't offset the reduction. auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); @@ -3598,7 +4651,7 @@ void Planner::visit(const ::internal::tflite::op::L2Normalization::Node &node) if (::internal::arm_compute::isGpuMode()) { - auto fn = nnfw::make_unique<::arm_compute::CLNormalizationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNormalizationLayerEx>(); fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), norm_info); @@ -3606,9 +4659,9 @@ void Planner::visit(const ::internal::tflite::op::L2Normalization::Node &node) } else { - auto fn = nnfw::make_unique<::arm_compute::NENormalizationLayer>(); + auto fn = nnfw::cpp14::make_unique<::arm_compute::NENormalizationLayerEx>(); - fn->configure(CAST_NE(ifm_alloc), CAST_NE(ofm_alloc), norm_info); + fn->configure(ifm_alloc, ofm_alloc, norm_info); builder.append("L2Normalize", std::move(fn)); } @@ -3647,10 +4700,12 @@ void Planner::visit(const ::internal::tflite::op::L2Pool2D::Implicit::Node &node assert((ANEURALNETWORKS_PADDING_SAME == padding_type) || (ANEURALNETWORKS_PADDING_VALID == padding_type)); - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); struct Param { @@ -3678,7 +4733,7 @@ void Planner::visit(const ::internal::tflite::op::L2Pool2D::Implicit::Node &node param.stride.horizontal = hstride; param.padding = (padding_type == ANEURALNETWORKS_PADDING_SAME) - ? same_padding(ifm_shape, ofm_shape, param.stride, kw, kh) + ? same_padding(ifm_shape, param.stride, kw, kh) : valid_padding(); param.activation = static_cast<FuseCode>(_ctx.at(activation_index).asScalar<int32_t>()); @@ -3731,9 +4786,6 @@ void Planner::visit(const ::internal::tflite::op::L2Pool2D::Explicit::Node &node const ::internal::tflite::operand::Index activation_index{node.param().activation_index}; - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(); - const int32_t kh = _ctx.at(kh_index).asScalar<int32_t>(); const int32_t kw = _ctx.at(kw_index).asScalar<int32_t>(); @@ -3745,10 +4797,12 @@ void Planner::visit(const ::internal::tflite::op::L2Pool2D::Explicit::Node &node const int32_t padding_top = _ctx.at(padding_top_index).asScalar<int32_t>(); const int32_t padding_bottom = _ctx.at(padding_bottom_index).asScalar<int32_t>(); - _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), - _ctx.at(ofm_index).type())); - _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), - _ctx.at(ifm_index).type())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); // Construct operation parameters struct Param @@ -3876,11 +4930,27 @@ void Planner::visit(const ::internal::tflite::op::EmbeddingLookup::Node &node) auto lookups_alloc = ctx.at(::internal::tflite::operand::Index{param.lookups_index}); auto values_alloc = ctx.at(::internal::tflite::operand::Index{param.values_index}); - auto fn = nnfw::make_unique<SimpleEmbeddingLookup>(); + if (from_env<bool>(std::getenv("USE_SIMPLE_EMBEDDINGLOOKUP"))) + { + auto fn = nnfw::cpp14::make_unique<SimpleEmbeddingLookup>(); - fn->configure(lookups_alloc, values_alloc, output_alloc); + fn->configure(lookups_alloc, values_alloc, output_alloc); - builder.append("EmbeddingLookup", std::move(fn)); + builder.append("EmbeddingLookup", std::move(fn)); + } + else if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLEmbeddingLookup>(); + + fn->configure(CAST_CL(values_alloc), CAST_CL(output_alloc), CAST_CL(lookups_alloc)); + + builder.append("EmbeddingLookup", std::move(fn)); + } + else + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } }; _builder.addStage(stage); @@ -3888,8 +4958,857 @@ void Planner::visit(const ::internal::tflite::op::EmbeddingLookup::Node &node) void Planner::visit(const ::internal::tflite::op::HashtableLookup::Node &node) { - // TODO Implement HashtableLookup - throw std::runtime_error("Not supported"); + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index hits_index{node.param().hits_index}; + const ::internal::tflite::operand::Index lookups_index{node.param().lookups_index}; + const ::internal::tflite::operand::Index values_index{node.param().values_index}; + const ::internal::tflite::operand::Index keys_index{node.param().keys_index}; + + const auto &lookups_obj = _ctx.at(lookups_index); + const auto &keys_obj = _ctx.at(keys_index); + const auto &hits_obj = _ctx.at(hits_index); + const auto &values_obj = _ctx.at(values_index); + const auto &output_obj = _ctx.at(output_index); + + assert(lookups_obj.type() == ANEURALNETWORKS_TENSOR_INT32); + assert(keys_obj.type() == ANEURALNETWORKS_TENSOR_INT32); + assert(hits_obj.type() == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); + + const auto &lookups_shape = lookups_obj.shape(); + const auto &keys_shape = keys_obj.shape(); + const auto &hits_shape = hits_obj.shape(); + const auto &values_shape = values_obj.shape(); + const auto &output_shape = output_obj.shape(); + + assert(values_shape.rank() == output_shape.rank()); + + assert(lookups_shape.rank() == 1); + assert(keys_shape.rank() == 1); + assert(values_shape.dim(0) == keys_shape.dim(0)); + assert(lookups_shape.dim(0) == output_shape.dim(0)); + + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(hits_index, + asTensorInfo(asTensorShape(_ctx.at(hits_index).shape()), + _ctx.at(hits_index).type(), _ctx.at(hits_index).type(), + _ctx.at(hits_index).zeroPoint())); + + _builder.addShapeConstr(lookups_index, asTensorInfo(asTensorShape(_ctx.at(lookups_index).shape()), + _ctx.at(lookups_index).type(), + _ctx.at(lookups_index).scale(), + _ctx.at(lookups_index).zeroPoint())); + _builder.addShapeConstr(values_index, + asTensorInfo(asTensorShape(_ctx.at(values_index).shape()), + _ctx.at(values_index).type(), _ctx.at(values_index).scale(), + _ctx.at(values_index).zeroPoint())); + _builder.addShapeConstr(keys_index, + asTensorInfo(asTensorShape(_ctx.at(keys_index).shape()), + _ctx.at(keys_index).type(), _ctx.at(keys_index).scale(), + _ctx.at(keys_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int32_t output_index; + int32_t hits_index; + int32_t lookups_index; + int32_t values_index; + int32_t keys_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.hits_index = hits_index.asInt(); + param.lookups_index = lookups_index.asInt(); + param.values_index = values_index.asInt(); + param.keys_index = keys_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto hits_alloc = ctx.at(::internal::tflite::operand::Index{param.hits_index}); + auto lookups_alloc = ctx.at(::internal::tflite::operand::Index{param.lookups_index}); + auto values_alloc = ctx.at(::internal::tflite::operand::Index{param.values_index}); + auto keys_alloc = ctx.at(::internal::tflite::operand::Index{param.keys_index}); + + if (from_env<bool>(std::getenv("USE_SIMPLE_HASHTABLELOOKUP"))) + { + auto fn = nnfw::cpp14::make_unique<SimpleHashtableLookupLayer>(); + + fn->configure(lookups_alloc, keys_alloc, values_alloc, output_alloc, hits_alloc); + + builder.append("HashtableLookup", std::move(fn)); + } + else if (::internal::arm_compute::isGpuMode()) // GPU + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLHashtableLookup>(); + + fn->configure(CAST_CL(lookups_alloc), CAST_CL(keys_alloc), CAST_CL(values_alloc), + CAST_CL(output_alloc), CAST_CL(hits_alloc)); + + builder.append("HashtableLookup", std::move(fn)); + } + else // NEON + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::LocalResponseNormalization::Node &node) +{ + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index radius_index{node.param().radius_index}; + const ::internal::tflite::operand::Index bias_index{node.param().bias_index}; + const ::internal::tflite::operand::Index alpha_index{node.param().alpha_index}; + const ::internal::tflite::operand::Index beta_index{node.param().beta_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int ofm_index; + int ifm_index; + int32_t radius; + float bias; + float alpha; + float beta; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + + param.radius = _ctx.at(radius_index).asScalar<int32_t>(); + param.alpha = _ctx.at(alpha_index).asScalar<float>(); + param.beta = _ctx.at(beta_index).asScalar<float>(); + param.bias = _ctx.at(bias_index).asScalar<float>(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + const auto norm_info = + ::arm_compute::NormalizationLayerInfo(::arm_compute::NormType::CROSS_MAP, param.radius, + param.alpha, param.beta, param.bias, false); + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNormalizationLayerEx>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), norm_info); + + builder.append("LocalResponseNormalization", std::move(fn)); + } + else + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::NENormalizationLayerEx>(); + + fn->configure(ifm_alloc, ofm_alloc, norm_info); + + builder.append("LocalResponseNormalization", std::move(fn)); + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::DepthToSpace::Node &node) +{ + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input_index{node.param().input_index}; + const ::internal::tflite::operand::Index block_size_index{node.param().block_size_index}; + + assert(_ctx.at(input_index).shape().rank() == 4); + assert(_ctx.at(output_index).shape().rank() == 4); + + int32_t block_size = _ctx.at(block_size_index).asScalar<int32_t>(); + assert(block_size > 0); + + { // assertions block + const auto output_shape = _ctx.at(output_index).shape(); + const auto input_shape = _ctx.at(input_index).shape(); + assert(output_shape.dim(0) == input_shape.dim(0)); + assert(output_shape.dim(1) == input_shape.dim(1) * block_size); + assert(output_shape.dim(2) == input_shape.dim(2) * block_size); + assert(input_shape.dim(3) % (block_size * block_size) == 0); + assert(output_shape.dim(3) == input_shape.dim(3) / (block_size * block_size)); + } + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape(), false), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + _builder.addShapeConstr(input_index, + asTensorInfo(asTensorShape(_ctx.at(input_index).shape(), false), + _ctx.at(input_index).type(), _ctx.at(input_index).scale(), + _ctx.at(input_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input_index; + int32_t block_size; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input_index = input_index.asInt(); + param.block_size = block_size; + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.input_index}); + + if (from_env<bool>(std::getenv("USE_SIMPLE_DEPTHTOSPACE"))) + { + // USE CPU VERSION OF DEPTHTOSPACE + auto rank = 4; + auto fn = nnfw::cpp14::make_unique<SimpleDepthToSpace>(); + + fn->configure(input_alloc, output_alloc, param.block_size, getARMComputeAxises(rank)); + + builder.append("DepthToSpace", std::move(fn)); + } + else + { + if (::internal::arm_compute::isGpuMode()) // GPU + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLDepthToSpace>(); + + fn->configure(CAST_CL(input_alloc), CAST_CL(output_alloc), param.block_size); + + builder.append("DepthToSpace", std::move(fn)); + } + else // NEON + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::Unpack::Node &node) +{ + VERBOSE(Unpack) << "Configure Unpack operation" << std::endl; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + uint32_t input_rank = _ctx.at(ifm_index).shape().rank(); + + assert(input_rank == 4 || input_rank == 3 || input_rank == 2); + _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), + _ctx.at(ifm_index).type())); + + int32_t axis = + _ctx.at(::internal::tflite::operand::Index{node.param().axis_index}).asScalar<int32_t>(); + // int32_t num_split = + // _ctx.at(::internal::tflite::operand::Index{node.param().num_split_index}).asScalar<int32_t>(); + + for (const auto &index : node.param().ofm_indexes) + { + const ::internal::tflite::operand::Index ofm_index{index}; + _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), + _ctx.at(ofm_index).type())); + } + + struct Param + { + std::vector<int32_t> ofm_indexes; + int ifm_index; + int axis; + }; + + if (input_rank == 4) + { + Param param; + param.ifm_index = ifm_index.asInt(); + param.axis = axis; + for (const auto &index : node.param().ofm_indexes) + { + param.ofm_indexes.push_back(index); + } + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto input_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<SimpleUnpackLayer>(); + std::vector<::arm_compute::ICLTensor *> outputs; + for (const auto &index : param.ofm_indexes) + { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{index}); + outputs.push_back(CAST_CL(output_alloc)); + } + fn->configure(CAST_CL(input_alloc), outputs, param.axis); + + builder.append("Unpack", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + }; + + _builder.addStage(stage); + } + else if (input_rank == 3) + { + // TODO: generate test case for this and generalize 4D method all cases. + throw std::runtime_error("UNPACK_3D not implemented"); + } + else if (input_rank == 2) + { + throw std::runtime_error("UNPACK_2D not implemented"); + } + else + { + throw std::runtime_error("UNPACK axis is not valid"); + } +} + +void Planner::visit(const ::internal::tflite::op::Pack::Node &node) +{ + VERBOSE(Pack) << "Configure Pack operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const uint32_t output_rank = _ctx.at(ofm_index).shape().rank(); + const uint32_t input_rank = output_rank - 1; + + assert(output_rank == 4 || output_rank == 3 || output_rank == 2); + + for (const auto &index : node.param().ifm_indexes) + { + const ::internal::tflite::operand::Index ifm_index{index}; + assert(_ctx.at(ifm_index).shape().rank() == input_rank); + _builder.addShapeConstr(ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), + _ctx.at(ifm_index).type())); + } + + _builder.addShapeConstr(ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), + _ctx.at(ofm_index).type())); + + int32_t axis = + _ctx.at(::internal::tflite::operand::Index{node.param().axis_index}).asScalar<int32_t>(); + + struct Param + { + std::vector<int32_t> ifm_indexes; + int ofm_index; + int axis; + }; + + if (input_rank == 3) + { + Param param; + param.ofm_index = ofm_index.asInt(); + param.axis = axis; + + // TODO: Fix this once all permutations are present. + if (param.axis != 0) + { + throw std::runtime_error("This axis not supported, some 4D permutations are missing"); + } + + for (const auto &index : node.param().ifm_indexes) + { + param.ifm_indexes.push_back(index); + } + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<SimplePackLayer>(); + std::vector<::arm_compute::ICLTensor *> inputs; + for (const auto &index : param.ifm_indexes) + { + auto input_alloc = ctx.at(::internal::tflite::operand::Index{index}); + inputs.push_back(CAST_CL(input_alloc)); + } + fn->configure(inputs, CAST_CL(output_alloc), param.axis); + + builder.append("Pack", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + }; + + _builder.addStage(stage); + } + else if (input_rank == 2) + { + // TODO: generate test case for this and generalize 4D method all cases. + throw std::runtime_error("PACK_2D not implemented"); + } + else if (input_rank == 1) + { + throw std::runtime_error("PACK_1D not implemented"); + } + else + { + throw std::runtime_error("PACK axis is not valid"); + } +} + +void Planner::visit(const ::internal::tflite::op::Neg::Node &node) +{ + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int ofm_index; + int ifm_index; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + // NOTE SimpleNeg is quite slow, but may be useful for debugging + if (from_env<bool>(std::getenv("USE_SIMPLE_NEG"))) + { + auto fn = nnfw::cpp14::make_unique<SimpleNeg>(); + + fn->configure(ifm_alloc, ofm_alloc); + builder.append("Neg", std::move(fn)); + } + else if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLNeg>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc)); + builder.append("Neg", std::move(fn)); + } + else + { + // TODO Enable NEON Support + throw std::runtime_error("Not supported, yet"); + } + + }; + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::Exp::Node &node) +{ + VERBOSE(Exp) << "Configure Exp operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + + // Set shape constraints + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + struct Param + { + int ofm_index; + int ifm_index; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLExp>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc)); + + builder.append("Exp", std::move(fn)); + } + else + { + throw std::runtime_error("Not supported"); + } + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::ReduceSum::Node &node) +{ + VERBOSE(ReduceSum) << "Configure ReduceSum operation" << std::endl; + + const ::internal::tflite::operand::Index ofm_index{node.param().ofm_index}; + const ::internal::tflite::operand::Index ifm_index{node.param().ifm_index}; + const ::internal::tflite::operand::Index axis_index{node.param().axis_index}; + + const auto ifm_shape = _ctx.at(ifm_index).shape(); + const auto ofm_shape = _ctx.at(ofm_index).shape(); + const auto axis_shape = _ctx.at(axis_index).shape(); + + assert(ifm_shape.rank() <= 4); + assert(ofm_shape.rank() <= ifm_shape.rank()); + assert(_ctx.at(axis_index).hasData()); + assert(axis_shape.rank() == 0 || axis_shape.rank() == 1); + + // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only + // supports cases reducing height and width or reducing depth. + // TODO We have to support all cases of dimensions up to 4. + // For correct permuting, we have to set output's shape to be equal in dimension position of the + // input. But the positions of the same dimensions in the input and output may be set differently. + // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original + // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to + // extend it in 4 dimensions, it should be {1,1,3,5}. + // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of + // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the + // next operation is not desired. + if (ifm_shape.rank() == 4 && ifm_shape.rank() != ofm_shape.rank()) + { + if (ofm_shape.rank() == 2) + { + // Reducing HW + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(3) == ofm_shape.dim(1)); + } + else if (ofm_shape.rank() == 3) + { + // Reducing C or + // (Reducing H and C(ifm and ofm) == 1) or (Reducing W and C(ifm and ofm) == 1) + assert(ifm_shape.dim(0) == ofm_shape.dim(0) && ifm_shape.dim(1) == ofm_shape.dim(1) && + ifm_shape.dim(2) == ofm_shape.dim(2) || + (ifm_shape.dim(0) == ofm_shape.dim(0) && + (ifm_shape.dim(1) == ofm_shape.dim(1) || ifm_shape.dim(2) == ofm_shape.dim(1)) && + ifm_shape.dim(3) == 1 && ofm_shape.dim(2) == 1)); + } + } + + // Set shape constraints + _builder.addShapeConstr( + ofm_index, asTensorInfo(asTensorShape(_ctx.at(ofm_index).shape()), _ctx.at(ofm_index).type(), + _ctx.at(ofm_index).scale(), _ctx.at(ofm_index).zeroPoint())); + _builder.addShapeConstr( + ifm_index, asTensorInfo(asTensorShape(_ctx.at(ifm_index).shape()), _ctx.at(ifm_index).type(), + _ctx.at(ifm_index).scale(), _ctx.at(ifm_index).zeroPoint())); + + uint32_t input_rank = ifm_shape.rank(); + std::set<uint32_t> axis; + int32_t axis_rank = axis_shape.rank(); + + if (axis_rank == 0) + { + int32_t axis_value = _ctx.at(axis_index).asScalar<int32_t>(); + if (axis_value < 0) + { + axis_value += input_rank; + } + axis.insert(ToARMComputeAxis(input_rank, axis_value).value()); + } + else if (axis_rank == 1) + { + const auto axis_base = _ctx.at(axis_index).data().base(); + const auto axis_size = _ctx.at(axis_index).shape().asVector(); + + // If axis's data does not exist as constant values and can be gotten as input data, we have to + // find a way to infer output shape when sinking output. + assert(axis_base != nullptr); + for (uint32_t n = 0; n < axis_size; ++n) + { + int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n); + if (axis_value < 0) + { + axis_value += input_rank; + } + axis.insert(ToARMComputeAxis(input_rank, axis_value).value()); + } + } + else + { + throw std::runtime_error("Not supported axis"); + } + + struct Param + { + int ofm_index; + int ifm_index; + std::set<uint32_t> axis; + }; + + Param param; + + param.ofm_index = ofm_index.asInt(); + param.ifm_index = ifm_index.asInt(); + param.axis = axis; + + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto ofm_alloc = ctx.at(::internal::tflite::operand::Index{param.ofm_index}); + auto ifm_alloc = ctx.at(::internal::tflite::operand::Index{param.ifm_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLReduceOperation>(); + + fn->configure(CAST_CL(ifm_alloc), CAST_CL(ofm_alloc), param.axis, + ::arm_compute::ReduceOperation::SUM); + + builder.append("ReduceSum", std::move(fn)); + } + else + throw std::runtime_error("Not supported, yet"); + }; + + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::Abs::Node &node) +{ + // TODO Implement Abs op + throw std::runtime_error("Not supported yet"); +} + +void Planner::visit(const ::internal::tflite::op::NotEqual::Node &node) +{ + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input1_index{node.param().input1_index}; + const ::internal::tflite::operand::Index input2_index{node.param().input2_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + + if (!(_ctx.at(input1_index).shape() == _ctx.at(input2_index).shape())) + { + const auto broadcast_rank = + std::max(_ctx.at(input1_index).shape().rank(), _ctx.at(input2_index).shape().rank()); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input1_index).shape()) + .extendRank(broadcast_rank); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input2_index).shape()) + .extendRank(broadcast_rank); + } + _builder.addShapeConstr(input1_index, + asTensorInfo(asTensorShape(_ctx.at(input1_index).shape()), + _ctx.at(input1_index).type(), _ctx.at(input1_index).scale(), + _ctx.at(input1_index).zeroPoint())); + _builder.addShapeConstr(input2_index, + asTensorInfo(asTensorShape(_ctx.at(input2_index).shape()), + _ctx.at(input2_index).type(), _ctx.at(input2_index).scale(), + _ctx.at(input2_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input1_index; + int input2_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input1_index = input1_index.asInt(); + param.input2_index = input2_index.asInt(); + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input1_alloc = ctx.at(::internal::tflite::operand::Index{param.input1_index}); + auto input2_alloc = ctx.at(::internal::tflite::operand::Index{param.input2_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLComparisonOp>(); + + fn->configure(CAST_CL(input1_alloc), CAST_CL(input2_alloc), CAST_CL(output_alloc), + ::arm_compute::ComparisonOperation::NOT_EQUAL); + + builder.append("NotEqual", std::move(fn)); + } + else + { + // TODO Add NEON support + + throw std::runtime_error("Not supported yet"); + } + }; + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::LogicalAnd::Node &node) +{ + VERBOSE(Logical_AND) << "Configure Logical_AND operation" << std::endl; + + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input1_index{node.param().input1_index}; + const ::internal::tflite::operand::Index input2_index{node.param().input2_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + + if (!(_ctx.at(input1_index).shape() == _ctx.at(input2_index).shape())) + { + const auto broadcast_rank = + std::max(_ctx.at(input1_index).shape().rank(), _ctx.at(input2_index).shape().rank()); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input1_index).shape()) + .extendRank(broadcast_rank); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input2_index).shape()) + .extendRank(broadcast_rank); + } + _builder.addShapeConstr(input1_index, + asTensorInfo(asTensorShape(_ctx.at(input1_index).shape()), + _ctx.at(input1_index).type(), _ctx.at(input1_index).scale(), + _ctx.at(input1_index).zeroPoint())); + _builder.addShapeConstr(input2_index, + asTensorInfo(asTensorShape(_ctx.at(input2_index).shape()), + _ctx.at(input2_index).type(), _ctx.at(input2_index).scale(), + _ctx.at(input2_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input1_index; + int input2_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input1_index = input1_index.asInt(); + param.input2_index = input2_index.asInt(); + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input1_alloc = ctx.at(::internal::tflite::operand::Index{param.input1_index}); + auto input2_alloc = ctx.at(::internal::tflite::operand::Index{param.input2_index}); + + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBinaryLogicalOp>(); + + fn->configure(CAST_CL(input1_alloc), CAST_CL(input2_alloc), CAST_CL(output_alloc), + ::arm_compute::BinaryLogicalOperation::AND); + + builder.append("LogicalAnd", std::move(fn)); + } + else + { + // TODO Add NEON support + + throw std::runtime_error("Not supported yet"); + } + }; + _builder.addStage(stage); +} + +void Planner::visit(const ::internal::tflite::op::LogicalNot::Node &node) +{ + // TODO Implement LogicalNot op + throw std::runtime_error("Not supported yet"); +} + +void Planner::visit(const ::internal::tflite::op::LogicalOr::Node &node) +{ + VERBOSE(LogicalOr) << "Configure LogicalOr operation" << std::endl; + + const ::internal::tflite::operand::Index output_index{node.param().output_index}; + const ::internal::tflite::operand::Index input1_index{node.param().input1_index}; + const ::internal::tflite::operand::Index input2_index{node.param().input2_index}; + + // Set Shape Constraints and TensorInfo + _builder.addShapeConstr(output_index, + asTensorInfo(asTensorShape(_ctx.at(output_index).shape()), + _ctx.at(output_index).type(), _ctx.at(output_index).scale(), + _ctx.at(output_index).zeroPoint())); + + if (!(_ctx.at(input1_index).shape() == _ctx.at(input2_index).shape())) + { + const auto broadcast_rank = + std::max(_ctx.at(input1_index).shape().rank(), _ctx.at(input2_index).shape().rank()); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input1_index).shape()) + .extendRank(broadcast_rank); + const_cast<::internal::tflite::operand::Shape &>(_ctx.at(input2_index).shape()) + .extendRank(broadcast_rank); + } + + _builder.addShapeConstr(input1_index, + asTensorInfo(asTensorShape(_ctx.at(input1_index).shape()), + _ctx.at(input1_index).type(), _ctx.at(input1_index).scale(), + _ctx.at(input1_index).zeroPoint())); + _builder.addShapeConstr(input2_index, + asTensorInfo(asTensorShape(_ctx.at(input2_index).shape()), + _ctx.at(input2_index).type(), _ctx.at(input2_index).scale(), + _ctx.at(input2_index).zeroPoint())); + + // Construct operation parameters + struct Param + { + int output_index; + int input1_index; + int input2_index; + }; + + Param param; + + param.output_index = output_index.asInt(); + param.input1_index = input1_index.asInt(); + param.input2_index = input2_index.asInt(); + auto stage = [param](const IAllocationContext &ctx, IExecutionBuilder &builder) { + auto output_alloc = ctx.at(::internal::tflite::operand::Index{param.output_index}); + auto input1_alloc = ctx.at(::internal::tflite::operand::Index{param.input1_index}); + auto input2_alloc = ctx.at(::internal::tflite::operand::Index{param.input2_index}); + if (::internal::arm_compute::isGpuMode()) + { + auto fn = nnfw::cpp14::make_unique<::arm_compute::CLBinaryLogicalOp>(); + + fn->configure(CAST_CL(input1_alloc), CAST_CL(input2_alloc), CAST_CL(output_alloc), + ::arm_compute::BinaryLogicalOperation::OR); + + builder.append("LogicalOr", std::move(fn)); + } + else + { + // TODO Add NEON support + + throw std::runtime_error("Not supported yet"); + } + }; + _builder.addStage(stage); } class AllocationContext final : public IAllocationContext @@ -3939,32 +5858,69 @@ private: ::internal::arm_compute::Plan &_plan; }; +/** + * @brief Class to provide methods of compilation plan builder + */ class PlanBuilder final : public IPlanBuilder { public: + /** + * @brief Construct a new PlanBuilder object with Plan + * @param [in] plan The Plan object + */ PlanBuilder(::internal::arm_compute::Plan &plan) : _plan{plan} { // DO NOTHING } public: + /** + * @brief Add TensorInfo with Shape Constraints + * @param [in] ind Index of operand + * @param [in] info TensorInfo value to set to index of operand + * @return N/A + */ void addShapeConstr(const ::internal::tflite::operand::Index &ind, const ::arm_compute::TensorInfo &info) override; public: + /** + * @brief Add Subsumption constraints + * @param [in] ind Index of operand + * @param [in] base Index of base operand of Subsumption + * @param [in] offset Offset of Subsumption + * @param [in] shape Shape of Subsumption + * @param [in] extend_parent extend_parent value of Subsumption + * @return N/A + */ void addSubsumptionConstr(const ::internal::tflite::operand::Index &ind, const ::internal::tflite::operand::Index &base, const ::arm_compute::Coordinates &offset, const ::arm_compute::TensorShape &shape, bool extend_parent) override; public: + /** + * @brief Add Initializer lambda with ITensor param + * @param [in] ind Index of operand + * @param [in] initializer Initializer to add + * @return N/A + */ void addInitializer(const ::internal::tflite::operand::Index &ind, const Initializer &initializer) override; public: + /** + * @brief Add Stage lambda with IAllocationContext and IExecutionBuilder params + * @param [in] stage Stage to add + * @return N/A + */ void addStage(const Stage &stage) override; public: + /** + * @brief Finilize(build) the Plan + * @return N/A + */ void finalize(void) const; private: @@ -4197,6 +6153,8 @@ void PlanBuilder::finalize(void) const auto type = operands.at(operand_idx).type(); auto shape = operands.at(operand_idx).shape(); + // Need to support scalar types (ANEURALNETWORKS_FLOAT32 and ANEURALNETWORKS_INT32) + // for rank > 1 tensor, because it can be operand of broadcast operation switch (rank) { case 0: // scalar @@ -4240,12 +6198,14 @@ void PlanBuilder::finalize(void) const auto size = shape.asVector(); switch (type) { + case ANEURALNETWORKS_FLOAT32: case ANEURALNETWORKS_TENSOR_FLOAT32: { auto initializer = std::bind(initVectorTensor<float>, _1, base, size); _plan.operands().at(operand_idx).access(initializer); break; } + case ANEURALNETWORKS_INT32: case ANEURALNETWORKS_TENSOR_INT32: { auto initializer = std::bind(initVectorTensor<int32_t>, _1, base, size); @@ -4270,12 +6230,14 @@ void PlanBuilder::finalize(void) const auto size = operands.at(operand_idx).data().size(); switch (type) { + case ANEURALNETWORKS_FLOAT32: case ANEURALNETWORKS_TENSOR_FLOAT32: { auto initializer = std::bind(initMatrixTensor<float>, _1, matrix_shape, base, size); _plan.operands().at(operand_idx).access(initializer); break; } + case ANEURALNETWORKS_INT32: case ANEURALNETWORKS_TENSOR_INT32: { auto initializer = std::bind(initMatrixTensor<int32_t>, _1, matrix_shape, base, size); @@ -4300,12 +6262,14 @@ void PlanBuilder::finalize(void) const auto size = operands.at(operand_idx).data().size(); switch (type) { + case ANEURALNETWORKS_FLOAT32: case ANEURALNETWORKS_TENSOR_FLOAT32: { auto initializer = std::bind(initTensor3D<float>, _1, tensor_shape, base, size); _plan.operands().at(operand_idx).access(initializer); break; } + case ANEURALNETWORKS_INT32: case ANEURALNETWORKS_TENSOR_INT32: { auto initializer = std::bind(initTensor3D<int32_t>, _1, tensor_shape, base, size); @@ -4330,12 +6294,14 @@ void PlanBuilder::finalize(void) const auto size = operands.at(operand_idx).data().size(); switch (type) { + case ANEURALNETWORKS_FLOAT32: case ANEURALNETWORKS_TENSOR_FLOAT32: { auto initializer = std::bind(initFeatureTensor<float>, _1, feature_shape, base, size); _plan.operands().at(operand_idx).access(initializer); break; } + case ANEURALNETWORKS_INT32: case ANEURALNETWORKS_TENSOR_INT32: { auto initializer = @@ -4417,8 +6383,10 @@ int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation *compilation) if (::internal::arm_compute::isGpuMode()) { arm_compute::CLScheduler::get().default_init(); - arm_compute::CLKernelLibraryEx::get().init("./cl_kernels/", cl::Context::getDefault(), - cl::Device::getDefault()); + // NOTE CLKernelLibraryEx must use the same context as CLScheduler + // It did not check whether another device is available. + arm_compute::CLKernelLibraryEx::get().init( + "./cl_kernels/", arm_compute::CLScheduler::get().context(), cl::Device::getDefault()); } const auto &operands = compilation->plan().model().operands(); diff --git a/runtimes/pure_arm_compute/src/compilation.h b/runtimes/pure_arm_compute/src/compilation.h index dd3613b2d..1a06d06b9 100644 --- a/runtimes/pure_arm_compute/src/compilation.h +++ b/runtimes/pure_arm_compute/src/compilation.h @@ -14,15 +14,28 @@ * limitations under the License. */ +/** + * @file compilation.h + * @brief This file defines ANeuralNetworksCompilation class for handling Compilation NNAPI + * @ingroup COM_AI_RUNTIME + */ + #ifndef __COMPILATION_H__ #define __COMPILATION_H__ #include "internal/Model.h" #include "internal/arm_compute.h" +/** + * @brief struct to define Compilation of NNAPI + */ struct ANeuralNetworksCompilation { public: + /** + * @brief Construct with params + * @param [in] model Pointer of internal::tflite::Model to set internal::arm_compute::Plan + */ ANeuralNetworksCompilation(const std::shared_ptr<const internal::tflite::Model> &model) : _plan{new internal::arm_compute::Plan{model}} { @@ -30,11 +43,28 @@ public: } public: + /** + * @brief Get reference of internal::arm_compute::Plan + * @return Reference of internal::arm_compute::Plan + */ internal::arm_compute::Plan &plan(void) { return *_plan; } public: + /** + * @brief Publish internal Plan to param + * @param [out] plan Pointer of internal::arm_compute::Plan to be set + * @return N/A + */ void publish(std::shared_ptr<const internal::arm_compute::Plan> &plan) { plan = _plan; } + /** + * @brief Get @c true if ANeuralNetworksCompilation_finish has been called, otherwise @c false + * @return @c true if ANeuralNetworksCompilation_finish has been called, otherwise @c false + */ bool isFinished(void) { return _isFinished; } + /** + * @brief Mark compilation process finished + * @return N/A + */ void markAsFinished() { _isFinished = true; } private: diff --git a/runtimes/pure_arm_compute/src/event.h b/runtimes/pure_arm_compute/src/event.h index 5d41dca84..b5595583c 100644 --- a/runtimes/pure_arm_compute/src/event.h +++ b/runtimes/pure_arm_compute/src/event.h @@ -14,9 +14,18 @@ * limitations under the License. */ +/** + * @file event.h + * @brief This file defines ANeuralNetworksEvent struct for handling Event NNAPI + * @ingroup COM_AI_RUNTIME + */ + #ifndef __EVENT_H__ #define __EVENT_H__ +/** + * @brief struct to define Event of NNAPI + */ struct ANeuralNetworksEvent { }; diff --git a/runtimes/pure_arm_compute/src/execution.cc b/runtimes/pure_arm_compute/src/execution.cc index 778a22155..b7eba1cef 100644 --- a/runtimes/pure_arm_compute/src/execution.cc +++ b/runtimes/pure_arm_compute/src/execution.cc @@ -18,8 +18,8 @@ #include "compilation.h" #include "execution.h" -#include "util/profiling/profiling.h" -#include "util/profiling/profiler.h" +#include "profiling/profiling.h" +#include "profiling/profiler.h" #include "event.h" #include "internal/VectorSource.h" @@ -34,7 +34,7 @@ #include "internal/Tensor3DSink.h" #include "internal/FeatureSink.h" -#include "util/feature/IndexIterator.h" +#include "misc/feature/IndexIterator.h" #include <arm_compute/runtime/CL/CLScheduler.h> @@ -70,7 +70,7 @@ static void asVectorSource(ANeuralNetworksExecution *execution, int32_t type, in } static void asMatrixSource(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::matrix::Shape &shape, const void *buffer, + const nnfw::misc::matrix::Shape &shape, const void *buffer, size_t length) { switch (type) @@ -100,7 +100,7 @@ static void asMatrixSource(ANeuralNetworksExecution *execution, int32_t type, in } static void asTensor3DSource(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::tensor::Shape &shape, const void *buffer, + const nnfw::misc::tensor::Shape &shape, const void *buffer, size_t length) { switch (type) @@ -130,7 +130,7 @@ static void asTensor3DSource(ANeuralNetworksExecution *execution, int32_t type, } static void asTensorSource(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::tensor::Shape &shape, const void *buffer, + const nnfw::misc::tensor::Shape &shape, const void *buffer, size_t length) { switch (type) @@ -160,7 +160,7 @@ static void asTensorSource(ANeuralNetworksExecution *execution, int32_t type, in } static void asFeatureSource(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::feature::Shape &shape, const void *buffer, + const nnfw::misc::feature::Shape &shape, const void *buffer, size_t length) { switch (type) @@ -244,7 +244,7 @@ static void asMatrixSink(ANeuralNetworksExecution *execution, int32_t type, int3 } static void asFeatureSink(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::feature::Shape &shape, void *buffer, size_t length) + const nnfw::misc::feature::Shape &shape, void *buffer, size_t length) { switch (type) { @@ -272,7 +272,7 @@ static void asFeatureSink(ANeuralNetworksExecution *execution, int32_t type, int } static void asTensor3DSink(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::tensor::Shape &shape, void *buffer, size_t length) + const nnfw::misc::tensor::Shape &shape, void *buffer, size_t length) { assert(shape.rank() == 3); @@ -302,7 +302,7 @@ static void asTensor3DSink(ANeuralNetworksExecution *execution, int32_t type, in } static void asTensorSink(ANeuralNetworksExecution *execution, int32_t type, int32_t index, - const nnfw::util::tensor::Shape &shape, void *buffer, size_t length) + const nnfw::misc::tensor::Shape &shape, void *buffer, size_t length) { switch (type) { @@ -420,9 +420,9 @@ int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution *execution, int32 // squeeze(shape) eliminates all the dimensions whose dimensionality is 1 // For example, squeeze([3, 1, 3]) returns [3, 3] -static nnfw::util::tensor::Shape squeeze(const nnfw::util::tensor::Shape &shape) +static nnfw::misc::tensor::Shape squeeze(const nnfw::misc::tensor::Shape &shape) { - nnfw::util::tensor::Shape res(0); + nnfw::misc::tensor::Shape res(0); for (uint32_t axis = 0; axis < shape.rank(); ++axis) { @@ -497,7 +497,7 @@ int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution, return ANEURALNETWORKS_UNEXPECTED_NULL; } - const bool sync = profiling::Context::get().sync().enabled(); + const bool sync = profiling::Context::get().sync(); const auto &plan = execution->plan(); const auto &model = plan.model(); diff --git a/runtimes/pure_arm_compute/src/execution.h b/runtimes/pure_arm_compute/src/execution.h index c036fe2c8..f55ab3fbf 100644 --- a/runtimes/pure_arm_compute/src/execution.h +++ b/runtimes/pure_arm_compute/src/execution.h @@ -14,6 +14,13 @@ * limitations under the License. */ +/** + * @file execution.h + * @brief This file contains ANeuralNetworksExecution class for handling Execution NNAPI such as + * ANeuralNetworksExecution_create, ANeuralNetworksExecution_setInput + * @ingroup COM_AI_RUNTIME + */ + #ifndef __EXECUTION_H__ #define __EXECUTION_H__ @@ -21,9 +28,16 @@ #include "internal/Sink.h" #include "internal/Source.h" +/** + * @brief struct to express Execution of NNAPI + */ struct ANeuralNetworksExecution { public: + /** + * @brief Construct with params + * @param [in] plan Pointer to get internal::arm_compute::Plan + */ ANeuralNetworksExecution(const std::shared_ptr<const internal::arm_compute::Plan> &plan) : _plan{plan} { @@ -32,31 +46,69 @@ public: } public: + /** + * @brief Get reference of internal::arm_compute::Plan + * @return Const reference of internal::arm_compute::Plan + */ const internal::arm_compute::Plan &plan(void) const { return *_plan; } private: std::shared_ptr<const internal::arm_compute::Plan> _plan; public: + /** + * @brief Set the nth source with param + * @param [in] n Index of the nth source + * @param [in] source Pointer to set the nth source from + * @return N/A + */ // TODO Use InputIndex instead of int void source(int n, std::unique_ptr<Source> &&source) { _sources.at(n) = std::move(source); } + /** + * @brief Set the nth source with param + * @param [in] n Index of the nth source + * @param [in] args Arguments to set the nth source from + * @return N/A + */ template <typename T, typename... Args> void source(int n, Args &&... args) { source(n, std::unique_ptr<T>{new T{std::forward<Args>(args)...}}); } public: + /** + * @brief Get the nth source + * @param [in] n Index of the nth source + * @return Const reference of Source + */ const Source &source(int n) const { return *(_sources.at(n)); } public: + /** + * @brief Set the nth sink with param + * @param [in] n Index of the nth sink + * @param [in] sink Pointer to set the nth sink from + * @return N/A + */ // TODO Use OutputIndex instead of int void sink(int n, std::unique_ptr<Sink> &&sink) { _sinks.at(n) = std::move(sink); } + /** + * @brief Set the nth sink with param + * @param [in] n Index of the nth sink + * @param [in] args Arguments to set the nth sink from + * @return N/A + */ template <typename T, typename... Args> void sink(int n, Args &&... args) { sink(n, std::unique_ptr<T>{new T{std::forward<Args>(args)...}}); } public: + /** + * @brief Get the nth sink + * @param [in] n Index of the nth sink + * @return Const reference of Sink + */ const Sink &sink(int n) const { return *(_sinks.at(n)); } private: diff --git a/runtimes/pure_arm_compute/src/internal/FeatureSink.h b/runtimes/pure_arm_compute/src/internal/FeatureSink.h index 9e4412c2a..7c6884141 100644 --- a/runtimes/pure_arm_compute/src/internal/FeatureSink.h +++ b/runtimes/pure_arm_compute/src/internal/FeatureSink.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file FeatureSink.h + * @brief This file contains FeatureSink class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_FEATURE_SINK_H__ #define __INTERNAL_FEATURE_SINK_H__ @@ -21,22 +27,36 @@ #include "internal/nnapi/feature/View.h" #include "internal/arm_compute/feature/View.h" -#include <util/feature/Shape.h> -#include "util/feature/IndexIterator.h" +#include <misc/feature/Shape.h> +#include "misc/feature/IndexIterator.h" -// -// FeatureSink -// +/** + * @brief Class to store Feature(4D) output data. + * This is for pulling data to internal tensor from other tensor. + * @tparam T Type of the data elements + */ template <typename T> class FeatureSink final : public Sink { public: - FeatureSink(const nnfw::util::feature::Shape &shape, T *base, const size_t size) + /** + * @brief Construct a FeatureSink object + * + * @param[in] shape 4D tensor dimensions for this feature + * @param[in] base Base pointer of the actual data + * @param[in] size Size of the data + */ + FeatureSink(const nnfw::misc::feature::Shape &shape, T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Pull the data into the internal structure + * @param[in] tensor The tensor which contains source data + * @return N/A + */ void pull(::arm_compute::ITensor &tensor) const override { const ::internal::arm_compute::feature::View<T> from{&tensor}; @@ -44,7 +64,7 @@ public: // Inevitably casting must be done. ::internal::nnapi::feature::View<T> into{_shape, _base, _size}; - ::nnfw::util::feature::iterate(_shape) + ::nnfw::misc::feature::iterate(_shape) << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { const auto value = from.at(batch, ch, row, col); into.at(batch, ch, row, col) = value; @@ -52,7 +72,7 @@ public: } private: - const nnfw::util::feature::Shape _shape; + const nnfw::misc::feature::Shape _shape; T *const _base; const size_t _size; }; diff --git a/runtimes/pure_arm_compute/src/internal/FeatureSource.h b/runtimes/pure_arm_compute/src/internal/FeatureSource.h index fca56e341..772beb701 100644 --- a/runtimes/pure_arm_compute/src/internal/FeatureSource.h +++ b/runtimes/pure_arm_compute/src/internal/FeatureSource.h @@ -14,31 +14,54 @@ * limitations under the License. */ +/** + * @file FeatureSource.h + * @brief This file contains FeatureSource class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_FEATURE_SOURCE_H__ #define __INTERNAL_FEATURE_SOURCE_H__ -#include <util/feature/Shape.h> -#include <util/feature/IndexIterator.h> +#include <misc/feature/Shape.h> +#include <misc/feature/IndexIterator.h> #include "internal/nnapi/feature/Reader.h" #include "internal/arm_compute/feature/View.h" +/** + * @brief Class to store feature(4D) input data. + * This is for push out the data to another tensor. + * @tparam T Type of the data elements + */ template <typename T> class FeatureSource final : public Source { public: - FeatureSource(const nnfw::util::feature::Shape &shape, const T *base, const size_t size) + /** + * @brief Construct a FeatureSource object + * + * @param[in] shape 4D tensor dimensions for this feature + * @param[in] base Base pointer of the actual data + * @param[in] size Size of the data + */ + FeatureSource(const nnfw::misc::feature::Shape &shape, const T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Push the data out to the another tensor + * @param[out] The tensor that output data will be stored + * @return N/A + */ void push(::arm_compute::ITensor &tensor) const override { const ::internal::nnapi::feature::Reader<T> from{_shape, _base, _size}; ::internal::arm_compute::feature::View<T> into{&tensor}; - ::nnfw::util::feature::iterate(_shape) + ::nnfw::misc::feature::iterate(_shape) << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { const auto value = from.at(batch, ch, row, col); into.at(batch, ch, row, col) = value; @@ -46,7 +69,7 @@ public: } private: - const nnfw::util::feature::Shape _shape; + const nnfw::misc::feature::Shape _shape; const T *const _base; const size_t _size; }; diff --git a/runtimes/pure_arm_compute/src/internal/IExecutionBuilder.h b/runtimes/pure_arm_compute/src/internal/IExecutionBuilder.h index aa1e67177..2a6e2a743 100644 --- a/runtimes/pure_arm_compute/src/internal/IExecutionBuilder.h +++ b/runtimes/pure_arm_compute/src/internal/IExecutionBuilder.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file IExecutionBuilder.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines interface of ExecutionBuilder + */ #ifndef __INTERNAL_IEXECUTION_BUILDER_H__ #define __INTERNAL_IEXECUTION_BUILDER_H__ @@ -22,10 +27,22 @@ #include <memory> #include <string> +/** + * @brief Struct to define interface of ExecutionBuilder + */ struct IExecutionBuilder { + /** + * @brief Destroy the IExecutionBuilder object + */ virtual ~IExecutionBuilder() = default; + /** + * @brief Append function to execute + * @param[in] name Name of function + * @param[in] f Function to append + * @return N/A + */ virtual void append(const std::string &name, std::unique_ptr<::arm_compute::IFunction> &&f) = 0; }; diff --git a/runtimes/pure_arm_compute/src/internal/MatrixSink.h b/runtimes/pure_arm_compute/src/internal/MatrixSink.h index 32bd49dc6..23ecc112b 100644 --- a/runtimes/pure_arm_compute/src/internal/MatrixSink.h +++ b/runtimes/pure_arm_compute/src/internal/MatrixSink.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file MatrixSink.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines MatrixSink class + */ #ifndef __INTERNAL_MATRIX_SINK_H__ #define __INTERNAL_MATRIX_SINK_H__ @@ -27,9 +32,19 @@ #include <cstring> #include <cassert> +/** + * @brief Class to get matrix data from arm compute tensor + */ template <typename T> class MatrixSink final : public Sink { public: + /** + * @brief Construct a new Matrix Sink object + * @param[in] H Height of matrix + * @param[in] W Width of matrix + * @param[in] base Pointer to get data + * @param[in] size Size of matrix + */ MatrixSink(const int32_t H, const int32_t W, T *base, const size_t size) : _height{H}, _width{W}, _base{base} { @@ -37,6 +52,11 @@ public: } public: + /** + * @brief Get matrix data from arm compute tensor to base + * @param[in] tensor Tensor object of arm compute to get data + * @return N/A + */ void pull(::arm_compute::ITensor &tensor) const override { assert(tensor.info()->dimension(0) == _width); diff --git a/runtimes/pure_arm_compute/src/internal/MatrixSource.h b/runtimes/pure_arm_compute/src/internal/MatrixSource.h index 2f5d92484..71d6a804f 100644 --- a/runtimes/pure_arm_compute/src/internal/MatrixSource.h +++ b/runtimes/pure_arm_compute/src/internal/MatrixSource.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file MatrixSource.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines MatrixSource class + */ #ifndef __INTERNAL_MATRIX_SOURCE_H__ #define __INTERNAL_MATRIX_SOURCE_H__ @@ -23,16 +28,30 @@ #include "internal/Source.h" +/** + * @brief Class to push matrix data to arm compute tensor + */ template <typename T> class MatrixSource final : public Source { public: - MatrixSource(const nnfw::util::matrix::Shape &shape, const T *base, const size_t size) + /** + * @brief Construct a new MatrixSource object + * @param[in] shape Shape of matrix + * @param[in] base Pointer of matrix data to push + * @param[in] size Size of matrix + */ + MatrixSource(const nnfw::misc::matrix::Shape &shape, const T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // do nothing } public: + /** + * @brief Push matrix data to arm compute tensor + * @param[out] tensor Tensor object of arm compute to push matrix data + * @return N/A + */ void push(::arm_compute::ITensor &tensor) const override { using ::arm_compute::Window; @@ -55,7 +74,7 @@ public: } private: - const nnfw::util::matrix::Shape _shape; + const nnfw::misc::matrix::Shape _shape; const T *const _base; const size_t _size; }; diff --git a/runtimes/pure_arm_compute/src/internal/Model.cc b/runtimes/pure_arm_compute/src/internal/Model.cc index 3a31f9911..03753fea2 100644 --- a/runtimes/pure_arm_compute/src/internal/Model.cc +++ b/runtimes/pure_arm_compute/src/internal/Model.cc @@ -16,8 +16,6 @@ #include "internal/Model.h" -#include <cassert> - namespace internal { namespace tflite @@ -25,7 +23,7 @@ namespace tflite namespace operand { -Shape::Shape(uint32_t rank) : nnfw::util::tensor::Shape(rank) +Shape::Shape(uint32_t rank) : nnfw::misc::tensor::Shape(rank) { // DO NOTHING } @@ -37,17 +35,17 @@ int32_t Shape::asVector(void) const return dim(0); } -nnfw::util::matrix::Shape Shape::asMatrix(void) const +nnfw::misc::matrix::Shape Shape::asMatrix(void) const { assert(rank() == 2); const auto height = dim(0); const auto width = dim(1); - return nnfw::util::matrix::Shape(height, width); + return nnfw::misc::matrix::Shape(height, width); } -nnfw::util::feature::Shape Shape::asFeature(void) const +nnfw::misc::feature::Shape Shape::asFeature(void) const { assert(rank() == 4); @@ -62,15 +60,15 @@ nnfw::util::feature::Shape Shape::asFeature(void) const const auto height = dim(1); const auto width = dim(2); - return nnfw::util::feature::Shape(batch, depth, height, width); + return nnfw::misc::feature::Shape(batch, depth, height, width); } -nnfw::util::tensor::Shape Shape::asTensor(void) const +nnfw::misc::tensor::Shape Shape::asTensor(void) const { - return nnfw::util::tensor::Shape(*this); // this shape represents shape of NNAPI + return nnfw::misc::tensor::Shape(*this); // this shape represents shape of NNAPI } -nnfw::util::kernel::Shape Shape::asKernel(void) const +nnfw::misc::kernel::Shape Shape::asKernel(void) const { assert(rank() == 4); @@ -84,7 +82,7 @@ nnfw::util::kernel::Shape Shape::asKernel(void) const const auto height = dim(1); const auto width = dim(2); - return nnfw::util::kernel::Shape(count, depth, height, width); + return nnfw::misc::kernel::Shape(count, depth, height, width); } // Extended dimension is filled with 1. @@ -120,6 +118,11 @@ const Object &Set::at(const Index &index) const { return *(_objects.at(index.asI Object &Set::at(const Index &index) { return *(_objects.at(index.asInt())); } +bool Set::exist(const Index &index) const +{ + return index.asInt() >= 0 && index.asInt() < _objects.size(); +} + } // namespace operand } // namespace tflite } // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/Model.h b/runtimes/pure_arm_compute/src/internal/Model.h index 33ba3a8fd..bdcf32f6f 100644 --- a/runtimes/pure_arm_compute/src/internal/Model.h +++ b/runtimes/pure_arm_compute/src/internal/Model.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Model.h + * @brief This file contains classes for handle internal Model object + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_MODEL_H__ #define __INTERNAL_MODEL_H__ @@ -24,15 +30,26 @@ namespace tflite namespace operand { +/** + * @brief Class to express index of operand. + */ class Index { public: + /** + * @brief Construct a new Index object for operand with param. + * @param [in] value The number of index + */ explicit Index(int value) : _value{value} { // DO NOTHING } public: + /** + * @brief Get index value as int + * @return Index value as int + */ int asInt(void) const { return _value; } private: @@ -46,10 +63,10 @@ private: #include <vector> #include <cstdint> -#include "util/feature/Shape.h" -#include "util/matrix/Shape.h" -#include "util/kernel/Shape.h" -#include "util/tensor/Shape.h" +#include "misc/feature/Shape.h" +#include "misc/matrix/Shape.h" +#include "misc/kernel/Shape.h" +#include "misc/tensor/Shape.h" namespace internal { @@ -58,19 +75,51 @@ namespace tflite namespace operand { -struct Shape : public nnfw::util::tensor::Shape +/** + * @brief Class to express shape of operand. + */ +struct Shape : public nnfw::misc::tensor::Shape { public: + /** + * @brief Construct a new Shape object for operand with param. + * @param [in] rank The rank value of shape + */ Shape(uint32_t rank); public: + /** + * @brief Get dimension value of tensor as vector + * @return Dimension value(int32_t) of tensor as vector + */ int32_t asVector(void) const; - nnfw::util::feature::Shape asFeature(void) const; - nnfw::util::matrix::Shape asMatrix(void) const; - nnfw::util::kernel::Shape asKernel(void) const; - nnfw::util::tensor::Shape asTensor(void) const; + /** + * @brief Get dimension values of tensor as feature::Shape + * @return Dimension values of tensor as feature::Shape + */ + nnfw::misc::feature::Shape asFeature(void) const; + /** + * @brief Get dimension values of tensor as matrix::Shape + * @return Dimension values of tensor as matrix::Shape + */ + nnfw::misc::matrix::Shape asMatrix(void) const; + /** + * @brief Get dimension values of tensor as kernel::Shape + * @return Dimension values of tensor as kernel::Shape + */ + nnfw::misc::kernel::Shape asKernel(void) const; + /** + * @brief Get dimension values of tensor::Shape + * @return Dimension values of tensor::Shape + */ + nnfw::misc::tensor::Shape asTensor(void) const; public: + /** + * @brief Extend rank of Shape object for operand with param. + * @param [in] to_rank The rank value to be extended to + * @return N/A + */ void extendRank(size_t); }; @@ -87,27 +136,60 @@ namespace tflite namespace operand { +/** + * @brief Class to have data of operand. + */ struct Data { + /** + * @brief Destruct this object + */ virtual ~Data() = default; + /** + * @brief Get size of data + * @return size of data + */ virtual size_t size(void) const = 0; + /** + * @brief Get the base address of data + * @return the base address of data + */ virtual const uint8_t *base(void) const = 0; }; +/** + * @brief Class to have cached data of operand. + */ class CachedData final : public Data { public: + /** + * @brief Construct a new CachedData object for operand with param. + * @param [in] base the base address of data + * @param [in] size the size of data + */ CachedData(const uint8_t *base, size_t size) : _base{new uint8_t[size]}, _size{size} { std::copy(base, base + size, _base); } public: + /** + * @brief Destruct this object + */ ~CachedData() { delete[] _base; } public: + /** + * @brief Get size of data + * @return size of data + */ size_t size(void) const override { return _size; } + /** + * @brief Get the base address of data + * @return the base address of data + */ const uint8_t *base(void) const override { return _base; } private: @@ -115,16 +197,32 @@ private: size_t _size; }; +/** + * @brief Class to have external data of operand. + */ class ExternalData final : public Data { public: + /** + * @brief Construct a new ExternalData object for operand with param. + * @param [in] base the base address of data + * @param [in] size the size of data + */ ExternalData(const uint8_t *base, size_t size) : _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Get size of data + * @return size of data + */ size_t size(void) const override { return _size; } + /** + * @brief Get the base address of data + * @return the base address of data + */ const uint8_t *base(void) const override { return _base; } private: @@ -148,9 +246,19 @@ namespace tflite namespace operand { +/** + * @brief Class to express operand as object. + */ class Object { public: + /** + * @brief Construct a new Object object for operand with param. + * @param [in] shape shape of operand + * @param [in] type type of operand + * @param [in] scale scale of operand + * @param [in] zeroPoint zeroPoint of operand + */ explicit Object(const Shape &shape, const int32_t type, const float scale, const int32_t zeroPoint) : _shape{shape}, _type{type}, _scale{scale}, _zeroPoint{zeroPoint} @@ -159,25 +267,58 @@ public: } public: + /** + * @brief Get shape of operand + * @return Reference of shape of operand + */ const Shape &shape(void) const { return _shape; } + /** + * @brief Get type of operand + * @return type of operand + */ const int32_t type(void) const { return _type; } + /** + * @brief Get scale of operand + * @return scale of operand + */ const float scale(void) const { return _scale; } + /** + * @brief Get zeroPoint of operand + * @return zeroPoint of operand + */ const int32_t zeroPoint(void) const { return _zeroPoint; } private: void data(std::unique_ptr<Data> &&data) { _data = std::move(data); } public: + /** + * @brief Get data of operand + * @return Reference of data of operand + */ const Data &data(void) const { return *_data; } + /** + * @brief Get true if Object has data, otherwise @c false + * @return @c true if Object has data, otherwise @c false + */ bool hasData(void) const { return _data != nullptr; } public: + /** + * @brief Set data of operand with param + * @param [in] args arguments of data to be set + * @return N/A + */ template <typename T, typename... Args> void data(Args &&... args) { data(std::unique_ptr<T>(new T{std::forward<Args>(args)...})); } public: + /** + * @brief Get value of data as scalar + * @return value of data as scalar + */ template <typename T> T asScalar(void) const { assert((_shape.rank() == 0) || ((_shape.rank() == 1) && (_shape.dim(0) == 1))); @@ -188,6 +329,11 @@ public: } public: + /** + * @brief Get value of data as ReorderBits + * @param [in] numOfBits The number of bits to be reordered to + * @return value of data as ReorderBits + */ template <typename T> T asReorderBits(size_t numOfBits) const { assert((_shape.rank() == 0) || ((_shape.rank() == 1) && (_shape.dim(0) == 1))); @@ -209,8 +355,6 @@ private: } // namespace tflite } // namespace internal -#include <memory> - namespace internal { namespace tflite @@ -218,9 +362,17 @@ namespace tflite namespace operand { +/** + * @brief Class to have object instances in a kind of set + */ class Set { public: + /** + * @brief Iterate objects with fn + * @param [in] fn function to be iterated + * @return N/A + */ void iterate(const std::function<void(const Index &)> &fn) { for (uint32_t n = 0; n < _objects.size(); ++n) @@ -231,12 +383,35 @@ public: } public: + /** + * @brief Append Object for operand with param + * @param [in] shape shape of operand + * @param [in] type type of operand + * @param [in] scale scale of operand + * @param [in] zeroPoint zeroPoint of operand + * @return Value of Index which has been appended to + */ Index append(const Shape &, int32_t type, float scale, int32_t zeroPoint); public: + /** + * @brief Get Object at Index + * @param [in] index Index to be at + * @return Const refernece of Object + */ const Object &at(const Index &) const; + /** + * @brief Get Object at Index + * @param [in] index Index to be at + * @return Refernece of Object + */ Object &at(const Index &); + /** + * @brief Get size of operands in Set + * @return Value of size + */ size_t size(void) const { return _objects.size(); } + bool exist(const Index &) const; private: std::vector<std::unique_ptr<Object>> _objects; @@ -255,16 +430,36 @@ namespace tflite namespace op { +/** + * @brief Class to have sequence operators. + */ class Sequence { public: + /** + * @brief Construct a new Sequence object for operator as default + */ Sequence() = default; public: + /** + * @brief Get size of operators in Sequence + * @return Value of size + */ uint32_t size(void) const { return _ops.size(); } public: + /** + * @brief Get op::Node at Index + * @param [in] nth index to be at + * @return Refernece of op::Node + */ op::Node &at(uint32_t nth) { return *(_ops.at(nth)); } + /** + * @brief Get op::Node at Index + * @param [in] nth index to be at + * @return Const refernece of op::Node + */ const op::Node &at(uint32_t nth) const { return *(_ops.at(nth)); } private: @@ -275,6 +470,11 @@ private: } public: + /** + * @brief Add op::Node with param + * @param [in] args arguments of op::Node to be set + * @return Reference of Sequence + */ template <typename T, typename... Args> Sequence &emplace_back(Args &&... args) { return emplace_back(std::unique_ptr<T>(new T{std::forward<Args>(args)...})); @@ -293,14 +493,33 @@ namespace internal namespace tflite { +/** + * @brief Class to have operand::Set as operands and op::Sequence as operators + */ class Model { public: + /** + * @brief Get operand::Set + * @return Reference of operand::Set + */ operand::Set &operands(void) { return _operands; } + /** + * @brief Get operand::Set + * @return Const reference of operand::Set + */ const operand::Set &operands(void) const { return _operands; } public: + /** + * @brief Get op::Sequence + * @return Reference of op::Sequence + */ op::Sequence &operations(void) { return _operations; } + /** + * @brief Get op::Sequence + * @return Const reference of op::Sequence + */ const op::Sequence &operations(void) const { return _operations; } private: @@ -309,8 +528,8 @@ private: public: // TODO Hide these fields - std::vector<operand::Index> inputs; - std::vector<operand::Index> outputs; + std::vector<operand::Index> inputs; /**< indexes of operand as input */ + std::vector<operand::Index> outputs; /**< indexes of operand as output */ }; } // namespace tflite diff --git a/runtimes/pure_arm_compute/src/internal/Sink.h b/runtimes/pure_arm_compute/src/internal/Sink.h index af3b37e61..6f44561ea 100644 --- a/runtimes/pure_arm_compute/src/internal/Sink.h +++ b/runtimes/pure_arm_compute/src/internal/Sink.h @@ -14,15 +14,31 @@ * limitations under the License. */ +/** + * @file Sink.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Sink struct + */ #ifndef __INTERNAL_SINK_H__ #define __INTERNAL_SINK_H__ #include <arm_compute/core/ITensor.h> +/** + * @brief Struct to get tensor data from arm compute tensor (abstract) + */ struct Sink { + /** + * @brief Destroy the Sink object + */ virtual ~Sink() = default; + /** + * @brief Get tensor data from arm compute tensor + * @param[in] tensor Tensor object of arm compute to get data + * @return N/A + */ virtual void pull(::arm_compute::ITensor &tensor) const = 0; }; diff --git a/runtimes/pure_arm_compute/src/internal/Sinks.h b/runtimes/pure_arm_compute/src/internal/Sinks.h index e8a7d5966..7317c67c1 100644 --- a/runtimes/pure_arm_compute/src/internal/Sinks.h +++ b/runtimes/pure_arm_compute/src/internal/Sinks.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Sinks.h + * @brief This file contains TensorSink class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_SINKS_H__ #define __INTERNAL_SINKS_H__ @@ -28,29 +34,46 @@ #include "internal/nnapi/tensor/View.h" #include "internal/arm_compute/tensor/View.h" -#include "util/tensor/IndexIterator.h" +#include "misc/tensor/IndexIterator.h" +/** + * @brief Class to store NN model output data for general-shaped tensors. + * This is for pulling data to internal tensor from other tensor. + * @tparam T Type of the data elements + */ template <typename T> class TensorSink final : public Sink { public: - TensorSink(const nnfw::util::tensor::Shape &shape, T *base, const size_t size) + /** + * @brief Construct a TensorSink object + * + * @param[in] shape general-shaped tensor dimensions + * @param[in] base Base pointer of the actual data + * @param[in] size Size of the data + */ + TensorSink(const nnfw::misc::tensor::Shape &shape, T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Pull the data into the internal structure + * @param[in] tensor The tensor which contains source data + * @return N/A + */ void pull(::arm_compute::ITensor &tensor) const override { const ::internal::arm_compute::tensor::View<T> from{&tensor}; ::internal::nnapi::tensor::View<T> into{_shape, _base, _size}; - using ::nnfw::util::tensor::iterate; - using ::nnfw::util::tensor::Index; + using ::nnfw::misc::tensor::iterate; + using ::nnfw::misc::tensor::Index; const uint32_t rank = _shape.rank(); - ::nnfw::util::tensor::iterate(_shape) << [&](const Index &raw) { + ::nnfw::misc::tensor::iterate(_shape) << [&](const Index &raw) { Index permuted(raw.rank()); for (uint32_t axis = 0; axis < rank; ++axis) @@ -64,7 +87,7 @@ public: } private: - const nnfw::util::tensor::Shape _shape; + const nnfw::misc::tensor::Shape _shape; private: T *const _base; diff --git a/runtimes/pure_arm_compute/src/internal/Source.h b/runtimes/pure_arm_compute/src/internal/Source.h index a159e5092..fa8f1e811 100644 --- a/runtimes/pure_arm_compute/src/internal/Source.h +++ b/runtimes/pure_arm_compute/src/internal/Source.h @@ -14,15 +14,32 @@ * limitations under the License. */ +/** + * @file Source.h + * @brief This file contains Source struct for pushing ITensor + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_SOURCE_H__ #define __INTERNAL_SOURCE_H__ #include <arm_compute/core/ITensor.h> +/** + * @brief Struct to push inner source to ITensor. + */ struct Source { + /** + * @brief Destructor as default + */ virtual ~Source() = default; + /** + * @brief Push inner source to ITensor + * @param [in] tensor ITensor to be pushed into + * @return N/A + */ virtual void push(::arm_compute::ITensor &tensor) const = 0; }; diff --git a/runtimes/pure_arm_compute/src/internal/Swizzle.h b/runtimes/pure_arm_compute/src/internal/Swizzle.h index 66bf7aef6..f127b8a3b 100644 --- a/runtimes/pure_arm_compute/src/internal/Swizzle.h +++ b/runtimes/pure_arm_compute/src/internal/Swizzle.h @@ -14,28 +14,53 @@ * limitations under the License. */ +/** + * @file Swizzle.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines ARMComputeAxis class and utility functions to support mapping + * between arm compute axis and NNAPI axis + */ #ifndef __SWIZZLE_H__ #define __SWIZZLE_H__ +/** + * @brief Class to represent arm compute axis + */ class ARMComputeAxis { public: + /** + * @brief Construct a new ARMComputeAxis object + */ ARMComputeAxis() = default; public: + /** + * @brief Construct a new ARMComputeAxis object + * @param[in] value Raw axis number + */ explicit ARMComputeAxis(uint32_t value) : _value{value} { // DO NOTHING } public: + /** + * @brief Get raw axis number + * @return Raw axis number + */ uint32_t value(void) const { return _value; } private: uint32_t _value; }; -// Convert T/F Lite / NNAPI axis (based on ...NHWC) to ARMCompute axis (WHCN...) +/** + * @brief Convert T/F Lite / NNAPI axis (based on ...NHWC) to arm compute axis (WHCN...) + * @param[in] rank Rank of shape + * @param[in] axis Axis to map + * @return ARMComputeAxis including arm compute axis info + */ inline ARMComputeAxis ToARMComputeAxis(uint32_t rank, uint32_t axis) { assert(rank > axis); @@ -68,6 +93,12 @@ inline ARMComputeAxis ToARMComputeAxis(uint32_t rank, uint32_t axis) #include <cassert> +/** + * @brief Covert bitmask info from NNAPI axis to arm compute axis + * @param[in] in Bitmask data + * @param[in] numOfBits Used bits (rank) + * @return Coverted bitmask + */ template <typename T> inline T ReorderBits(T in, size_t numOfBits) { assert(numOfBits > 0); diff --git a/runtimes/pure_arm_compute/src/internal/Tensor3DSink.h b/runtimes/pure_arm_compute/src/internal/Tensor3DSink.h index 20de3b9e8..1e14e2d6c 100644 --- a/runtimes/pure_arm_compute/src/internal/Tensor3DSink.h +++ b/runtimes/pure_arm_compute/src/internal/Tensor3DSink.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file Tensor3DSink.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Tensor3DSink class + */ #ifndef __TENSOR3D_SINK_H__ #define __TENSOR3D_SINK_H__ @@ -26,16 +31,30 @@ #include <arm_compute/core/Window.h> #include <arm_compute/core/Helpers.h> +/** + * @brief Class to get tensor data from arm compute tensor + */ template <typename T> class Tensor3DSink final : public Sink { public: - Tensor3DSink(const nnfw::util::tensor::Shape &shape, T *base, const size_t size) + /** + * @brief Construct a new Tensor3DSink object + * @param[in] shape Shape of tensor + * @param[in] base Pointer to get data + * @param[in] size Size of tensor + */ + Tensor3DSink(const nnfw::misc::tensor::Shape &shape, T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Get tensor data from arm compute tensor to base + * @param[in] tensor Tensor object of arm compute to get data + * @return N/A + */ void pull(::arm_compute::ITensor &tensor) const override { using ::arm_compute::Window; @@ -60,7 +79,7 @@ public: } private: - const nnfw::util::tensor::Shape _shape; + const nnfw::misc::tensor::Shape _shape; private: T *const _base; diff --git a/runtimes/pure_arm_compute/src/internal/Tensor3DSource.h b/runtimes/pure_arm_compute/src/internal/Tensor3DSource.h index c100bbdd2..3d8d1b958 100644 --- a/runtimes/pure_arm_compute/src/internal/Tensor3DSource.h +++ b/runtimes/pure_arm_compute/src/internal/Tensor3DSource.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file Tensor3DSource.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Tensor3DSource class + */ #ifndef __TENSOR3D_SOURCE_H__ #define __TENSOR3D_SOURCE_H__ @@ -26,16 +31,30 @@ #include <arm_compute/core/Window.h> #include <arm_compute/core/Helpers.h> +/** + * @brief Class to push tensor data to arm compute tensor + */ template <typename T> class Tensor3DSource final : public Source { public: - Tensor3DSource(const nnfw::util::tensor::Shape &shape, const T *base, const size_t size) + /** + * @brief Construct a new Tensor3DSource object + * @param[in] shape Shape of tensor + * @param[in] base Pointer of tensor data to push + * @param[in] size Size of tensor + */ + Tensor3DSource(const nnfw::misc::tensor::Shape &shape, const T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Push tensor data to arm compute tensor + * @param[out] tensor Tensor object of arm compute to push tensor data + * @return N/A + */ void push(::arm_compute::ITensor &tensor) const override { using ::arm_compute::Window; @@ -60,7 +79,7 @@ public: } private: - const nnfw::util::tensor::Shape _shape; + const nnfw::misc::tensor::Shape _shape; private: const T *const _base; diff --git a/runtimes/pure_arm_compute/src/internal/TensorSource.h b/runtimes/pure_arm_compute/src/internal/TensorSource.h index 0ddc44855..114d3588e 100644 --- a/runtimes/pure_arm_compute/src/internal/TensorSource.h +++ b/runtimes/pure_arm_compute/src/internal/TensorSource.h @@ -14,11 +14,17 @@ * limitations under the License. */ +/** + * @file TensorSource.h + * @brief This file contains TensorSource class which is inherited from Source class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_TENSOR_SOURCE_H__ #define __INTERNAL_TENSOR_SOURCE_H__ -#include <util/tensor/Shape.h> -#include <util/tensor/IndexIterator.h> +#include <misc/tensor/Shape.h> +#include <misc/tensor/IndexIterator.h> #include "internal/Source.h" #include "internal/Swizzle.h" @@ -26,24 +32,38 @@ #include "internal/arm_compute/tensor/View.h" // NOTE TensorSource is much slower than specialized Source(s) +/** + * @brief Class to define constructor and push function + */ template <typename T> class TensorSource final : public Source { public: - TensorSource(const nnfw::util::tensor::Shape &shape, const T *base, const size_t size) + /** + * @brief Construct a new TensorSource object with params + * @param [in] shape Shape of tensor + * @param [in] base Base address + * @param [in] size Size of tensor + */ + TensorSource(const nnfw::misc::tensor::Shape &shape, const T *base, const size_t size) : _shape{shape}, _base{base}, _size{size} { // DO NOTHING } public: + /** + * @brief Function for pushing tensor + * @param [in] tensor Tensor to be pushed + * @return N/A + */ void push(::arm_compute::ITensor &tensor) const override { const ::internal::nnapi::tensor::Reader<T> from{_shape, _base, _size}; ::internal::arm_compute::tensor::View<T> into{&tensor}; - ::nnfw::util::tensor::iterate(_shape) << [&](const nnfw::util::tensor::Index &index_nnapi) { + ::nnfw::misc::tensor::iterate(_shape) << [&](const nnfw::misc::tensor::Index &index_nnapi) { const auto rank = index_nnapi.rank(); - nnfw::util::tensor::Index index_ACL(rank); + nnfw::misc::tensor::Index index_ACL(rank); for (uint32_t axis = 0; axis < rank; ++axis) { @@ -55,7 +75,7 @@ public: } private: - const nnfw::util::tensor::Shape _shape; + const nnfw::misc::tensor::Shape _shape; const T *const _base; const size_t _size; }; diff --git a/runtimes/pure_arm_compute/src/internal/VectorSink.h b/runtimes/pure_arm_compute/src/internal/VectorSink.h index d1bf962e2..a630ef1c1 100644 --- a/runtimes/pure_arm_compute/src/internal/VectorSink.h +++ b/runtimes/pure_arm_compute/src/internal/VectorSink.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file VectorSink.h + * @brief This file contains VectorSink class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_VECTOR_SINK_H__ #define __INTERNAL_VECTOR_SINK_H__ @@ -23,18 +29,31 @@ #include <cassert> -// -// VectorSink -// +/** + * @brief Class to store vector(2D) output data. + * This is for pulling out the data to another tensor. + * @tparam T Type of the data elements + */ template <typename T> class VectorSink final : public Sink { public: + /** + * @brief Construct a VectorSink object + * @param[in] vlen Length of the vector + * @param[in] base Base pointer of the actual data + * @param[in] size Size of the data + */ VectorSink(const int32_t vlen, T *base, const size_t size) : _vlen{vlen}, _base{base} { assert(size >= _vlen * sizeof(T)); } public: + /** + * @brief Pull the data into the internal structure + * @param[in] tensor The tensor which contains source data + * @return N/A + */ void pull(::arm_compute::ITensor &tensor) const override { for (int32_t n = 0; n < _vlen; ++n) diff --git a/runtimes/pure_arm_compute/src/internal/VectorSource.h b/runtimes/pure_arm_compute/src/internal/VectorSource.h index 41aab07e4..48d3d3209 100644 --- a/runtimes/pure_arm_compute/src/internal/VectorSource.h +++ b/runtimes/pure_arm_compute/src/internal/VectorSource.h @@ -14,20 +14,42 @@ * limitations under the License. */ +/** + * @file VectorSource.h + * @brief This file contains VectorSource class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_VECTOR_SOURCE_H__ #define __INTERNAL_VECTOR_SOURCE_H__ #include "internal/Source.h" +/** + * @brief Class to store vector(2D) input data. + * This is for push out the data to another tensor. + * @tparam T Type of the data elements + */ template <typename T> class VectorSource final : public Source { public: + /** + * @brief Construct a VectorSource object + * @param[in] vlen Length of the vector + * @param[in] base Base pointer of the actual data + * @param[in] size Size of the data + */ VectorSource(const int32_t vlen, const T *base, const size_t size) : _vlen{vlen}, _base{base} { assert(size >= _vlen * sizeof(T)); } public: + /** + * @brief Push the data out to the another tensor + * @param[out] The tensor that output data will be stored + * @return N/A + */ void push(::arm_compute::ITensor &tensor) const override { for (int32_t n = 0; n < _vlen; ++n) diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute.h b/runtimes/pure_arm_compute/src/internal/arm_compute.h index ef43ed45c..fb6acaf81 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file arm_compute.h + * @brief This file contains arm_compute library related classes + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_ARM_COMPUTE_H__ #define __INTERNAL_ARM_COMPUTE_H__ @@ -28,6 +34,9 @@ namespace arm_compute namespace operand { +/** + * @brief Class to access the tensor object + */ class Object { public: @@ -40,12 +49,21 @@ public: } public: + /** + * @brief Get the tensor pointer + * @return The tensor pointer + */ ::arm_compute::ITensor *ptr(void) const { return _tensor.get(); } private: std::shared_ptr<::arm_compute::ITensor> _tensor; public: + /** + * @brief Access the tensor object and run the given function + : @param[in] fn The actual behavior when accessing the tensor object + * @return N/A + */ void access(const std::function<void(::arm_compute::ITensor &tensor)> &fn) const; }; @@ -64,24 +82,48 @@ namespace arm_compute namespace operand { +/** + * @brief Class to manage Object instances + */ class Context { public: + /** + * @brief Set index and tensor pair + * @param[in] ind The operand index + * @param[in] tensor The tensor object + * @return This object reference + */ Context &set(const ::internal::tflite::operand::Index &ind, const std::shared_ptr<::arm_compute::ITensor> &tensor); public: + /** + * @brief Check if the tensor for given index is exist + * @param[in] ind The operand Index + * @return @c true if the entry for ind is exist, otherwise @c false + */ bool exist(const ::internal::tflite::operand::Index &ind) const { return _objects.find(ind.asInt()) != _objects.end(); } public: + /** + * @brief Lookup the tensor with the given index + * @param[in] ind The index as the key + * @return The object const reference + */ const Object &at(const ::internal::tflite::operand::Index &ind) const { return _objects.at(ind.asInt()); } + /** + * @brief Lookup the tensor with the given index + * @param[in] ind The index as the key + * @return The object reference + */ Object &at(const ::internal::tflite::operand::Index &ind) { return _objects.at(ind.asInt()); } private: @@ -101,19 +143,38 @@ namespace arm_compute namespace op { +/** + * @brief Class to wrap IFunction + */ class Step { public: + /** + * @brief Construct a Step object + * @param[in] func The compiled code to be executed + */ Step(std::unique_ptr<::arm_compute::IFunction> &&func) : _func{std::move(func)} { // DO NOTHING } public: + /** + * @brief Run _func + * @return N/A + */ void run(void) const { _func->run(); } public: + /** + * @brief Get member @c _name + * @return The name as const reference + */ const std::string &name(void) const { return _name; } + /** + * @brief Get member @c _name + * @return The name as reference + */ std::string &name(void) { return _name; } private: @@ -121,7 +182,15 @@ private: std::unique_ptr<::arm_compute::IFunction> _func; #ifdef TFLITE_PROFILING_ENABLED public: + /** + * @brief Get member @c _op_index + * @return The operation index as value + */ int op_idx() const { return _op_idx; } + /** + * @brief Get member @c _op_index + * @return The operation index as reference + */ int &op_idx() { return _op_idx; } private: int _op_idx; @@ -139,12 +208,24 @@ namespace arm_compute namespace op { +/** + * @brief Class managing compiled operation code Sequence + */ class Sequence { public: + /** + * @brief Get size of sequence + * @return Number of sequence steps + */ uint32_t size(void) const { return _functions.size(); } public: + /** + * @brief Append a Function to the sequence + * @param[in] func Function to be appended + * @return This object reference + */ Sequence &append(std::unique_ptr<::arm_compute::IFunction> &&func) { _functions.emplace_back(std::move(func)); @@ -152,7 +233,17 @@ public: } public: + /** + * @brief Get the step entry on the index @c n + * @param[in] n The index + * @return The step object as reference + */ Step &at(uint32_t n) { return _functions.at(n); } + /** + * @brief Get the step entry on the index @c n + * @param[in] n The index + * @return The step object as const reference + */ const Step &at(uint32_t n) const { return _functions.at(n); } private: @@ -169,23 +260,50 @@ namespace internal namespace arm_compute { +/** + * @brief Class to manage compiled operation sequence + */ class Plan { public: + /** + * @brief Construct a Plan object + * @param[in] model Model that we want to compile + */ Plan(const std::shared_ptr<const ::internal::tflite::Model> &model) : _model(model) { // DO NOTHING } public: + /** + * @brief Get the model object + * @return The model object as const reference + */ const ::internal::tflite::Model &model(void) const { return *_model; } public: + /** + * @brief Get operand context + * @return The operand context as reference + */ operand::Context &operands(void) { return _operands; } + /** + * @brief Get operand context + * @return The operand context as const reference + */ const operand::Context &operands(void) const { return _operands; } public: + /** + * @brief Get operation sequence + * @return The operation sequence as reference + */ op::Sequence &operations(void) { return _ops; } + /** + * @brief Get operation sequence + * @return The operation sequence as const reference + */ const op::Sequence &operations(void) const { return _ops; } private: @@ -204,7 +322,10 @@ namespace internal namespace arm_compute { -// check if this runtime runs on GPU or NEON +/** + * @brief Check if this runtime runs on GPU or NEON + * @return @c true if GPU mode, otherwise @c false + */ bool isGpuMode(); #define CAST_CL(tensor) static_cast<::arm_compute::CLTensor *>(tensor) diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc new file mode 100644 index 000000000..ff2f79309 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.cc @@ -0,0 +1,165 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/arm_compute/Cast.h" + +#include "internal/Swizzle.h" + +::arm_compute::Coordinates getARMComputeAxises(uint32_t rank) +{ + ::arm_compute::Coordinates res{}; + + res.set_num_dimensions(rank); + + for (uint32_t axis = 0; axis < rank; ++axis) + { + res.set(axis, ToARMComputeAxis(rank, axis).value()); + } + + return res; +} + +::arm_compute::Coordinates asARMComputeCoordinates(const ::arm_compute::Coordinates &runtime_coord, + const ::arm_compute::Coordinates &axises) +{ + ::arm_compute::Coordinates id{}; + assert(runtime_coord.num_dimensions() == axises.num_dimensions()); + for (size_t i = 0; i < runtime_coord.num_dimensions(); ++i) + { + id.set(axises[i], runtime_coord[i]); + } + return id; +} + +// Restructure runtime_permutationVector to ACL_permutationVector +::arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank, + const int32_t *runtime_pv) +{ + // rank upto 4 is supported + assert(rank <= 4); + assert(runtime_pv != nullptr); + + int new_pv[4] = {0}; + ::arm_compute::Coordinates axises = getARMComputeAxises(rank); + + if (rank == 4) + { + /** + axises = {3,1,0,2} + NNAPI PermutationVector + N 0 3 + H 1 1 + W 2 0 + C 3 2 + **/ + new_pv[0] = axises[runtime_pv[2]]; + new_pv[1] = axises[runtime_pv[1]]; + new_pv[2] = axises[runtime_pv[3]]; + new_pv[3] = axises[runtime_pv[0]]; + } + else + { + /** + mapping/axises = {rank-1 to 0} + CHW --------> WHC + or + WH ----------> HW + **/ + for (int id = 0; id < rank; ++id) + { + new_pv[id] = axises[runtime_pv[rank - id - 1]]; + } + } + + return ::arm_compute::PermutationVector{new_pv[0], new_pv[1], new_pv[2], new_pv[3]}; +} + +::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape, + bool apply_dim_correction) +{ + const uint32_t rank = shape.rank(); + + ::arm_compute::TensorShape res{}; + + res.set_num_dimensions(rank); + + for (uint32_t axis = 0; axis < rank; ++axis) + { + // NOTE In some cases, in incorrect dimensions is required. + // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of + // LSTM is used as the weight of the FullyConnected. + // The FullyConnected's weight must be greater or equal than 2-dimensions. + // However, if the dimension correction is applied to input_to_input_weights with input_size + // equal to 1, it will be changed to 1-D. + // So input_to_input_weights is not used by the weight of FullyConnected. + res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction); + } + + return res; +} + +::arm_compute::DataType asDataType(const int32_t type) +{ + switch (type) + { + case ANEURALNETWORKS_FLOAT32: + case ANEURALNETWORKS_TENSOR_FLOAT32: + return ::arm_compute::DataType::F32; + case ANEURALNETWORKS_INT32: + case ANEURALNETWORKS_TENSOR_INT32: + return ::arm_compute::DataType::S32; + case ANEURALNETWORKS_UINT32: + return ::arm_compute::DataType::U32; + case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM: + return ::arm_compute::DataType::QASYMM8; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code) +{ + switch (code) + { + case ANEURALNETWORKS_FUSED_NONE: + return ::arm_compute::ActivationLayerInfo{}; + case ANEURALNETWORKS_FUSED_RELU: + return ::arm_compute::ActivationLayerInfo{ + ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; + case ANEURALNETWORKS_FUSED_RELU1: + return ::arm_compute::ActivationLayerInfo{ + ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f}; + case ANEURALNETWORKS_FUSED_RELU6: + return ::arm_compute::ActivationLayerInfo{ + ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f}; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset) +{ + return ::arm_compute::QuantizationInfo(scale, offset); +} + +::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape, const int32_t type, + const float scale, const int32_t zeroPoint) +{ + return ::arm_compute::TensorInfo(shape, 1, asDataType(type), + asQuantizationInfo(scale, zeroPoint)); +} diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h index e2ceb8fef..42b547feb 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/Cast.h @@ -14,104 +14,98 @@ * limitations under the License. */ +/** + * @file Cast.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines casting functions from internal object to arm compute object + */ #ifndef __ARM_COMPUTE_CAST_H__ +#define __ARM_COMPUTE_CAST_H__ +#include <arm_compute/core/Coordinates.h> +#include <arm_compute/core/TensorInfo.h> #include <arm_compute/core/TensorShape.h> +#include <arm_compute/core/Types.h> -#include "internal/Swizzle.h" -#include "internal/Model.h" - -inline ::arm_compute::Coordinates getARMComputeAxises(uint32_t rank) -{ - ::arm_compute::Coordinates res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - res.set(axis, ToARMComputeAxis(rank, axis).value()); - } - - return res; -} - -inline ::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape, - bool apply_dim_correction = true) -{ - const uint32_t rank = shape.rank(); +#include <NeuralNetworks.h> - ::arm_compute::TensorShape res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - // NOTE In some cases, in incorrect dimensions is required. - // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of - // LSTM is used as the weight of the FullyConnected. - // The FullyConnected's weight must be greater or equal than 2-dimensions. - // However, if the dimension correction is applied to input_to_input_weights with input_size - // equal to 1, it will be changed to 1-D. - // So input_to_input_weights is not used by the weight of FullyConnected. - res.set(ToARMComputeAxis(rank, axis).value(), shape.dim(axis), apply_dim_correction); - } - - return res; -} +#include "internal/Model.h" -::arm_compute::DataType asDataType(const int32_t type) -{ - switch (type) - { - case ANEURALNETWORKS_FLOAT32: - case ANEURALNETWORKS_TENSOR_FLOAT32: - return ::arm_compute::DataType::F32; - case ANEURALNETWORKS_INT32: - case ANEURALNETWORKS_TENSOR_INT32: - return ::arm_compute::DataType::S32; - case ANEURALNETWORKS_UINT32: - return ::arm_compute::DataType::U32; - case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM: - return ::arm_compute::DataType::QASYMM8; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} +/** + * @brief Generate arm compute coordinate object from rank + * @param[in] rank Rank number + * @return Coordinate object + */ +::arm_compute::Coordinates getARMComputeAxises(uint32_t rank); + +/** + * @brief Generate arm compute coordinate object from runtime coordinate object + * @param[in] runtime_coord Runtime coordinates object + * @param[in] axises Coordinates for axises to map runtime-coordinates to + * arm_compute-coordinates + * @return Arm_compute coordinate object + */ +::arm_compute::Coordinates asARMComputeCoordinates(const ::arm_compute::Coordinates &runtime_coord, + const ::arm_compute::Coordinates &axises); + +/** +* @brief Generate arm compute permutation vector from runtime permutation vector +* @param[in] rank Rank number supported upto 4 +* @param[in] runtime_pv Integer array for runtime permutation vector +* @return Permutation vector of arm compute +*/ +::arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank, + const int32_t *runtime_pv); +/** + * @brief Cast from shape of internal to arm compute + * @param[in] shape Internal shape object + * @param[in] apply_dim_correction Flag to state whether apply dimension correction after setting + * one dimension in arm compute + * @return TensorShape object of arm compute + */ +::arm_compute::TensorShape asTensorShape(const internal::tflite::operand::Shape &shape, + bool apply_dim_correction = true); -::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code) -{ - switch (code) - { - case ANEURALNETWORKS_FUSED_NONE: - return ::arm_compute::ActivationLayerInfo{}; - case ANEURALNETWORKS_FUSED_RELU: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; - case ANEURALNETWORKS_FUSED_RELU1: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f}; - case ANEURALNETWORKS_FUSED_RELU6: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f}; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} +/** + * @brief Cast from data type enum of NNAPI to arm compute + * @param[in] type NNAPI data type + * @return Data type of arm compute + */ +::arm_compute::DataType asDataType(const int32_t type); -::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset) -{ - return ::arm_compute::QuantizationInfo(scale, offset); -} +/** + * @brief Cast from NNAPI activation type enum to activation object of arm compute + * @param[in] code NNAPI activation type + * @return ActivationLayerInfo object of arm compute + */ +::arm_compute::ActivationLayerInfo asActivationInfo(FuseCode code); +/** + * @brief Generate quantization info object of arm compute + * @param[in] scale Scale of quantization + * @param[in] offset Offset of quantization + * @return QuantizationInfo object of arm compute + */ +::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset); + +/** + * @brief Cast from internal tensor info to tensor info object of arm compute + * @param[in] shape Tensor shape + * @param[in] type Tensor type + * @param[in] scale Scale of tensor quantization + * @param[in] zeroPoint Zeropoint of tensor quantization + * @return TensorInfo object of arm compute + */ ::arm_compute::TensorInfo asTensorInfo(const ::arm_compute::TensorShape &shape, const int32_t type, - const float scale = 0.0f, const int32_t zeroPoint = 0) -{ - return ::arm_compute::TensorInfo(shape, 1, asDataType(type), - asQuantizationInfo(scale, zeroPoint)); -} - + const float scale = 0.0f, const int32_t zeroPoint = 0); + +/** + * @brief Set value to arm compute tensor with casting + * @param[in] value Value to set + * @param[out] to Target tensor of arm compute + * @param[in] id Position of element + * @return N/A + */ template <typename FromT> void copyCast(const FromT value, ::arm_compute::ITensor *to, const ::arm_compute::Coordinates &id) { diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/feature/View.h b/runtimes/pure_arm_compute/src/internal/arm_compute/feature/View.h index 9d19021ae..c989ef4c2 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/feature/View.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/feature/View.h @@ -14,10 +14,15 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::arm_compute::feature::View class + */ #ifndef __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ #define __INTERNAL_ARM_COMPUTE_FEATURE_VIEW_H__ -#include "util/feature/Reader.h" +#include "misc/feature/Reader.h" #include <arm_compute/core/ITensor.h> @@ -28,15 +33,29 @@ namespace arm_compute namespace feature { -template <typename T> class View final : public nnfw::util::feature::Reader<T> +/** + * @brief Class to access feature's element + */ +template <typename T> class View final : public nnfw::misc::feature::Reader<T> { public: + /** + * @brief Construct a new View object + * @param[in] tensor Feature to support access + */ View(::arm_compute::ITensor *tensor) : _tensor{tensor} { // DO NOTHING } public: + /** + * @brief Get value of element in 3D feature using channel, row and column + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t ch, uint32_t row, uint32_t col) const override { const auto offset = feature_index_to_byte_offset(ch, row, col); @@ -46,6 +65,14 @@ public: return *ptr; } + /** + * @brief Get value of element in 4D feature using batch, channel, row and column + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override { const auto offset = feature_index_to_byte_offset(batch, ch, row, col); @@ -56,6 +83,13 @@ public: } public: + /** + * @brief Get reference of element in 3D feature using channel, row and column + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ T &at(uint32_t ch, uint32_t row, uint32_t col) { const auto offset = feature_index_to_byte_offset(ch, row, col); @@ -65,6 +99,14 @@ public: return *ptr; } + /** + * @brief Get reference of element in 4D feature using batch, channel, row and column + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { const auto offset = feature_index_to_byte_offset(batch, ch, row, col); @@ -75,12 +117,27 @@ public: } private: + /** + * @brief Get offset of element in 3D feature + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Offset of element + */ size_t feature_index_to_byte_offset(uint32_t ch, uint32_t row, uint32_t col) const { // ARM Compute uses CHW ordering return _tensor->info()->offset_element_in_bytes(::arm_compute::Coordinates{col, row, ch}); } + /** + * @brief Get offset of element in 4D feature + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Offset of element + */ size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const { // ARM Compute uses CHW ordering diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/kernel/View.h b/runtimes/pure_arm_compute/src/internal/arm_compute/kernel/View.h index 28054d7c8..399cdf913 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/kernel/View.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/kernel/View.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internel::arm_compute::kernel::View class + */ #ifndef __INTERNAL_ARM_COMPUTE_KERNEL_VIEW_H__ #define __INTERNAL_ARM_COMPUTE_KERNEL_VIEW_H__ -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" +#include "misc/kernel/Shape.h" +#include "misc/kernel/Reader.h" #include <arm_compute/core/ITensor.h> @@ -29,15 +34,30 @@ namespace arm_compute namespace kernel { -template <typename T> class View final : public nnfw::util::kernel::Reader<T> +/** + * @brief Class to access kernel's element + */ +template <typename T> class View final : public nnfw::misc::kernel::Reader<T> { public: + /** + * @brief Construct a new View object + * @param[in] tensor Kernel to support access + */ View(::arm_compute::ITensor *tensor) : _tensor{tensor} { // DO NOTHING } public: + /** + * @brief Get value of element in kernel + * @param[in] nth Kernel index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override { const auto offset = kernel_index_to_byte_offset(nth, ch, row, col); @@ -48,6 +68,14 @@ public: } public: + /** + * @brief Get reference of element in kernel + * @param[in] nth Kernel index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ T &at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) { const auto offset = kernel_index_to_byte_offset(nth, ch, row, col); @@ -58,6 +86,14 @@ public: } private: + /** + * @brief Get offset of element in kernel + * @param[in] nth Kernel index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Offset of element + */ size_t kernel_index_to_byte_offset(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const { return _tensor->info()->offset_element_in_bytes(::arm_compute::Coordinates{col, row, ch, nth}); diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/matrix/View.h b/runtimes/pure_arm_compute/src/internal/arm_compute/matrix/View.h index e3534294f..305fff729 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/matrix/View.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/matrix/View.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::arm_compute::matrix::View class + */ #ifndef __INTERNAL_ARM_COMPUTE_MATRIX_VIEW_H__ #define __INTERNAL_ARM_COMPUTE_MATRIX_VIEW_H__ -#include "util/matrix/Shape.h" -#include "util/matrix/Reader.h" +#include "misc/matrix/Shape.h" +#include "misc/matrix/Reader.h" #include <arm_compute/core/ITensor.h> @@ -29,15 +34,28 @@ namespace arm_compute namespace matrix { -template <typename T> class View final : public nnfw::util::matrix::Reader<T> +/** + * @brief Class to access matrix's element + */ +template <typename T> class View final : public nnfw::misc::matrix::Reader<T> { public: + /** + * @brief Construct a new View object + * @param[in] tensor Matrix to support access + */ View(::arm_compute::ITensor *tensor) : _tensor{tensor} { // DO NOTHING } public: + /** + * @brief Get value of element in matrix + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t row, uint32_t col) const override { const auto offset = matrix_index_to_byte_offset(row, col); @@ -48,6 +66,12 @@ public: } public: + /** + * @brief Get reference of element in matrix + * @param[in] row Row index + * @param[in] col Column index + * @return Refence of element + */ T &at(uint32_t row, uint32_t col) { const auto offset = matrix_index_to_byte_offset(row, col); @@ -58,6 +82,12 @@ public: } private: + /** + * @brief Get offset of element in matrix + * @param[in] row Row index + * @param[in] col Column index + * @return Offset of element + */ size_t matrix_index_to_byte_offset(uint32_t row, uint32_t col) const { return _tensor->info()->offset_element_in_bytes(::arm_compute::Coordinates{col, row}); diff --git a/runtimes/pure_arm_compute/src/internal/arm_compute/tensor/View.h b/runtimes/pure_arm_compute/src/internal/arm_compute/tensor/View.h index 0d8f2ab81..372bd682d 100644 --- a/runtimes/pure_arm_compute/src/internal/arm_compute/tensor/View.h +++ b/runtimes/pure_arm_compute/src/internal/arm_compute/tensor/View.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::arm_compute::tensor::View class + */ #ifndef __INTERNAL_ARM_COMPUTE_TENSOR_VIEW_H__ #define __INTERNAL_ARM_COMPUTE_TENSOR_VIEW_H__ -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" #include <arm_compute/core/ITensor.h> @@ -29,16 +34,28 @@ namespace arm_compute namespace tensor { +/** + * @brief Class to access tensor's element + */ template <typename T> class View { public: + /** + * @brief Construct a new View object + * @param[in] tensor Tensor to support access + */ View(::arm_compute::ITensor *tensor) : _tensor{tensor} { // DO NOTHING } private: - uint32_t byte_offset_of(const nnfw::util::tensor::Index &index) const + /** + * @brief Get offset of element in tensor + * @param[in] index Index of element + * @return Offset of element + */ + uint32_t byte_offset_of(const nnfw::misc::tensor::Index &index) const { // NOTE index.rank() >= _tensor->info()->num_dimensions() should hold here const uint32_t rank = index.rank(); @@ -56,7 +73,12 @@ private: } public: - T at(const nnfw::util::tensor::Index &index) const + /** + * @brief Get value of element in tensor + * @param[in] index Index of element + * @return Value of element + */ + T at(const nnfw::misc::tensor::Index &index) const { const auto offset = byte_offset_of(index); @@ -65,7 +87,12 @@ public: return *ptr; } - T &at(const nnfw::util::tensor::Index &index) + /** + * @brief Get reference of element in tensor + * @param[in] index Index of element + * @return Reference of element + */ + T &at(const nnfw::misc::tensor::Index &index) { const auto offset = byte_offset_of(index); diff --git a/runtimes/pure_arm_compute/src/internal/layers/FeatureLoggingLayer.h b/runtimes/pure_arm_compute/src/internal/layers/FeatureLoggingLayer.h index 502a1ee0e..83ae7c17b 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/FeatureLoggingLayer.h +++ b/runtimes/pure_arm_compute/src/internal/layers/FeatureLoggingLayer.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file FeatureLoggingLayer.h + * @brief This file contains FeatureLoggingLayer class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __FEATURE_LOGGING_LAYER_H__ #define __FEATURE_LOGGING_LAYER_H__ @@ -27,9 +33,24 @@ #include "internal/arm_compute.h" +/** + * @brief Class to run FeatureLogging Layer + */ class FeatureLoggingLayer : public ::arm_compute::IFunction { public: + FeatureLoggingLayer(void) : _tag(""), _target(nullptr) + { + // DO NOTHING + } + +public: + /** + * @brief Configure the layer + * @param[in] tag Text tag for this layer + * @param[in] target The feature tensor to be printed + * @return N/A + */ void configure(const std::string &tag, ::arm_compute::ITensor *target) { _tag = tag; @@ -37,6 +58,10 @@ public: } public: + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run(void) override { if (::internal::arm_compute::isGpuMode()) diff --git a/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.cc index 311284efc..28789a801 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.cc +++ b/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.cc @@ -17,8 +17,6 @@ #include "GenericFullyConnectedLayer.h" #include "internal/arm_compute.h" -#include <arm_compute/core/Helpers.h> - void GenericFullyConnectedLayer::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *weights, ::arm_compute::ITensor *biases, @@ -56,9 +54,9 @@ void GenericFullyConnectedLayer::configure(::arm_compute::ITensor *input, { // reshape auto_init_if_empty(*_neon_buffer.info(), _input->info()->clone()->set_tensor_shape(reshape)); - _generic_reshape.configure(CAST_NE(_input), &_neon_buffer); + _generic_reshape.configure(_input, &_neon_buffer); - _neon_fc.configure(&_neon_buffer, CAST_NE(_weights), CAST_NE(_biases), CAST_NE(_output)); + _neon_fc.configure(&_neon_buffer, _weights, _biases, _output); // NOTE _neon_buffer is inaccessible from outside, and thus it is safe to invoke allocate // here. @@ -66,7 +64,7 @@ void GenericFullyConnectedLayer::configure(::arm_compute::ITensor *input, } else { - _neon_fc.configure(CAST_NE(_input), CAST_NE(_weights), CAST_NE(_biases), CAST_NE(_output)); + _neon_fc.configure(_input, _weights, _biases, _output); } } } diff --git a/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.h b/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.h index 55d8683da..f1519f54d 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.h +++ b/runtimes/pure_arm_compute/src/internal/layers/GenericFullyConnectedLayer.h @@ -14,23 +14,52 @@ * limitations under the License. */ +/** + * @file GenericFullyConnectedLayer.h + * @brief This file contains GenericFullyConnectedLayer class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __GENERIC_FULLY_CONNECTED_LAYER_H__ #define __GENERIC_FULLY_CONNECTED_LAYER_H__ -#include <arm_compute/runtime/Tensor.h> -#include <arm_compute/runtime/CL/CLTensor.h> #include <arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h> #include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h> #include "internal/layers/GenericReshapeLayer.h" +/** + * @brief Class to run FullyConnected Layer with both CPU and GPU + */ class GenericFullyConnectedLayer : public ::arm_compute::IFunction { public: + GenericFullyConnectedLayer(void) + : _input(nullptr), _weights(nullptr), _biases(nullptr), _output(nullptr), _cl_buffer{}, + _neon_buffer{}, _cl_fc{}, _neon_fc{}, _generic_reshape{}, _needs_reshape(false) + { + // DO NOTHING + } + +public: + /** + * @brief Configure the layer + * @param[in] input The source tensor + * @param[in] weights The tensor that is filled with weight values + * @param[in] biases The tensor that is filled with biase values + * @param[in] output The destination tensor + * @param[in] needs_reshape Whether it needs to be reshaped or not + * @param[in] reshape The tensor shape to be reshaped. Only valid when needs_reshape is true. + * @return N/A + */ void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *weights, ::arm_compute::ITensor *biases, ::arm_compute::ITensor *output, bool needs_reshape, ::arm_compute::TensorShape reshape); public: + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run(void) override; private: diff --git a/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.cc index 2cdfe1b6e..c38c2e9e3 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.cc +++ b/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.cc @@ -43,8 +43,8 @@ void GenericReshapeLayer::configure(::arm_compute::ITensor *input, ::arm_compute } else { - _neon_permute.configure(CAST_NE(input), &_neon_permuted, pv); - _neon_reshape.configure(&_neon_permuted, CAST_NE(output)); + _neon_permute.configure(input, &_neon_permuted, pv); + _neon_reshape.configure(&_neon_permuted, output); // NOTE _permuted is inaccessible from outside, and thus it is safe to invoke allocate here. _neon_permuted.allocator()->allocate(); diff --git a/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.h b/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.h index 1def21085..a22c14c8b 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.h +++ b/runtimes/pure_arm_compute/src/internal/layers/GenericReshapeLayer.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file GenericReshapeLayer.h + * @brief This file contains GenericReshapeLayer class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __GENERIC_RESHAPE_LAYER_H__ #define __GENERIC_RESHAPE_LAYER_H__ @@ -25,12 +31,33 @@ #include <arm_compute/runtime/NEON/functions/NEPermute.h> #include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h> +/** + * @brief Class to run Reshape Layer with both CPU and GPU + */ class GenericReshapeLayer : public ::arm_compute::IFunction { public: + GenericReshapeLayer(void) + : _input(nullptr), _output(nullptr), _cl_permuted{}, _neon_permuted{}, _cl_permute{}, + _cl_reshape{}, _neon_permute{}, _neon_reshape{} + { + // DO NOTHING + } + +public: + /** + * @brief Configure the layer + * @param[in] input The source tensor + * @param[in] output The destination tensor + * @return N/A + */ void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output); public: + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run(void) override; private: diff --git a/runtimes/pure_arm_compute/src/internal/layers/PadLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/PadLayer.cc deleted file mode 100644 index 4a5370587..000000000 --- a/runtimes/pure_arm_compute/src/internal/layers/PadLayer.cc +++ /dev/null @@ -1,78 +0,0 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <iostream>
-#include "PadLayer.h"
-#include <arm_compute/runtime/CL/CLScheduler.h>
-
-void PadLayer::configure(::arm_compute::ICLTensor *input, ::arm_compute::ICLTensor *output,
- unsigned int border_width)
-{
- _input = input;
- _output = output;
- _border_width = border_width;
- _output_height = _output->info()->dimension(0);
- _output_width = _output->info()->dimension(1);
-
- uint8_t constant_border_value = 0;
- ::arm_compute::PixelValue constant_pixel_value = ::arm_compute::PixelValue(constant_border_value);
-
- unsigned int padding_size = _border_width;
- input->info()->extend_padding(::arm_compute::PaddingSize{padding_size});
- _fillborderkernel.configure(input, _border_width, ::arm_compute::BorderMode::CONSTANT,
- constant_pixel_value);
-}
-
-void PadLayer::run(void)
-{
- _fillborderkernel.run();
-
- ::arm_compute::Coordinates coordinates =
- ::arm_compute::Coordinates(-_border_width, -_border_width);
- ::arm_compute::TensorShape new_tensor_shape =
- ::arm_compute::TensorShape(_output_height, _output_width);
-
- /* NOTE: The cl kernel fills the data in the borders(not in the tensor).
- Once the tensor is received back at NNAPI, we are adjusting
- the valid region in such a way that the padding becomes part of the tensor itself
- and matches the size of output. */
- _input->info()->set_valid_region(::arm_compute::ValidRegion(coordinates, new_tensor_shape));
-
- /* NOTE: Since cl kernel does not have an argument for output tensor while NNAPI does.
- We need to map the input (tensor that is passed to the cl kernel) back to
- output. */
-
- // TODO: Write a modified CLCopy kernel to do this job.
- populateOutput();
-}
-
-void PadLayer::populateOutput()
-{
- auto &queue = ::arm_compute::CLScheduler::get().queue();
- _input->map(queue);
- _output->map(queue);
-
- auto input_tensor = static_cast<::arm_compute::ITensor *>(_input);
- auto const source_data = input_tensor->buffer();
-
- auto output_tensor = static_cast<::arm_compute::ITensor *>(_output);
- auto dst_data = output_tensor->buffer();
-
- memmove(dst_data, source_data, _output_height * _output_width * 4);
-
- _input->unmap(queue);
- _output->unmap(queue);
-}
diff --git a/runtimes/pure_arm_compute/src/internal/layers/PadLayer.h b/runtimes/pure_arm_compute/src/internal/layers/PadLayer.h deleted file mode 100644 index cb3f36337..000000000 --- a/runtimes/pure_arm_compute/src/internal/layers/PadLayer.h +++ /dev/null @@ -1,41 +0,0 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __PAD_LAYER_H__
-#define __PAD_LAYER_H__
-
-#include <arm_compute/runtime/CL/CLTensor.h>
-#include <arm_compute/runtime/CL/functions/CLFillBorder.h>
-
-class PadLayer : public ::arm_compute::IFunction
-{
-public:
- void configure(::arm_compute::ICLTensor *input, ::arm_compute::ICLTensor *output,
- unsigned int border_width);
- void run(void) override;
-
-private:
- ::arm_compute::ICLTensor *_input;
- ::arm_compute::ICLTensor *_output;
- int _border_width;
- int _output_height;
- int _output_width;
-
- ::arm_compute::CLFillBorder _fillborderkernel;
- void populateOutput();
-};
-
-#endif // __PAD_LAYER_H__
diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.cc new file mode 100644 index 000000000..6d348e814 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.cc @@ -0,0 +1,181 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleArgMinMax.h" +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleArgMinMax::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + std::vector<uint32_t> axis, ::arm_compute::ArgOperation op) +{ + _input = input; + _output = output; + _axis = axis; + _input_rank = input->info()->num_dimensions(); + _op_type = op; +} + +inline const ::arm_compute::TensorShape +inferOutputShape(const ::arm_compute::TensorShape &input_shape, const std::vector<uint32_t> &axis, + int input_rank) +{ + ::arm_compute::TensorShape out_shape{}; + size_t dim = 1; + for (int i = 0; i < input_rank; ++i) + { + dim = input_shape[i]; + out_shape.set(i, dim); + } + + for (int i = 0; i < axis.size(); ++i) + { + out_shape.set(axis[i], 1); + } + + return out_shape; +} + +template <typename T> +inline T getArgMinMaxEle(const ::arm_compute::ITensor *input, + const ::arm_compute::TensorShape &input_shape, + const ::arm_compute::TensorShape &output_shape, const size_t b, + const size_t d, const size_t h, const size_t w, const int axis, + const ::arm_compute::ArgOperation op_type) +{ + // If output[dimention] == 1, will check all values of that dimension because of reducing + // dimension. + // Else will check only one value. + const size_t start_b = output_shape[3] == 1 ? 0 : b; + const size_t start_d = output_shape[2] == 1 ? 0 : d; + const size_t start_h = output_shape[1] == 1 ? 0 : h; + const size_t start_w = output_shape[0] == 1 ? 0 : w; + const size_t stop_b = output_shape[3] == 1 ? input_shape[3] - 1 : b; + const size_t stop_d = output_shape[2] == 1 ? input_shape[2] - 1 : d; + const size_t stop_h = output_shape[1] == 1 ? input_shape[1] - 1 : h; + const size_t stop_w = output_shape[0] == 1 ? input_shape[0] - 1 : w; + + ::arm_compute::Coordinates id{w, h, d, b}; + ::arm_compute::Coordinates min_max_id{w, h, d, b}; + + T value = *reinterpret_cast<T *>(input->ptr_to_element(id)); + T tval = *reinterpret_cast<T *>(input->ptr_to_element(id)); + + for (size_t in_b = start_b; in_b <= stop_b; ++in_b) + { + id.set(3, in_b); + for (size_t in_d = start_d; in_d <= stop_d; ++in_d) + { + id.set(2, in_d); + for (size_t in_h = start_h; in_h <= stop_h; ++in_h) + { + id.set(1, in_h); + for (size_t in_w = start_w; in_w <= stop_w; ++in_w) + { + id.set(0, in_w); + if (op_type == ::arm_compute::ArgOperation::MIN) + { + value = std::min<T>(value, *reinterpret_cast<T *>(input->ptr_to_element(id))); + } + else if (op_type == ::arm_compute::ArgOperation::MAX) + { + value = std::max<T>(value, *reinterpret_cast<T *>(input->ptr_to_element(id))); + } + else + throw std::runtime_error("This Arg operation is not supported, yet"); + + if (tval != value) + { + min_max_id = id; + tval = value; + } + } + } + } + } + + return min_max_id[axis]; +} + +template <typename T> +inline void +getArgMinMax(const ::arm_compute::ITensor *input, const ::arm_compute::TensorShape &input_shape, + const ::arm_compute::TensorShape &output_shape, ::arm_compute::ITensor *output, + const int axis, const ::arm_compute::ArgOperation op_type) +{ + ::arm_compute::Coordinates id; + for (size_t out_b = 0; out_b < output_shape[3]; ++out_b) + { + id.set(3, out_b); + for (size_t out_d = 0; out_d < output_shape[2]; ++out_d) + { + id.set(2, out_d); + for (size_t out_h = 0; out_h < output_shape[1]; ++out_h) + { + id.set(1, out_h); + for (size_t out_w = 0; out_w < output_shape[0]; ++out_w) + { + id.set(0, out_w); + *reinterpret_cast<int *>(output->ptr_to_element(id)) = getArgMinMaxEle<T>( + input, input_shape, output_shape, out_b, out_d, out_h, out_w, axis, op_type); + } + } + } + } +} + +void SimpleArgMinMax::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_output)->map(q); + } + + ::arm_compute::TensorShape input_shape = _input->info()->tensor_shape(); + + // Axis dimension is 1 and size is 1. + // TODO support axis size > 1. + int axis_val = _axis[0]; + ::arm_compute::TensorShape output_shape = inferOutputShape(input_shape, _axis, _input_rank); + + _output->info()->set_tensor_shape(output_shape); + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::QASYMM8: + getArgMinMax<uint8_t>(_input, input_shape, output_shape, _output, axis_val, _op_type); + break; + case ::arm_compute::DataType::S32: + getArgMinMax<int32_t>(_input, input_shape, output_shape, _output, axis_val, _op_type); + break; + case ::arm_compute::DataType::F32: + getArgMinMax<float>(_input, input_shape, output_shape, _output, axis_val, _op_type); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + _output->info()->set_tensor_shape(output_shape); + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_output)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.h new file mode 100644 index 000000000..b90e74579 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleArgMinMax.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_ARG_MIN_MAX_H__ +#define __SIMPLE_ARG_MIN_MAX_H__ + +#include "internal/arm_compute.h" +#include "arm_compute/core/TypesEx.h" + +class SimpleArgMinMax : public ::arm_compute::IFunction +{ +public: + SimpleArgMinMax(void) : _input(nullptr), _output(nullptr), _axis(), _input_rank(0) + { + // DO NOTHING + } + +public: + /** Initialise input and output + * + * @param[in] input First tensor input. + * @param[out] output Output tensor. + * @param[in] axis Dimension along which to find Min or Max Index. + */ + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + std::vector<uint32_t> axis, ::arm_compute::ArgOperation _op_type); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; + std::vector<uint32_t> _axis; + int _input_rank; + ::arm_compute::ArgOperation _op_type; +}; + +#endif /*__SIMPLE_ARG_MIN_MAX_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleArithmeticAddition.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleArithmeticAddition.h index 31c927b4f..aed9ae286 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleArithmeticAddition.h +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleArithmeticAddition.h @@ -14,15 +14,36 @@ * limitations under the License. */ +/** + * @file SimpleArithmeticAddition.h + * @brief This file contains SimpleArithmeticAddition class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __SIMPLE_ARITHMETIC_ADDITION_H__ #define __SIMPLE_ARITHMETIC_ADDITION_H__ #include "internal/arm_compute.h" #include <arm_compute/core/ITensor.h> +/** + * @brief Class to run SimpleArithmeticAddition Layer + */ class SimpleArithmeticAddition : public ::arm_compute::IFunction { public: + SimpleArithmeticAddition(void) : _lhs(nullptr), _rhs(nullptr), _out(nullptr) + { + // DO NOTHING + } + + /** + * @brief Configure the layer + * @param[in] lhs Lefthand-side operand + * @param[in] rhs Righthand-side operand + * @param[in] out The destination tensor(Result operand) + * @return N/A + */ void configure(::arm_compute::ITensor *lhs, ::arm_compute::ITensor *rhs, ::arm_compute::ITensor *out) { @@ -32,6 +53,10 @@ public: } public: + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run(void) override { if (::internal::arm_compute::isGpuMode()) diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.cc new file mode 100644 index 000000000..87175ee1a --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.cc @@ -0,0 +1,110 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleBatchToSpaceNd.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleBatchToSpaceND::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + const int32_t *block_size, + const ::arm_compute::Coordinates &axises) +{ + const auto rank = axises.num_dimensions(); + assert(rank == 4); + + for (int i = 0; i < rank; ++i) + assert(axises[i] >= 0 && axises[i] < rank); + + _input = input; + _output = output; + _block_size = block_size; + _axises = axises; +} + +template <typename T> +inline void BatchToSpaceND(const ::arm_compute::ITensor *input, + const ::arm_compute::TensorShape &input_shape, + const int32_t *block_size_data, ::arm_compute::ITensor *output, + const ::arm_compute::TensorShape &output_shape, + const ::arm_compute::Coordinates &axises) +{ + const int output_batch = output_shape[axises[0]]; + const int output_height = output_shape[axises[1]]; + const int output_width = output_shape[axises[2]]; + const int depth = output_shape[axises[3]]; + + for (int out_b = 0; out_b < output_batch; ++out_b) + { + for (int out_h = 0; out_h < output_height; ++out_h) + { + for (int out_w = 0; out_w < output_width; ++out_w) + { + for (int out_d = 0; out_d < depth; ++out_d) + { + const int in_d = out_d; + const int in_h = out_h / block_size_data[0]; + const int in_w = out_w / block_size_data[1]; + const int in_b = + out_b + + ((out_h % block_size_data[0]) * block_size_data[1] + out_w % block_size_data[1]) * + output_batch; + + auto input_id = + asARMComputeCoordinates(::arm_compute::Coordinates{in_b, in_h, in_w, in_d}, axises); + auto output_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{out_b, out_h, out_w, out_d}, axises); + + *reinterpret_cast<T *>(output->ptr_to_element(output_id)) = + *reinterpret_cast<T *>(input->ptr_to_element(input_id)); + } + } + } + } +} +void SimpleBatchToSpaceND::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_output)->map(q); + } + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::U8: + case ::arm_compute::DataType::QASYMM8: + BatchToSpaceND<uint8_t>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); + break; + case ::arm_compute::DataType::F32: + BatchToSpaceND<float>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_output)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.h new file mode 100644 index 000000000..5695d9719 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleBatchToSpaceNd.h @@ -0,0 +1,51 @@ +/* + *Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_BATCH_TO_SPACE_ND_H__ +#define __SIMPLE_BATCH_TO_SPACE_ND_H__ + +#include "internal/arm_compute.h" +#include "internal/arm_compute/Cast.h" + +class SimpleBatchToSpaceND : public ::arm_compute::IFunction +{ +public: + SimpleBatchToSpaceND(void) : _input(nullptr), _output(nullptr), _block_size(nullptr), _axises{} + { + // DO NOTHING + } + + /** Initialise input and output + * + * @param[in] input First tensor input. + * @param[out] output Output tensor. + * @param[in] block_size Block size. + * @param[in] axises Axises of rank 4 + */ + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + const int32_t *block_size, + const ::arm_compute::Coordinates &axises = getARMComputeAxises(4)); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; + const int32_t *_block_size; + ::arm_compute::Coordinates _axises; +}; + +#endif /*__SIMPLE_BATCH_TO_SPACE_ND_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.cc new file mode 100644 index 000000000..7c7706a78 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.cc @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "internal/layers/SimpleCastLayer.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleCastLayer::castData(::arm_compute::ITensor *in, ::arm_compute::ITensor *out, + const arm_compute::Coordinates &id) +{ + switch (in->info()->data_type()) + { + case ::arm_compute::DataType::F32: + { + copyCast(*reinterpret_cast<float *>(in->ptr_to_element(id)), out, id); + break; + } + case ::arm_compute::DataType::S32: + { + copyCast(*reinterpret_cast<int32_t *>(in->ptr_to_element(id)), out, id); + break; + } + case ::arm_compute::DataType::U32: + { + copyCast(*reinterpret_cast<uint32_t *>(in->ptr_to_element(id)), out, id); + break; + } + case ::arm_compute::DataType::QASYMM8: + { + const uint8_t quantizedValue = *(in->ptr_to_element(id)); + copyCast(in->info()->quantization_info().dequantize(quantizedValue), out, id); + break; + } + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +void SimpleCastLayer::configure(::arm_compute::ITensor *in, ::arm_compute::ITensor *out) +{ + _in = in; + _out = out; +} + +void SimpleCastLayer::run(void) +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + CAST_CL(_in)->map(q); + CAST_CL(_out)->map(q); + } + + arm_compute::Window window; + window.use_tensor_dimensions(_out->info()->tensor_shape()); + + execute_window_loop(window, + [this](const arm_compute::Coordinates &id) { castData(_in, _out, id); }); + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + CAST_CL(_out)->unmap(q); + CAST_CL(_in)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.h index fa3006438..f9a48b481 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.h +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleCastLayer.h @@ -14,80 +14,55 @@ * limitations under the License. */ +/** + * @file SimpleCastLayer.h + * @brief This file contains SimpleCastLayer class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __SIMPLE_CAST_LAYER_H__ #define __SIMPLE_CAST_LAYER_H__ -#include <arm_compute/core/ITensor.h> - #include "internal/arm_compute.h" -#include "internal/op/Cast.h" +#include "internal/arm_compute/Cast.h" +/** + * @brief Class to run SimpleCast Layer + */ class SimpleCastLayer : public ::arm_compute::IFunction { public: - void configure(::arm_compute::ITensor *in, ::arm_compute::ITensor *out) + SimpleCastLayer(void) : _in(nullptr), _out(nullptr) { - _in = in; - _out = out; + // DO NOTHING } -public: - void run(void) override - { - if (::internal::arm_compute::isGpuMode()) - { - auto &q = ::arm_compute::CLScheduler::get().queue(); - CAST_CL(_in)->map(q); - CAST_CL(_out)->map(q); - } - - arm_compute::Window window; - window.use_tensor_dimensions(_out->info()->tensor_shape()); + /** + * @brief Configure the layer + * @param[in] in The source tensor + * @param[in] out The destination tensor + * @return N/A + */ + void configure(::arm_compute::ITensor *in, ::arm_compute::ITensor *out); - execute_window_loop(window, - [this](const arm_compute::Coordinates &id) { castData(_in, _out, id); }); - - if (::internal::arm_compute::isGpuMode()) - { - auto &q = ::arm_compute::CLScheduler::get().queue(); - CAST_CL(_out)->unmap(q); - CAST_CL(_in)->unmap(q); - } - } + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ + void run(void) override; +private: + /** + * @brief Cast and copy data from one tensor to another + * + * @param[in] in The source tensor + * @param[out] out The destination tensor + * @param[in] id Coordinates to copy + * @return N/A + */ void castData(::arm_compute::ITensor *in, ::arm_compute::ITensor *out, - const arm_compute::Coordinates &id) - { - switch (in->info()->data_type()) - { - case ::arm_compute::DataType::F32: - { - copyCast(*reinterpret_cast<float *>(in->ptr_to_element(id)), out, id); - break; - } - case ::arm_compute::DataType::S32: - { - copyCast(*reinterpret_cast<int32_t *>(in->ptr_to_element(id)), out, id); - break; - } - case ::arm_compute::DataType::U32: - { - copyCast(*reinterpret_cast<uint32_t *>(in->ptr_to_element(id)), out, id); - break; - } - case ::arm_compute::DataType::QASYMM8: - { - const uint8_t quantizedValue = *(in->ptr_to_element(id)); - copyCast(in->info()->quantization_info().dequantize(quantizedValue), out, id); - break; - } - default: - throw std::runtime_error("Not supported, yet"); - break; - } - } + const arm_compute::Coordinates &id); -private: ::arm_compute::ITensor *_in; ::arm_compute::ITensor *_out; }; diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.cc new file mode 100644 index 000000000..d62a8321b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.cc @@ -0,0 +1,110 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleDepthToSpace.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleDepthToSpace::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + int32_t block_size, const ::arm_compute::Coordinates &axises) +{ + const auto rank = axises.num_dimensions(); + assert(rank == 4); + for (int i = 0; i < rank; ++i) + { + assert(axises[i] >= 0); + assert(axises[i] < rank); + } + + _input = input; + _output = output; + _block_size = block_size; + _axises = axises; +} + +template <typename T> +inline void DepthToSpace(const ::arm_compute::ITensor *input, + const ::arm_compute::TensorShape &input_shape, int32_t block_size, + ::arm_compute::ITensor *output, + const ::arm_compute::TensorShape &output_shape, + const ::arm_compute::Coordinates &axises) +{ + const int output_batch = output_shape[axises[0]]; + const int output_height = output_shape[axises[1]]; + const int output_width = output_shape[axises[2]]; + const int output_depth = output_shape[axises[3]]; + + for (int out_b = 0; out_b < output_batch; ++out_b) + { + for (int out_h = 0; out_h < output_height; ++out_h) + { + for (int out_w = 0; out_w < output_width; ++out_w) + { + for (int out_d = 0; out_d < output_depth; ++out_d) + { + const int in_b = out_b; + const int in_h = out_h / block_size; + const int in_w = out_w / block_size; + const int in_d = + out_d + ((out_h % block_size) * block_size + out_w % block_size) * output_depth; + + auto input_id = + asARMComputeCoordinates(::arm_compute::Coordinates{in_b, in_h, in_w, in_d}, axises); + auto output_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{out_b, out_h, out_w, out_d}, axises); + + *reinterpret_cast<T *>(output->ptr_to_element(output_id)) = + *reinterpret_cast<T *>(input->ptr_to_element(input_id)); + } + } + } + } +} + +void SimpleDepthToSpace::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_output)->map(q); + } + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::U8: + case ::arm_compute::DataType::QASYMM8: + DepthToSpace<uint8_t>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); + break; + case ::arm_compute::DataType::F32: + DepthToSpace<float>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_output)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.h new file mode 100644 index 000000000..1032aaa47 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleDepthToSpace.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_DEPTH_TO_SPACE_H__ +#define __SIMPLE_DEPTH_TO_SPACE_H__ + +#include "internal/arm_compute.h" +#include "internal/arm_compute/Cast.h" + +class SimpleDepthToSpace : public ::arm_compute::IFunction +{ +public: + SimpleDepthToSpace(void) : _input(nullptr), _output(nullptr), _block_size(0), _axises{} + { + // DO NOTHING + } + +public: + /** Initialise input and output + * + * @param[in] input First tensor input. + * @param[out] output Output tensor. + * @param[in] block_size Block size. + * @param[in] axises Axises of rank 4 + */ + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, int32_t block_size, + const ::arm_compute::Coordinates &axises = getARMComputeAxises(4)); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; + int32_t _block_size; + ::arm_compute::Coordinates _axises; +}; + +#endif /*__SIMPLE_DEPTH_TO_SPACE_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.cc index 089c783c1..ae740bb10 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.cc +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.cc @@ -1,3 +1,18 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ #include "internal/layers/SimpleEmbeddingLookup.h" #include <arm_compute/runtime/CL/CLScheduler.h> @@ -6,7 +21,8 @@ void SimpleEmbeddingLookup::configure(::arm_compute::ITensor *lookups, ::arm_compute::ITensor *values, ::arm_compute::ITensor *output) { - // Assume that verification of operands are already done at Planner::visit() + assert(values->info()->num_dimensions() == output->info()->num_dimensions()); + assert(values->info()->num_dimensions() > 1 && values->info()->num_dimensions() <= 4); _lookups = lookups; _values = values; _output = output; @@ -25,85 +41,62 @@ void SimpleEmbeddingLookup::run() // type of elements of lookups is always integer const int32_t *lookups_buf = reinterpret_cast<int32_t *>(_lookups->buffer()); - const auto values_buf = _values->buffer(); - auto output_buf = _output->buffer(); const auto lookups_info = _lookups->info(); const auto values_info = _values->info(); const auto output_info = _output->info(); - // TODO Refactor below duplicated code! - const auto values_rank = values_info->num_dimensions(); - switch (values_rank) + // NOTE The first dimension's position is always at the end of dimensions. + const auto first_dim_pos = values_info->num_dimensions() - 1; + + const size_t first_dim = values_info->dimension(first_dim_pos); + for (size_t i = 0; i < lookups_info->dimension(0); ++i) { - case 2: - // (H,W) in nnapi -> (W,H) in acl - { - const size_t row_size = values_info->dimension(1); - const size_t row_bytes = values_info->total_size() / row_size; - for (size_t i = 0; i < lookups_info->dimension(0); ++i) - { - if (lookups_buf[i] < 0 || lookups_buf[i] >= row_size) - throw std::runtime_error("Embedding Lookup: index out of bounds."); - - size_t idx = lookups_buf[i]; - size_t row_offset_by_idx = values_info->offset_element_in_bytes({0, idx}); - size_t row_offset_by_i = output_info->offset_element_in_bytes({0, i}); - - unsigned char *sink_addr = output_buf + row_offset_by_i; - unsigned char *source_addr = values_buf + row_offset_by_idx; - memcpy(sink_addr, source_addr, row_bytes); - } - } - break; - case 3: - // (B,H,W) in nnapi -> (W,H,B) in acl - { - const size_t row_size = values_info->dimension(2); - const size_t row_bytes = values_info->total_size() / row_size; - for (size_t i = 0; i < lookups_info->dimension(0); ++i) - { - if (lookups_buf[i] < 0 || lookups_buf[i] >= row_size) - throw std::runtime_error("Embedding Lookup: index out of bounds."); - - size_t idx = lookups_buf[i]; - size_t row_offset_by_idx = values_info->offset_element_in_bytes({0, 0, idx}); - size_t row_offset_by_i = output_info->offset_element_in_bytes({0, 0, i}); - - unsigned char *sink_addr = output_buf + row_offset_by_i; - unsigned char *source_addr = values_buf + row_offset_by_idx; - memcpy(sink_addr, source_addr, row_bytes); - } - } - break; - case 4: - // (N,H,W,C) in nnapi -> (N,C,H,W) in acl - { - const size_t row_size = values_info->dimension(3); - const size_t row_bytes = values_info->total_size() / row_size; - for (size_t i = 0; i < lookups_info->dimension(0); ++i) - { - if (lookups_buf[i] < 0 || lookups_buf[i] >= row_size) - throw std::runtime_error("Embedding Lookup: index out of bounds."); - - size_t idx = lookups_buf[i]; - size_t row_offset_by_idx = values_info->offset_element_in_bytes({0, 0, 0, idx}); - size_t row_offset_by_i = output_info->offset_element_in_bytes({0, 0, 0, i}); - - unsigned char *sink_addr = output_buf + row_offset_by_i; - unsigned char *source_addr = values_buf + row_offset_by_idx; - memcpy(sink_addr, source_addr, row_bytes); - } - } - break; - case 1: - // In this case, shape of values actually is matrix but the height(row size) is 1 in acl. If - // row size is 1, this op is not needed and it means this situtation could be wrong. - throw std::runtime_error("Wrong usage of EmbeddingLookup op!"); - default: - throw std::runtime_error("Not supported rank!"); + if (lookups_buf[i] < 0 || lookups_buf[i] >= first_dim) + throw std::runtime_error("Embedding Lookup: index out of bounds."); } + // If each strides of values and output are different, applied padding size of the two tensors are + // different, therefore, it can not be copied at once. + auto can_copy_at_once = [&]() -> bool { + const auto &values_strides = values_info->strides_in_bytes(); + const auto &output_strides = output_info->strides_in_bytes(); + + for (size_t i = 0; i < first_dim_pos; ++i) + { + if (values_strides[i] != values_strides[i]) + return false; + } + + return true; + }; + + using ::arm_compute::Window; + using ::arm_compute::Iterator; + + size_t copy_bytes; + Window window; + if (can_copy_at_once()) + { + copy_bytes = values_info->total_size() / first_dim; + window.use_tensor_dimensions(output_info->tensor_shape(), first_dim_pos); + } + else + { + copy_bytes = values_info->dimension(0) * values_info->element_size(); + window.use_tensor_dimensions(output_info->tensor_shape(), Window::DimY); + } + + Iterator it(_output, window); + execute_window_loop(window, + [&](const ::arm_compute::Coordinates &id) { + ::arm_compute::Coordinates values_id = id; + const int idx = id[first_dim_pos]; + values_id.set(first_dim_pos, lookups_buf[idx]); + memcpy(it.ptr(), _values->ptr_to_element(values_id), copy_bytes); + }, + it); + if (::internal::arm_compute::isGpuMode()) { auto &q = ::arm_compute::CLScheduler::get().queue(); diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.h index 9f2cd977f..fd499437f 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.h +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleEmbeddingLookup.h @@ -1,16 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ #ifndef __SIMPLE_EMBEDDING_LOOKUP_H__ #define __SIMPLE_EMBEDDING_LOOKUP_H__ #include "internal/arm_compute.h" -#include <arm_compute/core/ITensor.h> -#include <arm_compute/runtime/IFunction.h> +/** + * @file SimpleEmbeddingLookup.h + * @brief This file contains SimpleEmbeddingLookup class + * @ingroup COM_AI_RUNTIME + */ + +/** + * @brief Class to run SimpleEmbeddingLookup Layer + */ class SimpleEmbeddingLookup : public ::arm_compute::IFunction { public: + SimpleEmbeddingLookup(void) : _lookups(nullptr), _values(nullptr), _output(nullptr) + { + // DO NOTHING + } + +public: + /** + * @brief Configure the layer + * @param[in] lookups 1D tensor which contains lookup values + * @param[in] values The source tensor + * @param[in] output The destination tensor + * @return N/A + */ void configure(::arm_compute::ITensor *lookups, ::arm_compute::ITensor *values, ::arm_compute::ITensor *output); + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run() override; private: diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.cc new file mode 100644 index 000000000..7f8ae2505 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.cc @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleHashtableLookupLayer.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleHashtableLookupLayer::configure(::arm_compute::ITensor *lookups, + ::arm_compute::ITensor *keys, + ::arm_compute::ITensor *values, + ::arm_compute::ITensor *output, + ::arm_compute::ITensor *hits) +{ + _lookups = lookups; + _keys = keys; + _values = values; + _output = output; + _hits = hits; + _lookup_indices.resize(lookups->info()->dimension(0), -1); +} + +void SimpleHashtableLookupLayer::run() +{ + auto &queue = ::arm_compute::CLScheduler::get().queue(); + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_lookups)->map(queue); + CAST_CL(_keys)->map(queue); + CAST_CL(_values)->map(queue); + CAST_CL(_output)->map(queue); + CAST_CL(_hits)->map(queue); + } + + const int32_t *lookups_buf = reinterpret_cast<int32_t *>(_lookups->buffer()); + const int32_t *keys_buf = reinterpret_cast<int32_t *>(_keys->buffer()); + uint8_t *hits_buf = reinterpret_cast<uint8_t *>(_hits->buffer()); + + const auto lookups_info = _lookups->info(); + const auto values_info = _values->info(); + const auto keys_info = _keys->info(); + const auto output_info = _output->info(); + + // NOTE The first dimension's position must be always at the end of dimensions. + const auto first_dim_pos = values_info->num_dimensions() - 1; + const size_t first_dim = values_info->dimension(first_dim_pos); + + std::map<int32_t, size_t> key_map; + const int keys_num = keys_info->dimension(0); + for (size_t key_index = 0; key_index < keys_num; key_index++) + { + key_map[keys_buf[key_index]] = key_index; + } + + const int lookups_num = lookups_info->dimension(0); + for (size_t i = 0; i < lookups_num; ++i) + { + const auto lookup_value = lookups_buf[i]; + const auto it = key_map.find(lookup_value); + if (it != key_map.end()) + { + if (it->second >= first_dim) + throw std::runtime_error("HashTable Lookup: index out of bounds."); + _lookup_indices[i] = it->second; + } + } + + // If each strides of values and output are different, applied padding size of the two tensors are + // different, therefore, it can not be copied at once. + auto can_copy_at_once = [&]() -> bool { + const auto &values_strides = values_info->strides_in_bytes(); + const auto &output_strides = output_info->strides_in_bytes(); + + for (size_t i = 0; i < first_dim_pos; ++i) + { + if (values_strides[i] != values_strides[i]) + return false; + } + + return true; + }; + + using ::arm_compute::Window; + using ::arm_compute::Iterator; + using ::arm_compute::Coordinates; + + size_t copy_bytes; + Window window; + if (can_copy_at_once()) + { + copy_bytes = values_info->total_size() / first_dim; + window.use_tensor_dimensions(output_info->tensor_shape(), first_dim_pos); + } + else + { + copy_bytes = values_info->dimension(0) * values_info->element_size(); + window.use_tensor_dimensions(output_info->tensor_shape(), Window::DimY); + } + + Iterator it(_output, window); + execute_window_loop(window, + [&](const Coordinates &id) { + Coordinates values_id = id; + const int idx = id[first_dim_pos]; + const int lookup_index = _lookup_indices[idx]; + if (lookup_index >= 0) + { + values_id.set(first_dim_pos, lookup_index); + memcpy(it.ptr(), _values->ptr_to_element(values_id), copy_bytes); + hits_buf[lookup_index] = 1; + } + else + { + memset(it.ptr(), 0, copy_bytes); + hits_buf[lookup_index] = 0; + } + }, + it); + + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_lookups)->unmap(queue); + CAST_CL(_keys)->unmap(queue); + CAST_CL(_values)->unmap(queue); + CAST_CL(_output)->unmap(queue); + CAST_CL(_hits)->unmap(queue); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.h new file mode 100644 index 000000000..ba9d2ec0d --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleHashtableLookupLayer.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_HASHTABLE_LOOKUP_H__ +#define __SIMPLE_HASHTABLE_LOOKUP_H__ + +#include "internal/arm_compute.h" + +class SimpleHashtableLookupLayer : public ::arm_compute::IFunction +{ +public: + SimpleHashtableLookupLayer(void) + : _lookups(nullptr), _keys(nullptr), _values(nullptr), _output(nullptr), _hits(nullptr) + { + // DO NOTHING + } + + void configure(::arm_compute::ITensor *lookups, ::arm_compute::ITensor *keys, + ::arm_compute::ITensor *values, ::arm_compute::ITensor *output, + ::arm_compute::ITensor *hits); + + void run() override; + +private: + ::arm_compute::ITensor *_lookups; + ::arm_compute::ITensor *_keys; + ::arm_compute::ITensor *_values; + ::arm_compute::ITensor *_output; + ::arm_compute::ITensor *_hits; + std::vector<int32_t> _lookup_indices; +}; + +#endif /*__SIMPLE_HASHTABLE_LOOKUP_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.cc new file mode 100644 index 000000000..d3943ad40 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.cc @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleNeg.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleNeg::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output) +{ + _input = input; + _output = output; +} + +void SimpleNeg::run() +{ + auto &queue = ::arm_compute::CLScheduler::get().queue(); + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_input)->map(queue); + CAST_CL(_output)->map(queue); + } + + arm_compute::Window window; + window.use_tensor_dimensions(_output->info()->tensor_shape()); + + execute_window_loop(window, [this](const arm_compute::Coordinates &id) { + // NOTE Must be two input tensors of identical type + // Must be output tensor of the same type as input0. + assert(_input->info()->data_type() == _output->info()->data_type()); + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::F32: + { + const auto input_value = *reinterpret_cast<float *>(_input->ptr_to_element(id)); + *reinterpret_cast<float *>(_output->ptr_to_element(id)) = -input_value; + break; + } + case ::arm_compute::DataType::S32: + { + const auto input_value = *reinterpret_cast<int32_t *>(_input->ptr_to_element(id)); + *reinterpret_cast<int32_t *>(_output->ptr_to_element(id)) = -input_value; + break; + } + case ::arm_compute::DataType::U32: + { + const auto input_value = *reinterpret_cast<uint32_t *>(_input->ptr_to_element(id)); + *reinterpret_cast<uint32_t *>(_output->ptr_to_element(id)) = -input_value; + break; + } + default: + throw std::runtime_error("Not supported, yet"); + break; + } + }); + + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_input)->unmap(queue); + CAST_CL(_output)->unmap(queue); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.h new file mode 100644 index 000000000..4ca88e7f8 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleNeg.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_NEG_H__ +#define __SIMPLE_NEG_H__ + +#include "internal/arm_compute.h" + +class SimpleNeg : public ::arm_compute::IFunction +{ +public: + SimpleNeg(void) : _input(nullptr), _output(nullptr) + { + // DO NOTHING + } + + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; +}; + +#endif /*__SIMPLE_NEG_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.cc new file mode 100644 index 000000000..2a0a25f0c --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.cc @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "internal/arm_compute.h" +#include "SimplePackLayer.h" + +void SimplePackLayer::configure(const std::vector<::arm_compute::ICLTensor *> &input_vector, + ::arm_compute::ICLTensor *output, int32_t axis) +{ + uint32_t nr_inputs = input_vector.size(); + uint32_t output_rank = output->info()->num_dimensions(); + const ::arm_compute::PermutationVector pv{1, 2, 0}; + _cl_permuted_vector.resize(nr_inputs); + _cl_permute_vector.resize(nr_inputs); + + _output = output; + // A negative axis implies axis from the end. + // For example, axis = -1 implies the first axis from the end, i.e. axis = Rank - 1. + // Similarly, axis = -2 imples second axis from the end, i.e. axis = Rank - 2. + if (axis < 0) + { + axis += output_rank; + } + _axis = ToARMComputeAxis(output_rank, axis).value(); + _cl_reshape_vector.resize(nr_inputs); + + ::arm_compute::TensorShape subTensor_shape{}; + for (int i = 0; i < output_rank; i++) + { + if (i != _axis) + { + subTensor_shape.set(i, _output->info()->tensor_shape()[i]); + } + else + { + subTensor_shape.set(i, 1); + } + } + + auto subTensor_offset = ::arm_compute::Coordinates{}; + subTensor_offset.set_num_dimensions(output_rank); + + for (int i = 0; i < input_vector.size(); i++) + { + _input_vector.push_back(input_vector[i]); + subTensor_offset[_axis] = i; + auto temp_tensor = std::make_shared<::arm_compute::CLSubTensor>( + CAST_CL(_output), subTensor_shape, subTensor_offset, true); + _sub_tensor_vector.push_back(temp_tensor); + // configure to resize of input tensor in sub tensor offseted, dimension expansion will be + // automatic + _cl_permute_vector[i].configure(CAST_CL(_input_vector[i]), &_cl_permuted_vector[i], pv); + _cl_reshape_vector[i].configure(&_cl_permuted_vector[i], _sub_tensor_vector[i].get()); + _cl_permuted_vector[i].allocator()->allocate(); + } +} + +void SimplePackLayer::run(void) +{ + for (int i = 0; i < _input_vector.size(); i++) + { + _cl_permute_vector[i].run(); + _cl_reshape_vector[i].run(); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.h b/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.h new file mode 100644 index 000000000..2c2fc37f2 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimplePackLayer.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __SIMPLE_PACK_LAYER_H__ +#define __SIMPLE_PACK_LAYER_H__ + +#include <arm_compute/runtime/CL/CLTensor.h> +#include <arm_compute/runtime/CL/CLSubTensor.h> +#include <arm_compute/runtime/CL/functions/CLReshapeLayer.h> +#include <arm_compute/runtime/CL/functions/CLPermute.h> + +class SimplePackLayer : public ::arm_compute::IFunction +{ +public: + SimplePackLayer(void) + : _cl_permuted_vector{}, _input_vector{}, _sub_tensor_vector{}, _cl_reshape_vector{}, + _cl_permute_vector{}, _output(nullptr), _axis(0) + { + // DO NOTHING + } + +public: + void configure(const std::vector<::arm_compute::ICLTensor *> &input_vector, + ::arm_compute::ICLTensor *output, int axis); + +public: + void run(void) override; + +private: + std::vector<::arm_compute::CLTensor> _cl_permuted_vector; + std::vector<::arm_compute::ICLTensor *> _input_vector; + std::vector<std::shared_ptr<::arm_compute::CLSubTensor>> _sub_tensor_vector; + std::vector<::arm_compute::CLReshapeLayer> _cl_reshape_vector; + std::vector<::arm_compute::CLPermute> _cl_permute_vector; + ::arm_compute::ICLTensor *_output; + int _axis; +}; + +#endif // __SIMPLE_PACK_LAYER_H__ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc new file mode 100644 index 000000000..64236603f --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc @@ -0,0 +1,172 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimplePadLayer.h" +#include <arm_compute/runtime/CL/CLScheduler.h> + +namespace +{ +bool validate_arg(const ::arm_compute::ITensor *input, const ::arm_compute::ITensor *output, + const ::arm_compute::ITensor *padding_size, + const ::arm_compute::Coordinates &axises) +{ + const int input_batch = input->info()->tensor_shape()[axises[0]]; + const int input_height = input->info()->tensor_shape()[axises[1]]; + const int input_width = input->info()->tensor_shape()[axises[2]]; + const int input_depth = input->info()->tensor_shape()[axises[3]]; + + const int output_batch = output->info()->tensor_shape()[axises[0]]; + const int output_height = output->info()->tensor_shape()[axises[1]]; + const int output_width = output->info()->tensor_shape()[axises[2]]; + const int output_depth = output->info()->tensor_shape()[axises[3]]; + + auto pad_batch_up = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 0})); + auto pad_batch_down = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 0})); + auto pad_height_top = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 1})); + auto pad_height_bottom = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 1})); + auto pad_width_left = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 2})); + auto pad_width_right = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 2})); + auto pad_depth_front = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 3})); + auto pad_depth_back = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 3})); + + const int padded_batch = input_batch + pad_batch_up + pad_batch_down; + const int padded_height = input_height + pad_height_top + pad_height_bottom; + const int padded_width = input_width + pad_width_left + pad_width_right; + const int padded_depth = input_depth + pad_depth_front + pad_depth_back; + + return (padded_batch == output_batch) && (padded_height == output_height) && + (padded_width == output_width) && (padded_depth == output_depth); +} +} // namespace + +void SimplePadLayer::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + ::arm_compute::ITensor *padding_size, + const ::arm_compute::Coordinates &axises) +{ + + const auto rank = axises.num_dimensions(); + assert(rank == 4); + assert(input != nullptr && output != nullptr && padding_size != nullptr); + + for (int i = 0; i < rank; ++i) + { + assert(axises[i] >= 0); + assert(axises[i] < rank); + } + + _input = input; + _output = output; + _padding_size = padding_size; + _axises = axises; +} + +template <typename T> +inline void ApplyPadding(const ::arm_compute::ITensor *input_data, + const ::arm_compute::TensorShape &input_shape, + const ::arm_compute::ITensor *padding_size, + ::arm_compute::ITensor *output_data, + const ::arm_compute::TensorShape &output_shape, + const ::arm_compute::Coordinates &axises, T zero_value) +{ + + assert(validate_arg(input_data, output_data, padding_size, axises) && + "Padded Input shape does not match to output shape"); + + const int input_batch = input_shape[axises[0]]; + const int input_height = input_shape[axises[1]]; + const int input_width = input_shape[axises[2]]; + const int input_depth = input_shape[axises[3]]; + + const int output_batch = output_shape[axises[0]]; + const int output_height = output_shape[axises[1]]; + const int output_width = output_shape[axises[2]]; + const int output_depth = output_shape[axises[3]]; + + // Padding size for Up, Top, Left and Front are required. + auto pad_batch_up = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 0})); + auto pad_height_top = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 1})); + auto pad_width_left = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 2})); + auto pad_depth_front = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 3})); + + for (int out_b = 0; out_b < output_batch; ++out_b) + { + for (int out_h = 0; out_h < output_height; ++out_h) + { + for (int out_w = 0; out_w < output_width; ++out_w) + { + for (int out_d = 0; out_d < output_depth; ++out_d) + { + auto output_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{out_b, out_h, out_w, out_d}, axises); + + if (out_b < pad_batch_up || out_b >= (input_batch + pad_batch_up) || + out_h < pad_height_top || out_h >= (input_height + pad_height_top) || + out_w < pad_width_left || out_w >= (input_width + pad_width_left) || + out_d < pad_depth_front || out_d >= (input_depth + pad_depth_front)) + { + *reinterpret_cast<T *>(output_data->ptr_to_element(output_id)) = zero_value; + } + else + { + auto input_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{out_b - pad_batch_up, out_h - pad_height_top, + out_w - pad_width_left, out_d - pad_depth_front}, + axises); + *reinterpret_cast<T *>(output_data->ptr_to_element(output_id)) = + *reinterpret_cast<T *>(input_data->ptr_to_element(input_id)); + } + } + } + } + } +} +void SimplePadLayer::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_output)->map(q); + CAST_CL(_padding_size)->map(q); + } + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::U8: + case ::arm_compute::DataType::QASYMM8: + ApplyPadding<uint8_t>(_input, _input->info()->tensor_shape(), _padding_size, _output, + _output->info()->tensor_shape(), _axises, + _input->info()->quantization_info().offset); + break; + case ::arm_compute::DataType::F32: + ApplyPadding<float>(_input, _input->info()->tensor_shape(), _padding_size, _output, + _output->info()->tensor_shape(), _axises, 0.0f); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_output)->unmap(q); + CAST_CL(_padding_size)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.h b/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.h new file mode 100644 index 000000000..8cb6659ce --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_PAD_LAYER_H__ +#define __SIMPLE_PAD_LAYER_H__ + +#include "internal/arm_compute.h" +#include "internal/arm_compute/Cast.h" + +class SimplePadLayer : public ::arm_compute::IFunction +{ +public: + SimplePadLayer(void) : _input(nullptr), _output(nullptr), _padding_size(nullptr), _axises{} + { + // DO NOTHING + } + + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, + ::arm_compute::ITensor *padding_size, + const ::arm_compute::Coordinates &axises = getARMComputeAxises(4)); + + void run(void) override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; + ::arm_compute::ITensor *_padding_size; + ::arm_compute::Coordinates _axises; +}; + +#endif // __SIMPLE_PAD_LAYER_H__ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.cc new file mode 100644 index 000000000..b5b3a0950 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.cc @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleSQRT.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleSQRT::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output) +{ + _input = input; + _output = output; +} + +void SimpleSQRT::run() +{ + auto &queue = ::arm_compute::CLScheduler::get().queue(); + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_input)->map(queue); + CAST_CL(_output)->map(queue); + } + + arm_compute::Window window; + window.use_tensor_dimensions(_output->info()->tensor_shape()); + + execute_window_loop(window, [this](const arm_compute::Coordinates &id) { + // NOTE Must be two input tensors of identical type + // Must be output tensor of the same type as input0. + assert(_input->info()->data_type() == _output->info()->data_type()); + + const auto input_value = *reinterpret_cast<float *>(_input->ptr_to_element(id)); + *reinterpret_cast<float *>(_output->ptr_to_element(id)) = sqrt(input_value); + }); + + if (::internal::arm_compute::isGpuMode()) + { + CAST_CL(_input)->unmap(queue); + CAST_CL(_output)->unmap(queue); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.h new file mode 100644 index 000000000..b05a9e32e --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSQRT.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_SQRT_H__ +#define __SIMPLE_SQRT_H__ + +#include "internal/arm_compute.h" + +class SimpleSQRT : public ::arm_compute::IFunction +{ +public: + SimpleSQRT(void) : _input(nullptr), _output(nullptr) + { + // DO NOTHING + } + + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_output; +}; + +#endif /*__SIMPLE_SQRT_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.cc new file mode 100644 index 000000000..f53675b99 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.cc @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleSpaceToBatchND.h" + +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleSpaceToBatchND::configure(::arm_compute::ITensor *input, + ::arm_compute::ITensor *block_size, + ::arm_compute::ITensor *padding_size, + ::arm_compute::ITensor *output) +{ + const auto rank = input->info()->num_dimensions(); + assert(rank == 4); + + _input = input; + _block_size = block_size; + _padding_size = padding_size; + _output = output; +} + +template <typename T> +inline void +SpaceToBatchND(const ::arm_compute::ITensor *input, const ::arm_compute::TensorShape &input_shape, + const ::arm_compute::ITensor *block_size, const ::arm_compute::ITensor *padding_size, + const ::arm_compute::ITensor *output, const ::arm_compute::TensorShape &output_shape, + T zero_value) +{ + const int input_batch = input_shape[3]; + const int input_height = input_shape[1]; + const int input_width = input_shape[0]; + + const int depth = output_shape[2]; + + const int padding_height_left = *reinterpret_cast<int *>(padding_size->ptr_to_element({0, 1})); + const int padding_height_right = *reinterpret_cast<int *>(padding_size->ptr_to_element({1, 1})); + const int padding_width_left = *reinterpret_cast<int *>(padding_size->ptr_to_element({0, 0})); + const int padding_width_right = *reinterpret_cast<int *>(padding_size->ptr_to_element({1, 0})); + const int padded_height = input_height + padding_height_left + padding_height_right; + const int padded_width = input_width + padding_width_left + padding_width_right; + + const int block_size_height = *reinterpret_cast<int *>(block_size->ptr_to_element({1})); + const int block_size_width = *reinterpret_cast<int *>(block_size->ptr_to_element({0})); + + assert(padding_height_left >= 0); + assert(padding_height_right >= 0); + assert(padding_width_left >= 0); + assert(padding_width_right >= 0); + assert(block_size_height >= 1); + assert(block_size_width >= 1); + assert(padded_height % block_size_height == 0); + assert(padded_width % block_size_width == 0); + assert(output->info()->dimension(3) == + input->info()->dimension(3) * (block_size_height * block_size_width)); + + for (int in_b = 0; in_b < input_batch; ++in_b) + { + for (int in_d = 0; in_d < depth; ++in_d) + { + for (int in_h = 0; in_h < padded_height; ++in_h) + { + for (int in_w = 0; in_w < padded_width; ++in_w) + { + const int out_d = in_d; + const int out_h = in_h / block_size_height; + const int out_w = in_w / block_size_width; + const int out_b = + in_b + + ((in_h % block_size_height) * block_size_width + in_w % block_size_width) * + input_batch; + + const ::arm_compute::Coordinates output_id{out_w, out_h, out_d, out_b}; + + if (in_h < padding_height_left || in_h >= (input_height + padding_height_left) || + in_w < padding_width_left || in_w >= (input_width + padding_width_left)) + { + *reinterpret_cast<T *>(output->ptr_to_element(output_id)) = zero_value; + } + else + { + const ::arm_compute::Coordinates input_id{in_w - padding_width_left, + in_h - padding_height_left, in_d, in_b}; + *reinterpret_cast<T *>(output->ptr_to_element(output_id)) = + *reinterpret_cast<T *>(input->ptr_to_element(input_id)); + } + } + } + } + } +} +void SimpleSpaceToBatchND::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_block_size)->map(q); + CAST_CL(_padding_size)->map(q); + CAST_CL(_output)->map(q); + } + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::U8: + case ::arm_compute::DataType::QASYMM8: + SpaceToBatchND<uint8_t>(_input, _input->info()->tensor_shape(), _block_size, _padding_size, + _output, _output->info()->tensor_shape(), + _input->info()->quantization_info().offset); + break; + case ::arm_compute::DataType::F32: + SpaceToBatchND<float>(_input, _input->info()->tensor_shape(), _block_size, _padding_size, + _output, _output->info()->tensor_shape(), 0.0f); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_block_size)->unmap(q); + CAST_CL(_padding_size)->unmap(q); + CAST_CL(_output)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.h new file mode 100644 index 000000000..4af961d34 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToBatchND.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SIMPLE_SPACE_TO_BATCHND_H__ +#define __SIMPLE_SPACE_TO_BATCHND_H__ + +#include "internal/arm_compute.h" + +class SimpleSpaceToBatchND : public ::arm_compute::IFunction +{ +public: + SimpleSpaceToBatchND(void) + : _input(nullptr), _block_size(nullptr), _padding_size(nullptr), _output(nullptr) + { + // DO NOTHING + } + + /** Initialise input and output + * + * @param[in] input First tensor input. + * @param[in] block_size Block size. + * @param[in] padding_size Padding size. + * @param[out] output Output tensor. + */ + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *block_size, + ::arm_compute::ITensor *padding_size, ::arm_compute::ITensor *output); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_block_size; + ::arm_compute::ITensor *_padding_size; + ::arm_compute::ITensor *_output; +}; + +#endif /*__SIMPLE_SPACE_TO_BATCHND_H__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.cc index 682295f81..3519da1f3 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.cc +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.cc @@ -19,11 +19,8 @@ #include <arm_compute/runtime/CL/CLScheduler.h> void SimpleSpaceToDepth::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, - int32_t block_size, - const ::arm_compute::Coordinates &axises = {3, 1, 0, 2}) + int32_t block_size, const ::arm_compute::Coordinates &axises) { - assert(input->info()->num_dimensions() == 4); - assert(output->info()->num_dimensions() == 4); const auto rank = axises.num_dimensions(); assert(rank == 4); for (int i = 0; i < rank; ++i) @@ -38,26 +35,10 @@ void SimpleSpaceToDepth::configure(::arm_compute::ITensor *input, ::arm_compute: _axises = axises; } -inline int32_t Offset4D(const ::arm_compute::TensorShape &shape, int32_t b, int32_t h, int32_t w, - int32_t d, const ::arm_compute::Coordinates &axises) -{ - // b, h, w, d >= 0 - size_t indexes[4]; - indexes[axises[0]] = b; - indexes[axises[1]] = h; - indexes[axises[2]] = w; - indexes[axises[3]] = d; - - int32_t offset = indexes[3] * shape[2] * shape[1] * shape[0]; - offset += indexes[2] * shape[1] * shape[0]; - offset += indexes[1] * shape[0]; - offset += indexes[0]; - return offset; -} - template <typename T> -inline void SpaceToDepth(const T *input_data, const ::arm_compute::TensorShape &input_shape, - int32_t block_size, T *output_data, +inline void SpaceToDepth(const ::arm_compute::ITensor *input, + const ::arm_compute::TensorShape &input_shape, int32_t block_size, + ::arm_compute::ITensor *output, const ::arm_compute::TensorShape &output_shape, const ::arm_compute::Coordinates &axises) { @@ -66,16 +47,6 @@ inline void SpaceToDepth(const T *input_data, const ::arm_compute::TensorShape & const int input_width = input_shape[axises[2]]; const int input_depth = input_shape[axises[3]]; - const int output_batch = output_shape[axises[0]]; - const int output_height = output_shape[axises[1]]; - const int output_width = output_shape[axises[2]]; - const int output_depth = output_shape[axises[3]]; - - assert(input_batch == output_batch); - assert(input_height == output_height * block_size); - assert(input_width == output_width * block_size); - assert(input_depth * block_size * block_size == output_depth); - for (int in_b = 0; in_b < input_batch; ++in_b) { for (int in_h = 0; in_h < input_height; ++in_h) @@ -90,10 +61,13 @@ inline void SpaceToDepth(const T *input_data, const ::arm_compute::TensorShape & const int out_d = in_d + ((in_h % block_size) * block_size + in_w % block_size) * input_depth; - const int input_index = Offset4D(input_shape, in_b, in_h, in_w, in_d, axises); - const int output_index = Offset4D(output_shape, out_b, out_h, out_w, out_d, axises); + auto input_id = + asARMComputeCoordinates(::arm_compute::Coordinates{in_b, in_h, in_w, in_d}, axises); + auto output_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{out_b, out_h, out_w, out_d}, axises); - output_data[output_index] = input_data[input_index]; + *reinterpret_cast<T *>(output->ptr_to_element(output_id)) = + *reinterpret_cast<T *>(input->ptr_to_element(input_id)); } } } @@ -110,35 +84,16 @@ void SimpleSpaceToDepth::run() CAST_CL(_output)->map(q); } - auto input_buf = _input->buffer(); - auto output_buf = _output->buffer(); switch (_input->info()->data_type()) { case ::arm_compute::DataType::U8: case ::arm_compute::DataType::QASYMM8: - SpaceToDepth(reinterpret_cast<const uint8_t *>(input_buf), _input->info()->tensor_shape(), - _block_size, reinterpret_cast<uint8_t *>(output_buf), - _output->info()->tensor_shape(), _axises); - break; - case ::arm_compute::DataType::S8: - SpaceToDepth(reinterpret_cast<const int8_t *>(input_buf), _input->info()->tensor_shape(), - _block_size, reinterpret_cast<int8_t *>(output_buf), - _output->info()->tensor_shape(), _axises); - break; - case ::arm_compute::DataType::U32: - SpaceToDepth(reinterpret_cast<const uint32_t *>(input_buf), _input->info()->tensor_shape(), - _block_size, reinterpret_cast<uint32_t *>(output_buf), - _output->info()->tensor_shape(), _axises); - break; - case ::arm_compute::DataType::S32: - SpaceToDepth(reinterpret_cast<const int32_t *>(input_buf), _input->info()->tensor_shape(), - _block_size, reinterpret_cast<int32_t *>(output_buf), - _output->info()->tensor_shape(), _axises); + SpaceToDepth<uint8_t>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); break; case ::arm_compute::DataType::F32: - SpaceToDepth(reinterpret_cast<const float *>(input_buf), _input->info()->tensor_shape(), - _block_size, reinterpret_cast<float *>(output_buf), - _output->info()->tensor_shape(), _axises); + SpaceToDepth<float>(_input, _input->info()->tensor_shape(), _block_size, _output, + _output->info()->tensor_shape(), _axises); break; default: ARM_COMPUTE_ERROR("DataType not supported"); diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.h index f5e028b1c..9e87c364c 100644 --- a/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.h +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleSpaceToDepth.h @@ -14,25 +14,44 @@ * limitations under the License. */ +/** + * @file SimpleSpaceToDepth.h + * @brief This file contains SimpleSpaceToDepth class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __SIMPLE_SPACE_TO_DEPTH_H__ #define __SIMPLE_SPACE_TO_DEPTH_H__ #include "internal/arm_compute.h" -#include <arm_compute/core/ITensor.h> -#include <arm_compute/runtime/IFunction.h> +#include "internal/arm_compute/Cast.h" +/** + * @brief Class to run SimpleEmbeddingLookup Layer + */ class SimpleSpaceToDepth : public ::arm_compute::IFunction { public: - /** Initialise input and output - * - * @param[in] input First tensor input. - * @param[out] output Output tensor. - * @param[in] block_size Block size. + SimpleSpaceToDepth(void) : _input(nullptr), _output(nullptr), _block_size(0), _axises{} + { + // DO NOTHING + } + + /** + * @brief Configure the layer + * @param[in] input First tensor input. + * @param[in] output Output tensor. + * @param[in] block_size Block size. + * @param[in] axises Axises of rank 4 + * @return N/A */ void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output, int32_t block_size, - const ::arm_compute::Coordinates &axises); + const ::arm_compute::Coordinates &axises = getARMComputeAxises(4)); + /** + * @brief Run the operation. Must be called after configure(). + * @return N/A + */ void run() override; private: diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.cc new file mode 100644 index 000000000..abc291289 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.cc @@ -0,0 +1,155 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/layers/SimpleTransposeConv.h" +#include <arm_compute/runtime/CL/CLScheduler.h> + +void SimpleTransposeConv::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *weights, + ::arm_compute::ITensor *output, + ::arm_compute::PadStrideInfo &tconv_info, + ::arm_compute::Coordinates axises) +{ + auto rank = axises.num_dimensions(); + + assert(rank == 4); + + _input = input; + _weights = weights; + _output = output; + _stride_width = tconv_info.stride().first; + _stride_height = tconv_info.stride().second; + _pad_width = tconv_info.pad_left(); + _pad_height = tconv_info.pad_top(); + _axises = axises; +} + +template <typename T> +inline void ApplyTransposeConv( + const ::arm_compute::TensorShape &input_shape, const ::arm_compute::ITensor *input_data, + const ::arm_compute::TensorShape &filter_shape, const ::arm_compute::ITensor *filter_data, + const ::arm_compute::TensorShape &output_shape, const ::arm_compute::ITensor *output_data, + const int32_t stride_width, const int32_t stride_height, const int32_t pad_width, + const int32_t pad_height, const ::arm_compute::Coordinates axises) +{ + const int batches = input_shape[axises[0]]; + const int input_height = input_shape[axises[1]]; + const int input_width = input_shape[axises[2]]; + const int input_depth = input_shape[axises[3]]; + + const int filter_height = filter_shape[axises[1]]; + const int filter_width = filter_shape[axises[2]]; + + const int output_height = output_shape[axises[1]]; + const int output_width = output_shape[axises[2]]; + const int output_depth = output_shape[axises[3]]; + + assert(batches == output_shape[axises[0]]); + assert(input_depth == filter_shape[axises[3]]); + assert(filter_shape[axises[0]] == output_depth); + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a + // "scatter" access pattern, where we loop through all the input elements, + // computing their influence on the output, rather than looping through the + // output elements in the typical "gather" access pattern of a conv. We + // therefore must initialize the output array to zero. + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) + { + for (int in_y = 0; in_y < input_height; ++in_y) + { + for (int in_x = 0; in_x < input_width; ++in_x) + { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) + { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) + { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) + { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) + { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) + { + auto input_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{batch, in_y, in_x, in_channel}, axises); + auto filter_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{in_channel, filter_y, filter_x, out_channel}, + axises); + auto output_id = asARMComputeCoordinates( + ::arm_compute::Coordinates{batch, out_y, out_x, out_channel}, axises); + T input_value = *reinterpret_cast<T *>(input_data->ptr_to_element(input_id)); + T filter_value = *reinterpret_cast<T *>(filter_data->ptr_to_element(filter_id)); + *reinterpret_cast<T *>(output_data->ptr_to_element(output_id)) += + input_value * filter_value; + } + } + } + } + } + } + } + } +} + +void SimpleTransposeConv::run() +{ + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->map(q); + CAST_CL(_weights)->map(q); + CAST_CL(_output)->map(q); + } + + switch (_input->info()->data_type()) + { + case ::arm_compute::DataType::S32: + ApplyTransposeConv<int32_t>(_input->info()->tensor_shape(), _input, + _weights->info()->tensor_shape(), _weights, + _output->info()->tensor_shape(), _output, _stride_width, + _stride_height, _pad_width, _pad_height, _axises); + break; + case ::arm_compute::DataType::F32: + ApplyTransposeConv<float>(_input->info()->tensor_shape(), _input, + _weights->info()->tensor_shape(), _weights, + _output->info()->tensor_shape(), _output, _stride_width, + _stride_height, _pad_width, _pad_height, _axises); + break; + default: + ARM_COMPUTE_ERROR("DataType not supported"); + break; + } + + if (::internal::arm_compute::isGpuMode()) + { + auto &q = ::arm_compute::CLScheduler::get().queue(); + + CAST_CL(_input)->unmap(q); + CAST_CL(_weights)->unmap(q); + CAST_CL(_output)->unmap(q); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.h new file mode 100644 index 000000000..c5519828b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleTransposeConv.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TRANSPOSE_CONV_EX__ +#define __TRANSPOSE_CONV_EX__ + +#include "internal/arm_compute.h" +#include "internal/arm_compute/Cast.h" + +class SimpleTransposeConv : public ::arm_compute::IFunction +{ +public: + SimpleTransposeConv() + : _input(nullptr), _weights(nullptr), _output(nullptr), _stride_width(0), _stride_height(0), + _pad_width(0), _pad_height(0) + { + // DO NOTHING + } + + /** Initialise input and output + * + * @param[in] input First tensor input. + * @param[in] weights Weights + * @param[out] output Output tensor. + * @param[in] tc_info Contains padding and policies to be used in the deconvolution, + * this is decribed in @ref PadStrideInfo. + * @param[in] axises Axises of rank 4 + */ + void configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *weights, + ::arm_compute::ITensor *output, ::arm_compute::PadStrideInfo &tconv_info, + ::arm_compute::Coordinates axises = getARMComputeAxises(4)); + + void run() override; + +private: + ::arm_compute::ITensor *_input; + ::arm_compute::ITensor *_weights; + ::arm_compute::ITensor *_output; + int32_t _stride_width; + int32_t _stride_height; + int32_t _pad_width; + int32_t _pad_height; + ::arm_compute::Coordinates _axises; +}; + +#endif /*__TRANSPOSE_CONV_EX__ */ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.cc new file mode 100644 index 000000000..910595a44 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.cc @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "internal/arm_compute.h" +#include "SimpleUnpackLayer.h" + +void SimpleUnpackLayer::configure(::arm_compute::ICLTensor *input, + const std::vector<::arm_compute::ICLTensor *> &output_vector, + int32_t axis) +{ + uint32_t nr_outputs = output_vector.size(); + _cl_permuted_vector.resize(nr_outputs); + _cl_permute_vector.resize(nr_outputs); + uint32_t input_rank = input->info()->num_dimensions(); + const ::arm_compute::PermutationVector pv{2, 0, 1}; + _input = input; + // Negatige axis is supported, -1 implies R-1 axis where R is input rank + if (axis < 0) + { + axis += input_rank; + } + _axis = ToARMComputeAxis(input_rank, axis).value(); + _cl_reshape_vector.resize(nr_outputs); + + ::arm_compute::TensorShape subTensor_shape{}; + for (int i = 0; i < input_rank; i++) + { + if (i != _axis) + { + subTensor_shape.set(i, _input->info()->tensor_shape()[i]); + } + else + { + subTensor_shape.set(i, 1); + } + } + + auto subTensor_offset = ::arm_compute::Coordinates{}; + subTensor_offset.set_num_dimensions(input_rank); + + for (int i = 0; i < output_vector.size(); i++) + { + _output_vector.push_back(output_vector[i]); + subTensor_offset[_axis] = i; + auto temp_tensor = std::make_shared<::arm_compute::CLSubTensor>( + CAST_CL(_input), subTensor_shape, subTensor_offset, true); + _sub_tensor_vector.push_back(temp_tensor); + // Copies into the subtensor + _cl_permute_vector[i].configure(_sub_tensor_vector[i].get(), &_cl_permuted_vector[i], pv); + _cl_reshape_vector[i].configure(&_cl_permuted_vector[i], CAST_CL(_output_vector[i])); + _cl_permuted_vector[i].allocator()->allocate(); + } +} + +void SimpleUnpackLayer::run(void) +{ + for (int i = 0; i < _output_vector.size(); i++) + { + _cl_permute_vector[i].run(); + _cl_reshape_vector[i].run(); + } +} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.h b/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.h new file mode 100644 index 000000000..52fc7513d --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/layers/SimpleUnpackLayer.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef __UNPACK_LAYER_H__ +#define __UNPACK_LAYER_H__ + +#include <arm_compute/runtime/CL/CLTensor.h> +#include <arm_compute/runtime/CL/CLSubTensor.h> +#include <arm_compute/runtime/CL/functions/CLReshapeLayer.h> +#include <arm_compute/runtime/CL/functions/CLPermute.h> + +class SimpleUnpackLayer : public ::arm_compute::IFunction +{ +public: + SimpleUnpackLayer(void) + : _cl_permuted_vector{}, _output_vector{}, _sub_tensor_vector{}, _cl_reshape_vector{}, + _cl_permute_vector{}, _input(nullptr), _axis(0) + { + // DO NOTHING + } + +public: + void configure(::arm_compute::ICLTensor *input, + const std::vector<::arm_compute::ICLTensor *> &output_vector, int32_t axis); + +public: + void run(void) override; + +private: + std::vector<::arm_compute::CLTensor> _cl_permuted_vector; + std::vector<::arm_compute::ICLTensor *> _output_vector; + std::vector<std::shared_ptr<::arm_compute::CLSubTensor>> _sub_tensor_vector; + std::vector<::arm_compute::CLReshapeLayer> _cl_reshape_vector; + std::vector<::arm_compute::CLPermute> _cl_permute_vector; + ::arm_compute::ICLTensor *_input; + int32_t _axis; +}; + +#endif // __UNPACK_LAYER_H__ diff --git a/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.cc b/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.cc deleted file mode 100644 index 3f988a819..000000000 --- a/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.cc +++ /dev/null @@ -1,40 +0,0 @@ -#include "SquaredDifferenceOperation.h" -#include "internal/arm_compute.h" - -void SquaredDifferenceOperation::configure(::arm_compute::ITensor *input1, - ::arm_compute::ITensor *input2, - ::arm_compute::ITensor *output, - ::arm_compute::ConvertPolicy ConvertPolicy, float scale, - ::arm_compute::RoundingPolicy RoundingPolicy) -{ - _input1 = input1; - _input2 = input2; - _output = output; - - if (::internal::arm_compute::isGpuMode()) - { - _cl_sub.configure(CAST_CL(input1), CAST_CL(input2), CAST_CL(output), ConvertPolicy); - _cl_mul.configure(CAST_CL(output), CAST_CL(output), CAST_CL(output), scale, ConvertPolicy, - RoundingPolicy); - } - else - { - _neon_sub.configure(CAST_NE(input1), CAST_NE(input2), CAST_NE(output), ConvertPolicy); - _neon_mul.configure(CAST_NE(output), CAST_NE(output), CAST_NE(output), scale, ConvertPolicy, - RoundingPolicy); - } -} - -void SquaredDifferenceOperation::run(void) -{ - if (::internal::arm_compute::isGpuMode()) - { - _cl_sub.run(); - _cl_mul.run(); - } - else - { - _neon_sub.run(); - _neon_mul.run(); - } -} diff --git a/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.h b/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.h deleted file mode 100644 index 3782c4e8c..000000000 --- a/runtimes/pure_arm_compute/src/internal/layers/SquaredDifferenceOperation.h +++ /dev/null @@ -1,35 +0,0 @@ -#ifndef __SQUARED_DIFFERENCE_OPERATION_H__ -#define __SQUARED_DIFFERENCE_OPERATION_H__ - -#include <arm_compute/runtime/Tensor.h> -#include <arm_compute/runtime/CL/CLTensor.h> - -#include <arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h> -#include <arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h> -#include <arm_compute/runtime/NEON/functions/NEArithmeticSubtraction.h> -#include <arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h> - -class SquaredDifferenceOperation : public ::arm_compute::IFunction -{ -public: - void configure(::arm_compute::ITensor *input1, ::arm_compute::ITensor *input2, - ::arm_compute::ITensor *output, ::arm_compute::ConvertPolicy ConvertPolicy, - float scale, ::arm_compute::RoundingPolicy RoundingPolicy); - -public: - void run(void) override; - -private: - ::arm_compute::ITensor *_input1; - ::arm_compute::ITensor *_input2; - - ::arm_compute::ITensor *_output; - -private: - ::arm_compute::CLArithmeticSubtraction _cl_sub; - ::arm_compute::CLPixelWiseMultiplication _cl_mul; - - ::arm_compute::NEArithmeticSubtraction _neon_sub; - ::arm_compute::NEPixelWiseMultiplication _neon_mul; -}; -#endif // __SQUARED_DIFFERENCE_OPERATION_H__ diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/feature/Reader.h b/runtimes/pure_arm_compute/src/internal/nnapi/feature/Reader.h index 764b9b13a..ac25692a1 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/feature/Reader.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/feature/Reader.h @@ -14,12 +14,17 @@ * limitations under the License. */ +/** + * @file Reader.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::nnapi::feature::Reader + */ #ifndef __INTERNAL_NNAPI_FEATURE_READER_H__ #define __INTERNAL_NNAPI_FEATURE_READER_H__ #include "internal/nnapi/feature/Utils.h" -#include "util/feature/Reader.h" +#include "misc/feature/Reader.h" namespace internal { @@ -28,20 +33,40 @@ namespace nnapi namespace feature { -template <typename T> class Reader final : public nnfw::util::feature::Reader<T> +/** + * @brief Class to support reading element in feature(3D, 4D) + */ +template <typename T> class Reader final : public nnfw::misc::feature::Reader<T> { public: + /** + * @brief Construct a new Reader object + * @param[in] shape Shape of feature + * @param[in] ptr Pointer to feature data + * @param[in] len Size of tensor (byte) + */ // NOTE The parameter len denotes the number of bytes. - Reader(const ::nnfw::util::feature::Shape &shape, const T *ptr, size_t len) + Reader(const ::nnfw::misc::feature::Shape &shape, const T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); } public: - const nnfw::util::feature::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape of feature + * @return Shape of feature + */ + const nnfw::misc::feature::Shape &shape(void) const { return _shape; } public: + /** + * @brief Get value of element using channel, row, and column index for 3D feature + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t ch, uint32_t row, uint32_t col) const override { uint32_t index = index_of(_shape, ch, row, col); @@ -51,6 +76,14 @@ public: return arr[index]; } + /** + * @brief Get value of element using batch, channel, row, and column index for 4D feature + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override { uint32_t index = index_of(_shape, batch, ch, row, col); @@ -59,7 +92,7 @@ public: } private: - nnfw::util::feature::Shape _shape; + nnfw::misc::feature::Shape _shape; private: const T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/feature/Utils.h b/runtimes/pure_arm_compute/src/internal/nnapi/feature/Utils.h index a64ff5d63..ee59d217e 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/feature/Utils.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/feature/Utils.h @@ -14,10 +14,15 @@ * limitations under the License. */ +/** + * @file Utils.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines utility functions used in internal::nnapi::feature namespace + */ #ifndef __INTERNAL_NNAPI_FEATURE_UTILS_H__ #define __INTERNAL_NNAPI_FEATURE_UTILS_H__ -#include "util/feature/Shape.h" +#include "misc/feature/Shape.h" namespace internal { @@ -26,7 +31,15 @@ namespace nnapi namespace feature { -inline uint32_t index_of(const ::nnfw::util::feature::Shape &shape, uint32_t ch, uint32_t row, +/** + * @brief Get position of element using channel, row, and column for 3D feature + * @param[in] shape Shape of feature + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Position of element + */ +inline uint32_t index_of(const ::nnfw::misc::feature::Shape &shape, uint32_t ch, uint32_t row, uint32_t col) { uint32_t res = 0; @@ -39,7 +52,16 @@ inline uint32_t index_of(const ::nnfw::util::feature::Shape &shape, uint32_t ch, return res; } -inline uint32_t index_of(const ::nnfw::util::feature::Shape &shape, uint32_t batch, uint32_t ch, +/** + * @brief Get position of element using batch, channel, row, and column for 4D feature + * @param[in] shape Shape of feature + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Position of element + */ +inline uint32_t index_of(const ::nnfw::misc::feature::Shape &shape, uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { uint32_t res = 0; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/feature/View.h b/runtimes/pure_arm_compute/src/internal/nnapi/feature/View.h index 083b6b055..965e42f1c 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/feature/View.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/feature/View.h @@ -14,12 +14,17 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::nnapi::feature::View class + */ #ifndef __INTERNAL_NNAPI_FEATURE_VIEW_H__ #define __INTERNAL_NNAPI_FEATURE_VIEW_H__ #include "internal/nnapi/feature/Utils.h" -#include "util/feature/Reader.h" +#include "misc/feature/Reader.h" namespace internal { @@ -28,25 +33,55 @@ namespace nnapi namespace feature { -template <typename T> class View final : public nnfw::util::feature::Reader<T> +/** + * @brief Class to access feature's element information using index + */ +template <typename T> class View final : public nnfw::misc::feature::Reader<T> { public: + /** + * @brief Construct a new View object + * @param[in] shape Shape of feature + * @param[in] ptr Pointer to feature data + * @param[in] len Size of feature (byte) + * @return + */ // NOTE The parameter len denotes the number of bytes. - View(const ::nnfw::util::feature::Shape &shape, T *ptr, size_t len) : _shape{shape}, _ptr{ptr} + View(const ::nnfw::misc::feature::Shape &shape, T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); } public: - const nnfw::util::feature::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape of feature + * @return Shape of feature + */ + const nnfw::misc::feature::Shape &shape(void) const { return _shape; } public: + /** + * @brief Get value of element in 3D feature using channel, row, and column index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t ch, uint32_t row, uint32_t col) const override { uint32_t index = index_of(_shape, ch, row, col); return _ptr[index]; } + + /** + * @brief Get value of element in 4D feature using batch, channel, row and column index + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const override { uint32_t index = index_of(_shape, batch, ch, row, col); @@ -54,12 +89,28 @@ public: return _ptr[index]; } + /** + * @brief Get reference of element in 3D feature using channel, row, and column index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ T &at(uint32_t ch, uint32_t row, uint32_t col) { uint32_t index = index_of(_shape, ch, row, col); return _ptr[index]; } + + /** + * @brief Get reference of element in 4D feature using batch, channel, row and column index + * @param[in] batch Batch index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Reference of element + */ T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { uint32_t index = index_of(_shape, batch, ch, row, col); @@ -68,7 +119,7 @@ public: } private: - nnfw::util::feature::Shape _shape; + nnfw::misc::feature::Shape _shape; private: T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/kernel/Reader.h b/runtimes/pure_arm_compute/src/internal/nnapi/kernel/Reader.h index 0853a8c89..ae964f74c 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/kernel/Reader.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/kernel/Reader.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file Reader.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::nnapi::kernel::Reader class + */ #ifndef __INTERNAL_NNAPI_KERNEL_READER_H__ #define __INTERNAL_NNAPI_KERNEL_READER_H__ -#include "util/kernel/Shape.h" -#include "util/kernel/Reader.h" +#include "misc/kernel/Shape.h" +#include "misc/kernel/Reader.h" namespace internal { @@ -27,20 +32,41 @@ namespace nnapi namespace kernel { -template <typename T> class Reader final : public nnfw::util::kernel::Reader<T> +/** + * @brief Class to support reading element in kernel + */ +template <typename T> class Reader final : public nnfw::misc::kernel::Reader<T> { public: + /** + * @brief Construct a new Reader object + * @param[in] shape Shape of kernel + * @param[in] ptr Pointer to kernel data + * @param[in] len Size of kernel (byte) + */ // NOTE The parameter len denotes the number of bytes. - Reader(const ::nnfw::util::kernel::Shape &shape, const T *ptr, size_t len) + Reader(const ::nnfw::misc::kernel::Shape &shape, const T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); } public: - const nnfw::util::kernel::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape of kernel + * @return Shape of kernel + */ + const nnfw::misc::kernel::Shape &shape(void) const { return _shape; } public: + /** + * @brief Get value of element for kernel + * @param[in] nth Kernel index + * @param[in] ch Channel index + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override { // NNAPI uses NHWC ordering @@ -55,7 +81,7 @@ public: } private: - nnfw::util::kernel::Shape _shape; + nnfw::misc::kernel::Shape _shape; private: const T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/matrix/Reader.h b/runtimes/pure_arm_compute/src/internal/nnapi/matrix/Reader.h index f6f0f3908..f03a4be31 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/matrix/Reader.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/matrix/Reader.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file Reader.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::nnapi::matrix::Reader class + */ #ifndef __INTERNAL_NNAPI_MATRIX_READER_H__ #define __INTERNAL_NNAPI_MATRIX_READER_H__ -#include "util/matrix/Shape.h" -#include "util/matrix/Reader.h" +#include "misc/matrix/Shape.h" +#include "misc/matrix/Reader.h" namespace internal { @@ -27,20 +32,39 @@ namespace nnapi namespace matrix { -template <typename T> class Reader final : public nnfw::util::matrix::Reader<T> +/** + * @brief Class to support reading element in matrix + */ +template <typename T> class Reader final : public nnfw::misc::matrix::Reader<T> { public: + /** + * @brief Construct a new Reader object + * @param[in] shape Shape of matrix + * @param[in] ptr Pointer to matrix data + * @param[in] len Size of matrix (byte) + */ // NOTE The parameter len denotes the number of bytes. - Reader(const ::nnfw::util::matrix::Shape &shape, const T *ptr, size_t len) + Reader(const ::nnfw::misc::matrix::Shape &shape, const T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.H * shape.W * sizeof(T) == len); } public: - const nnfw::util::matrix::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape of matrix + * @return Shape of matrix + */ + const nnfw::misc::matrix::Shape &shape(void) const { return _shape; } public: + /** + * @brief Get value of element for matrix + * @param[in] row Row index + * @param[in] col Column index + * @return Value of element + */ T at(uint32_t row, uint32_t col) const override { // NNAPI uses NHWC ordering @@ -53,7 +77,7 @@ public: } private: - nnfw::util::matrix::Shape _shape; + nnfw::misc::matrix::Shape _shape; private: const T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/ConstView.h b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/ConstView.h index 38d1b291b..6a3fff646 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/ConstView.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/ConstView.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file ConstView.h + * @brief This file contains ConstView class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_NNAPI_TENSOR_CONST_VIEW_H__ #define __INTERNAL_NNAPI_TENSOR_CONST_VIEW_H__ @@ -27,21 +33,36 @@ namespace nnapi namespace tensor { +/** + * @brief Wrapper class to read tensor values + * @tparam T The tensor element type + */ template <typename T> class ConstView { public: - ConstView(const ::nnfw::util::tensor::Shape &shape, const uint8_t *ptr, size_t len) + /** + * @brief Construct a ConstView class + * @param[in] shape Tensor shape + * @param[in] ptr The base pointer of actual data + * @param[in] len The number of bytes + */ + ConstView(const ::nnfw::misc::tensor::Shape &shape, const uint8_t *ptr, size_t len) : _shape{shape}, _ptr{ptr}, _len{len} { // DO NOTHING } public: - const nnfw::util::tensor::Shape &shape(void) const { return _shape; } + const nnfw::misc::tensor::Shape &shape(void) const { return _shape; } private: // TODO Make this as a helper function, and share it for both View<T> and ConstView<T> - uint32_t offset_of(const nnfw::util::tensor::Index &index) const + /** + * @brief Calculate offset for the given tensor index + * @param[in] index Tensor index + * @return The calculated offset + */ + uint32_t offset_of(const nnfw::misc::tensor::Index &index) const { if (_shape.rank() == 0) { @@ -61,7 +82,12 @@ private: } public: - T at(const nnfw::util::tensor::Index &index) const + /** + * @brief Get the value on the given index + * @param[in] index Flattened tensor index + * @return The value on the given index + */ + T at(const nnfw::misc::tensor::Index &index) const { const auto offset = offset_of(index); @@ -71,7 +97,7 @@ public: } private: - const nnfw::util::tensor::Shape _shape; + const nnfw::misc::tensor::Shape _shape; private: const uint8_t *const _ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/Reader.h b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/Reader.h index fe89e572e..cc51db594 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/Reader.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/Reader.h @@ -14,11 +14,17 @@ * limitations under the License. */ +/** + * @file Reader.h + * @brief This file contains Reader class + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_NNAPI_TENSOR_READER_H__ #define __INTERNAL_NNAPI_TENSOR_READER_H__ #include <vector> -#include "util/tensor/Reader.h" +#include "misc/tensor/Reader.h" namespace internal { @@ -27,11 +33,20 @@ namespace nnapi namespace tensor { -template <typename T> class Reader final : public nnfw::util::tensor::Reader<T> +/** + * @brief Wrapper class to read tensor values + * @tparam T The tensor element type + */ +template <typename T> class Reader final : public nnfw::misc::tensor::Reader<T> { public: - // NOTE The parameter len denotes the number of bytes. - Reader(const ::nnfw::util::tensor::Shape &shape, const T *ptr, size_t len) + /** + * @brief Construct a Reader class + * @param[in] shape Tensor shape + * @param[in] ptr The base pointer of actual data + * @param[in] len The number of bytes + */ + Reader(const ::nnfw::misc::tensor::Shape &shape, const T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.element_nums() * sizeof(T) == len); @@ -39,10 +54,19 @@ public: } public: - const nnfw::util::tensor::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape object + * @return The shape as const reference + */ + const nnfw::misc::tensor::Shape &shape(void) const { return _shape; } public: - T at(const nnfw::util::tensor::Index &index_nnapi) const override + /** + * @brief Get the value on the given index + * @param[in] index_nnapi Flattened tensor index + * @return The value on the given index + */ + T at(const nnfw::misc::tensor::Index &index_nnapi) const override { uint32_t offset = 0; @@ -53,17 +77,19 @@ public: } private: - /* - Assuming that shape is [d4, .. , d1] and data is stored at a pointer ptr, - we need to calculate the offset of index [i4, .. i1] as follows: - offset = i4 * (d3 * d2 * d1) + - i3 * (d2 * d1) + - i2 * (d1) + - i1 - So (d4 * d3 * d2 * d1) or (d3 * d2 * d1) or (d2 * d1) happens whenever offset is calculate. - To minimize this repetitive calculation, - _stridess[n] contains _spape[n-1]*_spape[n-2]*_spape[0] - */ + /** + * @brief Initializes @c _stridess + * @return N/A + * @note Assuming that shape is [d4, .. , d1] and data is stored at a pointer ptr, + we need to calculate the offset of index [i4, .. i1] as follows: + offset = i4 * (d3 * d2 * d1) + + i3 * (d2 * d1) + + i2 * (d1) + + i1 + So (d4 * d3 * d2 * d1) or (d3 * d2 * d1) or (d2 * d1) happens whenever offset is + calculate. To minimize this repetitive calculation, + _stridess[n] contains _spape[n-1]*_spape[n-2]*_spape[0] + */ void initialize(void) { for (int r = 0; r < _shape.rank(); r++) @@ -76,7 +102,7 @@ private: } private: - nnfw::util::tensor::Shape _shape; + nnfw::misc::tensor::Shape _shape; private: const T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/View.h b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/View.h index 80e1bb057..f8f297f97 100644 --- a/runtimes/pure_arm_compute/src/internal/nnapi/tensor/View.h +++ b/runtimes/pure_arm_compute/src/internal/nnapi/tensor/View.h @@ -14,11 +14,16 @@ * limitations under the License. */ +/** + * @file View.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::nnapi::tensor::View class + */ #ifndef __INTERNAL_NNAPI_TENSOR_VIEW_H__ #define __INTERNAL_NNAPI_TENSOR_VIEW_H__ -#include "util/tensor/Shape.h" -#include "util/tensor/Index.h" +#include "misc/tensor/Shape.h" +#include "misc/tensor/Index.h" namespace internal { @@ -27,20 +32,38 @@ namespace nnapi namespace tensor { +/** + * @brief Class to access tensor's element information using index + */ template <typename T> class View { public: + /** + * @brief Construct a new View object + * @param[in] shape Shape of tensor + * @param[in] ptr Pointer to tensor data + * @param[in] len Size of tensor (byte) + */ // NOTE The parameter len denotes the number of bytes. - View(const ::nnfw::util::tensor::Shape &shape, T *ptr, size_t len) : _shape{shape}, _ptr{ptr} + View(const ::nnfw::misc::tensor::Shape &shape, T *ptr, size_t len) : _shape{shape}, _ptr{ptr} { assert(shape.element_nums() * sizeof(T) == len); } public: - const nnfw::util::tensor::Shape &shape(void) const { return _shape; } + /** + * @brief Get shape of tensor + * @return Shape of tensor + */ + const nnfw::misc::tensor::Shape &shape(void) const { return _shape; } private: - uint32_t offset_of(const nnfw::util::tensor::Index &index) const + /** + * @brief Get position of element using index in tensor + * @param[in] index Index of element + * @return Position of element + */ + uint32_t offset_of(const nnfw::misc::tensor::Index &index) const { if (_shape.rank() == 0) { @@ -60,14 +83,24 @@ private: } public: - T at(const nnfw::util::tensor::Index &index) const + /** + * @brief Get value of element at index + * @param[in] index Index of element + * @return Value of element at index + */ + T at(const nnfw::misc::tensor::Index &index) const { const auto offset = offset_of(index); return _ptr[offset]; } - T &at(const nnfw::util::tensor::Index &index) + /** + * @brief Get reference of element at index + * @param[in] index Index of element + * @return Reference of element at index + */ + T &at(const nnfw::misc::tensor::Index &index) { const auto offset = offset_of(index); @@ -75,7 +108,7 @@ public: } private: - nnfw::util::tensor::Shape _shape; + nnfw::misc::tensor::Shape _shape; private: T *_ptr; diff --git a/runtimes/pure_arm_compute/src/internal/op/Abs.cc b/runtimes/pure_arm_compute/src/internal/op/Abs.cc new file mode 100644 index 000000000..e23a9538c --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Abs.cc @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Abs.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Abs +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Abs +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Abs +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 1 && outputCount == 1); + + output_index = outputs[0]; + input_index = inputs[0]; +} + +} // namespace Abs +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Abs.h b/runtimes/pure_arm_compute/src/internal/op/Abs.h new file mode 100644 index 000000000..0be8b0205 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Abs.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_ABS_H__ +#define __INTERNAL_OP_ABS_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Abs +{ + +struct Param +{ + int32_t output_index; + int32_t input_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Abs +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_ABS_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Add.h b/runtimes/pure_arm_compute/src/internal/op/Add.h index 42ed5b976..a7804a569 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Add.h +++ b/runtimes/pure_arm_compute/src/internal/op/Add.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Add.h + * @brief This file contains accept function and params for Add operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_ADD_H__ #define __INTERNAL_OP_ADD_H__ @@ -30,33 +36,66 @@ namespace op namespace Add { +/** + * @brief Struct of Add operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t lhs_index; - int32_t rhs_index; - int32_t activation_index; + int32_t lhs_index; /**< Left hand side index */ + int32_t rhs_index; /**< Right hand side index */ + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for Add as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Add with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Add + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for Add with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for Add + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for Add + * @return Parameters of Add + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for Add + * @param [in] v Node visitor for invoking visit function of Add + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ArgMax.cc b/runtimes/pure_arm_compute/src/internal/op/ArgMax.cc new file mode 100644 index 000000000..485430377 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ArgMax.cc @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "internal/op/ArgMax.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ArgMax +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace ArgMax +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ArgMax +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Axis Tensor Index + ifm_index = inputs[0]; + axis_index = inputs[1]; +} + +} // namespace ArgMax +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/ArgMax.h b/runtimes/pure_arm_compute/src/internal/op/ArgMax.h new file mode 100644 index 000000000..780af2232 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ArgMax.h @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_ARGMAX_H__ +#define __INTERNAL_OP_ARGMAX_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ArgMax +{ + +struct Param +{ + int32_t ofm_index; + + int32_t ifm_index; + int32_t axis_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace ArgMax +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_ARGMAX_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/AvgPool2D.h b/runtimes/pure_arm_compute/src/internal/op/AvgPool2D.h index 729f6043c..cf9061ca9 100644 --- a/runtimes/pure_arm_compute/src/internal/op/AvgPool2D.h +++ b/runtimes/pure_arm_compute/src/internal/op/AvgPool2D.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file AvgPool2D.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::AvgPool2D Param structs + * and internal::tflite::op::AvgPool2D Node classes + */ #ifndef __INTERNAL_OP_AVG_POOL_2D_H__ #define __INTERNAL_OP_AVG_POOL_2D_H__ @@ -32,44 +38,75 @@ namespace AvgPool2D namespace Explicit { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Index of output feature map */ - int32_t ifm_index; + int32_t ifm_index; /**< Index of input feature map */ - int32_t kw_index; - int32_t kh_index; + int32_t kw_index; /**< Index of kernel width */ + int32_t kh_index; /**< Index of kernel height */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ - int32_t padding_left_index; - int32_t padding_right_index; - int32_t padding_top_index; - int32_t padding_bottom_index; - - int32_t activation_index; + int32_t padding_left_index; /**< Index of padding left */ + int32_t padding_right_index; /**< Index of padding right */ + int32_t padding_top_index; /**< Index of padding top */ + int32_t padding_bottom_index; /**< Index of padding bottom */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: @@ -81,40 +118,71 @@ private: namespace Implicit { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; + int32_t ofm_index; /**< Index of output feature map */ - int32_t kw_index; - int32_t kh_index; + int32_t ifm_index; /**< Index of input feature map */ - int32_t hstride_index; - int32_t vstride_index; + int32_t kw_index; /**< Index of kernel width */ + int32_t kh_index; /**< Index of kernel height */ - int32_t padding_index; - int32_t activation_index; + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ + int32_t padding_index; /**< Index of padding */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.cc b/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.cc new file mode 100644 index 000000000..0768039d0 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.cc @@ -0,0 +1,63 @@ +/*Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "internal/op/BatchToSpaceNd.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace BatchToSpaceNd +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace BatchToSpaceNd +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace BatchToSpaceNd +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Block size Index + input_index = inputs[0]; + block_size_index = inputs[1]; +} + +} // namespace BatchToSpaceNd +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.h b/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.h new file mode 100644 index 000000000..a514cb44c --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/BatchToSpaceNd.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_BATCHTOSPACE_ND_H__ +#define __INTERNAL_OP_BATCHTOSPACE_ND_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace BatchToSpaceNd +{ + +struct Param +{ + int32_t output_index; + + int32_t input_index; + int32_t block_size_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace BatchToSpaceNd +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace BatchToSpaceNd +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace BatchToSpaceNd +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_BATCHTOSPACE_Nd_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Cast.h b/runtimes/pure_arm_compute/src/internal/op/Cast.h index 3b3795189..8af741a16 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Cast.h +++ b/runtimes/pure_arm_compute/src/internal/op/Cast.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Cast.h + * @brief This file contains accept function and params for Cast operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_CAST_H__ #define __INTERNAL_OP_CAST_H__ @@ -30,31 +36,64 @@ namespace op namespace Cast { +/** + * @brief Struct of Cast operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t input_index; + int32_t input_index; /**< Input index */ + /** + * @brief Construct a new Param object for Cast as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Cast with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Cast + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for Cast with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for Cast + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for Cast + * @return Parameters of Cast + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for Cast + * @param [in] v Node visitor for invoking visit function of Cast + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Concat.h b/runtimes/pure_arm_compute/src/internal/op/Concat.h index 185cba3e1..207f964fb 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Concat.h +++ b/runtimes/pure_arm_compute/src/internal/op/Concat.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Concat.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Concat node + */ + #ifndef __INTERNAL_OP_CONCAT_H__ #define __INTERNAL_OP_CONCAT_H__ @@ -31,36 +37,68 @@ namespace op namespace Concat { +/** + * @brief Struct to manipulate parameter for Concat operation + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; //!< index for output - std::vector<int32_t> ifm_indexes; - int32_t axis_index; + std::vector<int32_t> ifm_indexes; //!< index for input + int32_t axis_index; //!< index for axis + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define Concat Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Concat Node object + * @param param Parameter for Concat Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for Concat node }; } // namespace Concat diff --git a/runtimes/pure_arm_compute/src/internal/op/Conv2D.h b/runtimes/pure_arm_compute/src/internal/op/Conv2D.h index b04b8c85f..de46fbb9c 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Conv2D.h +++ b/runtimes/pure_arm_compute/src/internal/op/Conv2D.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Conv2D.h + * @brief This file contains accept function and params for Conv2D operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_CONV_2D_H__ #define __INTERNAL_OP_CONV_2D_H__ @@ -32,43 +38,76 @@ namespace Conv2D namespace Explicit { +/** + * @brief Struct of Conv2D(explicit) operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; - int32_t ker_index; - int32_t bias_index; + int32_t ifm_index; /**< Input format index */ + int32_t ker_index; /**< Kernel index */ + int32_t bias_index; /**< Bias index */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Horizontal stride index */ + int32_t vstride_index; /**< Vertical stride index */ - int32_t padding_left_index; - int32_t padding_right_index; - int32_t padding_top_index; - int32_t padding_bottom_index; + int32_t padding_left_index; /**< Left padding index */ + int32_t padding_right_index; /**< Right padding index */ + int32_t padding_top_index; /**< Top padding index */ + int32_t padding_bottom_index; /**< Bottomd padding index */ - int32_t activation_index; + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for Conv2D(explicit) as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Conv2D(explicit) with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Conv2D(explicit) + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for conv2D(explicit) with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for conv2D(explicit) + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for conv2D(explicit) + * @return Parameters of conv2D(explicit) + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for conv2D(explicit) + * @param [in] v Node visitor for invoking visit function of conv2D(explicit) + * @return N/A + */ void accept(NodeVisitor &&) const override; private: @@ -80,39 +119,72 @@ private: namespace Implicit { +/** + * @brief Struct of Conv2D(implicit) operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; - int32_t ker_index; - int32_t bias_index; + int32_t ifm_index; /**< Input format index */ + int32_t ker_index; /**< Kernel index */ + int32_t bias_index; /**< Bias index */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Horizontal stride index */ + int32_t vstride_index; /**< Vertical stride index */ - int32_t padding_index; - int32_t activation_index; + int32_t padding_index; /**< Padding index */ + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for Conv2D(implicit) as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Conv2D(implicit) with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Conv2D(implicit) + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for conv2D(implicit) with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for conv2D(implicit) + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for conv2D(implicit) + * @return Parameters of conv2D(implicit) + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for conv2D(implicit) + * @param [in] v Node visitor for invoking visit function of conv2D(implicit) + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.cc b/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.cc new file mode 100644 index 000000000..db164a148 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/DepthToSpace.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace DepthToSpace +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace DepthToSpace +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace DepthToSpace +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Block size Index + input_index = inputs[0]; + block_size_index = inputs[1]; +} + +} // namespace DepthToSpace +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.h b/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.h new file mode 100644 index 000000000..dd4c5c914 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/DepthToSpace.h @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_DEPTHTOSPACE_H__ +#define __INTERNAL_OP_DEPTHTOSPACE_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace DepthToSpace +{ + +struct Param +{ + int32_t output_index; + + int32_t input_index; + int32_t block_size_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace DepthToSpace +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_DEPTHTOSPACE_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/DepthwiseConv2D.h b/runtimes/pure_arm_compute/src/internal/op/DepthwiseConv2D.h index 77ab4b63e..c63e30aae 100644 --- a/runtimes/pure_arm_compute/src/internal/op/DepthwiseConv2D.h +++ b/runtimes/pure_arm_compute/src/internal/op/DepthwiseConv2D.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file DepthwiseConv2D.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::DepthwiseConv2D Param structs + * and internal::tflite::op::DepthwiseConv2D Node classes + */ #ifndef __INTERNAL_OP_DEPTHWISE_CONV_2D_H__ #define __INTERNAL_OP_DEPTHWISE_CONV_2D_H__ @@ -32,44 +38,75 @@ namespace DepthwiseConv2D namespace Explicit { +/** + * @brief Struct to have indexes for explicit padding DepthwiseConv2D operation parameter + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Index of output feature map */ - int32_t ifm_index; - int32_t ker_index; - int32_t bias_index; + int32_t ifm_index; /**< Index of input feature map */ + int32_t ker_index; /**< Index of kernel */ + int32_t bias_index; /**< Index of bias */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ - int32_t padding_left_index; - int32_t padding_right_index; - int32_t padding_top_index; - int32_t padding_bottom_index; - - int32_t multipler_index; - int32_t activation_index; + int32_t padding_left_index; /**< Index of padding left */ + int32_t padding_right_index; /**< Index of padding right */ + int32_t padding_top_index; /**< Index of padding top */ + int32_t padding_bottom_index; /**< Index of padding bottom */ + int32_t multipler_index; /**< Index of multipler */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an explicit padding DepthwiseConv2D operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: @@ -78,43 +115,74 @@ private: } // namespace Explicit +/** + * @brief Struct to have indexes for implicit padding DepthwiseConv2D operation parameter + */ namespace Implicit { struct Param { - int32_t ofm_index; - - int32_t ifm_index; - int32_t ker_index; - int32_t bias_index; + int32_t ofm_index; /**< Index of output feature map */ - int32_t hstride_index; - int32_t vstride_index; + int32_t ifm_index; /**< Index of input feature map */ + int32_t ker_index; /**< Index of kernel */ + int32_t bias_index; /**< Index of bias */ - int32_t padding_index; - int32_t multipler_index; - int32_t activation_index; + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ + int32_t padding_index; /**< Index of padding */ + int32_t multipler_index; /**< Index of multipler */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an implicit padding DepthwiseConv2D operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Dequantize.h b/runtimes/pure_arm_compute/src/internal/op/Dequantize.h index b0645d136..f19898e9e 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Dequantize.h +++ b/runtimes/pure_arm_compute/src/internal/op/Dequantize.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Dequantize.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Dequantize::Param struct + * and internal::tflite::op::Dequantize::Node class + */ #ifndef __INTERNAL_OP_DEQUANTIZE_H__ #define __INTERNAL_OP_DEQUANTIZE_H__ @@ -30,31 +36,62 @@ namespace op namespace Dequantize { +/** + * @brief Struct to have indexes for Dequantize operation parameter + */ struct Param { - int32_t output_index; - - int32_t input_index; + int32_t output_index; /**< Index of output feature map */ + int32_t input_index; /**< Index of input feature map */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an Dequantize operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Div.h b/runtimes/pure_arm_compute/src/internal/op/Div.h index 06ed7ec21..d5fc09d19 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Div.h +++ b/runtimes/pure_arm_compute/src/internal/op/Div.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Div.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Div::Param struct + * and internal::tflite::op::Div::Node class + */ #ifndef __INTERNAL_OP_DIV_H__ #define __INTERNAL_OP_DIV_H__ @@ -30,33 +36,64 @@ namespace op namespace Div { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t lhs_index; - int32_t rhs_index; - int32_t activation_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t lhs_index; /**< Index of lhs */ + int32_t rhs_index; /**< Index of rhs */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/EmbeddingLookup.h b/runtimes/pure_arm_compute/src/internal/op/EmbeddingLookup.h index 4547f27c7..17e8485f7 100644 --- a/runtimes/pure_arm_compute/src/internal/op/EmbeddingLookup.h +++ b/runtimes/pure_arm_compute/src/internal/op/EmbeddingLookup.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file EmbeddingLookup.h + * @brief This file contains accept function and params for EmbeddingLookup operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_EMBEDDING_LOOKUP_H__ #define __INTERNAL_OP_EMBEDDING_LOOKUP_H__ @@ -30,32 +36,65 @@ namespace op namespace EmbeddingLookup { +/** + * @brief Struct of EmbeddingLookup operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t lookups_index; - int32_t values_index; + int32_t lookups_index; /**< Lookups index */ + int32_t values_index; /**< Values index */ + /** + * @brief Construct a new Param object for EmbeddingLookup as default + */ Param() = default; + + /** + * @brief Construct a new Param object for EmbeddingLookup with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for EmbeddingLookup + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for EmbeddingLookup with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for EmbeddingLookup + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for EmbeddingLookup + * @return Parameters of EmbeddingLookup + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for EmbeddingLookup + * @param [in] v Node visitor for invoking visit function of EmbeddingLookup + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Equal.cc b/runtimes/pure_arm_compute/src/internal/op/Equal.cc new file mode 100644 index 000000000..b9cccc6a9 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Equal.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Equal.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Equal +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Equal +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Equal +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input1 Tensor Index + // 1 -> input2 Tensor Index + input1_index = inputs[0]; + input2_index = inputs[1]; +} + +} // namespace Equal +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Equal.h b/runtimes/pure_arm_compute/src/internal/op/Equal.h new file mode 100644 index 000000000..78b9f846f --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Equal.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_EQUAL_H__ +#define __INTERNAL_OP_EQUAL_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Equal +{ + +struct Param +{ + int32_t output_index; + + int32_t input1_index; + int32_t input2_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace Equal +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Equal +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Equal +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_EQUAL_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Exp.cc b/runtimes/pure_arm_compute/src/internal/op/Exp.cc new file mode 100644 index 000000000..6f1aa8f42 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Exp.cc @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Exp.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Exp +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Exp +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Exp +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 1 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + ifm_index = inputs[0]; +} + +} // namespace Exp +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Exp.h b/runtimes/pure_arm_compute/src/internal/op/Exp.h new file mode 100644 index 000000000..ac7f244b7 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Exp.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_EXP_H__ +#define __INTERNAL_OP_EXP_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Exp +{ + +struct Param +{ + int32_t ofm_index; + + int32_t ifm_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Exp +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_EXP_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Floor.h b/runtimes/pure_arm_compute/src/internal/op/Floor.h index 8cf2a841c..5264ec10c 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Floor.h +++ b/runtimes/pure_arm_compute/src/internal/op/Floor.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Floor.h + * @brief This file contains accept function and params for Floor operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_FLOOR_H__ #define __INTERNAL_OP_FLOOR_H__ @@ -30,31 +36,64 @@ namespace op namespace Floor { +/** + * @brief Struct of Floor operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t input_index; + int32_t input_index; /**< Input index */ + /** + * @brief Construct a new Param object for Floor as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Floor with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Floor + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for Floor with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for Floor + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for Floor + * @return Parameters of Floor + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for Floor + * @param [in] v Node visitor for invoking visit function of Floor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/FullyConnected.h b/runtimes/pure_arm_compute/src/internal/op/FullyConnected.h index 7a425a6af..434308435 100644 --- a/runtimes/pure_arm_compute/src/internal/op/FullyConnected.h +++ b/runtimes/pure_arm_compute/src/internal/op/FullyConnected.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file FullyConnected.h + * @brief This file contains accept function and params for FullyConnected operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_FULLY_CONNTECTED_H__ #define __INTERNAL_OP_FULLY_CONNTECTED_H__ @@ -30,34 +36,70 @@ namespace op namespace FullyConnected { +/** + * @brief Struct of FullyConnected operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t input_index; - int32_t weight_index; - int32_t bias_index; - int32_t activation_index; + int32_t input_index; /**< Input index */ + int32_t weight_index; /**< Weight index */ + int32_t bias_index; /**< Bias index */ + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for FullyConnected as default + */ Param() = default; + + /** + * @brief Construct a new Param object for FullyConnected with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for FullyConnected + */ class Node final : public op::Node { + /** + * @brief Construct a new Node object for FullyConnected with param + * @param [in] param Parameters for Node + */ public: + /** + * @brief Destroy the Node object for FullyConnected + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for FullyConnected + */ virtual ~Node() = default; public: + /** + * @brief Parameter Get parameters for FullyConnected + * @return _param Parameters of FullyConnected + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for FullyConnected + * @param [in] v Node visitor for invoking visit function of FullyConnected + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Gather.h b/runtimes/pure_arm_compute/src/internal/op/Gather.h index 5f7fe956f..4470236eb 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Gather.h +++ b/runtimes/pure_arm_compute/src/internal/op/Gather.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Gather.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Gather operation + */ + #ifndef __INTERNAL_OP_GATHER_H__ #define __INTERNAL_OP_GATHER_H__ @@ -30,37 +36,69 @@ namespace op namespace Gather { +/** + * @brief Struct to manipulate parameter for Gather operation + */ struct Param { - int32_t ofm_index; // output + int32_t ofm_index; //!< index for output feature map - int32_t lhs_index; // input - int32_t rhs_index; // indexes - int32_t axis_index; // axis + int32_t lhs_index; //!< index for lhs tensor + int32_t rhs_index; //!< index for rhs tensor + int32_t axis_index; //!< index for axis + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define Gather Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Tanh Node object + * @param param Parameter for Tanh Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for Gather node }; } // namespace Gather diff --git a/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.cc b/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.cc index 30a853a64..7e04ecf82 100644 --- a/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.cc +++ b/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.cc @@ -1,52 +1,68 @@ -#include "internal/op/HashtableLookup.h"
-#include "internal/op/NodeVisitor.h"
-
-#include <cassert>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace HashtableLookup
-{
-
-void Node::accept(NodeVisitor &&v) const { v.visit(*this); }
-
-} // namespace HashtableLookup
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace HashtableLookup
-{
-
-Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount,
- const uint32_t *outputs)
-{
- assert(inputCount == 3 && outputCount == 2);
-
- output_index = outputs[0];
- hits_index = outputs[1];
-
- // Each input should be interpreted as follows:
- //
- // 0 -> Lookups Index
- // 1 -> Keys Index
- // 2 -> Values Index
- lookups_index = inputs[0];
- keys_index = inputs[1];
- values_index = inputs[2];
-}
-
-} // namespace HashtableLookup
-} // namespace op
-} // namespace tflite
-} // namespace internal
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/HashtableLookup.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace HashtableLookup +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace HashtableLookup +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace HashtableLookup +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 3 && outputCount == 2); + + output_index = outputs[0]; + hits_index = outputs[1]; + + // Each input should be interpreted as follows: + // + // 0 -> Lookups Index + // 1 -> Keys Index + // 2 -> Values Index + lookups_index = inputs[0]; + keys_index = inputs[1]; + values_index = inputs[2]; +} + +} // namespace HashtableLookup +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.h b/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.h index 192da2aae..a5b43d1c7 100644 --- a/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.h +++ b/runtimes/pure_arm_compute/src/internal/op/HashtableLookup.h @@ -1,56 +1,109 @@ -#ifndef __INTERNAL_OP_HASHTABLE_LOOKUP_H__
-#define __INTERNAL_OP_HASHTABLE_LOOKUP_H__
-
-#include "internal/op/Node.h"
-
-#include <cstdint>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace HashtableLookup
-{
-
-struct Param
-{
- int32_t output_index;
- int32_t hits_index;
-
- int32_t lookups_index;
- int32_t values_index;
- int32_t keys_index;
-
- Param() = default;
- Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs);
-};
-
-class Node final : public op::Node
-{
-public:
- Node(const Param ¶m) : _param(param)
- {
- // DO NOTHING
- }
-
-public:
- virtual ~Node() = default;
-
-public:
- const Param ¶m(void) const { return _param; }
-
-public:
- void accept(NodeVisitor &&) const override;
-
-private:
- const Param _param;
-};
-
-} // namespace HashtableLookup
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-#endif // __INTERNAL_OP_HASHTABLE_LOOKUP_H__
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file HashtableLookup.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::HashtableLookup::Param struct + * and internal::tflite::op::HashtableLookup::Node class + */ +#ifndef __INTERNAL_OP_HASHTABLE_LOOKUP_H__ +#define __INTERNAL_OP_HASHTABLE_LOOKUP_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace HashtableLookup +{ + +/** + * @brief Struct to have indexes for operation parameter + */ +struct Param +{ + int32_t output_index; /**< Index of output feature map */ + int32_t hits_index; /**< Index of hits */ + + int32_t lookups_index; /**< Index of lookups */ + int32_t values_index; /**< Index of values */ + int32_t keys_index; /**< Index of keys */ + /** + * @brief Construct as default + */ + Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to represent an operation of data structure + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destruct as default + */ + virtual ~Node() = default; + +public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace HashtableLookup +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_HASHTABLE_LOOKUP_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/L2Normalization.cc b/runtimes/pure_arm_compute/src/internal/op/L2Normalization.cc index 449540178..44a6ee63d 100644 --- a/runtimes/pure_arm_compute/src/internal/op/L2Normalization.cc +++ b/runtimes/pure_arm_compute/src/internal/op/L2Normalization.cc @@ -1,3 +1,19 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + #include "internal/op/L2Normalization.h" #include "internal/op/NodeVisitor.h" diff --git a/runtimes/pure_arm_compute/src/internal/op/L2Normalization.h b/runtimes/pure_arm_compute/src/internal/op/L2Normalization.h index 70fa2fb7e..2e94fac11 100644 --- a/runtimes/pure_arm_compute/src/internal/op/L2Normalization.h +++ b/runtimes/pure_arm_compute/src/internal/op/L2Normalization.h @@ -1,3 +1,25 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file L2Normalization.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::L2Normalization::Param struct + * and internal::tflite::op::L2Normalization::Node class + */ #ifndef __INTERNAL_OP_L2_NORMALIZATION_H__ #define __INTERNAL_OP_L2_NORMALIZATION_H__ @@ -14,31 +36,62 @@ namespace op namespace L2Normalization { +/** + * @brief Struct to have indexes for L2Normalization operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t ifm_index; /**< Index of input feature map */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an L2Normalization operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.cc b/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.cc index 73c1bb65c..64041ab49 100644 --- a/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.cc +++ b/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.cc @@ -1,124 +1,124 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "internal/op/L2Pool2D.h"
-#include "internal/op/NodeVisitor.h"
-
-#include <cassert>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace L2Pool2D
-{
-namespace Explicit
-{
-
-void Node::accept(NodeVisitor &&v) const { v.visit(*this); }
-
-} // namespace Explicit
-
-namespace Implicit
-{
-
-void Node::accept(NodeVisitor &&v) const { v.visit(*this); }
-
-} // namespace Implicit
-} // namespace L2Pool2D
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace L2Pool2D
-{
-namespace Explicit
-{
-
-Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount,
- const uint32_t *outputs)
-{
- assert(inputCount == 10 && outputCount == 1);
-
- ofm_index = outputs[0];
-
- // Each input should be interpreted as follows:
- //
- // 0 -> IFM Tensor Index
- // 1 -> Padding_left index
- // 2 -> Padding_right index
- // 3 -> Padding_top index
- // 4 -> Padding_bottom index
- // 5 -> Horizontal (over width) Stride Index
- // 6 -> Vertial (over height) Stride Index
- // 7 -> Filter Width Index
- // 8 -> Filter Height Index
- // 9 -> FuseCode (activation) Index
- ifm_index = inputs[0];
- padding_left_index = inputs[1];
- padding_right_index = inputs[2];
- padding_top_index = inputs[3];
- padding_bottom_index = inputs[4];
- hstride_index = inputs[5];
- vstride_index = inputs[6];
- kw_index = inputs[7];
- kh_index = inputs[8];
- activation_index = inputs[9];
-}
-
-} // namespace Explicit
-
-namespace Implicit
-{
-
-Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount,
- const uint32_t *outputs)
-{
- assert(inputCount == 7 && outputCount == 1);
-
- ofm_index = outputs[0];
-
- // Each input should be interpreted as follows:
- //
- // 0 -> IFM Tensor Index
- // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
- // 2 -> Horizontal (over width) Stride Index
- // 3 -> Vertial (over height) Stride Index
- // 4 -> Filter Width Index
- // 5 -> Filter Height Index
- // 6 -> FuseCode (activation) Index
- ifm_index = inputs[0];
- padding_index = inputs[1];
- hstride_index = inputs[2];
- vstride_index = inputs[3];
- kw_index = inputs[4];
- kh_index = inputs[5];
- activation_index = inputs[6];
-}
-
-} // namespace Implicit
-} // namespace L2Pool2D
-} // namespace op
-} // namespace tflite
-} // namespace internal
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/L2Pool2D.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace L2Pool2D +{ +namespace Explicit +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Explicit + +namespace Implicit +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Implicit +} // namespace L2Pool2D +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace L2Pool2D +{ +namespace Explicit +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 10 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> IFM Tensor Index + // 1 -> Padding_left index + // 2 -> Padding_right index + // 3 -> Padding_top index + // 4 -> Padding_bottom index + // 5 -> Horizontal (over width) Stride Index + // 6 -> Vertial (over height) Stride Index + // 7 -> Filter Width Index + // 8 -> Filter Height Index + // 9 -> FuseCode (activation) Index + ifm_index = inputs[0]; + padding_left_index = inputs[1]; + padding_right_index = inputs[2]; + padding_top_index = inputs[3]; + padding_bottom_index = inputs[4]; + hstride_index = inputs[5]; + vstride_index = inputs[6]; + kw_index = inputs[7]; + kh_index = inputs[8]; + activation_index = inputs[9]; +} + +} // namespace Explicit + +namespace Implicit +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 7 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> IFM Tensor Index + // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index + // 2 -> Horizontal (over width) Stride Index + // 3 -> Vertial (over height) Stride Index + // 4 -> Filter Width Index + // 5 -> Filter Height Index + // 6 -> FuseCode (activation) Index + ifm_index = inputs[0]; + padding_index = inputs[1]; + hstride_index = inputs[2]; + vstride_index = inputs[3]; + kw_index = inputs[4]; + kh_index = inputs[5]; + activation_index = inputs[6]; +} + +} // namespace Implicit +} // namespace L2Pool2D +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.h b/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.h index f4a25539b..facb223c7 100644 --- a/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.h +++ b/runtimes/pure_arm_compute/src/internal/op/L2Pool2D.h @@ -1,130 +1,198 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __INTERNAL_OP_L2_POOL_2D_H__
-#define __INTERNAL_OP_L2_POOL_2D_H__
-
-#include "internal/op/Node.h"
-
-#include <cstdint>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace L2Pool2D
-{
-namespace Explicit
-{
-
-struct Param
-{
- int32_t ofm_index;
-
- int32_t ifm_index;
-
- int32_t kw_index;
- int32_t kh_index;
-
- int32_t hstride_index;
- int32_t vstride_index;
-
- int32_t padding_left_index;
- int32_t padding_right_index;
- int32_t padding_top_index;
- int32_t padding_bottom_index;
-
- int32_t activation_index;
-
- Param() = default;
- Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs);
-};
-
-class Node final : public op::Node
-{
-public:
- Node(const Param ¶m) : _param(param)
- {
- // DO NOTHING
- }
-
-public:
- virtual ~Node() = default;
-
-public:
- const Param ¶m(void) const { return _param; }
-
-public:
- void accept(NodeVisitor &&) const override;
-
-private:
- const Param _param;
-};
-
-} // namespace Explicit
-
-namespace Implicit
-{
-
-struct Param
-{
- int32_t ofm_index;
-
- int32_t ifm_index;
-
- int32_t kw_index;
- int32_t kh_index;
-
- int32_t hstride_index;
- int32_t vstride_index;
-
- int32_t padding_index;
- int32_t activation_index;
-
- Param() = default;
- Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs);
-};
-
-class Node final : public op::Node
-{
-public:
- Node(const Param ¶m) : _param(param)
- {
- // DO NOTHING
- }
-
-public:
- virtual ~Node() = default;
-
-public:
- const Param ¶m(void) const { return _param; }
-
-public:
- void accept(NodeVisitor &&) const override;
-
-private:
- const Param _param;
-};
-
-} // namespace Implicit
-} // namespace L2Pool2D
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-#endif // __INTERNAL_OP_L2_POOL_2D_H__
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file L2Pool2D.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::L2Pool2D Param structs + * and internal::tflite::op::L2Pool2D Node classes + */ +#ifndef __INTERNAL_OP_L2_POOL_2D_H__ +#define __INTERNAL_OP_L2_POOL_2D_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace L2Pool2D +{ +namespace Explicit +{ + +/** + * @brief Struct to have indexes for operation parameter + */ +struct Param +{ + int32_t ofm_index; /**< Index of output feature map */ + + int32_t ifm_index; /**< Index of input feature map */ + + int32_t kw_index; /**< Index of kernel width */ + int32_t kh_index; /**< Index of kernel height */ + + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ + + int32_t padding_left_index; /**< Index of padding left */ + int32_t padding_right_index; /**< Index of padding right */ + int32_t padding_top_index; /**< Index of padding top */ + int32_t padding_bottom_index; /**< Index of padding bottom */ + + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ + Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to represent an operation of data structure + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destruct as default + */ + virtual ~Node() = default; + +public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Explicit + +namespace Implicit +{ + +/** + * @brief Struct to have indexes for operation parameter + */ +struct Param +{ + int32_t ofm_index; /**< Index of output feature map */ + + int32_t ifm_index; /**< Index of input feature map */ + + int32_t kw_index; /**< Index of kernel width */ + int32_t kh_index; /**< Index of kernel height */ + + int32_t hstride_index; /**< Index of horizontal stride */ + int32_t vstride_index; /**< Index of vertical stride */ + + int32_t padding_index; /**< Index of padding */ + int32_t activation_index; /**< Index of activation */ + /** + * @brief Construct as default + */ + Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to represent an operation of data structure + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destruct as default + */ + virtual ~Node() = default; + +public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Implicit +} // namespace L2Pool2D +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_L2_POOL_2D_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.cc b/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.cc new file mode 100644 index 000000000..b7419d923 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.cc @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/LocalResponseNormalization.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LocalResponseNormalization +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace LocalResponseNormalization +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LocalResponseNormalization +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 5 && outputCount == 1); + + ofm_index = outputs[0]; + + ifm_index = inputs[0]; + radius_index = inputs[1]; + bias_index = inputs[2]; + alpha_index = inputs[3]; + beta_index = inputs[4]; +} + +} // namespace LocalResponseNormalization +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.h b/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.h new file mode 100644 index 000000000..29e0699ad --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LocalResponseNormalization.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_LOCAL_RESPONSE_NORMALIZATION_H__ +#define __INTERNAL_OP_LOCAL_RESPONSE_NORMALIZATION_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LocalResponseNormalization +{ + +struct Param +{ + int32_t ofm_index; + + int32_t ifm_index; + int32_t radius_index; + int32_t bias_index; + int32_t alpha_index; + int32_t beta_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace LocalResponseNormalization +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_LOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.cc b/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.cc new file mode 100644 index 000000000..5b7da4d3b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/LogicalAnd.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalAnd +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace LogicalAnd +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalAnd +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input1 Tensor Index + // 1 -> input2 Tensor Index + input1_index = inputs[0]; + input2_index = inputs[1]; +} + +} // namespace LogicalAnd +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.h b/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.h new file mode 100644 index 000000000..2f53f756d --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalAnd.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_LOGICAL_AND_H__ +#define __INTERNAL_OP_LOGICAL_AND_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalAnd +{ + +struct Param +{ + int32_t output_index; + + int32_t input1_index; + int32_t input2_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace LogicalAnd +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalAnd +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace LogicalAnd +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_LOGICAL_AND_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalNot.cc b/runtimes/pure_arm_compute/src/internal/op/LogicalNot.cc new file mode 100644 index 000000000..4cb6a8e2a --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalNot.cc @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/LogicalNot.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalNot +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace LogicalNot +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalNot +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 1 && outputCount == 1); + + output_index = outputs[0]; + + input_index = inputs[0]; +} + +} // namespace LogicalNot +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalNot.h b/runtimes/pure_arm_compute/src/internal/op/LogicalNot.h new file mode 100644 index 000000000..9593deafe --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalNot.h @@ -0,0 +1,82 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_LOGICAL_NOT_H__ +#define __INTERNAL_OP_LOGICAL_NOT_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalNot +{ + +struct Param +{ + int32_t output_index; + + int32_t input_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace LogicalNot +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalNot +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace LogicalNot +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_LOGICAL_NOT_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalOr.cc b/runtimes/pure_arm_compute/src/internal/op/LogicalOr.cc new file mode 100644 index 000000000..8295f6f0b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalOr.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/LogicalOr.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalOr +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace LogicalOr +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalOr +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input1 Tensor Index + // 1 -> input2 Tensor Index + input1_index = inputs[0]; + input2_index = inputs[1]; +} + +} // namespace LogicalOr +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/LogicalOr.h b/runtimes/pure_arm_compute/src/internal/op/LogicalOr.h new file mode 100644 index 000000000..6487fa720 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/LogicalOr.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_LOGICAL_OR_H__ +#define __INTERNAL_OP_LOGICAL_OR_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalOr +{ + +struct Param +{ + int32_t output_index; + + int32_t input1_index; + int32_t input2_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace LogicalOr +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace LogicalOr +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace LogicalOr +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_LOGICAL_OR_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Logistic.h b/runtimes/pure_arm_compute/src/internal/op/Logistic.h index db8935846..a42fdc0d4 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Logistic.h +++ b/runtimes/pure_arm_compute/src/internal/op/Logistic.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Logistic.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Logistic::Param struct + * and internal::tflite::op::Logistic::Node class + */ #ifndef __INTERNAL_OP_LOGISTIC_H__ #define __INTERNAL_OP_LOGISTIC_H__ @@ -30,31 +36,61 @@ namespace op namespace Logistic { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t ifm_index; /**< Index of input feature map */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Lstm.h b/runtimes/pure_arm_compute/src/internal/op/Lstm.h index 056ac2ea7..f51f0402a 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Lstm.h +++ b/runtimes/pure_arm_compute/src/internal/op/Lstm.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Lstm.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::LSTM::Param struct + * and internal::tflite::op::LSTM::Node class + */ #ifndef __INTERNAL_OP_LSTM_H__ #define __INTERNAL_OP_LSTM_H__ @@ -30,56 +36,87 @@ namespace op namespace LSTM { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t scratch_buffer_index; - int32_t output_state_out_index; - int32_t cell_state_out_index; - int32_t output_index; + int32_t scratch_buffer_index; /**< Index of scartch buffer */ + int32_t output_state_out_index; /**< Index of output state out */ + int32_t cell_state_out_index; /**< Index of cell state out */ + int32_t output_index; /**< Index of output */ - int32_t input_index; - int32_t input_to_input_weights_index; - int32_t input_to_forget_weights_index; - int32_t input_to_cell_weights_index; - int32_t input_to_output_weights_index; - int32_t recurrent_to_input_weights_index; - int32_t recurrent_to_forget_weights_index; - int32_t recurrent_to_cell_weights_index; - int32_t recurrent_to_output_weights_index; - int32_t cell_to_input_weights_index; - int32_t cell_to_forget_weights_index; - int32_t cell_to_output_weights_index; - int32_t input_gate_bias_index; - int32_t forget_gate_bias_index; - int32_t cell_bias_index; - int32_t output_gate_bias_index; - int32_t projection_weights_index; - int32_t projection_bias_index; - int32_t output_state_in_index; - int32_t cell_state_in_index; - int32_t activation_index; - int32_t cell_threshold_index; - int32_t projection_threshold_index; + int32_t input_index; /**< Index of input */ + int32_t input_to_input_weights_index; /**< Index of input to input weights */ + int32_t input_to_forget_weights_index; /**< Index of input to forget weights */ + int32_t input_to_cell_weights_index; /**< Index of input to cell weights */ + int32_t input_to_output_weights_index; /**< Index of input to output weights */ + int32_t recurrent_to_input_weights_index; /**< Index of recurrent to input weights */ + int32_t recurrent_to_forget_weights_index; /**< Index of recurrent to forget weights */ + int32_t recurrent_to_cell_weights_index; /**< Index of recurrent to cell weights */ + int32_t recurrent_to_output_weights_index; /**< Index of recurrent to output weights */ + int32_t cell_to_input_weights_index; /**< Index of cell to input weights */ + int32_t cell_to_forget_weights_index; /**< Index of cell to forget weights */ + int32_t cell_to_output_weights_index; /**< Index of cell to output weights */ + int32_t input_gate_bias_index; /**< Index of input gate bias */ + int32_t forget_gate_bias_index; /**< Index of forget gate bias */ + int32_t cell_bias_index; /**< Index of cell bias */ + int32_t output_gate_bias_index; /**< Index of output gate bias */ + int32_t projection_weights_index; /**< Index of projection weights */ + int32_t projection_bias_index; /**< Index of projection bias */ + int32_t output_state_in_index; /**< Index of output state in */ + int32_t cell_state_in_index; /**< Index of cell state in */ + int32_t activation_index; /**< Index of activation */ + int32_t cell_threshold_index; /**< Index of cell threshold */ + int32_t projection_threshold_index; /**< Index of projection threshold */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/MaxPool2D.h b/runtimes/pure_arm_compute/src/internal/op/MaxPool2D.h index d5da17d66..329ccecb7 100644 --- a/runtimes/pure_arm_compute/src/internal/op/MaxPool2D.h +++ b/runtimes/pure_arm_compute/src/internal/op/MaxPool2D.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file MaxPool2D.h + * @brief This file contains accept function and params for MaxPool2D operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_MAX_POOL_2D_H__ #define __INTERNAL_OP_MAX_POOL_2D_H__ @@ -32,44 +38,77 @@ namespace MaxPool2D namespace Explicit { +/** + * @brief Struct of MaxPool2D(Explicit) operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; + int32_t ifm_index; /**< Input format index */ - int32_t kw_index; - int32_t kh_index; + int32_t kw_index; /**< Kernel width index */ + int32_t kh_index; /**< Kernel height index */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Horizontal stride index */ + int32_t vstride_index; /**< Vertical stride index */ - int32_t padding_left_index; - int32_t padding_right_index; - int32_t padding_top_index; - int32_t padding_bottom_index; + int32_t padding_left_index; /**< Left padding index */ + int32_t padding_right_index; /**< Right padding index */ + int32_t padding_top_index; /**< Top padding index */ + int32_t padding_bottom_index; /**< Bottom padding index */ - int32_t activation_index; + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for MaxPool2D(Explicit) as default + */ Param() = default; + + /** + * @brief Construct a new Param object for MaxPool2D(Explicit) with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for MaxPool2D(Explicit) + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for MaxPool2D(Explicit) with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for MaxPool2D(Explicit) + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for MaxPool2D(Explicit) + * @return Parameters of MaxPool2D(Explicit) + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for MaxPool2D(Explicit) + * @param [in] v Node visitor for invoking visit function of MaxPool2D(Explicit) + * @return N/A + */ void accept(NodeVisitor &&) const override; private: @@ -81,40 +120,73 @@ private: namespace Implicit { +/** + * @brief Struct of MaxPool2D(Implicit) operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; + int32_t ifm_index; /**< Input format index */ - int32_t kw_index; - int32_t kh_index; + int32_t kw_index; /**< Kernel width index */ + int32_t kh_index; /**< Kernel height index */ - int32_t hstride_index; - int32_t vstride_index; + int32_t hstride_index; /**< Horizontal stride index */ + int32_t vstride_index; /**< Vertical stride index */ - int32_t padding_index; - int32_t activation_index; + int32_t padding_index; /**< Padding index */ + int32_t activation_index; /**< Activation index */ + /** + * @brief Construct a new Param object for MaxPool2D(Implicit) as default + */ Param() = default; + + /** + * @brief Construct a new Param object for MaxPool2D(Implicit) with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for MaxPool2D(Implicit) + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for MaxPool2D(Implicit) with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for MaxPool2D(Implicit) + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for MaxPool2D(Implicit) + * @return Parameters of MaxPool2D(Implicit) + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for MaxPool2D(Implicit) + * @param [in] v Node visitor for invoking visit function of MaxPool2D(Implicit) + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Mean.h b/runtimes/pure_arm_compute/src/internal/op/Mean.h index 385b38dbf..f8e7ed308 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Mean.h +++ b/runtimes/pure_arm_compute/src/internal/op/Mean.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Mean.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Mean::Param struct + * and internal::tflite::op::Mean::Node class + */ #ifndef __INTERNAL_OP_MEAN_H__ #define __INTERNAL_OP_MEAN_H__ @@ -30,33 +36,64 @@ namespace op namespace Mean { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; // output - - int32_t ifm_index; // input - int32_t axis_index; // axis - int32_t keep_dims_index; // keep_dims + int32_t ofm_index; /**< Index of output feature map */ // output + int32_t ifm_index; /**< Index of input feature map */ // input + int32_t axis_index; /**< Index of axis */ // axis + int32_t keep_dims_index; /**< Index of keep dims */ // keep_dims + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Mul.h b/runtimes/pure_arm_compute/src/internal/op/Mul.h index ebb72c4be..9710dd057 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Mul.h +++ b/runtimes/pure_arm_compute/src/internal/op/Mul.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file Mul.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Mul class + */ #ifndef __INTERNAL_OP_MUL_H__ #define __INTERNAL_OP_MUL_H__ @@ -30,33 +35,63 @@ namespace op namespace Mul { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t lhs_index; - int32_t rhs_index; - int32_t activation_index; + int32_t ofm_index; /** Index of output feature map */ + int32_t lhs_index; /** Index of lhs */ + int32_t rhs_index; /** Index of rhs */ + int32_t activation_index; /** Index of activation */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Neg.cc b/runtimes/pure_arm_compute/src/internal/op/Neg.cc new file mode 100644 index 000000000..72fecf484 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Neg.cc @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Neg.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Neg +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Neg +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Neg +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 1 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + ifm_index = inputs[0]; +} + +} // namespace Neg +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Neg.h b/runtimes/pure_arm_compute/src/internal/op/Neg.h new file mode 100644 index 000000000..77507df3d --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Neg.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_NEG_H__ +#define __INTERNAL_OP_NEG_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Neg +{ + +struct Param +{ + int32_t ofm_index; + + int32_t ifm_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Neg +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_NEG_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Node.h b/runtimes/pure_arm_compute/src/internal/op/Node.h index 3927c20f0..be1cbdb5b 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Node.h +++ b/runtimes/pure_arm_compute/src/internal/op/Node.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Node.h + * @brief This file contains struct of Node and NodeVisitor + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_NODE_H__ #define __INTERNAL_OP_NODE_H__ @@ -24,12 +30,26 @@ namespace tflite namespace op { +/** + * @brief Struct of operation NodeVisitor + */ struct NodeVisitor; +/** + * @brief Struct of operation Node + */ struct Node { + /** + * @brief Destroy the Node object for operation + */ virtual ~Node() = default; + /** + * @brief Function for accepting node for operation + * @param [in] v Node visitor for invoking visit function of operation + * @return N/A + */ virtual void accept(NodeVisitor &&) const = 0; }; diff --git a/runtimes/pure_arm_compute/src/internal/op/NodeVisitor.h b/runtimes/pure_arm_compute/src/internal/op/NodeVisitor.h index 6d8d10af0..0c1a4001d 100644 --- a/runtimes/pure_arm_compute/src/internal/op/NodeVisitor.h +++ b/runtimes/pure_arm_compute/src/internal/op/NodeVisitor.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file NodeVisitor.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines NodeVisitor + */ + #ifndef __INTERNAL_OP_NODE_VISITOR_H__ #define __INTERNAL_OP_NODE_VISITOR_H__ @@ -26,6 +32,7 @@ #include "internal/op/Dequantize.h" #include "internal/op/MaxPool2D.h" #include "internal/op/AvgPool2D.h" +#include "internal/op/ArgMax.h" #include "internal/op/Concat.h" #include "internal/op/Reshape.h" #include "internal/op/ResizeBilinear.h" @@ -33,9 +40,11 @@ #include "internal/op/FullyConnected.h" #include "internal/op/Softmax.h" #include "internal/op/ReduceMax.h" +#include "internal/op/ReduceMin.h" #include "internal/op/Cast.h" #include "internal/op/TopKV2.h" #include "internal/op/Gather.h" +#include "internal/op/PReLU.h" #include "internal/op/ReLU.h" #include "internal/op/ReLU1.h" #include "internal/op/ReLU6.h" @@ -49,13 +58,30 @@ #include "internal/op/Floor.h" #include "internal/op/Split.h" #include "internal/op/RSQRT.h" +#include "internal/op/SQRT.h" #include "internal/op/Pad.h" #include "internal/op/SpaceToDepth.h" +#include "internal/op/SpaceToBatchND.h" #include "internal/op/L2Pool2D.h" #include "internal/op/EmbeddingLookup.h" #include "internal/op/HashtableLookup.h" #include "internal/op/L2Normalization.h" #include "internal/op/SquaredDifference.h" +#include "internal/op/LocalResponseNormalization.h" +#include "internal/op/DepthToSpace.h" +#include "internal/op/Unpack.h" +#include "internal/op/Neg.h" +#include "internal/op/Exp.h" +#include "internal/op/ReduceSum.h" +#include "internal/op/Equal.h" +#include "internal/op/BatchToSpaceNd.h" +#include "internal/op/TransposeConv.h" +#include "internal/op/Pack.h" +#include "internal/op/Abs.h" +#include "internal/op/NotEqual.h" +#include "internal/op/LogicalAnd.h" +#include "internal/op/LogicalNot.h" +#include "internal/op/LogicalOr.h" namespace internal { @@ -64,54 +90,400 @@ namespace tflite namespace op { +/** + * @brief Struct to define visitor for operation Nodes + */ struct NodeVisitor { + /** + * @brief Destruct NodeVisitor object with default + */ virtual ~NodeVisitor() = default; + /** + * @brief Visit an Add node + * @param[in] node Add node to visit + * @return N/A + */ virtual void visit(const Add::Node &) = 0; + /** + * @brief Visit a Mul node + * @param[in] node Mul node to visit + * @return N/A + */ virtual void visit(const Sub::Node &) = 0; + /** + * @brief Visit a Mul node + * @param[in] node Mul node to visit + * @return N/A + */ virtual void visit(const Mul::Node &) = 0; + /** + * @brief Visit a Div node + * @param[in] node Div node to visit + * @return N/A + */ virtual void visit(const Div::Node &) = 0; + /** + * @brief Visit a Conv2D node with implicit padding + * @param[in] node Conv2D node to visit + * @return N/A + */ virtual void visit(const Conv2D::Implicit::Node &) = 0; + /** + * @brief Visit a Conv2D node with explicit padding + * @param[in] node Conv2D node to visit + * @return N/A + */ virtual void visit(const Conv2D::Explicit::Node &) = 0; + /** + * @brief Visit a DepthwiseConv2D node with implicit padding + * @param[in] node DepthwiseConv2D node to visit + * @return N/A + */ virtual void visit(const DepthwiseConv2D::Implicit::Node &) = 0; + /** + * @brief Visit a DepthwiseConv2D node with explicit padding + * @param[in] node DepthwiseConv2D node to visit + * @return N/A + */ virtual void visit(const DepthwiseConv2D::Explicit::Node &) = 0; + /** + * @brief Visit a Dequantize node + * @param[in] node Dequantize node to visit + * @return N/A + */ virtual void visit(const Dequantize::Node &) = 0; + /** + * @brief Visit a MaxPool2D node with implicit padding + * @param[in] node MaxPool2D node to visit + * @return N/A + */ virtual void visit(const MaxPool2D::Implicit::Node &) = 0; + /** + * @brief Visit a MaxPool2D node with explicit padding + * @param[in] node MaxPool2D node to visit + * @return N/A + */ virtual void visit(const MaxPool2D::Explicit::Node &) = 0; + /** + * @brief Visit an AvgPool2D node with implicit padding + * @param[in] node AvgPool2D node to visit + * @return N/A + */ virtual void visit(const AvgPool2D::Implicit::Node &) = 0; + /** + * @brief Visit an AvgPool2D node with explicit padding + * @param[in] node AvgPool2D node to visit + * @return N/A + */ virtual void visit(const AvgPool2D::Explicit::Node &) = 0; + /** + * @brief Visit a Concat node + * @param[in] node Concat node to visit + * @return N/A + */ virtual void visit(const Concat::Node &) = 0; + /** + * @brief Visit a ArgMax node + * @param[in] node ArgMax node to visit + * @return N/A + */ + virtual void visit(const ArgMax::Node &) = 0; + /** + * @brief Visit an Reshape node + * @param[in] node Reshape node to visit + * @return N/A + */ virtual void visit(const Reshape::Node &) = 0; + /** + * @brief Visit an ResizeBilinear node + * @param[in] node ResizeBilinear node to visit + * @return N/A + */ virtual void visit(const ResizeBilinear::Node &) = 0; + /** + * @brief Visit a StridedSlice node + * @param[in] node StridedSlice node to visit + * @return N/A + */ virtual void visit(const StridedSlice::Node &) = 0; + /** + * @brief Visit a FullyConnected node + * @param[in] node FullyConnected node to visit + * @return N/A + */ virtual void visit(const FullyConnected::Node &) = 0; + /** + * @brief Visit a Softmax node + * @param[in] node Softmax node to visit + * @return N/A + */ virtual void visit(const Softmax::Node &) = 0; + /** + * @brief Visit a ReduceMax node + * @param[in] node ReduceMax node to visit + * @return N/A + */ virtual void visit(const ReduceMax::Node &) = 0; + /** + * @brief Visit a ReduceMin node + * @param[in] node ReduceMin node to visit + * @return N/A + */ + virtual void visit(const ReduceMin::Node &) = 0; + /** + * @brief Visit a Cast node + * @param[in] node Cast node to visit + * @return N/A + */ virtual void visit(const Cast::Node &) = 0; + /** + * @brief Visit a TopKV2 node + * @param[in] node TopKV2 node to visit + * @return N/A + */ virtual void visit(const TopKV2::Node &) = 0; + /** + * @brief Visit a Gather node + * @param[in] node Gather node to visit + * @return N/A + */ virtual void visit(const Gather::Node &) = 0; + /** + * @brief Visit an PReLU node + * @param[in] node PReLU node to visit + * @return N/A + */ + virtual void visit(const PReLU::Node &) = 0; + /** + * @brief Visit an ReLU node + * @param[in] node Relu node to visit + * @return N/A + */ virtual void visit(const ReLU::Node &) = 0; + /** + * @brief Visit a ReLU1 node + * @param[in] node ReLU1 node to visit + * @return N/A + */ virtual void visit(const ReLU1::Node &) = 0; + /** + * @brief Visit a ReLU6 node + * @param[in] node ReLU6 node to visit + * @return N/A + */ virtual void visit(const ReLU6::Node &) = 0; + /** + * @brief Visit a Tanh node + * @param[in] node Tanh node to visit + * @return N/A + */ virtual void visit(const Tanh::Node &) = 0; + /** + * @brief Visit a Squeeze node + * @param[in] node Squeeze node to visit + * @return N/A + */ virtual void visit(const Squeeze::Node &) = 0; + /** + * @brief Visit an Logistic node + * @param[in] node Logistic node to visit + * @return N/A + */ virtual void visit(const Logistic::Node &) = 0; + /** + * @brief Visit a Mean node + * @param[in] node Mean node to visit + * @return N/A + */ virtual void visit(const Mean::Node &) = 0; + /** + * @brief Visit an RNN node + * @param[in] node RNN node to visit + * @return N/A + */ virtual void visit(const RNN::Node &) = 0; + /** + * @brief Visit a Transpose node + * @param[in] node Transpose node to visit + * @return N/A + */ virtual void visit(const Transpose::Node &) = 0; + /** + * @brief Visit an LSTM node + * @param[in] node LSTM node to visit + * @return N/A + */ virtual void visit(const LSTM::Node &) = 0; + /** + * @brief Visit a Floor node + * @param[in] node Floor node to visit + * @return N/A + */ virtual void visit(const Floor::Node &) = 0; + /** + * @brief Visit a Split node + * @param[in] node Split node to visit + * @return N/A + */ virtual void visit(const Split::Node &) = 0; + /** + * @brief Visit an RSQRT node + * @param[in] node RSQRT node to visit + * @return N/A + */ virtual void visit(const RSQRT::Node &) = 0; + /** + * @brief Visit an SQRT node + * @param[in] node SQRT node to visit + * @return N/A + */ + virtual void visit(const SQRT::Node &) = 0; + /** + * @brief Visit a Pad node + * @param[in] node Pad node to visit + * @return N/A + */ virtual void visit(const Pad::Node &) = 0; + /** + * @brief Visit a SpaceToDepth node + * @param[in] node SpaceToDepth node to visit + * @return N/A + */ virtual void visit(const SpaceToDepth::Node &) = 0; + /** + * @brief Visit a SpaceToBatchND node + * @param[in] node SpaceToBatchND node to visit + * @return N/A + */ + virtual void visit(const SpaceToBatchND::Node &) = 0; + /** + * @brief Visit an L2Pool2D node with implicit padding + * @param[in] node L2Pool2D node to visit + * @return N/A + */ virtual void visit(const L2Pool2D::Implicit::Node &) = 0; + /** + * @brief Visit an L2Pool2D node with explicit padding + * @param[in] node L2Pool2D node to visit + * @return N/A + */ virtual void visit(const L2Pool2D::Explicit::Node &) = 0; + /** + * @brief Visit an EmbeddingLookup node + * @param[in] node EmbeddingLookup node to visit + * @return N/A + */ virtual void visit(const EmbeddingLookup::Node &) = 0; + /** + * @brief Visit a HashtableLookup node + * @param[in] node HashtableLookup node to visit + * @return N/A + */ virtual void visit(const HashtableLookup::Node &) = 0; + /** + * @brief Visit an L2Normalization node + * @param[in] node L2Normalization node to visit + * @return N/A + */ virtual void visit(const L2Normalization::Node &) = 0; + /** + * @brief Visit a SquaredDifference node + * @param[in] node SquaredDifference node to visit + * @return N/A + */ virtual void visit(const SquaredDifference::Node &) = 0; + /** + * @brief Visit a LocalResponseNormalization node + * @param[in] node LocalResponseNormalization node to visit + * @return N/A + */ + virtual void visit(const LocalResponseNormalization::Node &) = 0; + /** + * @brief Visit a DepthToSpace node + * @param[in] node DepthToSpace node to visit + * @return N/A + */ + virtual void visit(const DepthToSpace::Node &) = 0; + /** + * @brief Visit a Unpack node + * @param[in] node Unpack node to visit + * @return N/A + */ + virtual void visit(const Unpack::Node &) = 0; + /** + * @brief Visit a Neg node + * @param[in] node Neg node to visit + * @return N/A + */ + virtual void visit(const Neg::Node &) = 0; + /** + * @brief Visit a Exp node + * @param[in] node Exp node to visit + * @return N/A + */ + virtual void visit(const Exp::Node &) = 0; + /** + * @brief Visit a ReduceSum node + * @param[in] node ReduceSum node to visit + * @return N/A + */ + virtual void visit(const ReduceSum::Node &) = 0; + /** + * @brief Visit a Equal node + * @param[in] node Equal node to visit + * @return N/A + */ + virtual void visit(const Equal::Node &) = 0; + /** + * @brief Visit a BatchToSpaceNd node + * @param[in] node BatchToSpaceNd node to visit + * @return N/A + */ + virtual void visit(const BatchToSpaceNd::Node &) = 0; + /** + * @brief Visit a TransposeConv node + * @param[in] node TransposeConv node to visit + * @return N/A + */ + virtual void visit(const TransposeConv::Node &) = 0; + /** + * @brief Visit a Pack node + * @param[in] node Pack node to visit + * @return N/A + */ + virtual void visit(const Pack::Node &) = 0; + /** + * @brief Visit a Abs node + * @param[in] node Abs node to visit + * @return N/A + */ + virtual void visit(const Abs::Node &) = 0; + /** + * @brief Visit a NotEqual node + * @param[in] node NotEqual node to visit + * @return N/A + */ + virtual void visit(const NotEqual::Node &) = 0; + /** + * @brief Visit a LogicalAnd node + * @param[in] node LogicalAnd node to visit + * @return N/A + */ + virtual void visit(const LogicalAnd::Node &) = 0; + /** + * @brief Visit a LogicalNot node + * @param[in] node LogicalNot node to visit + * @return N/A + */ + virtual void visit(const LogicalNot::Node &) = 0; + /** + * @brief Visit a LogicalOr node + * @param[in] node LogicalOr node to visit + * @return N/A + */ + virtual void visit(const LogicalOr::Node &) = 0; }; } // namespace op diff --git a/runtimes/pure_arm_compute/src/internal/op/NotEqual.cc b/runtimes/pure_arm_compute/src/internal/op/NotEqual.cc new file mode 100644 index 000000000..2906e214b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/NotEqual.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/NotEqual.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace NotEqual +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace NotEqual +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace NotEqual +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input1 Tensor Index + // 1 -> input2 Tensor Index + input1_index = inputs[0]; + input2_index = inputs[1]; +} + +} // namespace NotEqual +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/NotEqual.h b/runtimes/pure_arm_compute/src/internal/op/NotEqual.h new file mode 100644 index 000000000..0d6130948 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/NotEqual.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_NOT_EQUAL_H__ +#define __INTERNAL_OP_NOT_EQUAL_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace NotEqual +{ + +struct Param +{ + int32_t output_index; + + int32_t input1_index; + int32_t input2_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +} // namespace NotEqual +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace NotEqual +{ +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace NotEqual +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_NOT_EQUAL_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/PReLU.cc b/runtimes/pure_arm_compute/src/internal/op/PReLU.cc new file mode 100644 index 000000000..25b06505b --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/PReLU.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/PReLU.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace PReLU +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace PReLU +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace PReLU +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input Tensor Index + // 1 -> alpha Tensor Index + ifm_index = inputs[0]; + alpha_index = inputs[1]; +} + +} // namespace PReLU +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/PReLU.h b/runtimes/pure_arm_compute/src/internal/op/PReLU.h new file mode 100644 index 000000000..ae754abb4 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/PReLU.h @@ -0,0 +1,109 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file ReLU.h + * @brief This file contains accept function and params for ReLU operation + * @ingroup COM_AI_RUNTIME + */ + +#ifndef __INTERNAL_OP_PRELU_H__ +#define __INTERNAL_OP_PRELU_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace PReLU +{ + +/** + * @brief Struct of PReLU operation's param + */ +struct Param +{ + int32_t ofm_index; /**< Output format index */ + + int32_t ifm_index; /**< Input format index */ + int32_t alpha_index; /**< Alpha input index */ + + /** + * @brief Construct a new Param object for ReLU as default + */ + Param() = default; + + /** + * @brief Construct a new Param object for PReLU with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to define operation node for PReLU + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object for PReLU with param + * @param [in] param Parameters for Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destroy the Node object for PReLU + */ + virtual ~Node() = default; + +public: + /** + * @brief Get parameters for PReLU + * @return Parameters of PReLU + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Function for accepting node for PReLU + * @param [in] v Node visitor for invoking visit function of PReLU + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace PReLU +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_PRELU_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Pack.cc b/runtimes/pure_arm_compute/src/internal/op/Pack.cc new file mode 100644 index 000000000..73f89b840 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Pack.cc @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Pack.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pack +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Pack +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pack +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(outputCount == 1); + + // Each input should be interpreted as follows: + // + // 0 .. n - 3 -> Input Tensor Index + // n - 2 -> Input Tensor counts (will be ignored) + // n - 1 -> Input Axis Index + ofm_index = outputs[0]; + axis_index = inputs[inputCount - 1]; + // last input is axis along which packing is required + for (uint32_t n = 0; n < inputCount - 2; ++n) + { + ifm_indexes.emplace_back(inputs[n]); + } +} + +} // namespace Pack +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Pack.h b/runtimes/pure_arm_compute/src/internal/op/Pack.h new file mode 100644 index 000000000..c5de01bd8 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Pack.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_PACK_H__ +#define __INTERNAL_OP_PACK_H__ + +#include "internal/op/Node.h" +#include <vector> + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pack +{ + +struct Param +{ + int32_t ofm_index; + // There are N+1 inputs, 0 to N-1 are tensors of same shape + // Nth input is axis index along which stack is needed to be done. + std::vector<int32_t> ifm_indexes; + int32_t axis_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Pack +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_PACK_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Pad.cc b/runtimes/pure_arm_compute/src/internal/op/Pad.cc index 24d08bf36..00938242b 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Pad.cc +++ b/runtimes/pure_arm_compute/src/internal/op/Pad.cc @@ -1,63 +1,63 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "internal/op/Pad.h"
-#include "internal/op/NodeVisitor.h"
-
-#include <cassert>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace Pad
-{
-
-void Node::accept(NodeVisitor &&v) const { v.visit(*this); }
-
-} // namespace Pad
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace Pad
-{
-
-Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount,
- const uint32_t *outputs)
-{
- assert(inputCount == 2 && outputCount == 1);
- ofm_index = outputs[0];
-
- // Each input should be interpreted as follows:
- //
- // 0 -> input Tensor Index
- // 1 -> paddings
- ifm_index = inputs[0];
- paddings_index = inputs[1];
-}
-} // namespace Pad
-} // namespace op
-} // namespace tflite
-} // namespace internal
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Pad.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pad +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Pad +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pad +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> input Tensor Index + // 1 -> paddings + ifm_index = inputs[0]; + paddings_index = inputs[1]; +} +} // namespace Pad +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Pad.h b/runtimes/pure_arm_compute/src/internal/op/Pad.h index e3ddae44c..68752a10e 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Pad.h +++ b/runtimes/pure_arm_compute/src/internal/op/Pad.h @@ -1,69 +1,107 @@ -/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef __INTERNAL_OP_PAD_H__
-#define __INTERNAL_OP_PAD_H__
-
-#include "internal/op/Node.h"
-
-#include <cstdint>
-
-namespace internal
-{
-namespace tflite
-{
-namespace op
-{
-namespace Pad
-{
-
-struct Param
-{
- int32_t ifm_index;
- int32_t paddings_index;
- int32_t ofm_index;
-
- Param() = default;
- Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs);
-};
-
-class Node final : public op::Node
-{
-public:
- Node(const Param ¶m) : _param(param)
- {
- // DO NOTHING
- }
-
-public:
- virtual ~Node() = default;
-
-public:
- const Param ¶m(void) const { return _param; }
-
-public:
- void accept(NodeVisitor &&) const override;
-
-private:
- const Param _param;
-};
-
-} // namespace Pad
-} // namespace op
-} // namespace tflite
-} // namespace internal
-
-#endif // __INTERNAL_OP_PAD_H_
+/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Pad.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Pad node + */ + +#ifndef __INTERNAL_OP_PAD_H__ +#define __INTERNAL_OP_PAD_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Pad +{ + +/** + * @brief Struct to manipulate parameter for Pad operation + */ +struct Param +{ + int32_t ifm_index; //!< index for input + int32_t paddings_index; //!< index for padding + int32_t ofm_index; //!< index for output + + /** + * @brief Default Constructor + */ + Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to define Pad Operation + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new RNN Node object + * @param param Parameter for RNN Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Default Destructor + */ + virtual ~Node() = default; + +public: + /** + * @brief Get parameter + * @return Param reference + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; //!< parameter for Pad node +}; + +} // namespace Pad +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_PAD_H_ diff --git a/runtimes/pure_arm_compute/src/internal/op/RSQRT.h b/runtimes/pure_arm_compute/src/internal/op/RSQRT.h index e384b27f2..e39d60241 100644 --- a/runtimes/pure_arm_compute/src/internal/op/RSQRT.h +++ b/runtimes/pure_arm_compute/src/internal/op/RSQRT.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file RSQRT.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::RSQRT::Param struct + * and internal::tflite::op::RSQRT::Node class + */ #ifndef __INTERNAL_OP_RSQRT_H__ #define __INTERNAL_OP_RSQRT_H__ @@ -30,31 +36,61 @@ namespace op namespace RSQRT { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t output_index; - - int32_t input_index; + int32_t output_index; /**< Index of output feature map */ + int32_t input_index; /**< Index of input feature map */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ReLU.h b/runtimes/pure_arm_compute/src/internal/op/ReLU.h index 64dcf2e14..aaa39b523 100644 --- a/runtimes/pure_arm_compute/src/internal/op/ReLU.h +++ b/runtimes/pure_arm_compute/src/internal/op/ReLU.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file ReLU.h + * @brief This file contains accept function and params for ReLU operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_RELU_H__ #define __INTERNAL_OP_RELU_H__ @@ -30,31 +36,64 @@ namespace op namespace ReLU { +/** + * @brief Struct of ReLU operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; + int32_t ifm_index; /**< Input format index */ + /** + * @brief Construct a new Param object for ReLU as default + */ Param() = default; + + /** + * @brief Construct a new Param object for ReLU with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for ReLU + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for ReLU with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for ReLU + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for ReLU + * @return Parameters of ReLU + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for ReLU + * @param [in] v Node visitor for invoking visit function of ReLU + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ReLU1.h b/runtimes/pure_arm_compute/src/internal/op/ReLU1.h index 997a9faff..330445af8 100644 --- a/runtimes/pure_arm_compute/src/internal/op/ReLU1.h +++ b/runtimes/pure_arm_compute/src/internal/op/ReLU1.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file ReLU1.h + * @brief This file contains accept function and params for ReLU1 operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_RELU1_H__ #define __INTERNAL_OP_RELU1_H__ @@ -30,31 +36,64 @@ namespace op namespace ReLU1 { +/** + * @brief Struct of ReLU1 operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; + int32_t ifm_index; /**< Input format index */ + /** + * @brief Construct a new Param object for ReLU1 as default + */ Param() = default; + + /** + * @brief Construct a new Param object for ReLU1 with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for ReLU1 + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for ReLU1 with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for ReLU1 + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for ReLU1 + * @return Parameters of ReLU1 + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for ReLU1 + * @param [in] v Node visitor for invoking visit function of ReLU1 + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ReLU6.h b/runtimes/pure_arm_compute/src/internal/op/ReLU6.h index 77c55b64c..6fc2c24fe 100644 --- a/runtimes/pure_arm_compute/src/internal/op/ReLU6.h +++ b/runtimes/pure_arm_compute/src/internal/op/ReLU6.h @@ -14,6 +14,11 @@ * limitations under the License. */ +/** + * @file ReLU6.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::ReLU6 class + */ #ifndef __INTERNAL_OP_RELU6_H__ #define __INTERNAL_OP_RELU6_H__ @@ -30,31 +35,61 @@ namespace op namespace ReLU6 { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; + int32_t ofm_index; /** Index of output feature map */ + int32_t ifm_index; /** Index of input feature map */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ReduceMax.h b/runtimes/pure_arm_compute/src/internal/op/ReduceMax.h index e3278aacf..77d8bd869 100644 --- a/runtimes/pure_arm_compute/src/internal/op/ReduceMax.h +++ b/runtimes/pure_arm_compute/src/internal/op/ReduceMax.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file ReduceMax.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::ReduceMax::Param struct + * and internal::tflite::op::ReduceMax::Node class + */ #ifndef __INTERNAL_OP_REDUCEMAX_H__ #define __INTERNAL_OP_REDUCEMAX_H__ @@ -30,32 +36,63 @@ namespace op namespace ReduceMax { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; - int32_t axis_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t ifm_index; /**< Index of input feature map */ + int32_t axis_index; /**< Index of axis */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/ReduceMin.cc b/runtimes/pure_arm_compute/src/internal/op/ReduceMin.cc new file mode 100644 index 000000000..72b6079d4 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ReduceMin.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/ReduceMin.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceMin +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace ReduceMin +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceMin +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Axis Tensor Index + ifm_index = inputs[0]; + axis_index = inputs[1]; +} + +} // namespace ReduceMin +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/ReduceMin.h b/runtimes/pure_arm_compute/src/internal/op/ReduceMin.h new file mode 100644 index 000000000..5dd82ec43 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ReduceMin.h @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file ReduceMin.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::ReduceMin::Param struct + * and internal::tflite::op::ReduceMin::Node class + */ +#ifndef __INTERNAL_OP_REDUCEMIN_H__ +#define __INTERNAL_OP_REDUCEMIN_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceMin +{ + +/** + * @brief Struct to have indexes for operation parameter + */ +struct Param +{ + int32_t ofm_index; /**< Index of output feature map */ + + int32_t ifm_index; /**< Index of input feature map */ + int32_t axis_index; /**< Index of axis */ + /** + * @brief Construct as default + */ + Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to represent an operation of data structure + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destruct as default + */ + virtual ~Node() = default; + +public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace ReduceMin +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_REDUCEMIN_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/ReduceSum.cc b/runtimes/pure_arm_compute/src/internal/op/ReduceSum.cc new file mode 100644 index 000000000..4d83c1734 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ReduceSum.cc @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/ReduceSum.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceSum +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace ReduceSum +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceSum +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 2 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Axis Tensor Index + ifm_index = inputs[0]; + axis_index = inputs[1]; +} + +} // namespace ReduceSum +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/ReduceSum.h b/runtimes/pure_arm_compute/src/internal/op/ReduceSum.h new file mode 100644 index 000000000..9c661f63a --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/ReduceSum.h @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_REDUCESUM_H__ +#define __INTERNAL_OP_REDUCESUM_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace ReduceSum +{ + +struct Param +{ + int32_t ofm_index; + + int32_t ifm_index; + int32_t axis_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace ReduceSum +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_REDUCESUM_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Reshape.h b/runtimes/pure_arm_compute/src/internal/op/Reshape.h index ab77ade8c..7152eaece 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Reshape.h +++ b/runtimes/pure_arm_compute/src/internal/op/Reshape.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Reshape.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Reshape node + */ + #ifndef __INTERNAL_OP_RESHAPE_H__ #define __INTERNAL_OP_RESHAPE_H__ @@ -30,36 +36,68 @@ namespace op namespace Reshape { +/** + * @brief Struct to manipulate parameter for Reshape operation + */ struct Param { - int32_t output_index; + int32_t output_index; //!< index for output feature map - int32_t input_index; - int32_t shape_index; + int32_t input_index; //!< index for input feature map + int32_t shape_index; //!< index for shape + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define Reshape Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Reshape Node object + * @param param Parameter for Reshape Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for Reshape node }; } // namespace Reshape diff --git a/runtimes/pure_arm_compute/src/internal/op/ResizeBilinear.h b/runtimes/pure_arm_compute/src/internal/op/ResizeBilinear.h index bf216b75f..f2eab4aaf 100644 --- a/runtimes/pure_arm_compute/src/internal/op/ResizeBilinear.h +++ b/runtimes/pure_arm_compute/src/internal/op/ResizeBilinear.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file ResizeBilinear.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::ResizeBilinear::Param struct + * and internal::tflite::op::ResizeBilinear::Node class + */ #ifndef __INTERNAL_OP_RESIZE_BILINEAR_H__ #define __INTERNAL_OP_RESIZE_BILINEAR_H__ @@ -30,33 +36,64 @@ namespace op namespace ResizeBilinear { +/** + * @brief Struct to have indexes for ResizeBilinear operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t ifm_index; - int32_t height_index; - int32_t width_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t ifm_index; /**< Index of input feature map */ + int32_t height_index; /**< Index of height */ + int32_t width_index; /**< Index of width */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an ResizeBilinear operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Rnn.h b/runtimes/pure_arm_compute/src/internal/op/Rnn.h index c436a0987..7b2a10843 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Rnn.h +++ b/runtimes/pure_arm_compute/src/internal/op/Rnn.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Rnn.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines RNN node + */ + #ifndef __INTERNAL_OP_RNN_H__ #define __INTERNAL_OP_RNN_H__ @@ -30,41 +36,73 @@ namespace op namespace RNN { +/** + * @brief Struct to manipulate parameter for RNN operation + */ struct Param { - int32_t output_index; - int32_t hidden_state_out_index; + int32_t output_index; //!< index for ouuput + int32_t hidden_state_out_index; //!< index for hidden state output - int32_t input_index; - int32_t weights_index; - int32_t recurrent_weights_index; - int32_t bias_index; - int32_t hidden_state_in_index; - int32_t fused_activation_index; + int32_t input_index; //!< index for input + int32_t weights_index; //!< index for weight + int32_t recurrent_weights_index; //!< index for recurrent weights + int32_t bias_index; //!< index for bias + int32_t hidden_state_in_index; //!< index for hidden state input + int32_t fused_activation_index; //!< index for fused activation + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define RNN Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new RNN Node object + * @param param Parameter for RNN Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for RNN node }; } // namespace RNN diff --git a/runtimes/pure_arm_compute/src/internal/op/SQRT.cc b/runtimes/pure_arm_compute/src/internal/op/SQRT.cc new file mode 100644 index 000000000..70ce42e9c --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/SQRT.cc @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/SQRT.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SQRT +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace SQRT +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SQRT +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 1 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // 0 -> input Tensor Index + input_index = inputs[0]; +} + +} // namespace SQRT +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/SQRT.h b/runtimes/pure_arm_compute/src/internal/op/SQRT.h new file mode 100644 index 000000000..85dfb97a7 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/SQRT.h @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file SQRT.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::SQRT::Param struct + * and internal::tflite::op::SQRT::Node class + */ +#ifndef __INTERNAL_OP_SQRT_H__ +#define __INTERNAL_OP_SQRT_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SQRT +{ + +/** + * @brief Struct to have indexes for operation parameter + */ +struct Param +{ + int32_t output_index; /**< Index of output feature map */ + + int32_t input_index; /**< Index of input feature map */ + /** + * @brief Construct as default + */ + Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +/** + * @brief Class to represent an operation of data structure + */ +class Node final : public op::Node +{ +public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + /** + * @brief Destruct as default + */ + virtual ~Node() = default; + +public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ + const Param ¶m(void) const { return _param; } + +public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace SQRT +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_SQRT_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Softmax.h b/runtimes/pure_arm_compute/src/internal/op/Softmax.h index 746f6b4e6..6e631af5f 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Softmax.h +++ b/runtimes/pure_arm_compute/src/internal/op/Softmax.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Softmax.h + * @brief This file contains accept function and params for Softmax operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_SOFTMAX_H__ #define __INTERNAL_OP_SOFTMAX_H__ @@ -30,32 +36,65 @@ namespace op namespace Softmax { +/** + * @brief Struct of Softmax operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t input_index; - int32_t scale_index; + int32_t input_index; /**< Input index */ + int32_t scale_index; /**< Scale index */ + /** + * @brief Construct a new Param object for Softmax as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Softmax with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Softmax + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for Softmax with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for Softmax + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for Softmax + * @return Parameters of Softmax + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for Softmax + * @param [in] v Node visitor for invoking visit function of Softmax + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.cc b/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.cc new file mode 100644 index 000000000..9ab026cf4 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.cc @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/SpaceToBatchND.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SpaceToBatchND +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace SpaceToBatchND +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SpaceToBatchND +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 3 && outputCount == 1); + + output_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Input Tensor Index + // 1 -> Block size Index + // 2 -> Padding size Index + input_index = inputs[0]; + block_size_index = inputs[1]; + padding_size_index = inputs[2]; +} + +} // namespace SpaceToBatchND +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.h b/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.h new file mode 100644 index 000000000..650d068f4 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/SpaceToBatchND.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_SPACETOBATCHND_H__ +#define __INTERNAL_OP_SPACETOBATCHND_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace SpaceToBatchND +{ + +struct Param +{ + int32_t output_index; + + int32_t input_index; + int32_t block_size_index; + int32_t padding_size_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace SpaceToBatchND +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_SPACETOBATCHND_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/SpaceToDepth.h b/runtimes/pure_arm_compute/src/internal/op/SpaceToDepth.h index 81bfe5246..2e624006a 100644 --- a/runtimes/pure_arm_compute/src/internal/op/SpaceToDepth.h +++ b/runtimes/pure_arm_compute/src/internal/op/SpaceToDepth.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file SpaceToDepth.h + * @brief This file contains accept function and params for SpaceToDepth operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_SPACETODEPTH_H__ #define __INTERNAL_OP_SPACETODEPTH_H__ @@ -30,32 +36,65 @@ namespace op namespace SpaceToDepth { +/** + * @brief Struct of SpaceToDepth operation's param + */ struct Param { - int32_t output_index; + int32_t output_index; /**< Output index */ - int32_t input_index; - int32_t block_size_index; + int32_t input_index; /**< Input index */ + int32_t block_size_index; /**< Block size index */ + /** + * @brief Construct a new Param object for SpaceToDepth as default + */ Param() = default; + + /** + * @brief Construct a new Param object for SpaceToDepth with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for SpaceToDepth + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for SpaceToDepth with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for SpaceToDepth + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for SpaceToDepth + * @return Parameters of SpaceToDepth + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for SpaceToDepth + * @param [in] v Node visitor for invoking visit function of SpaceToDepth + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Split.h b/runtimes/pure_arm_compute/src/internal/op/Split.h index 8bea1000d..b2c6c2fd1 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Split.h +++ b/runtimes/pure_arm_compute/src/internal/op/Split.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Split.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines Split node + */ + #ifndef __INTERNAL_OP_SPLIT_H__ #define __INTERNAL_OP_SPLIT_H__ @@ -31,36 +37,68 @@ namespace op namespace Split { +/** + * @brief Struct to manipulate parameter for Split operation + */ struct Param { - int32_t axis_index; - int32_t ifm_index; + int32_t axis_index; //!< index for axis + int32_t ifm_index; //!< index for input feature map - std::vector<int32_t> ofm_indexes; + std::vector<int32_t> ofm_indexes; //!< index for output feature map + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define Split Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Split Node object + * @param param Parameter for Split Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for Split node }; } // namespace Split diff --git a/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.cc b/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.cc index c2c4f7242..f6c8bc5df 100644 --- a/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.cc +++ b/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.cc @@ -1,3 +1,19 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + #include "internal/op/SquaredDifference.h" #include "internal/op/NodeVisitor.h" @@ -30,7 +46,7 @@ namespace SquaredDifference Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs) { - assert(inputCount == 3 && outputCount == 1); + assert(inputCount == 2 && outputCount == 1); ofm_index = outputs[0]; @@ -38,10 +54,8 @@ Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, // // 0 -> LHS Tensor Index // 1 -> RHS Tensor Index - // 2 -> Activation Index lhs_index = inputs[0]; rhs_index = inputs[1]; - activation_index = inputs[2]; } } // namespace SquaredDifference diff --git a/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.h b/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.h index 7760405b9..ecbb03209 100644 --- a/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.h +++ b/runtimes/pure_arm_compute/src/internal/op/SquaredDifference.h @@ -1,3 +1,25 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file SquaredDifference.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::SquaredDifference::Param struct + * and internal::tflite::op::SquaredDifference::Node class + */ #ifndef __INTERNAL_OP_SQUAREDDIFFERENCE_H__ #define __INTERNAL_OP_SQUAREDDIFFERENCE_H__ @@ -14,33 +36,62 @@ namespace op namespace SquaredDifference { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t ofm_index; - - int32_t lhs_index; - int32_t rhs_index; - int32_t activation_index; + int32_t ofm_index; /**< Index of output feature map */ + int32_t lhs_index; /**< Index of lhs */ + int32_t rhs_index; /**< Index of rhs */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Squeeze.h b/runtimes/pure_arm_compute/src/internal/op/Squeeze.h index e871067f5..d5f36f85f 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Squeeze.h +++ b/runtimes/pure_arm_compute/src/internal/op/Squeeze.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Squeeze.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines internal::tflite::op::Squeeze::Param struct + * and internal::tflite::op::Squeeze::Node class + */ #ifndef __INTERNAL_OP_SQUEEZE_H__ #define __INTERNAL_OP_SQUEEZE_H__ @@ -30,32 +36,64 @@ namespace op namespace Squeeze { +/** + * @brief Struct to have indexes for operation parameter + */ struct Param { - int32_t output_index; - - int32_t input_index; - int32_t dims_index_optional = -1; // optional param. default is -1 + int32_t output_index; /**< Index of output feature map */ + int32_t input_index; /**< Index of input feature map */ + // optional param. default is -1 + int32_t dims_index_optional = -1; /**< Index of dims */ + /** + * @brief Construct as default + */ Param() = default; + /** + * @brief Construct a new Param object with params + * @param[in] inputCount Count of inputs + * @param[in] inputs Pointer of inputs + * @param[in] outputCount Count of outputs + * @param[in] outputs Pointer of outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to represent an operation of data structure + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object with param + * @param[in] param Param object that makes up a Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destruct as default + */ virtual ~Node() = default; public: + /** + * @brief Get a reference of Param object + * @return Reference of Param object + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Visit this Node by NodeVisitor + * @param[in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/StridedSlice.h b/runtimes/pure_arm_compute/src/internal/op/StridedSlice.h index 26bb81a8c..21dbb9e68 100644 --- a/runtimes/pure_arm_compute/src/internal/op/StridedSlice.h +++ b/runtimes/pure_arm_compute/src/internal/op/StridedSlice.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file StridedSlice.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines StridedSlice node + */ + #ifndef __INTERNAL_OP_STRIDEDSLICE_H__ #define __INTERNAL_OP_STRIDEDSLICE_H__ @@ -30,41 +36,73 @@ namespace op namespace StridedSlice { +/** + * @brief Struct to manipulate parameter for StridedSlice operation + */ struct Param { - int32_t outputData_index; + int32_t outputData_index; //!< index for output data - int32_t inputData_index; - int32_t startData_index; - int32_t endData_index; - int32_t stridesData_index; - int32_t beginMask_index; - int32_t endMask_index; - int32_t shrinkAxisMask_index; + int32_t inputData_index; //!< index for input data + int32_t startData_index; //!< index where slicing start from + int32_t endData_index; //!< index where slicing ends to + int32_t stridesData_index; //!< index for stride value + int32_t beginMask_index; //!< index for beginmask + int32_t endMask_index; //!< index for endmask + int32_t shrinkAxisMask_index; //!< index for shrink axis + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define StridedSlice Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new StridedSlice Node object + * @param param Parameter for StridedSlice Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for StridedSlice node }; } // namespace StridedSlice diff --git a/runtimes/pure_arm_compute/src/internal/op/Sub.h b/runtimes/pure_arm_compute/src/internal/op/Sub.h index 3da271029..864359d1e 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Sub.h +++ b/runtimes/pure_arm_compute/src/internal/op/Sub.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Sub.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines SUB Node + */ + #ifndef __INTERNAL_OP_SUB_H__ #define __INTERNAL_OP_SUB_H__ @@ -30,37 +36,69 @@ namespace op namespace Sub { +/** + * @brief Struct to manipulate parameters for SUB + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; //!< index for output feature map - int32_t lhs_index; - int32_t rhs_index; - int32_t activation_index; + int32_t lhs_index; //!< index for left-hand side + int32_t rhs_index; //!< index for right-hand side + int32_t activation_index; //!< index for activation function + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define SUB Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Sub Node object + * @param param Parameter for Sub Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for SUB node }; } // namespace Sub diff --git a/runtimes/pure_arm_compute/src/internal/op/Tanh.h b/runtimes/pure_arm_compute/src/internal/op/Tanh.h index f5a9f102e..fd87297f1 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Tanh.h +++ b/runtimes/pure_arm_compute/src/internal/op/Tanh.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Tanh.h + * @ingroup COM_AI_RUNTIME + * @brief This file defines TANH node + */ + #ifndef __INTERNAL_OP_TANH_H__ #define __INTERNAL_OP_TANH_H__ @@ -30,35 +36,67 @@ namespace op namespace Tanh { +/** + * @brief Struct to manipulate parameter for hyperbolic tangent operation + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; //!< index for output feature map - int32_t ifm_index; + int32_t ifm_index; //!< index for input feature map + /** + * @brief Default Constructor + */ Param() = default; + /** + * @brief Construct a new Param object + * @param[in] inputCount the number of inputs + * @param[in] inputs pointer for input data + * @param[in] outputCount the number of outputs + * @param[in] outputs pointer for input data + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define Tanh Operation + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Tanh Node object + * @param param Parameter for Tanh Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Default Destructor + */ virtual ~Node() = default; public: + /** + * @brief Get parameter + * @return Param reference + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Accept a NodeVisitor so that it can visit this node + * @param [in] v Visitor + * @return N/A + */ void accept(NodeVisitor &&) const override; private: - const Param _param; + const Param _param; //!< parameter for Tanh node }; } // namespace Tanh diff --git a/runtimes/pure_arm_compute/src/internal/op/TopKV2.h b/runtimes/pure_arm_compute/src/internal/op/TopKV2.h index 79bbd1f2e..02b7827e9 100644 --- a/runtimes/pure_arm_compute/src/internal/op/TopKV2.h +++ b/runtimes/pure_arm_compute/src/internal/op/TopKV2.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file TopKV2.h + * @brief This file contains accept function and params for TopKV2 operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_TOPKV2_H__ #define __INTERNAL_OP_TOPKV2_H__ @@ -30,33 +36,66 @@ namespace op namespace TopKV2 { +/** + * @brief Struct of TopKV2 operation's param + */ struct Param { - int32_t outputValues_index; - int32_t outputIndices_index; + int32_t outputValues_index; /**< Output values index */ + int32_t outputIndices_index; /**< Output indices index */ - int32_t inputData_index; - int32_t k_index; + int32_t inputData_index; /**< Input data index */ + int32_t k_index; /**< K value index */ + /** + * @brief Construct a new Param object for TopKV2 as default + */ Param() = default; + + /** + * @brief Construct a new Param object for TopKV2 with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for TopKV2 + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for TopKV2 with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for TopKV2 + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for TopKV2 + * @return Parameters of TopKV2 + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for TopKV2 + * @param [in] v Node visitor for invoking visit function of TopKV2 + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/Transpose.h b/runtimes/pure_arm_compute/src/internal/op/Transpose.h index dac2ef8f2..bb01bf322 100644 --- a/runtimes/pure_arm_compute/src/internal/op/Transpose.h +++ b/runtimes/pure_arm_compute/src/internal/op/Transpose.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file Transpose.h + * @brief This file contains accept function and params for Transpose operation + * @ingroup COM_AI_RUNTIME + */ + #ifndef __INTERNAL_OP_TRANSPOSE_H__ #define __INTERNAL_OP_TRANSPOSE_H__ @@ -30,32 +36,65 @@ namespace op namespace Transpose { +/** + * @brief Struct of Transpose operation's param + */ struct Param { - int32_t ofm_index; + int32_t ofm_index; /**< Output format index */ - int32_t ifm_index; - int32_t permu_index; + int32_t ifm_index; /**< Input format index */ + int32_t permu_index; /**< Permutation index */ + /** + * @brief Construct a new Param object for Transpose as default + */ Param() = default; + + /** + * @brief Construct a new Param object for Transpose with params + * @param [in] inputCount The number of input + * @param [in] inputs Array containing inputs + * @param [in] outputCount The number of output + * @param [in] outputs Array containing outputs + */ Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); }; +/** + * @brief Class to define operation node for Transpose + */ class Node final : public op::Node { public: + /** + * @brief Construct a new Node object for Transpose with param + * @param [in] param Parameters for Node + */ Node(const Param ¶m) : _param(param) { // DO NOTHING } public: + /** + * @brief Destroy the Node object for Transpose + */ virtual ~Node() = default; public: + /** + * @brief Get parameters for Transpose + * @return Parameters of Transpose + */ const Param ¶m(void) const { return _param; } public: + /** + * @brief Function for accepting node for Transpose + * @param [in] v Node visitor for invoking visit function of Transpose + * @return N/A + */ void accept(NodeVisitor &&) const override; private: diff --git a/runtimes/pure_arm_compute/src/internal/op/TransposeConv.cc b/runtimes/pure_arm_compute/src/internal/op/TransposeConv.cc new file mode 100644 index 000000000..502eff525 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/TransposeConv.cc @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/TransposeConv.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace TransposeConv +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace TransposeConv +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace TransposeConv +{ + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 6 && outputCount == 1); + + ofm_index = outputs[0]; + + // Each input should be interpreted as follows: + // + // 0 -> Output Shape Index + // 1 -> Weights Index + // 2 -> Input Tensor Index + // 3 -> Padding Type + // 4 -> Stride width + // 5 -> Stride height + + op_shape_index = inputs[0]; + ker_index = inputs[1]; + ifm_index = inputs[2]; + padding_index = inputs[3]; + hstride_index = inputs[4]; + vstride_index = inputs[5]; +} + +} // namespace TransposeConv +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/TransposeConv.h b/runtimes/pure_arm_compute/src/internal/op/TransposeConv.h new file mode 100644 index 000000000..b0122f82d --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/TransposeConv.h @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_TRANSPOSECONV_H__ +#define __INTERNAL_OP_TRANSPOSECONV_H__ + +#include "internal/op/Node.h" + +#include <cstdint> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace TransposeConv +{ + +struct Param +{ + int32_t ofm_index; + + int32_t op_shape_index; + int32_t ker_index; + int32_t ifm_index; + int32_t padding_index; + int32_t hstride_index; + int32_t vstride_index; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace TransposeConv +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_TRANSPOSECONV_H__ diff --git a/runtimes/pure_arm_compute/src/internal/op/Unpack.cc b/runtimes/pure_arm_compute/src/internal/op/Unpack.cc new file mode 100644 index 000000000..a1be0280c --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Unpack.cc @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "internal/op/Unpack.h" +#include "internal/op/NodeVisitor.h" + +#include <cassert> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Unpack +{ + +void Node::accept(NodeVisitor &&v) const { v.visit(*this); } + +} // namespace Unpack +} // namespace op +} // namespace tflite +} // namespace internal + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Unpack +{ +// There are three inputs: tensor which is to be unpacked, +// axis along which tensor needs to be unpacked +// and number of splits along the axis. + +Param::Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, + const uint32_t *outputs) +{ + assert(inputCount == 3); + + ifm_index = inputs[0]; + + for (uint32_t n = 0; n < outputCount; ++n) + { + ofm_indexes.emplace_back(outputs[n]); + } + num_split_index = inputs[1]; + axis_index = inputs[2]; +} + +} // namespace Unpack +} // namespace op +} // namespace tflite +} // namespace internal diff --git a/runtimes/pure_arm_compute/src/internal/op/Unpack.h b/runtimes/pure_arm_compute/src/internal/op/Unpack.h new file mode 100644 index 000000000..575e3d024 --- /dev/null +++ b/runtimes/pure_arm_compute/src/internal/op/Unpack.h @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __INTERNAL_OP_UNPACK_H__ +#define __INTERNAL_OP_UNPACK_H__ + +#include "internal/op/Node.h" + +#include <cstdint> +#include <vector> + +namespace internal +{ +namespace tflite +{ +namespace op +{ +namespace Unpack +{ + +struct Param +{ + int32_t ifm_index; + int32_t axis_index; + int32_t num_split_index; + // There are N outputs after Unpacking Input Tensor along axis + std::vector<int32_t> ofm_indexes; + + Param() = default; + Param(uint32_t inputCount, const uint32_t *inputs, uint32_t outputCount, const uint32_t *outputs); +}; + +class Node final : public op::Node +{ +public: + Node(const Param ¶m) : _param(param) + { + // DO NOTHING + } + +public: + virtual ~Node() = default; + +public: + const Param ¶m(void) const { return _param; } + +public: + void accept(NodeVisitor &&) const override; + +private: + const Param _param; +}; + +} // namespace Unpack +} // namespace op +} // namespace tflite +} // namespace internal + +#endif // __INTERNAL_OP_UNPACK_H__ diff --git a/runtimes/pure_arm_compute/src/logging.h b/runtimes/pure_arm_compute/src/logging.h index 61b434eda..914b63057 100644 --- a/runtimes/pure_arm_compute/src/logging.h +++ b/runtimes/pure_arm_compute/src/logging.h @@ -14,6 +14,12 @@ * limitations under the License. */ +/** + * @file logging.h + * @brief This file contains Context class for logging. + * @ingroup COM_AI_RUNTIME + */ + #ifndef __PURE_ARM_COMPUTE_LOGGING_H__ #define __PURE_ARM_COMPUTE_LOGGING_H__ @@ -22,9 +28,15 @@ namespace logging { +/** + * @brief class to define Context for logging + */ class Context { public: + /** + * @brief Construct default + */ Context() : _enabled{false} { auto env = std::getenv("PURE_ARM_COMPUTE_LOG_ENABLE"); @@ -36,12 +48,21 @@ public: } public: + /** + * @brief Get @c true if PURE_ARM_COMPUTE_LOG_ENABLE has been set as environment value, otherwise + * @c false + * @return @c true if PURE_ARM_COMPUTE_LOG_ENABLE has been set as environment value, otherwise @c + * false + */ bool enabled(void) const { return _enabled; } private: bool _enabled; }; +/** + * @brief static Context class for logging + */ static Context ctx; } // namespace logging diff --git a/runtimes/pure_arm_compute/src/memory.cc b/runtimes/pure_arm_compute/src/memory.cc index 2a9294f86..9e999661a 100644 --- a/runtimes/pure_arm_compute/src/memory.cc +++ b/runtimes/pure_arm_compute/src/memory.cc @@ -18,7 +18,7 @@ #include <sys/mman.h> #include <memory> -#include "nnfw/std/memory.h" +#include "cpp14/memory.h" #include "memory.h" int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, @@ -31,7 +31,7 @@ int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t // Use unique pointer to avoid memory leak std::unique_ptr<ANeuralNetworksMemory> memory_ptr = - nnfw::make_unique<ANeuralNetworksMemory>(size, protect, fd, offset); + nnfw::cpp14::make_unique<ANeuralNetworksMemory>(size, protect, fd, offset); if (memory_ptr == nullptr) { return ANEURALNETWORKS_OUT_OF_MEMORY; diff --git a/runtimes/pure_arm_compute/src/memory.h b/runtimes/pure_arm_compute/src/memory.h index 8bd43b0d6..ffac26ef6 100644 --- a/runtimes/pure_arm_compute/src/memory.h +++ b/runtimes/pure_arm_compute/src/memory.h @@ -14,20 +14,51 @@ * limitations under the License. */ +/** + * @file execution.h + * @brief This file defines ANeuralNetworksMemory class for handling Memory NNAPI + * @ingroup COM_AI_RUNTIME + */ + #ifndef __MEMORY_H__ #define __MEMORY_H__ #include <cstdint> +/** + * @brief struct to define Memory NNAPI + */ struct ANeuralNetworksMemory { public: + /** + * @brief Constructor with params + * @param [in] size The requested size in bytes + * @param [in] protect The desired memory protection for the mapping + * @param [in] fd The requested file descriptor + * @param [in] offset The offset to the beginning of the file of the area to map + */ ANeuralNetworksMemory(size_t size, int protect, int fd, size_t offset); + /** + * @brief Destructor default + */ ~ANeuralNetworksMemory(); public: + /** + * @brief Get size + * @return size + */ size_t size(void) const { return _size; } + /** + * @brief Get base pointer + * @return base pointer + */ uint8_t *base(void) { return _base; } + /** + * @brief Get base pointer + * @return const base pointer + */ const uint8_t *base(void) const { return _base; } private: diff --git a/runtimes/pure_arm_compute/src/model.cc b/runtimes/pure_arm_compute/src/model.cc index 49ea59f17..2c4120d7a 100644 --- a/runtimes/pure_arm_compute/src/model.cc +++ b/runtimes/pure_arm_compute/src/model.cc @@ -602,6 +602,28 @@ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_SPACE_TO_BATCH_ND: + { + using internal::tflite::op::SpaceToBatchND::Param; + using internal::tflite::op::SpaceToBatchND::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_BATCH_TO_SPACE_ND: + { + using internal::tflite::op::BatchToSpaceNd::Param; + using internal::tflite::op::BatchToSpaceNd::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } case ANEURALNETWORKS_L2_POOL_2D: { // Input count is 7 for Implicit Padding @@ -675,6 +697,29 @@ int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION: + { + + using internal::tflite::op::LocalResponseNormalization::Param; + using internal::tflite::op::LocalResponseNormalization::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_DEPTH_TO_SPACE: + { + using internal::tflite::op::DepthToSpace::Param; + using internal::tflite::op::DepthToSpace::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } default: throw std::runtime_error{"Not supported operation"}; }; @@ -706,6 +751,18 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_REDUCE_MIN_EX: + { + using internal::tflite::op::ReduceMin::Param; + using internal::tflite::op::ReduceMin::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } case ANEURALNETWORKS_TENSORFLOW_MAX_EX: { using internal::tflite::op::ReduceMax::Param; @@ -718,6 +775,53 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_PRELU_EX: + { + using internal::tflite::op::PReLU::Param; + using internal::tflite::op::PReLU::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_TRANSPOSE_CONV_EX: + { + using internal::tflite::op::TransposeConv::Param; + using internal::tflite::op::TransposeConv::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_LOGICAL_AND_EX: + { + using internal::tflite::op::LogicalAnd::Param; + using internal::tflite::op::LogicalAnd::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_LOGICAL_OR_EX: + { + using internal::tflite::op::LogicalOr::Param; + using internal::tflite::op::LogicalOr::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } case ANEURALNETWORKS_RSQRT_EX: { using internal::tflite::op::RSQRT::Param; @@ -730,6 +834,30 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_SQRT_EX: + { + using internal::tflite::op::SQRT::Param; + using internal::tflite::op::SQRT::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_EQUAL_EX: + { + using internal::tflite::op::Equal::Param; + using internal::tflite::op::Equal::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } case ANEURALNETWORKS_SQUARED_DIFFERENCE_EX: { using internal::tflite::op::SquaredDifference::Param; @@ -778,6 +906,101 @@ int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, break; } + case ANEURALNETWORKS_UNPACK_EX: + { + using internal::tflite::op::Unpack::Param; + using internal::tflite::op::Unpack::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_NEG_EX: + { + using internal::tflite::op::Neg::Param; + using internal::tflite::op::Neg::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_EXP_EX: + { + using internal::tflite::op::Exp::Param; + using internal::tflite::op::Exp::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_REDUCE_SUM_EX: + { + using internal::tflite::op::ReduceSum::Param; + using internal::tflite::op::ReduceSum::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_PACK_EX: + { + using internal::tflite::op::Pack::Param; + using internal::tflite::op::Pack::Node; + + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_ABS_EX: + { + using internal::tflite::op::Abs::Param; + using internal::tflite::op::Abs::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_ARGMAX_EX: + { + using internal::tflite::op::ArgMax::Param; + using internal::tflite::op::ArgMax::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + case ANEURALNETWORKS_NOT_EQUAL_EX: + { + using internal::tflite::op::NotEqual::Param; + using internal::tflite::op::NotEqual::Node; + + // Add 'operations' + auto &operations = model->deref().operations(); + + operations.emplace_back<Node>(Param{inputCount, inputs, outputCount, outputs}); + + break; + } + default: throw std::runtime_error{"Not supported operation"}; } diff --git a/runtimes/pure_arm_compute/src/model.h b/runtimes/pure_arm_compute/src/model.h index a7e606201..8acc894f4 100644 --- a/runtimes/pure_arm_compute/src/model.h +++ b/runtimes/pure_arm_compute/src/model.h @@ -14,22 +14,52 @@ * limitations under the License. */ +/** + * @file model.h + * @brief This file contains ANeuralNetworksModel classe for handling Model NNAPI such as + * ANeuralNetworksModel_create, ANeuralNetworksModel_addOperand + * @ingroup COM_AI_RUNTIME + */ + #ifndef __MODEL_H__ #define __MODEL_H__ #include "internal/Model.h" +/** + * @brief struct to express Model of NNAPI + */ struct ANeuralNetworksModel { public: + /** + * @brief Construct without params + */ ANeuralNetworksModel(); public: + /** + * @brief Get reference of internal::tflite::Model + * @return Reference of internal::tflite::Model + */ internal::tflite::Model &deref(void) { return *_model; } public: + /** + * @brief Release internal::tflite::Model pointer to param + * @param [in] model To get released internal::tflite::Model pointer + * @return N/A + */ void release(std::shared_ptr<const internal::tflite::Model> &model) { model = _model; } + /** + * @brief Get @c true if ANeuralNetworksModel_finish has been called, otherwise @c false + * @return @c true if ANeuralNetworksModel_finish has been called, otherwise @c false + */ bool isFinished() { return _isFinished == true; } + /** + * @brief Mark model process finished + * @return N/A + */ void markAsFinished() { _isFinished = true; } private: diff --git a/runtimes/template/CMakeLists.txt b/runtimes/template/CMakeLists.txt deleted file mode 100644 index 146824ddc..000000000 --- a/runtimes/template/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cc") - -add_library(nnapi_template SHARED ${SOURCES}) -target_include_directories(nnapi_template PUBLIC ${NNFW_INCLUDE_DIR}) -set_target_properties(nnapi_template PROPERTIES OUTPUT_NAME neuralnetworks) diff --git a/runtimes/template/src/compilation.cc b/runtimes/template/src/compilation.cc deleted file mode 100644 index db3f3e180..000000000 --- a/runtimes/template/src/compilation.cc +++ /dev/null @@ -1,14 +0,0 @@ -#include <NeuralNetworks.h> - -#include "compilation.h" - -int ANeuralNetworksCompilation_create(ANeuralNetworksModel *model, - ANeuralNetworksCompilation **compilation) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation *compilation) -{ - return ANEURALNETWORKS_NO_ERROR; -} diff --git a/runtimes/template/src/compilation.h b/runtimes/template/src/compilation.h deleted file mode 100644 index 7a28742b4..000000000 --- a/runtimes/template/src/compilation.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __COMPILATION_H__ -#define __COMPILATION_H__ - -struct ANeuralNetworksCompilation -{ -}; - -#endif diff --git a/runtimes/template/src/event.cc b/runtimes/template/src/event.cc deleted file mode 100644 index 56b794608..000000000 --- a/runtimes/template/src/event.cc +++ /dev/null @@ -1,13 +0,0 @@ -#include <NeuralNetworks.h> - -#include "event.h" - -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent *event) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksEvent_free(ANeuralNetworksEvent *event) -{ - delete event; -} diff --git a/runtimes/template/src/event.h b/runtimes/template/src/event.h deleted file mode 100644 index 7197f0410..000000000 --- a/runtimes/template/src/event.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __EVENT_H__ -#define __EVENT_H__ - -struct ANeuralNetworksEvent -{ -}; - -#endif diff --git a/runtimes/template/src/execution.cc b/runtimes/template/src/execution.cc deleted file mode 100644 index 3a418195a..000000000 --- a/runtimes/template/src/execution.cc +++ /dev/null @@ -1,33 +0,0 @@ -#include <NeuralNetworks.h> - -#include "execution.h" - -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation *compilation, - ANeuralNetworksExecution **execution) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, - const void *buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, void *buffer, - size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution, - ANeuralNetworksEvent **event) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksExecution_free(ANeuralNetworksExecution *execution) -{ -} diff --git a/runtimes/template/src/execution.h b/runtimes/template/src/execution.h deleted file mode 100644 index 84d4e88e9..000000000 --- a/runtimes/template/src/execution.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __EXECUTION_H__ -#define __EXECUTION_H__ - -struct ANeuralNetworksExecution -{ -}; - -#endif diff --git a/runtimes/template/src/memory.cc b/runtimes/template/src/memory.cc deleted file mode 100644 index 9579f6e71..000000000 --- a/runtimes/template/src/memory.cc +++ /dev/null @@ -1,16 +0,0 @@ -#include <NeuralNetworks.h> - -#include "memory.h" - -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, - ANeuralNetworksMemory **memory) -{ - *memory = new ANeuralNetworksMemory{}; - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksMemory_free(ANeuralNetworksMemory *memory) -{ - delete memory; -} diff --git a/runtimes/template/src/memory.h b/runtimes/template/src/memory.h deleted file mode 100644 index a4fed8221..000000000 --- a/runtimes/template/src/memory.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __MEMORY_H__ -#define __MEMORY_H__ - -struct ANeuralNetworksMemory -{ -}; - -#endif // __MEMORY_H__ diff --git a/runtimes/template/src/model.cc b/runtimes/template/src/model.cc deleted file mode 100644 index f2d65d2b2..000000000 --- a/runtimes/template/src/model.cc +++ /dev/null @@ -1,63 +0,0 @@ -#include <NeuralNetworks.h> -#include <NeuralNetworksEx.h> - -#include "model.h" - -int ANeuralNetworksModel_create(ANeuralNetworksModel **model) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksModel_free(ANeuralNetworksModel *model) -{ -} - -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model, - const ANeuralNetworksOperandType *type) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t index, - const void *buffer, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel *model, - int32_t index, - const ANeuralNetworksMemory *memory, - size_t offset, size_t length) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, - ANeuralNetworksOperationType type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel* model, - ANeuralNetworksOperationTypeEx type, uint32_t inputCount, - const uint32_t* inputs, uint32_t outputCount, - const uint32_t* outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, - uint32_t inputCount, - const uint32_t *inputs, - uint32_t outputCount, - const uint32_t *outputs) -{ - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_finish(ANeuralNetworksModel *model) -{ - return ANEURALNETWORKS_NO_ERROR; -} diff --git a/runtimes/template/src/model.h b/runtimes/template/src/model.h deleted file mode 100644 index 8da8f747f..000000000 --- a/runtimes/template/src/model.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef __MODEL_H__ -#define __MODEL_H__ - -struct ANeuralNetworksModel -{ -}; - -#endif // __MODEL_H__ diff --git a/runtimes/tests/CMakeLists.txt b/runtimes/tests/CMakeLists.txt deleted file mode 100644 index 795c5446f..000000000 --- a/runtimes/tests/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -set(NNRUNTIME_TEST_INC_COMMON ${NNFW_INCLUDE_DIR} - ${CMAKE_CURRENT_SOURCE_DIR}/include -) - -if (BUILD_ANDROID_NN_RUNTIME_TEST) - add_subdirectory(neural_networks_test) -endif() diff --git a/runtimes/tests/include/NeuralNetworksWrapper.h b/runtimes/tests/include/NeuralNetworksWrapper.h deleted file mode 100644 index 5662d6f5c..000000000 --- a/runtimes/tests/include/NeuralNetworksWrapper.h +++ /dev/null @@ -1,355 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (C) 2017 The Android Open Source Project - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// Provides C++ classes to more easily use the Neural Networks API. - -#ifndef __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ -#define __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ - -#include "NeuralNetworks.h" -#include "NeuralNetworksEx.h" - -#include <math.h> -#include <vector> - -namespace nnfw { -namespace rt { -namespace wrapper { - -enum class Type { - FLOAT32 = ANEURALNETWORKS_FLOAT32, - INT32 = ANEURALNETWORKS_INT32, - UINT32 = ANEURALNETWORKS_UINT32, - TENSOR_FLOAT32 = ANEURALNETWORKS_TENSOR_FLOAT32, - TENSOR_INT32 = ANEURALNETWORKS_TENSOR_INT32, - TENSOR_QUANT8_ASYMM = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, -}; - -enum class ExecutePreference { - PREFER_LOW_POWER = ANEURALNETWORKS_PREFER_LOW_POWER, - PREFER_FAST_SINGLE_ANSWER = ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER, - PREFER_SUSTAINED_SPEED = ANEURALNETWORKS_PREFER_SUSTAINED_SPEED -}; - -enum class Result { - NO_ERROR = ANEURALNETWORKS_NO_ERROR, - OUT_OF_MEMORY = ANEURALNETWORKS_OUT_OF_MEMORY, - INCOMPLETE = ANEURALNETWORKS_INCOMPLETE, - UNEXPECTED_NULL = ANEURALNETWORKS_UNEXPECTED_NULL, - BAD_DATA = ANEURALNETWORKS_BAD_DATA, -}; - -struct OperandType { - ANeuralNetworksOperandType operandType; - // int32_t type; - std::vector<uint32_t> dimensions; - - OperandType(Type type, const std::vector<uint32_t>& d, float scale = 0.0f, - int32_t zeroPoint = 0) - : dimensions(d) { - operandType.type = static_cast<int32_t>(type); - operandType.scale = scale; - operandType.zeroPoint = zeroPoint; - - operandType.dimensionCount = static_cast<uint32_t>(dimensions.size()); - operandType.dimensions = dimensions.data(); - } -}; - -class Memory { -public: - Memory(size_t size, int protect, int fd, size_t offset) { - mValid = ANeuralNetworksMemory_createFromFd(size, protect, fd, offset, &mMemory) == - ANEURALNETWORKS_NO_ERROR; - } - - ~Memory() { ANeuralNetworksMemory_free(mMemory); } - - // Disallow copy semantics to ensure the runtime object can only be freed - // once. Copy semantics could be enabled if some sort of reference counting - // or deep-copy system for runtime objects is added later. - Memory(const Memory&) = delete; - Memory& operator=(const Memory&) = delete; - - // Move semantics to remove access to the runtime object from the wrapper - // object that is being moved. This ensures the runtime object will be - // freed only once. - Memory(Memory&& other) { *this = std::move(other); } - Memory& operator=(Memory&& other) { - if (this != &other) { - mMemory = other.mMemory; - mValid = other.mValid; - other.mMemory = nullptr; - other.mValid = false; - } - return *this; - } - - ANeuralNetworksMemory* get() const { return mMemory; } - bool isValid() const { return mValid; } - -private: - ANeuralNetworksMemory* mMemory = nullptr; - bool mValid = true; -}; - -class Model { -public: - Model() { - // TODO handle the value returned by this call - ANeuralNetworksModel_create(&mModel); - } - ~Model() { ANeuralNetworksModel_free(mModel); } - - // Disallow copy semantics to ensure the runtime object can only be freed - // once. Copy semantics could be enabled if some sort of reference counting - // or deep-copy system for runtime objects is added later. - Model(const Model&) = delete; - Model& operator=(const Model&) = delete; - - // Move semantics to remove access to the runtime object from the wrapper - // object that is being moved. This ensures the runtime object will be - // freed only once. - Model(Model&& other) { *this = std::move(other); } - Model& operator=(Model&& other) { - if (this != &other) { - mModel = other.mModel; - mNextOperandId = other.mNextOperandId; - mValid = other.mValid; - other.mModel = nullptr; - other.mNextOperandId = 0; - other.mValid = false; - } - return *this; - } - - Result finish() { return static_cast<Result>(ANeuralNetworksModel_finish(mModel)); } - - uint32_t addOperand(const OperandType* type) { - if (ANeuralNetworksModel_addOperand(mModel, &(type->operandType)) != - ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - return mNextOperandId++; - } - - void setOperandValue(uint32_t index, const void* buffer, size_t length) { - if (ANeuralNetworksModel_setOperandValue(mModel, index, buffer, length) != - ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } - - void setOperandValueFromMemory(uint32_t index, const Memory* memory, uint32_t offset, - size_t length) { - if (ANeuralNetworksModel_setOperandValueFromMemory(mModel, index, memory->get(), offset, - length) != ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } - - void addOperation(ANeuralNetworksOperationType type, const std::vector<uint32_t>& inputs, - const std::vector<uint32_t>& outputs) { - if (ANeuralNetworksModel_addOperation(mModel, type, static_cast<uint32_t>(inputs.size()), - inputs.data(), static_cast<uint32_t>(outputs.size()), - outputs.data()) != ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } - - void addOperationEx(ANeuralNetworksOperationTypeEx type, const std::vector<uint32_t>& inputs, - const std::vector<uint32_t>& outputs) { - if (ANeuralNetworksModel_addOperationEx(mModel, type, static_cast<uint32_t>(inputs.size()), - inputs.data(), static_cast<uint32_t>(outputs.size()), - outputs.data()) != ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } - - void identifyInputsAndOutputs(const std::vector<uint32_t>& inputs, - const std::vector<uint32_t>& outputs) { - if (ANeuralNetworksModel_identifyInputsAndOutputs( - mModel, static_cast<uint32_t>(inputs.size()), inputs.data(), - static_cast<uint32_t>(outputs.size()), - outputs.data()) != ANEURALNETWORKS_NO_ERROR) { - mValid = false; - } - } - ANeuralNetworksModel* getHandle() const { return mModel; } - bool isValid() const { return mValid; } - -private: - ANeuralNetworksModel* mModel = nullptr; - // We keep track of the operand ID as a convenience to the caller. - uint32_t mNextOperandId = 0; - bool mValid = true; -}; - -class Event { -public: - Event() {} - ~Event() { ANeuralNetworksEvent_free(mEvent); } - - // Disallow copy semantics to ensure the runtime object can only be freed - // once. Copy semantics could be enabled if some sort of reference counting - // or deep-copy system for runtime objects is added later. - Event(const Event&) = delete; - Event& operator=(const Event&) = delete; - - // Move semantics to remove access to the runtime object from the wrapper - // object that is being moved. This ensures the runtime object will be - // freed only once. - Event(Event&& other) { *this = std::move(other); } - Event& operator=(Event&& other) { - if (this != &other) { - mEvent = other.mEvent; - other.mEvent = nullptr; - } - return *this; - } - - Result wait() { return static_cast<Result>(ANeuralNetworksEvent_wait(mEvent)); } - - // Only for use by Execution - void set(ANeuralNetworksEvent* newEvent) { - ANeuralNetworksEvent_free(mEvent); - mEvent = newEvent; - } - -private: - ANeuralNetworksEvent* mEvent = nullptr; -}; - -class Compilation { -public: - Compilation(const Model* model) { - int result = ANeuralNetworksCompilation_create(model->getHandle(), &mCompilation); - if (result != 0) { - // TODO Handle the error - } - } - - ~Compilation() { ANeuralNetworksCompilation_free(mCompilation); } - - Compilation(const Compilation&) = delete; - Compilation& operator=(const Compilation&) = delete; - - Compilation(Compilation&& other) { *this = std::move(other); } - Compilation& operator=(Compilation&& other) { - if (this != &other) { - mCompilation = other.mCompilation; - other.mCompilation = nullptr; - } - return *this; - } - - Result setPreference(ExecutePreference preference) { - return static_cast<Result>(ANeuralNetworksCompilation_setPreference( - mCompilation, static_cast<int32_t>(preference))); - } - - Result finish() { return static_cast<Result>(ANeuralNetworksCompilation_finish(mCompilation)); } - - ANeuralNetworksCompilation* getHandle() const { return mCompilation; } - -private: - ANeuralNetworksCompilation* mCompilation = nullptr; -}; - -class Execution { -public: - Execution(const Compilation* compilation) { - int result = ANeuralNetworksExecution_create(compilation->getHandle(), &mExecution); - if (result != 0) { - // TODO Handle the error - } - } - - ~Execution() { ANeuralNetworksExecution_free(mExecution); } - - // Disallow copy semantics to ensure the runtime object can only be freed - // once. Copy semantics could be enabled if some sort of reference counting - // or deep-copy system for runtime objects is added later. - Execution(const Execution&) = delete; - Execution& operator=(const Execution&) = delete; - - // Move semantics to remove access to the runtime object from the wrapper - // object that is being moved. This ensures the runtime object will be - // freed only once. - Execution(Execution&& other) { *this = std::move(other); } - Execution& operator=(Execution&& other) { - if (this != &other) { - mExecution = other.mExecution; - other.mExecution = nullptr; - } - return *this; - } - - Result setInput(uint32_t index, const void* buffer, size_t length, - const ANeuralNetworksOperandType* type = nullptr) { - return static_cast<Result>( - ANeuralNetworksExecution_setInput(mExecution, index, type, buffer, length)); - } - - Result setInputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, - uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { - return static_cast<Result>(ANeuralNetworksExecution_setInputFromMemory( - mExecution, index, type, memory->get(), offset, length)); - } - - Result setOutput(uint32_t index, void* buffer, size_t length, - const ANeuralNetworksOperandType* type = nullptr) { - return static_cast<Result>( - ANeuralNetworksExecution_setOutput(mExecution, index, type, buffer, length)); - } - - Result setOutputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, - uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { - return static_cast<Result>(ANeuralNetworksExecution_setOutputFromMemory( - mExecution, index, type, memory->get(), offset, length)); - } - - Result startCompute(Event* event) { - ANeuralNetworksEvent* ev = nullptr; - Result result = static_cast<Result>(ANeuralNetworksExecution_startCompute(mExecution, &ev)); - event->set(ev); - return result; - } - - Result compute() { - ANeuralNetworksEvent* event = nullptr; - Result result = - static_cast<Result>(ANeuralNetworksExecution_startCompute(mExecution, &event)); - if (result != Result::NO_ERROR) { - return result; - } - // TODO how to manage the lifetime of events when multiple waiters is not - // clear. - result = static_cast<Result>(ANeuralNetworksEvent_wait(event)); - ANeuralNetworksEvent_free(event); - return result; - } - -private: - ANeuralNetworksExecution* mExecution = nullptr; -}; - -} // namespace wrapper -} // namespace rt -} // namespace nnfw - -#endif // __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ diff --git a/runtimes/tests/neural_networks_test/CMakeLists.txt b/runtimes/tests/neural_networks_test/CMakeLists.txt deleted file mode 100644 index 1f870a106..000000000 --- a/runtimes/tests/neural_networks_test/CMakeLists.txt +++ /dev/null @@ -1,40 +0,0 @@ -# Executable `runtime_run` (Dummy runner executable using NN API) -set(RUNTIME_ANDROID_NN_TEST runtime_run_android_nn_test) -set(RUNTIME_ANDROID_NN_TEST_SRC TestMain.cpp - TestTrivialModel.cpp - TestValidation.cpp) - -# g++-6.3 can build TestGenerated.cpp. -if (CMAKE_COMPILER_IS_GNUCC AND NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 6.3) - set(RUNTIME_ANDROID_NN_TEST_SRC ${RUNTIME_ANDROID_NN_TEST_SRC} TestGenerated.cpp) -endif() - -# Both gbs and cross for tizen support building generated test -if (OBS_BUILD) - set(RUNTIME_ANDROID_NN_TEST_SRC ${RUNTIME_ANDROID_NN_TEST_SRC} TestGenerated.cpp) -endif() - -add_executable(${RUNTIME_ANDROID_NN_TEST} ${RUNTIME_ANDROID_NN_TEST_SRC}) - -nnfw_find_package(GTest) - -target_include_directories(${RUNTIME_ANDROID_NN_TEST} PRIVATE - ${NNRUNTIME_TEST_INC_COMMON} - ${CMAKE_CURRENT_SOURCE_DIR} - ${CMAKE_CURRENT_SOURCE_DIR}/include) -if (BUILD_NEURUN) - target_link_libraries(${RUNTIME_ANDROID_NN_TEST} ${LIB_NEURUN}) - set(SKIPLIST_FILE_NAME ${RUNTIME_ANDROID_NN_TEST}.skip.${TARGET_PLATFORM}.neurun) -elseif (BUILD_PURE_ARM_COMPUTE) - target_link_libraries(${RUNTIME_ANDROID_NN_TEST} ${LIB_PUREACL_RUNTIME}) - set(SKIPLIST_FILE_NAME ${RUNTIME_ANDROID_NN_TEST}.skip.${TARGET_PLATFORM}) -endif() -target_link_libraries(${RUNTIME_ANDROID_NN_TEST} gtest) -target_link_libraries(${RUNTIME_ANDROID_NN_TEST} ${LIB_PTHREAD}) - -install(TARGETS runtime_run_android_nn_test DESTINATION unittest) - -install(FILES ${SKIPLIST_FILE_NAME} - DESTINATION unittest - RENAME ${RUNTIME_ANDROID_NN_TEST}.skip - OPTIONAL) diff --git a/runtimes/tests/neural_networks_test/TestValidation.cpp b/runtimes/tests/neural_networks_test/TestValidation.cpp deleted file mode 100644 index e4f287aaf..000000000 --- a/runtimes/tests/neural_networks_test/TestValidation.cpp +++ /dev/null @@ -1,612 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright (C) 2017 The Android Open Source Project - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "NeuralNetworks.h" -#if 0 // TODO-NNRT : Enable if we support OEM -#include "NeuralNetworksOEM.h" -#endif -#include <gtest/gtest.h> -#include <string> -#include <sys/mman.h> -#include <stdio.h> -#include <stdlib.h> -// This file tests all the validations done by the Neural Networks API. -namespace { - -#define PATH_MAX 256 - -static int shmem_num = 0; -static int shmem_create_region(size_t size) -{ - char temp[PATH_MAX]; - snprintf(temp, sizeof(temp), "/tmp/nn-shmem-%d-%d-XXXXXXXXX", getpid(), shmem_num++); - - // Set umask and recover after generate temporary file to avoid security issue - mode_t umaskPrev = umask(S_IRUSR|S_IWUSR); - int fd = mkstemp(temp); - umask(umaskPrev); - - if (fd == -1) return -1; - - unlink(temp); - - if (TEMP_FAILURE_RETRY(ftruncate(fd, size)) == -1) { - close(fd); - return -1; - } - - return fd; -} - -class ValidationTest : public ::testing::Test { -protected: - virtual void SetUp() {} -}; - -class ValidationTestModel : public ValidationTest { -protected: - virtual void SetUp() { - ValidationTest::SetUp(); - ASSERT_EQ(ANeuralNetworksModel_create(&mModel), ANEURALNETWORKS_NO_ERROR); - } - virtual void TearDown() { - ANeuralNetworksModel_free(mModel); - ValidationTest::TearDown(); - } - ANeuralNetworksModel* mModel = nullptr; -}; - -class ValidationTestIdentify : public ValidationTestModel { - virtual void SetUp() { - ValidationTestModel::SetUp(); - - uint32_t dimensions[]{1}; - ANeuralNetworksOperandType tensorType{.type = ANEURALNETWORKS_TENSOR_FLOAT32, - .dimensionCount = 1, - .dimensions = dimensions}; - ANeuralNetworksOperandType scalarType{.type = ANEURALNETWORKS_INT32, - .dimensionCount = 0, - .dimensions = nullptr}; - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &scalarType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - uint32_t inList[3]{0, 1, 2}; - uint32_t outList[1]{3}; - ASSERT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_ADD, 3, inList, 1, - outList), - ANEURALNETWORKS_NO_ERROR); - } - virtual void TearDown() { - ValidationTestModel::TearDown(); - } -}; - -class ValidationTestCompilation : public ValidationTestModel { -protected: - virtual void SetUp() { - ValidationTestModel::SetUp(); - - uint32_t dimensions[]{1}; - ANeuralNetworksOperandType tensorType{.type = ANEURALNETWORKS_TENSOR_FLOAT32, - .dimensionCount = 1, - .dimensions = dimensions}; - ANeuralNetworksOperandType scalarType{.type = ANEURALNETWORKS_INT32, - .dimensionCount = 0, - .dimensions = nullptr}; - - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &scalarType), ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); - uint32_t inList[3]{0, 1, 2}; - uint32_t outList[1]{3}; - ASSERT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_ADD, 3, inList, 1, - outList), - ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), - ANEURALNETWORKS_NO_ERROR); - ASSERT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); - - ASSERT_EQ(ANeuralNetworksCompilation_create(mModel, &mCompilation), - ANEURALNETWORKS_NO_ERROR); - } - virtual void TearDown() { - ANeuralNetworksCompilation_free(mCompilation); - ValidationTestModel::TearDown(); - } - ANeuralNetworksCompilation* mCompilation = nullptr; -}; - -class ValidationTestExecution : public ValidationTestCompilation { -protected: - virtual void SetUp() { - ValidationTestCompilation::SetUp(); - - ASSERT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_NO_ERROR); - - ASSERT_EQ(ANeuralNetworksExecution_create(mCompilation, &mExecution), - ANEURALNETWORKS_NO_ERROR); - } - virtual void TearDown() { - ANeuralNetworksExecution_free(mExecution); - ValidationTestCompilation::TearDown(); - } - ANeuralNetworksExecution* mExecution = nullptr; -}; - -TEST_F(ValidationTest, CreateModel) { - EXPECT_EQ(ANeuralNetworksModel_create(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); -} - -TEST_F(ValidationTestModel, AddOperand) { - ANeuralNetworksOperandType floatType{ - .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(nullptr, &floatType), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); - - ANeuralNetworksOperandType quant8TypeInvalidScale{ - .type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, - .dimensionCount = 0, - .dimensions = nullptr, - // Scale has to be non-negative - .scale = -1.0f, - .zeroPoint = 0, - }; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &quant8TypeInvalidScale), - ANEURALNETWORKS_BAD_DATA); - - ANeuralNetworksOperandType quant8TypeInvalidZeroPoint{ - .type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, - .dimensionCount = 0, - .dimensions = nullptr, - .scale = 1.0f, - // zeroPoint has to be in [0, 255] - .zeroPoint = -1, - }; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &quant8TypeInvalidZeroPoint), - ANEURALNETWORKS_BAD_DATA); - - uint32_t dim = 2; - ANeuralNetworksOperandType invalidScalarType{ - .type = ANEURALNETWORKS_INT32, - // scalar types can only 0 dimensions. - .dimensionCount = 1, - .dimensions = &dim, - }; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &invalidScalarType), - ANEURALNETWORKS_BAD_DATA); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), - ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestModel, SetOptionalOperand) { - ANeuralNetworksOperandType floatType{ - .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); - - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, nullptr, 0), - ANEURALNETWORKS_NO_ERROR); -} - -TEST_F(ValidationTestModel, SetOperandValue) { - ANeuralNetworksOperandType floatType{ - .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); - - char buffer[20]; - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(nullptr, 0, buffer, sizeof(buffer)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, nullptr, sizeof(buffer)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since buffer is not the size of a float32. - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(buffer)), - ANEURALNETWORKS_BAD_DATA); - - // This should succeed. - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(float)), - ANEURALNETWORKS_NO_ERROR); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 1, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestModel, SetOperandValueFromMemory) { - uint32_t dimensions[]{1}; - ANeuralNetworksOperandType floatType{ - .type = ANEURALNETWORKS_TENSOR_FLOAT32, - .dimensionCount = 1, - .dimensions = dimensions}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); - - const size_t memorySize = 20; - int memoryFd = shmem_create_region(memorySize); - ASSERT_GT(memoryFd, 0); - - ANeuralNetworksMemory* memory; - EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, - memoryFd, 0, &memory), - ANEURALNETWORKS_NO_ERROR); - - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(nullptr, 0, - memory, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, - nullptr, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since the operand does not exist. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, -1, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since memory is not the size of a float32. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, - memory, 0, memorySize), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 1, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since offset is larger than memorySize. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, - memory, memorySize + 1, - sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since requested size is larger than the memory. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, - memory, memorySize - 3, - sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, - memory, 0, - sizeof(float)), - ANEURALNETWORKS_BAD_STATE); -} - - -#if 0 // TODO-NNRT : Enable if we support OEM OP. -TEST_F(ValidationTestModel, AddOEMOperand) { - ANeuralNetworksOperandType OEMScalarType{ - .type = ANEURALNETWORKS_OEM_SCALAR, .dimensionCount = 0, .dimensions = nullptr}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMScalarType), ANEURALNETWORKS_NO_ERROR); - char buffer[20]; - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(buffer)), - ANEURALNETWORKS_NO_ERROR); - - const size_t kByteSizeOfOEMTensor = 4; - uint32_t dimensions[]{kByteSizeOfOEMTensor}; - ANeuralNetworksOperandType OEMTensorType{ - .type = ANEURALNETWORKS_TENSOR_OEM_BYTE, - .dimensionCount = 1, - .dimensions = dimensions}; - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMTensorType), ANEURALNETWORKS_NO_ERROR); - EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 1, buffer, kByteSizeOfOEMTensor), - ANEURALNETWORKS_NO_ERROR); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMTensorType), ANEURALNETWORKS_BAD_STATE); -} -#endif // TODO-NNRT - -TEST_F(ValidationTestModel, AddOperation) { - uint32_t input = 0; - uint32_t output = 0; - EXPECT_EQ(ANeuralNetworksModel_addOperation(nullptr, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, - 1, &output), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 0, nullptr, - 1, &output), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, - 0, nullptr), - ANEURALNETWORKS_UNEXPECTED_NULL); - - ANeuralNetworksOperationType invalidOp = -1; - EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, invalidOp, 1, &input, - 1, &output), - ANEURALNETWORKS_BAD_DATA); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, - 1, &output), - ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestModel, IdentifyInputsAndOutputs) { - uint32_t input = 0; - uint32_t output = 0; - EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(nullptr, 1, &input, 1, &output), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 0, nullptr, 1, &output), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 1, &input, 0, nullptr), - ANEURALNETWORKS_UNEXPECTED_NULL); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 1, &input, 1, &output), - ANEURALNETWORKS_BAD_STATE); -} - -#if 0 // TODO-NNRT : Enable if we support 'RelaxComputationFloat32toFloat16' -TEST_F(ValidationTestModel, RelaxComputationFloat32toFloat16) { - EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(nullptr, true), - ANEURALNETWORKS_UNEXPECTED_NULL); - - ANeuralNetworksModel_finish(mModel); - // This should fail, as the model is already finished. - EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(mModel, true), - ANEURALNETWORKS_BAD_STATE); - EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(mModel, false), - ANEURALNETWORKS_BAD_STATE); -} -#endif - -TEST_F(ValidationTestModel, Finish) { - EXPECT_EQ(ANeuralNetworksModel_finish(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); - EXPECT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestModel, CreateCompilation) { - ANeuralNetworksCompilation* compilation = nullptr; - EXPECT_EQ(ANeuralNetworksCompilation_create(nullptr, &compilation), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksCompilation_create(mModel, nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksCompilation_create(mModel, &compilation), ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestIdentify, Ok) { - uint32_t inList[3]{0, 1, 2}; - uint32_t outList[1]{3}; - - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), - ANEURALNETWORKS_NO_ERROR); - - ASSERT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); -} - -TEST_F(ValidationTestIdentify, InputIsOutput) { - uint32_t inList[3]{0, 1, 2}; - uint32_t outList[2]{3, 0}; - - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestIdentify, OutputIsInput) { - uint32_t inList[4]{0, 1, 2, 3}; - uint32_t outList[1]{3}; - - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestIdentify, DuplicateInputs) { - uint32_t inList[4]{0, 1, 2, 0}; - uint32_t outList[1]{3}; - - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestIdentify, DuplicateOutputs) { - uint32_t inList[3]{0, 1, 2}; - uint32_t outList[2]{3, 3}; - - ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestCompilation, SetPreference) { - EXPECT_EQ(ANeuralNetworksCompilation_setPreference(nullptr, ANEURALNETWORKS_PREFER_LOW_POWER), - ANEURALNETWORKS_UNEXPECTED_NULL); - - EXPECT_EQ(ANeuralNetworksCompilation_setPreference(mCompilation, 40), ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestCompilation, CreateExecution) { - ANeuralNetworksExecution* execution = nullptr; - EXPECT_EQ(ANeuralNetworksExecution_create(nullptr, &execution), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, nullptr), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), - ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestCompilation, Finish) { - EXPECT_EQ(ANeuralNetworksCompilation_finish(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_NO_ERROR); - EXPECT_EQ(ANeuralNetworksCompilation_setPreference(mCompilation, - ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER), - ANEURALNETWORKS_BAD_STATE); - EXPECT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_BAD_STATE); -} - -TEST_F(ValidationTestExecution, SetInput) { - ANeuralNetworksExecution* execution; - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); - - char buffer[20]; - EXPECT_EQ(ANeuralNetworksExecution_setInput(nullptr, 0, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 0, nullptr, nullptr, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since memory is not the size of a float32. - EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 0, nullptr, buffer, 20), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 999, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, -1, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestExecution, SetOutput) { - ANeuralNetworksExecution* execution; - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); - - char buffer[20]; - EXPECT_EQ(ANeuralNetworksExecution_setOutput(nullptr, 0, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 0, nullptr, nullptr, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since memory is not the size of a float32. - EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 0, nullptr, buffer, 20), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 999, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, as this operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, -1, nullptr, buffer, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestExecution, SetInputFromMemory) { - ANeuralNetworksExecution* execution; - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); - - const size_t memorySize = 20; - int memoryFd = shmem_create_region(memorySize); - ASSERT_GT(memoryFd, 0); - - ANeuralNetworksMemory* memory; - EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, - memoryFd, 0, &memory), - ANEURALNETWORKS_NO_ERROR); - - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(nullptr, 0, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, - nullptr, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since the operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 999, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since the operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, -1, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since memory is not the size of a float32. - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, - memory, 0, memorySize), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since offset is larger than memorySize. - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, - memory, memorySize + 1, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since requested size is larger than the memory. - EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, - memory, memorySize - 3, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestExecution, SetOutputFromMemory) { - ANeuralNetworksExecution* execution; - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); - - const size_t memorySize = 20; - int memoryFd = shmem_create_region(memorySize); - ASSERT_GT(memoryFd, 0); - - ANeuralNetworksMemory* memory; - EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, - memoryFd, 0, &memory), - ANEURALNETWORKS_NO_ERROR); - - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(nullptr, 0, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, - nullptr, 0, sizeof(float)), - ANEURALNETWORKS_UNEXPECTED_NULL); - - // This should fail, since the operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 999, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since the operand does not exist. - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, -1, nullptr, - memory, 0, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since memory is not the size of a float32. - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, - memory, 0, memorySize), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since offset is larger than memorySize. - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, - memory, memorySize + 1, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); - - // This should fail, since requested size is larger than the memory. - EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, - memory, memorySize - 3, sizeof(float)), - ANEURALNETWORKS_BAD_DATA); -} - -TEST_F(ValidationTestExecution, StartCompute) { - ANeuralNetworksExecution* execution; - EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); - - ANeuralNetworksEvent* event; - EXPECT_EQ(ANeuralNetworksExecution_startCompute(nullptr, &event), - ANEURALNETWORKS_UNEXPECTED_NULL); - EXPECT_EQ(ANeuralNetworksExecution_startCompute(execution, nullptr), - ANEURALNETWORKS_UNEXPECTED_NULL); -} - -TEST_F(ValidationTestExecution, EventWait) { - EXPECT_EQ(ANeuralNetworksEvent_wait(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); -} -} // namespace diff --git a/runtimes/tests/neural_networks_test/generated/all_generated_tests.cpp b/runtimes/tests/neural_networks_test/generated/all_generated_tests.cpp deleted file mode 100644 index e163b602f..000000000 --- a/runtimes/tests/neural_networks_test/generated/all_generated_tests.cpp +++ /dev/null @@ -1,3124 +0,0 @@ -// DO NOT EDIT; -// Generated by nnfw/runtimes/tests/neural_networks_test/specs/generate_test.sh - -namespace add_broadcast_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated add_broadcast_quant8 test -#include "generated/examples/add_broadcast_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/add_broadcast_quant8.model.cpp" -} // namespace add_broadcast_quant8 -TEST_F(GeneratedTests, add_broadcast_quant8) { - execute(add_broadcast_quant8::CreateModel, - add_broadcast_quant8::is_ignored, - add_broadcast_quant8::examples); -} - -namespace add { -std::vector<MixedTypedExample> examples = { -// Generated add test -#include "generated/examples/add.example.cpp" -}; -// Generated model constructor -#include "generated/models/add.model.cpp" -} // namespace add -TEST_F(GeneratedTests, add) { - execute(add::CreateModel, - add::is_ignored, - add::examples); -} - -namespace add_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated add_quant8 test -#include "generated/examples/add_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/add_quant8.model.cpp" -} // namespace add_quant8 -TEST_F(GeneratedTests, add_quant8) { - execute(add_quant8::CreateModel, - add_quant8::is_ignored, - add_quant8::examples); -} - -namespace avg_pool_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_float_1 test -#include "generated/examples/avg_pool_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_float_1.model.cpp" -} // namespace avg_pool_float_1 -TEST_F(GeneratedTests, avg_pool_float_1) { - execute(avg_pool_float_1::CreateModel, - avg_pool_float_1::is_ignored, - avg_pool_float_1::examples); -} - -namespace avg_pool_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_float_2 test -#include "generated/examples/avg_pool_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_float_2.model.cpp" -} // namespace avg_pool_float_2 -TEST_F(GeneratedTests, avg_pool_float_2) { - execute(avg_pool_float_2::CreateModel, - avg_pool_float_2::is_ignored, - avg_pool_float_2::examples); -} - -namespace avg_pool_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_float_3 test -#include "generated/examples/avg_pool_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_float_3.model.cpp" -} // namespace avg_pool_float_3 -TEST_F(GeneratedTests, avg_pool_float_3) { - execute(avg_pool_float_3::CreateModel, - avg_pool_float_3::is_ignored, - avg_pool_float_3::examples); -} - -namespace avg_pool_float_4 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_float_4 test -#include "generated/examples/avg_pool_float_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_float_4.model.cpp" -} // namespace avg_pool_float_4 -TEST_F(GeneratedTests, avg_pool_float_4) { - execute(avg_pool_float_4::CreateModel, - avg_pool_float_4::is_ignored, - avg_pool_float_4::examples); -} - -namespace avg_pool_float_5 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_float_5 test -#include "generated/examples/avg_pool_float_5.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_float_5.model.cpp" -} // namespace avg_pool_float_5 -TEST_F(GeneratedTests, avg_pool_float_5) { - execute(avg_pool_float_5::CreateModel, - avg_pool_float_5::is_ignored, - avg_pool_float_5::examples); -} - -namespace avg_pool_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_quant8_1 test -#include "generated/examples/avg_pool_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_quant8_1.model.cpp" -} // namespace avg_pool_quant8_1 -TEST_F(GeneratedTests, avg_pool_quant8_1) { - execute(avg_pool_quant8_1::CreateModel, - avg_pool_quant8_1::is_ignored, - avg_pool_quant8_1::examples); -} - -namespace avg_pool_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_quant8_2 test -#include "generated/examples/avg_pool_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_quant8_2.model.cpp" -} // namespace avg_pool_quant8_2 -TEST_F(GeneratedTests, avg_pool_quant8_2) { - execute(avg_pool_quant8_2::CreateModel, - avg_pool_quant8_2::is_ignored, - avg_pool_quant8_2::examples); -} - -namespace avg_pool_quant8_3 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_quant8_3 test -#include "generated/examples/avg_pool_quant8_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_quant8_3.model.cpp" -} // namespace avg_pool_quant8_3 -TEST_F(GeneratedTests, avg_pool_quant8_3) { - execute(avg_pool_quant8_3::CreateModel, - avg_pool_quant8_3::is_ignored, - avg_pool_quant8_3::examples); -} - -namespace avg_pool_quant8_4 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_quant8_4 test -#include "generated/examples/avg_pool_quant8_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_quant8_4.model.cpp" -} // namespace avg_pool_quant8_4 -TEST_F(GeneratedTests, avg_pool_quant8_4) { - execute(avg_pool_quant8_4::CreateModel, - avg_pool_quant8_4::is_ignored, - avg_pool_quant8_4::examples); -} - -namespace avg_pool_quant8_5 { -std::vector<MixedTypedExample> examples = { -// Generated avg_pool_quant8_5 test -#include "generated/examples/avg_pool_quant8_5.example.cpp" -}; -// Generated model constructor -#include "generated/models/avg_pool_quant8_5.model.cpp" -} // namespace avg_pool_quant8_5 -TEST_F(GeneratedTests, avg_pool_quant8_5) { - execute(avg_pool_quant8_5::CreateModel, - avg_pool_quant8_5::is_ignored, - avg_pool_quant8_5::examples); -} - -namespace concat_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated concat_float_1 test -#include "generated/examples/concat_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_float_1.model.cpp" -} // namespace concat_float_1 -TEST_F(GeneratedTests, concat_float_1) { - execute(concat_float_1::CreateModel, - concat_float_1::is_ignored, - concat_float_1::examples); -} - -namespace concat_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated concat_float_2 test -#include "generated/examples/concat_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_float_2.model.cpp" -} // namespace concat_float_2 -TEST_F(GeneratedTests, concat_float_2) { - execute(concat_float_2::CreateModel, - concat_float_2::is_ignored, - concat_float_2::examples); -} - -namespace concat_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated concat_float_3 test -#include "generated/examples/concat_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_float_3.model.cpp" -} // namespace concat_float_3 -TEST_F(GeneratedTests, concat_float_3) { - execute(concat_float_3::CreateModel, - concat_float_3::is_ignored, - concat_float_3::examples); -} - -namespace concat_float_4D_axis3_1_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated concat_float_4D_axis3_1_nnfw test -#include "generated/examples/concat_float_4D_axis3_1_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_float_4D_axis3_1_nnfw.model.cpp" -} // namespace concat_float_4D_axis3_1_nnfw -TEST_F(GeneratedTests, concat_float_4D_axis3_1_nnfw) { - execute(concat_float_4D_axis3_1_nnfw::CreateModel, - concat_float_4D_axis3_1_nnfw::is_ignored, - concat_float_4D_axis3_1_nnfw::examples); -} - -namespace concat_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated concat_quant8_1 test -#include "generated/examples/concat_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_quant8_1.model.cpp" -} // namespace concat_quant8_1 -TEST_F(GeneratedTests, concat_quant8_1) { - execute(concat_quant8_1::CreateModel, - concat_quant8_1::is_ignored, - concat_quant8_1::examples); -} - -namespace concat_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated concat_quant8_2 test -#include "generated/examples/concat_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_quant8_2.model.cpp" -} // namespace concat_quant8_2 -TEST_F(GeneratedTests, concat_quant8_2) { - execute(concat_quant8_2::CreateModel, - concat_quant8_2::is_ignored, - concat_quant8_2::examples); -} - -namespace concat_quant8_3 { -std::vector<MixedTypedExample> examples = { -// Generated concat_quant8_3 test -#include "generated/examples/concat_quant8_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/concat_quant8_3.model.cpp" -} // namespace concat_quant8_3 -TEST_F(GeneratedTests, concat_quant8_3) { - execute(concat_quant8_3::CreateModel, - concat_quant8_3::is_ignored, - concat_quant8_3::examples); -} - -namespace conv_1_h3_w2_SAME { -std::vector<MixedTypedExample> examples = { -// Generated conv_1_h3_w2_SAME test -#include "generated/examples/conv_1_h3_w2_SAME.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_1_h3_w2_SAME.model.cpp" -} // namespace conv_1_h3_w2_SAME -TEST_F(GeneratedTests, conv_1_h3_w2_SAME) { - execute(conv_1_h3_w2_SAME::CreateModel, - conv_1_h3_w2_SAME::is_ignored, - conv_1_h3_w2_SAME::examples); -} - -namespace conv_1_h3_w2_VALID { -std::vector<MixedTypedExample> examples = { -// Generated conv_1_h3_w2_VALID test -#include "generated/examples/conv_1_h3_w2_VALID.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_1_h3_w2_VALID.model.cpp" -} // namespace conv_1_h3_w2_VALID -TEST_F(GeneratedTests, conv_1_h3_w2_VALID) { - execute(conv_1_h3_w2_VALID::CreateModel, - conv_1_h3_w2_VALID::is_ignored, - conv_1_h3_w2_VALID::examples); -} - -namespace conv_3_h3_w2_SAME { -std::vector<MixedTypedExample> examples = { -// Generated conv_3_h3_w2_SAME test -#include "generated/examples/conv_3_h3_w2_SAME.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_3_h3_w2_SAME.model.cpp" -} // namespace conv_3_h3_w2_SAME -TEST_F(GeneratedTests, conv_3_h3_w2_SAME) { - execute(conv_3_h3_w2_SAME::CreateModel, - conv_3_h3_w2_SAME::is_ignored, - conv_3_h3_w2_SAME::examples); -} - -namespace conv_3_h3_w2_VALID { -std::vector<MixedTypedExample> examples = { -// Generated conv_3_h3_w2_VALID test -#include "generated/examples/conv_3_h3_w2_VALID.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_3_h3_w2_VALID.model.cpp" -} // namespace conv_3_h3_w2_VALID -TEST_F(GeneratedTests, conv_3_h3_w2_VALID) { - execute(conv_3_h3_w2_VALID::CreateModel, - conv_3_h3_w2_VALID::is_ignored, - conv_3_h3_w2_VALID::examples); -} - -namespace conv_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_2 test -#include "generated/examples/conv_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_2.model.cpp" -} // namespace conv_float_2 -TEST_F(GeneratedTests, conv_float_2) { - execute(conv_float_2::CreateModel, - conv_float_2::is_ignored, - conv_float_2::examples); -} - -namespace conv_float_channels { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_channels test -#include "generated/examples/conv_float_channels.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_channels.model.cpp" -} // namespace conv_float_channels -TEST_F(GeneratedTests, conv_float_channels) { - execute(conv_float_channels::CreateModel, - conv_float_channels::is_ignored, - conv_float_channels::examples); -} - -namespace conv_float_channels_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_channels_weights_as_inputs test -#include "generated/examples/conv_float_channels_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_channels_weights_as_inputs.model.cpp" -} // namespace conv_float_channels_weights_as_inputs -TEST_F(GeneratedTests, conv_float_channels_weights_as_inputs) { - execute(conv_float_channels_weights_as_inputs::CreateModel, - conv_float_channels_weights_as_inputs::is_ignored, - conv_float_channels_weights_as_inputs::examples); -} - -namespace conv_float_large { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_large test -#include "generated/examples/conv_float_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_large.model.cpp" -} // namespace conv_float_large -TEST_F(GeneratedTests, conv_float_large) { - execute(conv_float_large::CreateModel, - conv_float_large::is_ignored, - conv_float_large::examples); -} - -namespace conv_float_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_large_weights_as_inputs test -#include "generated/examples/conv_float_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_large_weights_as_inputs.model.cpp" -} // namespace conv_float_large_weights_as_inputs -TEST_F(GeneratedTests, conv_float_large_weights_as_inputs) { - execute(conv_float_large_weights_as_inputs::CreateModel, - conv_float_large_weights_as_inputs::is_ignored, - conv_float_large_weights_as_inputs::examples); -} - -namespace conv_float { -std::vector<MixedTypedExample> examples = { -// Generated conv_float test -#include "generated/examples/conv_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float.model.cpp" -} // namespace conv_float -TEST_F(GeneratedTests, conv_float) { - execute(conv_float::CreateModel, - conv_float::is_ignored, - conv_float::examples); -} - -namespace conv_float_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_float_weights_as_inputs test -#include "generated/examples/conv_float_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_float_weights_as_inputs.model.cpp" -} // namespace conv_float_weights_as_inputs -TEST_F(GeneratedTests, conv_float_weights_as_inputs) { - execute(conv_float_weights_as_inputs::CreateModel, - conv_float_weights_as_inputs::is_ignored, - conv_float_weights_as_inputs::examples); -} - -namespace conv_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_2 test -#include "generated/examples/conv_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_2.model.cpp" -} // namespace conv_quant8_2 -TEST_F(GeneratedTests, conv_quant8_2) { - execute(conv_quant8_2::CreateModel, - conv_quant8_2::is_ignored, - conv_quant8_2::examples); -} - -namespace conv_quant8_channels { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_channels test -#include "generated/examples/conv_quant8_channels.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_channels.model.cpp" -} // namespace conv_quant8_channels -TEST_F(GeneratedTests, conv_quant8_channels) { - execute(conv_quant8_channels::CreateModel, - conv_quant8_channels::is_ignored, - conv_quant8_channels::examples); -} - -namespace conv_quant8_channels_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_channels_weights_as_inputs test -#include "generated/examples/conv_quant8_channels_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_channels_weights_as_inputs.model.cpp" -} // namespace conv_quant8_channels_weights_as_inputs -TEST_F(GeneratedTests, conv_quant8_channels_weights_as_inputs) { - execute(conv_quant8_channels_weights_as_inputs::CreateModel, - conv_quant8_channels_weights_as_inputs::is_ignored, - conv_quant8_channels_weights_as_inputs::examples); -} - -namespace conv_quant8_large { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_large test -#include "generated/examples/conv_quant8_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_large.model.cpp" -} // namespace conv_quant8_large -TEST_F(GeneratedTests, conv_quant8_large) { - execute(conv_quant8_large::CreateModel, - conv_quant8_large::is_ignored, - conv_quant8_large::examples); -} - -namespace conv_quant8_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_large_weights_as_inputs test -#include "generated/examples/conv_quant8_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_large_weights_as_inputs.model.cpp" -} // namespace conv_quant8_large_weights_as_inputs -TEST_F(GeneratedTests, conv_quant8_large_weights_as_inputs) { - execute(conv_quant8_large_weights_as_inputs::CreateModel, - conv_quant8_large_weights_as_inputs::is_ignored, - conv_quant8_large_weights_as_inputs::examples); -} - -namespace conv_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8 test -#include "generated/examples/conv_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8.model.cpp" -} // namespace conv_quant8 -TEST_F(GeneratedTests, conv_quant8) { - execute(conv_quant8::CreateModel, - conv_quant8::is_ignored, - conv_quant8::examples); -} - -namespace conv_quant8_overflow { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_overflow test -#include "generated/examples/conv_quant8_overflow.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_overflow.model.cpp" -} // namespace conv_quant8_overflow -TEST_F(GeneratedTests, conv_quant8_overflow) { - execute(conv_quant8_overflow::CreateModel, - conv_quant8_overflow::is_ignored, - conv_quant8_overflow::examples); -} - -namespace conv_quant8_overflow_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_overflow_weights_as_inputs test -#include "generated/examples/conv_quant8_overflow_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_overflow_weights_as_inputs.model.cpp" -} // namespace conv_quant8_overflow_weights_as_inputs -TEST_F(GeneratedTests, conv_quant8_overflow_weights_as_inputs) { - execute(conv_quant8_overflow_weights_as_inputs::CreateModel, - conv_quant8_overflow_weights_as_inputs::is_ignored, - conv_quant8_overflow_weights_as_inputs::examples); -} - -namespace conv_quant8_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated conv_quant8_weights_as_inputs test -#include "generated/examples/conv_quant8_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/conv_quant8_weights_as_inputs.model.cpp" -} // namespace conv_quant8_weights_as_inputs -TEST_F(GeneratedTests, conv_quant8_weights_as_inputs) { - execute(conv_quant8_weights_as_inputs::CreateModel, - conv_quant8_weights_as_inputs::is_ignored, - conv_quant8_weights_as_inputs::examples); -} - -namespace depth_to_space_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated depth_to_space_float_1 test -#include "generated/examples/depth_to_space_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/depth_to_space_float_1.model.cpp" -} // namespace depth_to_space_float_1 -TEST_F(GeneratedTests, depth_to_space_float_1) { - execute(depth_to_space_float_1::CreateModel, - depth_to_space_float_1::is_ignored, - depth_to_space_float_1::examples); -} - -namespace depth_to_space_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated depth_to_space_float_2 test -#include "generated/examples/depth_to_space_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/depth_to_space_float_2.model.cpp" -} // namespace depth_to_space_float_2 -TEST_F(GeneratedTests, depth_to_space_float_2) { - execute(depth_to_space_float_2::CreateModel, - depth_to_space_float_2::is_ignored, - depth_to_space_float_2::examples); -} - -namespace depth_to_space_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated depth_to_space_float_3 test -#include "generated/examples/depth_to_space_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/depth_to_space_float_3.model.cpp" -} // namespace depth_to_space_float_3 -TEST_F(GeneratedTests, depth_to_space_float_3) { - execute(depth_to_space_float_3::CreateModel, - depth_to_space_float_3::is_ignored, - depth_to_space_float_3::examples); -} - -namespace depth_to_space_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated depth_to_space_quant8_1 test -#include "generated/examples/depth_to_space_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/depth_to_space_quant8_1.model.cpp" -} // namespace depth_to_space_quant8_1 -TEST_F(GeneratedTests, depth_to_space_quant8_1) { - execute(depth_to_space_quant8_1::CreateModel, - depth_to_space_quant8_1::is_ignored, - depth_to_space_quant8_1::examples); -} - -namespace depth_to_space_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated depth_to_space_quant8_2 test -#include "generated/examples/depth_to_space_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/depth_to_space_quant8_2.model.cpp" -} // namespace depth_to_space_quant8_2 -TEST_F(GeneratedTests, depth_to_space_quant8_2) { - execute(depth_to_space_quant8_2::CreateModel, - depth_to_space_quant8_2::is_ignored, - depth_to_space_quant8_2::examples); -} - -namespace depthwise_conv2d_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_2 test -#include "generated/examples/depthwise_conv2d_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_2.model.cpp" -} // namespace depthwise_conv2d_float_2 -TEST_F(GeneratedTests, depthwise_conv2d_float_2) { - execute(depthwise_conv2d_float_2::CreateModel, - depthwise_conv2d_float_2::is_ignored, - depthwise_conv2d_float_2::examples); -} - -namespace depthwise_conv2d_float_large_2 { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_large_2 test -#include "generated/examples/depthwise_conv2d_float_large_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_large_2.model.cpp" -} // namespace depthwise_conv2d_float_large_2 -TEST_F(GeneratedTests, depthwise_conv2d_float_large_2) { - execute(depthwise_conv2d_float_large_2::CreateModel, - depthwise_conv2d_float_large_2::is_ignored, - depthwise_conv2d_float_large_2::examples); -} - -namespace depthwise_conv2d_float_large_2_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_large_2_weights_as_inputs test -#include "generated/examples/depthwise_conv2d_float_large_2_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_large_2_weights_as_inputs.model.cpp" -} // namespace depthwise_conv2d_float_large_2_weights_as_inputs -TEST_F(GeneratedTests, depthwise_conv2d_float_large_2_weights_as_inputs) { - execute(depthwise_conv2d_float_large_2_weights_as_inputs::CreateModel, - depthwise_conv2d_float_large_2_weights_as_inputs::is_ignored, - depthwise_conv2d_float_large_2_weights_as_inputs::examples); -} - -namespace depthwise_conv2d_float_large { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_large test -#include "generated/examples/depthwise_conv2d_float_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_large.model.cpp" -} // namespace depthwise_conv2d_float_large -TEST_F(GeneratedTests, depthwise_conv2d_float_large) { - execute(depthwise_conv2d_float_large::CreateModel, - depthwise_conv2d_float_large::is_ignored, - depthwise_conv2d_float_large::examples); -} - -namespace depthwise_conv2d_float_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_large_weights_as_inputs test -#include "generated/examples/depthwise_conv2d_float_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_large_weights_as_inputs.model.cpp" -} // namespace depthwise_conv2d_float_large_weights_as_inputs -TEST_F(GeneratedTests, depthwise_conv2d_float_large_weights_as_inputs) { - execute(depthwise_conv2d_float_large_weights_as_inputs::CreateModel, - depthwise_conv2d_float_large_weights_as_inputs::is_ignored, - depthwise_conv2d_float_large_weights_as_inputs::examples); -} - -namespace depthwise_conv2d_float { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float test -#include "generated/examples/depthwise_conv2d_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float.model.cpp" -} // namespace depthwise_conv2d_float -TEST_F(GeneratedTests, depthwise_conv2d_float) { - execute(depthwise_conv2d_float::CreateModel, - depthwise_conv2d_float::is_ignored, - depthwise_conv2d_float::examples); -} - -namespace depthwise_conv2d_float_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_float_weights_as_inputs test -#include "generated/examples/depthwise_conv2d_float_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_float_weights_as_inputs.model.cpp" -} // namespace depthwise_conv2d_float_weights_as_inputs -TEST_F(GeneratedTests, depthwise_conv2d_float_weights_as_inputs) { - execute(depthwise_conv2d_float_weights_as_inputs::CreateModel, - depthwise_conv2d_float_weights_as_inputs::is_ignored, - depthwise_conv2d_float_weights_as_inputs::examples); -} - -namespace depthwise_conv2d_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_quant8_2 test -#include "generated/examples/depthwise_conv2d_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_quant8_2.model.cpp" -} // namespace depthwise_conv2d_quant8_2 -TEST_F(GeneratedTests, depthwise_conv2d_quant8_2) { - execute(depthwise_conv2d_quant8_2::CreateModel, - depthwise_conv2d_quant8_2::is_ignored, - depthwise_conv2d_quant8_2::examples); -} - -namespace depthwise_conv2d_quant8_large { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_quant8_large test -#include "generated/examples/depthwise_conv2d_quant8_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_quant8_large.model.cpp" -} // namespace depthwise_conv2d_quant8_large -TEST_F(GeneratedTests, depthwise_conv2d_quant8_large) { - execute(depthwise_conv2d_quant8_large::CreateModel, - depthwise_conv2d_quant8_large::is_ignored, - depthwise_conv2d_quant8_large::examples); -} - -namespace depthwise_conv2d_quant8_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_quant8_large_weights_as_inputs test -#include "generated/examples/depthwise_conv2d_quant8_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_quant8_large_weights_as_inputs.model.cpp" -} // namespace depthwise_conv2d_quant8_large_weights_as_inputs -TEST_F(GeneratedTests, depthwise_conv2d_quant8_large_weights_as_inputs) { - execute(depthwise_conv2d_quant8_large_weights_as_inputs::CreateModel, - depthwise_conv2d_quant8_large_weights_as_inputs::is_ignored, - depthwise_conv2d_quant8_large_weights_as_inputs::examples); -} - -namespace depthwise_conv2d_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_quant8 test -#include "generated/examples/depthwise_conv2d_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_quant8.model.cpp" -} // namespace depthwise_conv2d_quant8 -TEST_F(GeneratedTests, depthwise_conv2d_quant8) { - execute(depthwise_conv2d_quant8::CreateModel, - depthwise_conv2d_quant8::is_ignored, - depthwise_conv2d_quant8::examples); -} - -namespace depthwise_conv2d_quant8_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv2d_quant8_weights_as_inputs test -#include "generated/examples/depthwise_conv2d_quant8_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv2d_quant8_weights_as_inputs.model.cpp" -} // namespace depthwise_conv2d_quant8_weights_as_inputs -TEST_F(GeneratedTests, depthwise_conv2d_quant8_weights_as_inputs) { - execute(depthwise_conv2d_quant8_weights_as_inputs::CreateModel, - depthwise_conv2d_quant8_weights_as_inputs::is_ignored, - depthwise_conv2d_quant8_weights_as_inputs::examples); -} - -namespace depthwise_conv { -std::vector<MixedTypedExample> examples = { -// Generated depthwise_conv test -#include "generated/examples/depthwise_conv.example.cpp" -}; -// Generated model constructor -#include "generated/models/depthwise_conv.model.cpp" -} // namespace depthwise_conv -TEST_F(GeneratedTests, depthwise_conv) { - execute(depthwise_conv::CreateModel, - depthwise_conv::is_ignored, - depthwise_conv::examples); -} - -namespace dequantize { -std::vector<MixedTypedExample> examples = { -// Generated dequantize test -#include "generated/examples/dequantize.example.cpp" -}; -// Generated model constructor -#include "generated/models/dequantize.model.cpp" -} // namespace dequantize -TEST_F(GeneratedTests, dequantize) { - execute(dequantize::CreateModel, - dequantize::is_ignored, - dequantize::examples); -} - -namespace embedding_lookup_2d_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated embedding_lookup_2d_nnfw test -#include "generated/examples/embedding_lookup_2d_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/embedding_lookup_2d_nnfw.model.cpp" -} // namespace embedding_lookup_2d_nnfw -TEST_F(GeneratedTests, embedding_lookup_2d_nnfw) { - execute(embedding_lookup_2d_nnfw::CreateModel, - embedding_lookup_2d_nnfw::is_ignored, - embedding_lookup_2d_nnfw::examples); -} - -namespace embedding_lookup_4d_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated embedding_lookup_4d_nnfw test -#include "generated/examples/embedding_lookup_4d_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/embedding_lookup_4d_nnfw.model.cpp" -} // namespace embedding_lookup_4d_nnfw -TEST_F(GeneratedTests, embedding_lookup_4d_nnfw) { - execute(embedding_lookup_4d_nnfw::CreateModel, - embedding_lookup_4d_nnfw::is_ignored, - embedding_lookup_4d_nnfw::examples); -} - -namespace embedding_lookup { -std::vector<MixedTypedExample> examples = { -// Generated embedding_lookup test -#include "generated/examples/embedding_lookup.example.cpp" -}; -// Generated model constructor -#include "generated/models/embedding_lookup.model.cpp" -} // namespace embedding_lookup -TEST_F(GeneratedTests, embedding_lookup) { - execute(embedding_lookup::CreateModel, - embedding_lookup::is_ignored, - embedding_lookup::examples); -} - -namespace floor_ { -std::vector<MixedTypedExample> examples = { -// Generated floor_ test -#include "generated/examples/floor_.example.cpp" -}; -// Generated model constructor -#include "generated/models/floor_.model.cpp" -} // namespace floor_ -TEST_F(GeneratedTests, floor_) { - execute(floor_::CreateModel, - floor_::is_ignored, - floor_::examples); -} - -namespace fully_connected_float_1_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_1_nnfw test -#include "generated/examples/fully_connected_float_1_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_1_nnfw.model.cpp" -} // namespace fully_connected_float_1_nnfw -TEST_F(GeneratedTests, fully_connected_float_1_nnfw) { - execute(fully_connected_float_1_nnfw::CreateModel, - fully_connected_float_1_nnfw::is_ignored, - fully_connected_float_1_nnfw::examples); -} - -namespace fully_connected_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_2 test -#include "generated/examples/fully_connected_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_2.model.cpp" -} // namespace fully_connected_float_2 -TEST_F(GeneratedTests, fully_connected_float_2) { - execute(fully_connected_float_2::CreateModel, - fully_connected_float_2::is_ignored, - fully_connected_float_2::examples); -} - -namespace fully_connected_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_3 test -#include "generated/examples/fully_connected_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_3.model.cpp" -} // namespace fully_connected_float_3 -TEST_F(GeneratedTests, fully_connected_float_3) { - execute(fully_connected_float_3::CreateModel, - fully_connected_float_3::is_ignored, - fully_connected_float_3::examples); -} - -namespace fully_connected_float_large { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_large test -#include "generated/examples/fully_connected_float_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_large.model.cpp" -} // namespace fully_connected_float_large -TEST_F(GeneratedTests, fully_connected_float_large) { - execute(fully_connected_float_large::CreateModel, - fully_connected_float_large::is_ignored, - fully_connected_float_large::examples); -} - -namespace fully_connected_float_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_large_weights_as_inputs test -#include "generated/examples/fully_connected_float_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_large_weights_as_inputs.model.cpp" -} // namespace fully_connected_float_large_weights_as_inputs -TEST_F(GeneratedTests, fully_connected_float_large_weights_as_inputs) { - execute(fully_connected_float_large_weights_as_inputs::CreateModel, - fully_connected_float_large_weights_as_inputs::is_ignored, - fully_connected_float_large_weights_as_inputs::examples); -} - -namespace fully_connected_float { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float test -#include "generated/examples/fully_connected_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float.model.cpp" -} // namespace fully_connected_float -TEST_F(GeneratedTests, fully_connected_float) { - execute(fully_connected_float::CreateModel, - fully_connected_float::is_ignored, - fully_connected_float::examples); -} - -namespace fully_connected_float_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_weights_as_inputs test -#include "generated/examples/fully_connected_float_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_weights_as_inputs.model.cpp" -} // namespace fully_connected_float_weights_as_inputs -TEST_F(GeneratedTests, fully_connected_float_weights_as_inputs) { - execute(fully_connected_float_weights_as_inputs::CreateModel, - fully_connected_float_weights_as_inputs::is_ignored, - fully_connected_float_weights_as_inputs::examples); -} - -namespace fully_connected_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_quant8_2 test -#include "generated/examples/fully_connected_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_quant8_2.model.cpp" -} // namespace fully_connected_quant8_2 -TEST_F(GeneratedTests, fully_connected_quant8_2) { - execute(fully_connected_quant8_2::CreateModel, - fully_connected_quant8_2::is_ignored, - fully_connected_quant8_2::examples); -} - -namespace fully_connected_quant8_large { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_quant8_large test -#include "generated/examples/fully_connected_quant8_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_quant8_large.model.cpp" -} // namespace fully_connected_quant8_large -TEST_F(GeneratedTests, fully_connected_quant8_large) { - execute(fully_connected_quant8_large::CreateModel, - fully_connected_quant8_large::is_ignored, - fully_connected_quant8_large::examples); -} - -namespace fully_connected_quant8_large_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_quant8_large_weights_as_inputs test -#include "generated/examples/fully_connected_quant8_large_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_quant8_large_weights_as_inputs.model.cpp" -} // namespace fully_connected_quant8_large_weights_as_inputs -TEST_F(GeneratedTests, fully_connected_quant8_large_weights_as_inputs) { - execute(fully_connected_quant8_large_weights_as_inputs::CreateModel, - fully_connected_quant8_large_weights_as_inputs::is_ignored, - fully_connected_quant8_large_weights_as_inputs::examples); -} - -namespace fully_connected_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_quant8 test -#include "generated/examples/fully_connected_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_quant8.model.cpp" -} // namespace fully_connected_quant8 -TEST_F(GeneratedTests, fully_connected_quant8) { - execute(fully_connected_quant8::CreateModel, - fully_connected_quant8::is_ignored, - fully_connected_quant8::examples); -} - -namespace fully_connected_quant8_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_quant8_weights_as_inputs test -#include "generated/examples/fully_connected_quant8_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_quant8_weights_as_inputs.model.cpp" -} // namespace fully_connected_quant8_weights_as_inputs -TEST_F(GeneratedTests, fully_connected_quant8_weights_as_inputs) { - execute(fully_connected_quant8_weights_as_inputs::CreateModel, - fully_connected_quant8_weights_as_inputs::is_ignored, - fully_connected_quant8_weights_as_inputs::examples); -} - -namespace hashtable_lookup_float { -std::vector<MixedTypedExample> examples = { -// Generated hashtable_lookup_float test -#include "generated/examples/hashtable_lookup_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/hashtable_lookup_float.model.cpp" -} // namespace hashtable_lookup_float -TEST_F(GeneratedTests, hashtable_lookup_float) { - execute(hashtable_lookup_float::CreateModel, - hashtable_lookup_float::is_ignored, - hashtable_lookup_float::examples); -} - -namespace hashtable_lookup_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated hashtable_lookup_quant8 test -#include "generated/examples/hashtable_lookup_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/hashtable_lookup_quant8.model.cpp" -} // namespace hashtable_lookup_quant8 -TEST_F(GeneratedTests, hashtable_lookup_quant8) { - execute(hashtable_lookup_quant8::CreateModel, - hashtable_lookup_quant8::is_ignored, - hashtable_lookup_quant8::examples); -} - -namespace l2_normalization_2 { -std::vector<MixedTypedExample> examples = { -// Generated l2_normalization_2 test -#include "generated/examples/l2_normalization_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_normalization_2.model.cpp" -} // namespace l2_normalization_2 -TEST_F(GeneratedTests, l2_normalization_2) { - execute(l2_normalization_2::CreateModel, - l2_normalization_2::is_ignored, - l2_normalization_2::examples); -} - -namespace l2_normalization_large { -std::vector<MixedTypedExample> examples = { -// Generated l2_normalization_large test -#include "generated/examples/l2_normalization_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_normalization_large.model.cpp" -} // namespace l2_normalization_large -TEST_F(GeneratedTests, l2_normalization_large) { - execute(l2_normalization_large::CreateModel, - l2_normalization_large::is_ignored, - l2_normalization_large::examples); -} - -namespace l2_normalization { -std::vector<MixedTypedExample> examples = { -// Generated l2_normalization test -#include "generated/examples/l2_normalization.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_normalization.model.cpp" -} // namespace l2_normalization -TEST_F(GeneratedTests, l2_normalization) { - execute(l2_normalization::CreateModel, - l2_normalization::is_ignored, - l2_normalization::examples); -} - -namespace l2_pool_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated l2_pool_float_2 test -#include "generated/examples/l2_pool_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_pool_float_2.model.cpp" -} // namespace l2_pool_float_2 -TEST_F(GeneratedTests, l2_pool_float_2) { - execute(l2_pool_float_2::CreateModel, - l2_pool_float_2::is_ignored, - l2_pool_float_2::examples); -} - -namespace l2_pool_float_large { -std::vector<MixedTypedExample> examples = { -// Generated l2_pool_float_large test -#include "generated/examples/l2_pool_float_large.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_pool_float_large.model.cpp" -} // namespace l2_pool_float_large -TEST_F(GeneratedTests, l2_pool_float_large) { - execute(l2_pool_float_large::CreateModel, - l2_pool_float_large::is_ignored, - l2_pool_float_large::examples); -} - -namespace l2_pool_float { -std::vector<MixedTypedExample> examples = { -// Generated l2_pool_float test -#include "generated/examples/l2_pool_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/l2_pool_float.model.cpp" -} // namespace l2_pool_float -TEST_F(GeneratedTests, l2_pool_float) { - execute(l2_pool_float::CreateModel, - l2_pool_float::is_ignored, - l2_pool_float::examples); -} - -namespace local_response_norm_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated local_response_norm_float_1 test -#include "generated/examples/local_response_norm_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/local_response_norm_float_1.model.cpp" -} // namespace local_response_norm_float_1 -TEST_F(GeneratedTests, local_response_norm_float_1) { - execute(local_response_norm_float_1::CreateModel, - local_response_norm_float_1::is_ignored, - local_response_norm_float_1::examples); -} - -namespace local_response_norm_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated local_response_norm_float_2 test -#include "generated/examples/local_response_norm_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/local_response_norm_float_2.model.cpp" -} // namespace local_response_norm_float_2 -TEST_F(GeneratedTests, local_response_norm_float_2) { - execute(local_response_norm_float_2::CreateModel, - local_response_norm_float_2::is_ignored, - local_response_norm_float_2::examples); -} - -namespace local_response_norm_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated local_response_norm_float_3 test -#include "generated/examples/local_response_norm_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/local_response_norm_float_3.model.cpp" -} // namespace local_response_norm_float_3 -TEST_F(GeneratedTests, local_response_norm_float_3) { - execute(local_response_norm_float_3::CreateModel, - local_response_norm_float_3::is_ignored, - local_response_norm_float_3::examples); -} - -namespace local_response_norm_float_4 { -std::vector<MixedTypedExample> examples = { -// Generated local_response_norm_float_4 test -#include "generated/examples/local_response_norm_float_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/local_response_norm_float_4.model.cpp" -} // namespace local_response_norm_float_4 -TEST_F(GeneratedTests, local_response_norm_float_4) { - execute(local_response_norm_float_4::CreateModel, - local_response_norm_float_4::is_ignored, - local_response_norm_float_4::examples); -} - -namespace logistic_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated logistic_float_1 test -#include "generated/examples/logistic_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/logistic_float_1.model.cpp" -} // namespace logistic_float_1 -TEST_F(GeneratedTests, logistic_float_1) { - execute(logistic_float_1::CreateModel, - logistic_float_1::is_ignored, - logistic_float_1::examples); -} - -namespace logistic_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated logistic_float_2 test -#include "generated/examples/logistic_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/logistic_float_2.model.cpp" -} // namespace logistic_float_2 -TEST_F(GeneratedTests, logistic_float_2) { - execute(logistic_float_2::CreateModel, - logistic_float_2::is_ignored, - logistic_float_2::examples); -} - -namespace logistic_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated logistic_quant8_1 test -#include "generated/examples/logistic_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/logistic_quant8_1.model.cpp" -} // namespace logistic_quant8_1 -TEST_F(GeneratedTests, logistic_quant8_1) { - execute(logistic_quant8_1::CreateModel, - logistic_quant8_1::is_ignored, - logistic_quant8_1::examples); -} - -namespace logistic_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated logistic_quant8_2 test -#include "generated/examples/logistic_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/logistic_quant8_2.model.cpp" -} // namespace logistic_quant8_2 -TEST_F(GeneratedTests, logistic_quant8_2) { - execute(logistic_quant8_2::CreateModel, - logistic_quant8_2::is_ignored, - logistic_quant8_2::examples); -} - -namespace lsh_projection_2 { -std::vector<MixedTypedExample> examples = { -// Generated lsh_projection_2 test -#include "generated/examples/lsh_projection_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/lsh_projection_2.model.cpp" -} // namespace lsh_projection_2 -TEST_F(GeneratedTests, lsh_projection_2) { - execute(lsh_projection_2::CreateModel, - lsh_projection_2::is_ignored, - lsh_projection_2::examples); -} - -namespace lsh_projection { -std::vector<MixedTypedExample> examples = { -// Generated lsh_projection test -#include "generated/examples/lsh_projection.example.cpp" -}; -// Generated model constructor -#include "generated/models/lsh_projection.model.cpp" -} // namespace lsh_projection -TEST_F(GeneratedTests, lsh_projection) { - execute(lsh_projection::CreateModel, - lsh_projection::is_ignored, - lsh_projection::examples); -} - -namespace lsh_projection_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated lsh_projection_weights_as_inputs test -#include "generated/examples/lsh_projection_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/lsh_projection_weights_as_inputs.model.cpp" -} // namespace lsh_projection_weights_as_inputs -TEST_F(GeneratedTests, lsh_projection_weights_as_inputs) { - execute(lsh_projection_weights_as_inputs::CreateModel, - lsh_projection_weights_as_inputs::is_ignored, - lsh_projection_weights_as_inputs::examples); -} - -namespace lstm2 { -std::vector<MixedTypedExample> examples = { -// Generated lstm2 test -#include "generated/examples/lstm2.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm2.model.cpp" -} // namespace lstm2 -TEST_F(GeneratedTests, lstm2) { - execute(lstm2::CreateModel, - lstm2::is_ignored, - lstm2::examples); -} - -namespace lstm2_state2 { -std::vector<MixedTypedExample> examples = { -// Generated lstm2_state2 test -#include "generated/examples/lstm2_state2.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm2_state2.model.cpp" -} // namespace lstm2_state2 -TEST_F(GeneratedTests, lstm2_state2) { - execute(lstm2_state2::CreateModel, - lstm2_state2::is_ignored, - lstm2_state2::examples); -} - -namespace lstm2_state { -std::vector<MixedTypedExample> examples = { -// Generated lstm2_state test -#include "generated/examples/lstm2_state.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm2_state.model.cpp" -} // namespace lstm2_state -TEST_F(GeneratedTests, lstm2_state) { - execute(lstm2_state::CreateModel, - lstm2_state::is_ignored, - lstm2_state::examples); -} - -namespace lstm3 { -std::vector<MixedTypedExample> examples = { -// Generated lstm3 test -#include "generated/examples/lstm3.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm3.model.cpp" -} // namespace lstm3 -TEST_F(GeneratedTests, lstm3) { - execute(lstm3::CreateModel, - lstm3::is_ignored, - lstm3::examples); -} - -namespace lstm3_state2 { -std::vector<MixedTypedExample> examples = { -// Generated lstm3_state2 test -#include "generated/examples/lstm3_state2.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm3_state2.model.cpp" -} // namespace lstm3_state2 -TEST_F(GeneratedTests, lstm3_state2) { - execute(lstm3_state2::CreateModel, - lstm3_state2::is_ignored, - lstm3_state2::examples); -} - -namespace lstm3_state3 { -std::vector<MixedTypedExample> examples = { -// Generated lstm3_state3 test -#include "generated/examples/lstm3_state3.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm3_state3.model.cpp" -} // namespace lstm3_state3 -TEST_F(GeneratedTests, lstm3_state3) { - execute(lstm3_state3::CreateModel, - lstm3_state3::is_ignored, - lstm3_state3::examples); -} - -namespace lstm3_state { -std::vector<MixedTypedExample> examples = { -// Generated lstm3_state test -#include "generated/examples/lstm3_state.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm3_state.model.cpp" -} // namespace lstm3_state -TEST_F(GeneratedTests, lstm3_state) { - execute(lstm3_state::CreateModel, - lstm3_state::is_ignored, - lstm3_state::examples); -} - -namespace lstm { -std::vector<MixedTypedExample> examples = { -// Generated lstm test -#include "generated/examples/lstm.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm.model.cpp" -} // namespace lstm -TEST_F(GeneratedTests, lstm) { - execute(lstm::CreateModel, - lstm::is_ignored, - lstm::examples); -} - -namespace lstm_state2 { -std::vector<MixedTypedExample> examples = { -// Generated lstm_state2 test -#include "generated/examples/lstm_state2.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm_state2.model.cpp" -} // namespace lstm_state2 -TEST_F(GeneratedTests, lstm_state2) { - execute(lstm_state2::CreateModel, - lstm_state2::is_ignored, - lstm_state2::examples); -} - -namespace lstm_state { -std::vector<MixedTypedExample> examples = { -// Generated lstm_state test -#include "generated/examples/lstm_state.example.cpp" -}; -// Generated model constructor -#include "generated/models/lstm_state.model.cpp" -} // namespace lstm_state -TEST_F(GeneratedTests, lstm_state) { - execute(lstm_state::CreateModel, - lstm_state::is_ignored, - lstm_state::examples); -} - -namespace max_pool_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_float_1 test -#include "generated/examples/max_pool_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_float_1.model.cpp" -} // namespace max_pool_float_1 -TEST_F(GeneratedTests, max_pool_float_1) { - execute(max_pool_float_1::CreateModel, - max_pool_float_1::is_ignored, - max_pool_float_1::examples); -} - -namespace max_pool_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_float_2 test -#include "generated/examples/max_pool_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_float_2.model.cpp" -} // namespace max_pool_float_2 -TEST_F(GeneratedTests, max_pool_float_2) { - execute(max_pool_float_2::CreateModel, - max_pool_float_2::is_ignored, - max_pool_float_2::examples); -} - -namespace max_pool_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_float_3 test -#include "generated/examples/max_pool_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_float_3.model.cpp" -} // namespace max_pool_float_3 -TEST_F(GeneratedTests, max_pool_float_3) { - execute(max_pool_float_3::CreateModel, - max_pool_float_3::is_ignored, - max_pool_float_3::examples); -} - -namespace max_pool_float_4 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_float_4 test -#include "generated/examples/max_pool_float_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_float_4.model.cpp" -} // namespace max_pool_float_4 -TEST_F(GeneratedTests, max_pool_float_4) { - execute(max_pool_float_4::CreateModel, - max_pool_float_4::is_ignored, - max_pool_float_4::examples); -} - -namespace max_pool_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_quant8_1 test -#include "generated/examples/max_pool_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_quant8_1.model.cpp" -} // namespace max_pool_quant8_1 -TEST_F(GeneratedTests, max_pool_quant8_1) { - execute(max_pool_quant8_1::CreateModel, - max_pool_quant8_1::is_ignored, - max_pool_quant8_1::examples); -} - -namespace max_pool_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_quant8_2 test -#include "generated/examples/max_pool_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_quant8_2.model.cpp" -} // namespace max_pool_quant8_2 -TEST_F(GeneratedTests, max_pool_quant8_2) { - execute(max_pool_quant8_2::CreateModel, - max_pool_quant8_2::is_ignored, - max_pool_quant8_2::examples); -} - -namespace max_pool_quant8_3 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_quant8_3 test -#include "generated/examples/max_pool_quant8_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_quant8_3.model.cpp" -} // namespace max_pool_quant8_3 -TEST_F(GeneratedTests, max_pool_quant8_3) { - execute(max_pool_quant8_3::CreateModel, - max_pool_quant8_3::is_ignored, - max_pool_quant8_3::examples); -} - -namespace max_pool_quant8_4 { -std::vector<MixedTypedExample> examples = { -// Generated max_pool_quant8_4 test -#include "generated/examples/max_pool_quant8_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/max_pool_quant8_4.model.cpp" -} // namespace max_pool_quant8_4 -TEST_F(GeneratedTests, max_pool_quant8_4) { - execute(max_pool_quant8_4::CreateModel, - max_pool_quant8_4::is_ignored, - max_pool_quant8_4::examples); -} - -namespace mobilenet_224_gender_basic_fixed { -std::vector<MixedTypedExample> examples = { -// Generated mobilenet_224_gender_basic_fixed test -#include "generated/examples/mobilenet_224_gender_basic_fixed.example.cpp" -}; -// Generated model constructor -#include "generated/models/mobilenet_224_gender_basic_fixed.model.cpp" -} // namespace mobilenet_224_gender_basic_fixed -TEST_F(GeneratedTests, mobilenet_224_gender_basic_fixed) { - execute(mobilenet_224_gender_basic_fixed::CreateModel, - mobilenet_224_gender_basic_fixed::is_ignored, - mobilenet_224_gender_basic_fixed::examples); -} - -namespace mobilenet_quantized { -std::vector<MixedTypedExample> examples = { -// Generated mobilenet_quantized test -#include "generated/examples/mobilenet_quantized.example.cpp" -}; -// Generated model constructor -#include "generated/models/mobilenet_quantized.model.cpp" -} // namespace mobilenet_quantized -TEST_F(GeneratedTests, mobilenet_quantized) { - execute(mobilenet_quantized::CreateModel, - mobilenet_quantized::is_ignored, - mobilenet_quantized::examples); -} - -namespace mul_broadcast_3D_1D_1_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated mul_broadcast_3D_1D_1_nnfw test -#include "generated/examples/mul_broadcast_3D_1D_1_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul_broadcast_3D_1D_1_nnfw.model.cpp" -} // namespace mul_broadcast_3D_1D_1_nnfw -TEST_F(GeneratedTests, mul_broadcast_3D_1D_1_nnfw) { - execute(mul_broadcast_3D_1D_1_nnfw::CreateModel, - mul_broadcast_3D_1D_1_nnfw::is_ignored, - mul_broadcast_3D_1D_1_nnfw::examples); -} - -namespace mul_broadcast_3D_1D_2_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated mul_broadcast_3D_1D_2_nnfw test -#include "generated/examples/mul_broadcast_3D_1D_2_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul_broadcast_3D_1D_2_nnfw.model.cpp" -} // namespace mul_broadcast_3D_1D_2_nnfw -TEST_F(GeneratedTests, mul_broadcast_3D_1D_2_nnfw) { - execute(mul_broadcast_3D_1D_2_nnfw::CreateModel, - mul_broadcast_3D_1D_2_nnfw::is_ignored, - mul_broadcast_3D_1D_2_nnfw::examples); -} - -namespace mul_broadcast_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated mul_broadcast_quant8 test -#include "generated/examples/mul_broadcast_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul_broadcast_quant8.model.cpp" -} // namespace mul_broadcast_quant8 -TEST_F(GeneratedTests, mul_broadcast_quant8) { - execute(mul_broadcast_quant8::CreateModel, - mul_broadcast_quant8::is_ignored, - mul_broadcast_quant8::examples); -} - -namespace mul { -std::vector<MixedTypedExample> examples = { -// Generated mul test -#include "generated/examples/mul.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul.model.cpp" -} // namespace mul -TEST_F(GeneratedTests, mul) { - execute(mul::CreateModel, - mul::is_ignored, - mul::examples); -} - -namespace mul_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated mul_quant8 test -#include "generated/examples/mul_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul_quant8.model.cpp" -} // namespace mul_quant8 -TEST_F(GeneratedTests, mul_quant8) { - execute(mul_quant8::CreateModel, - mul_quant8::is_ignored, - mul_quant8::examples); -} - -namespace mul_relu { -std::vector<MixedTypedExample> examples = { -// Generated mul_relu test -#include "generated/examples/mul_relu.example.cpp" -}; -// Generated model constructor -#include "generated/models/mul_relu.model.cpp" -} // namespace mul_relu -TEST_F(GeneratedTests, mul_relu) { - execute(mul_relu::CreateModel, - mul_relu::is_ignored, - mul_relu::examples); -} - -namespace relu1_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu1_float_1 test -#include "generated/examples/relu1_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu1_float_1.model.cpp" -} // namespace relu1_float_1 -TEST_F(GeneratedTests, relu1_float_1) { - execute(relu1_float_1::CreateModel, - relu1_float_1::is_ignored, - relu1_float_1::examples); -} - -namespace relu1_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu1_float_2 test -#include "generated/examples/relu1_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu1_float_2.model.cpp" -} // namespace relu1_float_2 -TEST_F(GeneratedTests, relu1_float_2) { - execute(relu1_float_2::CreateModel, - relu1_float_2::is_ignored, - relu1_float_2::examples); -} - -namespace relu1_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu1_quant8_1 test -#include "generated/examples/relu1_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu1_quant8_1.model.cpp" -} // namespace relu1_quant8_1 -TEST_F(GeneratedTests, relu1_quant8_1) { - execute(relu1_quant8_1::CreateModel, - relu1_quant8_1::is_ignored, - relu1_quant8_1::examples); -} - -namespace relu1_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu1_quant8_2 test -#include "generated/examples/relu1_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu1_quant8_2.model.cpp" -} // namespace relu1_quant8_2 -TEST_F(GeneratedTests, relu1_quant8_2) { - execute(relu1_quant8_2::CreateModel, - relu1_quant8_2::is_ignored, - relu1_quant8_2::examples); -} - -namespace relu6_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu6_float_1 test -#include "generated/examples/relu6_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu6_float_1.model.cpp" -} // namespace relu6_float_1 -TEST_F(GeneratedTests, relu6_float_1) { - execute(relu6_float_1::CreateModel, - relu6_float_1::is_ignored, - relu6_float_1::examples); -} - -namespace relu6_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu6_float_2 test -#include "generated/examples/relu6_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu6_float_2.model.cpp" -} // namespace relu6_float_2 -TEST_F(GeneratedTests, relu6_float_2) { - execute(relu6_float_2::CreateModel, - relu6_float_2::is_ignored, - relu6_float_2::examples); -} - -namespace relu6_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu6_quant8_1 test -#include "generated/examples/relu6_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu6_quant8_1.model.cpp" -} // namespace relu6_quant8_1 -TEST_F(GeneratedTests, relu6_quant8_1) { - execute(relu6_quant8_1::CreateModel, - relu6_quant8_1::is_ignored, - relu6_quant8_1::examples); -} - -namespace relu6_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu6_quant8_2 test -#include "generated/examples/relu6_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu6_quant8_2.model.cpp" -} // namespace relu6_quant8_2 -TEST_F(GeneratedTests, relu6_quant8_2) { - execute(relu6_quant8_2::CreateModel, - relu6_quant8_2::is_ignored, - relu6_quant8_2::examples); -} - -namespace relu_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu_float_1 test -#include "generated/examples/relu_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu_float_1.model.cpp" -} // namespace relu_float_1 -TEST_F(GeneratedTests, relu_float_1) { - execute(relu_float_1::CreateModel, - relu_float_1::is_ignored, - relu_float_1::examples); -} - -namespace relu_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu_float_2 test -#include "generated/examples/relu_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu_float_2.model.cpp" -} // namespace relu_float_2 -TEST_F(GeneratedTests, relu_float_2) { - execute(relu_float_2::CreateModel, - relu_float_2::is_ignored, - relu_float_2::examples); -} - -namespace relu_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated relu_quant8_1 test -#include "generated/examples/relu_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu_quant8_1.model.cpp" -} // namespace relu_quant8_1 -TEST_F(GeneratedTests, relu_quant8_1) { - execute(relu_quant8_1::CreateModel, - relu_quant8_1::is_ignored, - relu_quant8_1::examples); -} - -namespace relu_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated relu_quant8_2 test -#include "generated/examples/relu_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/relu_quant8_2.model.cpp" -} // namespace relu_quant8_2 -TEST_F(GeneratedTests, relu_quant8_2) { - execute(relu_quant8_2::CreateModel, - relu_quant8_2::is_ignored, - relu_quant8_2::examples); -} - -namespace reshape { -std::vector<MixedTypedExample> examples = { -// Generated reshape test -#include "generated/examples/reshape.example.cpp" -}; -// Generated model constructor -#include "generated/models/reshape.model.cpp" -} // namespace reshape -TEST_F(GeneratedTests, reshape) { - execute(reshape::CreateModel, - reshape::is_ignored, - reshape::examples); -} - -namespace reshape_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated reshape_quant8 test -#include "generated/examples/reshape_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/reshape_quant8.model.cpp" -} // namespace reshape_quant8 -TEST_F(GeneratedTests, reshape_quant8) { - execute(reshape_quant8::CreateModel, - reshape_quant8::is_ignored, - reshape_quant8::examples); -} - -namespace reshape_quant8_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated reshape_quant8_weights_as_inputs test -#include "generated/examples/reshape_quant8_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/reshape_quant8_weights_as_inputs.model.cpp" -} // namespace reshape_quant8_weights_as_inputs -TEST_F(GeneratedTests, reshape_quant8_weights_as_inputs) { - execute(reshape_quant8_weights_as_inputs::CreateModel, - reshape_quant8_weights_as_inputs::is_ignored, - reshape_quant8_weights_as_inputs::examples); -} - -namespace reshape_weights_as_inputs { -std::vector<MixedTypedExample> examples = { -// Generated reshape_weights_as_inputs test -#include "generated/examples/reshape_weights_as_inputs.example.cpp" -}; -// Generated model constructor -#include "generated/models/reshape_weights_as_inputs.model.cpp" -} // namespace reshape_weights_as_inputs -TEST_F(GeneratedTests, reshape_weights_as_inputs) { - execute(reshape_weights_as_inputs::CreateModel, - reshape_weights_as_inputs::is_ignored, - reshape_weights_as_inputs::examples); -} - -namespace resize_bilinear_2 { -std::vector<MixedTypedExample> examples = { -// Generated resize_bilinear_2 test -#include "generated/examples/resize_bilinear_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/resize_bilinear_2.model.cpp" -} // namespace resize_bilinear_2 -TEST_F(GeneratedTests, resize_bilinear_2) { - execute(resize_bilinear_2::CreateModel, - resize_bilinear_2::is_ignored, - resize_bilinear_2::examples); -} - -namespace resize_bilinear { -std::vector<MixedTypedExample> examples = { -// Generated resize_bilinear test -#include "generated/examples/resize_bilinear.example.cpp" -}; -// Generated model constructor -#include "generated/models/resize_bilinear.model.cpp" -} // namespace resize_bilinear -TEST_F(GeneratedTests, resize_bilinear) { - execute(resize_bilinear::CreateModel, - resize_bilinear::is_ignored, - resize_bilinear::examples); -} - -namespace rnn { -std::vector<MixedTypedExample> examples = { -// Generated rnn test -#include "generated/examples/rnn.example.cpp" -}; -// Generated model constructor -#include "generated/models/rnn.model.cpp" -} // namespace rnn -TEST_F(GeneratedTests, rnn) { - execute(rnn::CreateModel, - rnn::is_ignored, - rnn::examples); -} - -namespace rnn_state { -std::vector<MixedTypedExample> examples = { -// Generated rnn_state test -#include "generated/examples/rnn_state.example.cpp" -}; -// Generated model constructor -#include "generated/models/rnn_state.model.cpp" -} // namespace rnn_state -TEST_F(GeneratedTests, rnn_state) { - execute(rnn_state::CreateModel, - rnn_state::is_ignored, - rnn_state::examples); -} - -namespace softmax_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated softmax_float_1 test -#include "generated/examples/softmax_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/softmax_float_1.model.cpp" -} // namespace softmax_float_1 -TEST_F(GeneratedTests, softmax_float_1) { - execute(softmax_float_1::CreateModel, - softmax_float_1::is_ignored, - softmax_float_1::examples); -} - -namespace softmax_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated softmax_float_2 test -#include "generated/examples/softmax_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/softmax_float_2.model.cpp" -} // namespace softmax_float_2 -TEST_F(GeneratedTests, softmax_float_2) { - execute(softmax_float_2::CreateModel, - softmax_float_2::is_ignored, - softmax_float_2::examples); -} - -namespace softmax_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated softmax_quant8_1 test -#include "generated/examples/softmax_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/softmax_quant8_1.model.cpp" -} // namespace softmax_quant8_1 -TEST_F(GeneratedTests, softmax_quant8_1) { - execute(softmax_quant8_1::CreateModel, - softmax_quant8_1::is_ignored, - softmax_quant8_1::examples); -} - -namespace softmax_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated softmax_quant8_2 test -#include "generated/examples/softmax_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/softmax_quant8_2.model.cpp" -} // namespace softmax_quant8_2 -TEST_F(GeneratedTests, softmax_quant8_2) { - execute(softmax_quant8_2::CreateModel, - softmax_quant8_2::is_ignored, - softmax_quant8_2::examples); -} - -namespace space_to_depth_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_depth_float_1 test -#include "generated/examples/space_to_depth_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_depth_float_1.model.cpp" -} // namespace space_to_depth_float_1 -TEST_F(GeneratedTests, space_to_depth_float_1) { - execute(space_to_depth_float_1::CreateModel, - space_to_depth_float_1::is_ignored, - space_to_depth_float_1::examples); -} - -namespace space_to_depth_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_depth_float_2 test -#include "generated/examples/space_to_depth_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_depth_float_2.model.cpp" -} // namespace space_to_depth_float_2 -TEST_F(GeneratedTests, space_to_depth_float_2) { - execute(space_to_depth_float_2::CreateModel, - space_to_depth_float_2::is_ignored, - space_to_depth_float_2::examples); -} - -namespace space_to_depth_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_depth_float_3 test -#include "generated/examples/space_to_depth_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_depth_float_3.model.cpp" -} // namespace space_to_depth_float_3 -TEST_F(GeneratedTests, space_to_depth_float_3) { - execute(space_to_depth_float_3::CreateModel, - space_to_depth_float_3::is_ignored, - space_to_depth_float_3::examples); -} - -namespace space_to_depth_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_depth_quant8_1 test -#include "generated/examples/space_to_depth_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_depth_quant8_1.model.cpp" -} // namespace space_to_depth_quant8_1 -TEST_F(GeneratedTests, space_to_depth_quant8_1) { - execute(space_to_depth_quant8_1::CreateModel, - space_to_depth_quant8_1::is_ignored, - space_to_depth_quant8_1::examples); -} - -namespace space_to_depth_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_depth_quant8_2 test -#include "generated/examples/space_to_depth_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_depth_quant8_2.model.cpp" -} // namespace space_to_depth_quant8_2 -TEST_F(GeneratedTests, space_to_depth_quant8_2) { - execute(space_to_depth_quant8_2::CreateModel, - space_to_depth_quant8_2::is_ignored, - space_to_depth_quant8_2::examples); -} - -namespace svdf2 { -std::vector<MixedTypedExample> examples = { -// Generated svdf2 test -#include "generated/examples/svdf2.example.cpp" -}; -// Generated model constructor -#include "generated/models/svdf2.model.cpp" -} // namespace svdf2 -TEST_F(GeneratedTests, svdf2) { - execute(svdf2::CreateModel, - svdf2::is_ignored, - svdf2::examples); -} - -namespace svdf { -std::vector<MixedTypedExample> examples = { -// Generated svdf test -#include "generated/examples/svdf.example.cpp" -}; -// Generated model constructor -#include "generated/models/svdf.model.cpp" -} // namespace svdf -TEST_F(GeneratedTests, svdf) { - execute(svdf::CreateModel, - svdf::is_ignored, - svdf::examples); -} - -namespace svdf_state { -std::vector<MixedTypedExample> examples = { -// Generated svdf_state test -#include "generated/examples/svdf_state.example.cpp" -}; -// Generated model constructor -#include "generated/models/svdf_state.model.cpp" -} // namespace svdf_state -TEST_F(GeneratedTests, svdf_state) { - execute(svdf_state::CreateModel, - svdf_state::is_ignored, - svdf_state::examples); -} - -namespace tanh_ { -std::vector<MixedTypedExample> examples = { -// Generated tanh_ test -#include "generated/examples/tanh_.example.cpp" -}; -// Generated model constructor -#include "generated/models/tanh_.model.cpp" -} // namespace tanh_ -TEST_F(GeneratedTests, tanh_) { - execute(tanh_::CreateModel, - tanh_::is_ignored, - tanh_::examples); -} - -namespace batch_to_space_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated batch_to_space_float_1 test -#include "generated/examples/batch_to_space_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/batch_to_space_float_1.model.cpp" -} // namespace batch_to_space_float_1 -TEST_F(GeneratedTests, batch_to_space_float_1) { - execute(batch_to_space_float_1::CreateModel, - batch_to_space_float_1::is_ignored, - batch_to_space_float_1::examples); -} - -namespace batch_to_space { -std::vector<MixedTypedExample> examples = { -// Generated batch_to_space test -#include "generated/examples/batch_to_space.example.cpp" -}; -// Generated model constructor -#include "generated/models/batch_to_space.model.cpp" -} // namespace batch_to_space -TEST_F(GeneratedTests, batch_to_space) { - execute(batch_to_space::CreateModel, - batch_to_space::is_ignored, - batch_to_space::examples); -} - -namespace batch_to_space_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated batch_to_space_quant8_1 test -#include "generated/examples/batch_to_space_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/batch_to_space_quant8_1.model.cpp" -} // namespace batch_to_space_quant8_1 -TEST_F(GeneratedTests, batch_to_space_quant8_1) { - execute(batch_to_space_quant8_1::CreateModel, - batch_to_space_quant8_1::is_ignored, - batch_to_space_quant8_1::examples); -} - -namespace div_broadcast_float { -std::vector<MixedTypedExample> examples = { -// Generated div_broadcast_float test -#include "generated/examples/div_broadcast_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/div_broadcast_float.model.cpp" -} // namespace div_broadcast_float -TEST_F(GeneratedTests, div_broadcast_float) { - execute(div_broadcast_float::CreateModel, - div_broadcast_float::is_ignored, - div_broadcast_float::examples); -} - -namespace div_ { -std::vector<MixedTypedExample> examples = { -// Generated div_ test -#include "generated/examples/div_.example.cpp" -}; -// Generated model constructor -#include "generated/models/div_.model.cpp" -} // namespace div_ -TEST_F(GeneratedTests, div_) { - execute(div_::CreateModel, - div_::is_ignored, - div_::examples); -} - -namespace fully_connected_float_4d_simple { -std::vector<MixedTypedExample> examples = { -// Generated fully_connected_float_4d_simple test -#include "generated/examples/fully_connected_float_4d_simple.example.cpp" -}; -// Generated model constructor -#include "generated/models/fully_connected_float_4d_simple.model.cpp" -} // namespace fully_connected_float_4d_simple -TEST_F(GeneratedTests, fully_connected_float_4d_simple) { - execute(fully_connected_float_4d_simple::CreateModel, - fully_connected_float_4d_simple::is_ignored, - fully_connected_float_4d_simple::examples); -} - -namespace mean_axis01_1_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated mean_axis01_1_nnfw test -#include "generated/examples/mean_axis01_1_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_axis01_1_nnfw.model.cpp" -} // namespace mean_axis01_1_nnfw -TEST_F(GeneratedTests, mean_axis01_1_nnfw) { - execute(mean_axis01_1_nnfw::CreateModel, - mean_axis01_1_nnfw::is_ignored, - mean_axis01_1_nnfw::examples); -} - -namespace mean_axis01_2_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated mean_axis01_2_nnfw test -#include "generated/examples/mean_axis01_2_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_axis01_2_nnfw.model.cpp" -} // namespace mean_axis01_2_nnfw -TEST_F(GeneratedTests, mean_axis01_2_nnfw) { - execute(mean_axis01_2_nnfw::CreateModel, - mean_axis01_2_nnfw::is_ignored, - mean_axis01_2_nnfw::examples); -} - -namespace mean_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated mean_float_1 test -#include "generated/examples/mean_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_float_1.model.cpp" -} // namespace mean_float_1 -TEST_F(GeneratedTests, mean_float_1) { - execute(mean_float_1::CreateModel, - mean_float_1::is_ignored, - mean_float_1::examples); -} - -namespace mean_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated mean_float_2 test -#include "generated/examples/mean_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_float_2.model.cpp" -} // namespace mean_float_2 -TEST_F(GeneratedTests, mean_float_2) { - execute(mean_float_2::CreateModel, - mean_float_2::is_ignored, - mean_float_2::examples); -} - -namespace mean { -std::vector<MixedTypedExample> examples = { -// Generated mean test -#include "generated/examples/mean.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean.model.cpp" -} // namespace mean -TEST_F(GeneratedTests, mean) { - execute(mean::CreateModel, - mean::is_ignored, - mean::examples); -} - -namespace mean_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated mean_quant8_1 test -#include "generated/examples/mean_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_quant8_1.model.cpp" -} // namespace mean_quant8_1 -TEST_F(GeneratedTests, mean_quant8_1) { - execute(mean_quant8_1::CreateModel, - mean_quant8_1::is_ignored, - mean_quant8_1::examples); -} - -namespace mean_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated mean_quant8_2 test -#include "generated/examples/mean_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/mean_quant8_2.model.cpp" -} // namespace mean_quant8_2 -TEST_F(GeneratedTests, mean_quant8_2) { - execute(mean_quant8_2::CreateModel, - mean_quant8_2::is_ignored, - mean_quant8_2::examples); -} - -namespace pad_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated pad_float_1 test -#include "generated/examples/pad_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/pad_float_1.model.cpp" -} // namespace pad_float_1 -TEST_F(GeneratedTests, pad_float_1) { - execute(pad_float_1::CreateModel, - pad_float_1::is_ignored, - pad_float_1::examples); -} - -namespace pad { -std::vector<MixedTypedExample> examples = { -// Generated pad test -#include "generated/examples/pad.example.cpp" -}; -// Generated model constructor -#include "generated/models/pad.model.cpp" -} // namespace pad -TEST_F(GeneratedTests, pad) { - execute(pad::CreateModel, - pad::is_ignored, - pad::examples); -} - -namespace space_to_batch_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_float_1 test -#include "generated/examples/space_to_batch_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_float_1.model.cpp" -} // namespace space_to_batch_float_1 -TEST_F(GeneratedTests, space_to_batch_float_1) { - execute(space_to_batch_float_1::CreateModel, - space_to_batch_float_1::is_ignored, - space_to_batch_float_1::examples); -} - -namespace space_to_batch_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_float_2 test -#include "generated/examples/space_to_batch_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_float_2.model.cpp" -} // namespace space_to_batch_float_2 -TEST_F(GeneratedTests, space_to_batch_float_2) { - execute(space_to_batch_float_2::CreateModel, - space_to_batch_float_2::is_ignored, - space_to_batch_float_2::examples); -} - -namespace space_to_batch_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_float_3 test -#include "generated/examples/space_to_batch_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_float_3.model.cpp" -} // namespace space_to_batch_float_3 -TEST_F(GeneratedTests, space_to_batch_float_3) { - execute(space_to_batch_float_3::CreateModel, - space_to_batch_float_3::is_ignored, - space_to_batch_float_3::examples); -} - -namespace space_to_batch { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch test -#include "generated/examples/space_to_batch.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch.model.cpp" -} // namespace space_to_batch -TEST_F(GeneratedTests, space_to_batch) { - execute(space_to_batch::CreateModel, - space_to_batch::is_ignored, - space_to_batch::examples); -} - -namespace space_to_batch_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_quant8_1 test -#include "generated/examples/space_to_batch_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_quant8_1.model.cpp" -} // namespace space_to_batch_quant8_1 -TEST_F(GeneratedTests, space_to_batch_quant8_1) { - execute(space_to_batch_quant8_1::CreateModel, - space_to_batch_quant8_1::is_ignored, - space_to_batch_quant8_1::examples); -} - -namespace space_to_batch_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_quant8_2 test -#include "generated/examples/space_to_batch_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_quant8_2.model.cpp" -} // namespace space_to_batch_quant8_2 -TEST_F(GeneratedTests, space_to_batch_quant8_2) { - execute(space_to_batch_quant8_2::CreateModel, - space_to_batch_quant8_2::is_ignored, - space_to_batch_quant8_2::examples); -} - -namespace space_to_batch_quant8_3 { -std::vector<MixedTypedExample> examples = { -// Generated space_to_batch_quant8_3 test -#include "generated/examples/space_to_batch_quant8_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/space_to_batch_quant8_3.model.cpp" -} // namespace space_to_batch_quant8_3 -TEST_F(GeneratedTests, space_to_batch_quant8_3) { - execute(space_to_batch_quant8_3::CreateModel, - space_to_batch_quant8_3::is_ignored, - space_to_batch_quant8_3::examples); -} - -namespace squeeze_2D_float_1_nnfw { -std::vector<MixedTypedExample> examples = { -// Generated squeeze_2D_float_1_nnfw test -#include "generated/examples/squeeze_2D_float_1_nnfw.example.cpp" -}; -// Generated model constructor -#include "generated/models/squeeze_2D_float_1_nnfw.model.cpp" -} // namespace squeeze_2D_float_1_nnfw -TEST_F(GeneratedTests, squeeze_2D_float_1_nnfw) { - execute(squeeze_2D_float_1_nnfw::CreateModel, - squeeze_2D_float_1_nnfw::is_ignored, - squeeze_2D_float_1_nnfw::examples); -} - -namespace squeeze_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated squeeze_float_1 test -#include "generated/examples/squeeze_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/squeeze_float_1.model.cpp" -} // namespace squeeze_float_1 -TEST_F(GeneratedTests, squeeze_float_1) { - execute(squeeze_float_1::CreateModel, - squeeze_float_1::is_ignored, - squeeze_float_1::examples); -} - -namespace squeeze { -std::vector<MixedTypedExample> examples = { -// Generated squeeze test -#include "generated/examples/squeeze.example.cpp" -}; -// Generated model constructor -#include "generated/models/squeeze.model.cpp" -} // namespace squeeze -TEST_F(GeneratedTests, squeeze) { - execute(squeeze::CreateModel, - squeeze::is_ignored, - squeeze::examples); -} - -namespace squeeze_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated squeeze_quant8_1 test -#include "generated/examples/squeeze_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/squeeze_quant8_1.model.cpp" -} // namespace squeeze_quant8_1 -TEST_F(GeneratedTests, squeeze_quant8_1) { - execute(squeeze_quant8_1::CreateModel, - squeeze_quant8_1::is_ignored, - squeeze_quant8_1::examples); -} - -namespace strided_slice_float_10 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_10 test -#include "generated/examples/strided_slice_float_10.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_10.model.cpp" -} // namespace strided_slice_float_10 -TEST_F(GeneratedTests, strided_slice_float_10) { - execute(strided_slice_float_10::CreateModel, - strided_slice_float_10::is_ignored, - strided_slice_float_10::examples); -} - -namespace strided_slice_float_11 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_11 test -#include "generated/examples/strided_slice_float_11.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_11.model.cpp" -} // namespace strided_slice_float_11 -TEST_F(GeneratedTests, strided_slice_float_11) { - execute(strided_slice_float_11::CreateModel, - strided_slice_float_11::is_ignored, - strided_slice_float_11::examples); -} - -namespace strided_slice_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_1 test -#include "generated/examples/strided_slice_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_1.model.cpp" -} // namespace strided_slice_float_1 -TEST_F(GeneratedTests, strided_slice_float_1) { - execute(strided_slice_float_1::CreateModel, - strided_slice_float_1::is_ignored, - strided_slice_float_1::examples); -} - -namespace strided_slice_float_2 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_2 test -#include "generated/examples/strided_slice_float_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_2.model.cpp" -} // namespace strided_slice_float_2 -TEST_F(GeneratedTests, strided_slice_float_2) { - execute(strided_slice_float_2::CreateModel, - strided_slice_float_2::is_ignored, - strided_slice_float_2::examples); -} - -namespace strided_slice_float_3 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_3 test -#include "generated/examples/strided_slice_float_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_3.model.cpp" -} // namespace strided_slice_float_3 -TEST_F(GeneratedTests, strided_slice_float_3) { - execute(strided_slice_float_3::CreateModel, - strided_slice_float_3::is_ignored, - strided_slice_float_3::examples); -} - -namespace strided_slice_float_4 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_4 test -#include "generated/examples/strided_slice_float_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_4.model.cpp" -} // namespace strided_slice_float_4 -TEST_F(GeneratedTests, strided_slice_float_4) { - execute(strided_slice_float_4::CreateModel, - strided_slice_float_4::is_ignored, - strided_slice_float_4::examples); -} - -namespace strided_slice_float_5 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_5 test -#include "generated/examples/strided_slice_float_5.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_5.model.cpp" -} // namespace strided_slice_float_5 -TEST_F(GeneratedTests, strided_slice_float_5) { - execute(strided_slice_float_5::CreateModel, - strided_slice_float_5::is_ignored, - strided_slice_float_5::examples); -} - -namespace strided_slice_float_6 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_6 test -#include "generated/examples/strided_slice_float_6.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_6.model.cpp" -} // namespace strided_slice_float_6 -TEST_F(GeneratedTests, strided_slice_float_6) { - execute(strided_slice_float_6::CreateModel, - strided_slice_float_6::is_ignored, - strided_slice_float_6::examples); -} - -namespace strided_slice_float_7 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_7 test -#include "generated/examples/strided_slice_float_7.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_7.model.cpp" -} // namespace strided_slice_float_7 -TEST_F(GeneratedTests, strided_slice_float_7) { - execute(strided_slice_float_7::CreateModel, - strided_slice_float_7::is_ignored, - strided_slice_float_7::examples); -} - -namespace strided_slice_float_8 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_8 test -#include "generated/examples/strided_slice_float_8.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_8.model.cpp" -} // namespace strided_slice_float_8 -TEST_F(GeneratedTests, strided_slice_float_8) { - execute(strided_slice_float_8::CreateModel, - strided_slice_float_8::is_ignored, - strided_slice_float_8::examples); -} - -namespace strided_slice_float_9 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_float_9 test -#include "generated/examples/strided_slice_float_9.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_float_9.model.cpp" -} // namespace strided_slice_float_9 -TEST_F(GeneratedTests, strided_slice_float_9) { - execute(strided_slice_float_9::CreateModel, - strided_slice_float_9::is_ignored, - strided_slice_float_9::examples); -} - -namespace strided_slice { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice test -#include "generated/examples/strided_slice.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice.model.cpp" -} // namespace strided_slice -TEST_F(GeneratedTests, strided_slice) { - execute(strided_slice::CreateModel, - strided_slice::is_ignored, - strided_slice::examples); -} - -namespace strided_slice_qaunt8_10 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_qaunt8_10 test -#include "generated/examples/strided_slice_qaunt8_10.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_qaunt8_10.model.cpp" -} // namespace strided_slice_qaunt8_10 -TEST_F(GeneratedTests, strided_slice_qaunt8_10) { - execute(strided_slice_qaunt8_10::CreateModel, - strided_slice_qaunt8_10::is_ignored, - strided_slice_qaunt8_10::examples); -} - -namespace strided_slice_qaunt8_11 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_qaunt8_11 test -#include "generated/examples/strided_slice_qaunt8_11.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_qaunt8_11.model.cpp" -} // namespace strided_slice_qaunt8_11 -TEST_F(GeneratedTests, strided_slice_qaunt8_11) { - execute(strided_slice_qaunt8_11::CreateModel, - strided_slice_qaunt8_11::is_ignored, - strided_slice_qaunt8_11::examples); -} - -namespace strided_slice_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_1 test -#include "generated/examples/strided_slice_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_1.model.cpp" -} // namespace strided_slice_quant8_1 -TEST_F(GeneratedTests, strided_slice_quant8_1) { - execute(strided_slice_quant8_1::CreateModel, - strided_slice_quant8_1::is_ignored, - strided_slice_quant8_1::examples); -} - -namespace strided_slice_quant8_2 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_2 test -#include "generated/examples/strided_slice_quant8_2.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_2.model.cpp" -} // namespace strided_slice_quant8_2 -TEST_F(GeneratedTests, strided_slice_quant8_2) { - execute(strided_slice_quant8_2::CreateModel, - strided_slice_quant8_2::is_ignored, - strided_slice_quant8_2::examples); -} - -namespace strided_slice_quant8_3 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_3 test -#include "generated/examples/strided_slice_quant8_3.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_3.model.cpp" -} // namespace strided_slice_quant8_3 -TEST_F(GeneratedTests, strided_slice_quant8_3) { - execute(strided_slice_quant8_3::CreateModel, - strided_slice_quant8_3::is_ignored, - strided_slice_quant8_3::examples); -} - -namespace strided_slice_quant8_4 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_4 test -#include "generated/examples/strided_slice_quant8_4.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_4.model.cpp" -} // namespace strided_slice_quant8_4 -TEST_F(GeneratedTests, strided_slice_quant8_4) { - execute(strided_slice_quant8_4::CreateModel, - strided_slice_quant8_4::is_ignored, - strided_slice_quant8_4::examples); -} - -namespace strided_slice_quant8_5 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_5 test -#include "generated/examples/strided_slice_quant8_5.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_5.model.cpp" -} // namespace strided_slice_quant8_5 -TEST_F(GeneratedTests, strided_slice_quant8_5) { - execute(strided_slice_quant8_5::CreateModel, - strided_slice_quant8_5::is_ignored, - strided_slice_quant8_5::examples); -} - -namespace strided_slice_quant8_6 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_6 test -#include "generated/examples/strided_slice_quant8_6.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_6.model.cpp" -} // namespace strided_slice_quant8_6 -TEST_F(GeneratedTests, strided_slice_quant8_6) { - execute(strided_slice_quant8_6::CreateModel, - strided_slice_quant8_6::is_ignored, - strided_slice_quant8_6::examples); -} - -namespace strided_slice_quant8_7 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_7 test -#include "generated/examples/strided_slice_quant8_7.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_7.model.cpp" -} // namespace strided_slice_quant8_7 -TEST_F(GeneratedTests, strided_slice_quant8_7) { - execute(strided_slice_quant8_7::CreateModel, - strided_slice_quant8_7::is_ignored, - strided_slice_quant8_7::examples); -} - -namespace strided_slice_quant8_8 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_8 test -#include "generated/examples/strided_slice_quant8_8.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_8.model.cpp" -} // namespace strided_slice_quant8_8 -TEST_F(GeneratedTests, strided_slice_quant8_8) { - execute(strided_slice_quant8_8::CreateModel, - strided_slice_quant8_8::is_ignored, - strided_slice_quant8_8::examples); -} - -namespace strided_slice_quant8_9 { -std::vector<MixedTypedExample> examples = { -// Generated strided_slice_quant8_9 test -#include "generated/examples/strided_slice_quant8_9.example.cpp" -}; -// Generated model constructor -#include "generated/models/strided_slice_quant8_9.model.cpp" -} // namespace strided_slice_quant8_9 -TEST_F(GeneratedTests, strided_slice_quant8_9) { - execute(strided_slice_quant8_9::CreateModel, - strided_slice_quant8_9::is_ignored, - strided_slice_quant8_9::examples); -} - -namespace sub_broadcast_float { -std::vector<MixedTypedExample> examples = { -// Generated sub_broadcast_float test -#include "generated/examples/sub_broadcast_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/sub_broadcast_float.model.cpp" -} // namespace sub_broadcast_float -TEST_F(GeneratedTests, sub_broadcast_float) { - execute(sub_broadcast_float::CreateModel, - sub_broadcast_float::is_ignored, - sub_broadcast_float::examples); -} - -namespace sub { -std::vector<MixedTypedExample> examples = { -// Generated sub test -#include "generated/examples/sub.example.cpp" -}; -// Generated model constructor -#include "generated/models/sub.model.cpp" -} // namespace sub -TEST_F(GeneratedTests, sub) { - execute(sub::CreateModel, - sub::is_ignored, - sub::examples); -} - -namespace transpose_float_1 { -std::vector<MixedTypedExample> examples = { -// Generated transpose_float_1 test -#include "generated/examples/transpose_float_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/transpose_float_1.model.cpp" -} // namespace transpose_float_1 -TEST_F(GeneratedTests, transpose_float_1) { - execute(transpose_float_1::CreateModel, - transpose_float_1::is_ignored, - transpose_float_1::examples); -} - -namespace transpose { -std::vector<MixedTypedExample> examples = { -// Generated transpose test -#include "generated/examples/transpose.example.cpp" -}; -// Generated model constructor -#include "generated/models/transpose.model.cpp" -} // namespace transpose -TEST_F(GeneratedTests, transpose) { - execute(transpose::CreateModel, - transpose::is_ignored, - transpose::examples); -} - -namespace transpose_quant8_1 { -std::vector<MixedTypedExample> examples = { -// Generated transpose_quant8_1 test -#include "generated/examples/transpose_quant8_1.example.cpp" -}; -// Generated model constructor -#include "generated/models/transpose_quant8_1.model.cpp" -} // namespace transpose_quant8_1 -TEST_F(GeneratedTests, transpose_quant8_1) { - execute(transpose_quant8_1::CreateModel, - transpose_quant8_1::is_ignored, - transpose_quant8_1::examples); -} - -namespace cast_ex_float32_to_int32 { -std::vector<MixedTypedExample> examples = { -// Generated cast_ex_float32_to_int32 test -#include "generated/examples/cast_ex_float32_to_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/cast_ex_float32_to_int32.model.cpp" -} // namespace cast_ex_float32_to_int32 -TEST_F(GeneratedTests, cast_ex_float32_to_int32) { - execute(cast_ex_float32_to_int32::CreateModel, - cast_ex_float32_to_int32::is_ignored, - cast_ex_float32_to_int32::examples); -} - -namespace cast_ex_int32_to_float32 { -std::vector<MixedTypedExample> examples = { -// Generated cast_ex_int32_to_float32 test -#include "generated/examples/cast_ex_int32_to_float32.example.cpp" -}; -// Generated model constructor -#include "generated/models/cast_ex_int32_to_float32.model.cpp" -} // namespace cast_ex_int32_to_float32 -TEST_F(GeneratedTests, cast_ex_int32_to_float32) { - execute(cast_ex_int32_to_float32::CreateModel, - cast_ex_int32_to_float32::is_ignored, - cast_ex_int32_to_float32::examples); -} - -namespace gather_1D_float { -std::vector<MixedTypedExample> examples = { -// Generated gather_1D_float test -#include "generated/examples/gather_1D_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_1D_float.model.cpp" -} // namespace gather_1D_float -TEST_F(GeneratedTests, gather_1D_float) { - execute(gather_1D_float::CreateModel, - gather_1D_float::is_ignored, - gather_1D_float::examples); -} - -namespace gather_1D_int32 { -std::vector<MixedTypedExample> examples = { -// Generated gather_1D_int32 test -#include "generated/examples/gather_1D_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_1D_int32.model.cpp" -} // namespace gather_1D_int32 -TEST_F(GeneratedTests, gather_1D_int32) { - execute(gather_1D_int32::CreateModel, - gather_1D_int32::is_ignored, - gather_1D_int32::examples); -} - -namespace gather_1D_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated gather_1D_quant8 test -#include "generated/examples/gather_1D_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_1D_quant8.model.cpp" -} // namespace gather_1D_quant8 -TEST_F(GeneratedTests, gather_1D_quant8) { - execute(gather_1D_quant8::CreateModel, - gather_1D_quant8::is_ignored, - gather_1D_quant8::examples); -} - -namespace gather_2D_float { -std::vector<MixedTypedExample> examples = { -// Generated gather_2D_float test -#include "generated/examples/gather_2D_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_2D_float.model.cpp" -} // namespace gather_2D_float -TEST_F(GeneratedTests, gather_2D_float) { - execute(gather_2D_float::CreateModel, - gather_2D_float::is_ignored, - gather_2D_float::examples); -} - -namespace gather_2D_int32 { -std::vector<MixedTypedExample> examples = { -// Generated gather_2D_int32 test -#include "generated/examples/gather_2D_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_2D_int32.model.cpp" -} // namespace gather_2D_int32 -TEST_F(GeneratedTests, gather_2D_int32) { - execute(gather_2D_int32::CreateModel, - gather_2D_int32::is_ignored, - gather_2D_int32::examples); -} - -namespace gather_2D_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated gather_2D_quant8 test -#include "generated/examples/gather_2D_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/gather_2D_quant8.model.cpp" -} // namespace gather_2D_quant8 -TEST_F(GeneratedTests, gather_2D_quant8) { - execute(gather_2D_quant8::CreateModel, - gather_2D_quant8::is_ignored, - gather_2D_quant8::examples); -} - -namespace tensorflowmax_ex_2D_float { -std::vector<MixedTypedExample> examples = { -// Generated tensorflowmax_ex_2D_float test -#include "generated/examples/tensorflowmax_ex_2D_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/tensorflowmax_ex_2D_float.model.cpp" -} // namespace tensorflowmax_ex_2D_float -TEST_F(GeneratedTests, tensorflowmax_ex_2D_float) { - execute(tensorflowmax_ex_2D_float::CreateModel, - tensorflowmax_ex_2D_float::is_ignored, - tensorflowmax_ex_2D_float::examples); -} - -namespace tensorflowmax_ex_2D_int32 { -std::vector<MixedTypedExample> examples = { -// Generated tensorflowmax_ex_2D_int32 test -#include "generated/examples/tensorflowmax_ex_2D_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/tensorflowmax_ex_2D_int32.model.cpp" -} // namespace tensorflowmax_ex_2D_int32 -TEST_F(GeneratedTests, tensorflowmax_ex_2D_int32) { - execute(tensorflowmax_ex_2D_int32::CreateModel, - tensorflowmax_ex_2D_int32::is_ignored, - tensorflowmax_ex_2D_int32::examples); -} - -namespace topk_v2_1D_float { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_1D_float test -#include "generated/examples/topk_v2_1D_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_1D_float.model.cpp" -} // namespace topk_v2_1D_float -TEST_F(GeneratedTests, topk_v2_1D_float) { - execute(topk_v2_1D_float::CreateModel, - topk_v2_1D_float::is_ignored, - topk_v2_1D_float::examples); -} - -namespace topk_v2_1D_int32 { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_1D_int32 test -#include "generated/examples/topk_v2_1D_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_1D_int32.model.cpp" -} // namespace topk_v2_1D_int32 -TEST_F(GeneratedTests, topk_v2_1D_int32) { - execute(topk_v2_1D_int32::CreateModel, - topk_v2_1D_int32::is_ignored, - topk_v2_1D_int32::examples); -} - -namespace topk_v2_1D_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_1D_quant8 test -#include "generated/examples/topk_v2_1D_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_1D_quant8.model.cpp" -} // namespace topk_v2_1D_quant8 -TEST_F(GeneratedTests, topk_v2_1D_quant8) { - execute(topk_v2_1D_quant8::CreateModel, - topk_v2_1D_quant8::is_ignored, - topk_v2_1D_quant8::examples); -} - -namespace topk_v2_2D_float { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_2D_float test -#include "generated/examples/topk_v2_2D_float.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_2D_float.model.cpp" -} // namespace topk_v2_2D_float -TEST_F(GeneratedTests, topk_v2_2D_float) { - execute(topk_v2_2D_float::CreateModel, - topk_v2_2D_float::is_ignored, - topk_v2_2D_float::examples); -} - -namespace topk_v2_2D_int32 { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_2D_int32 test -#include "generated/examples/topk_v2_2D_int32.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_2D_int32.model.cpp" -} // namespace topk_v2_2D_int32 -TEST_F(GeneratedTests, topk_v2_2D_int32) { - execute(topk_v2_2D_int32::CreateModel, - topk_v2_2D_int32::is_ignored, - topk_v2_2D_int32::examples); -} - -namespace topk_v2_2D_quant8 { -std::vector<MixedTypedExample> examples = { -// Generated topk_v2_2D_quant8 test -#include "generated/examples/topk_v2_2D_quant8.example.cpp" -}; -// Generated model constructor -#include "generated/models/topk_v2_2D_quant8.model.cpp" -} // namespace topk_v2_2D_quant8 -TEST_F(GeneratedTests, topk_v2_2D_quant8) { - execute(topk_v2_2D_quant8::CreateModel, - topk_v2_2D_quant8::is_ignored, - topk_v2_2D_quant8::examples); -} diff --git a/runtimes/tests/neural_networks_test/generated/examples/add.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/add.example.cpp deleted file mode 100644 index 540131854..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/add.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: add.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f}}, {1, {3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {4.0f, 6.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/add_broadcast_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/add_broadcast_quant8.example.cpp deleted file mode 100644 index d9eebee84..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/add_broadcast_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: add_broadcast_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2}}, {1, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {3, 6, 5, 8}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/add_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/add_quant8.example.cpp deleted file mode 100644 index 3f23cbcd9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/add_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: add_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2}}, {1, {3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {5, 8}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_1.example.cpp deleted file mode 100644 index 4ed1b0059..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_2.example.cpp deleted file mode 100644 index f62eba158..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 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Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_5.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_5.example.cpp deleted file mode 100644 index 924f87081..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_float_5.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_float_5.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 6, 2, 4, 3, 2, 10, 7}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.75f, 5.75f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_1.example.cpp deleted file mode 100644 index 03211dc0a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_2.example.cpp deleted file mode 100644 index e04521866..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_quant8_2.mod.py). 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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_3.example.cpp deleted file mode 100644 index 98697448e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_quant8_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 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6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6, 0, 2, 4, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_4.example.cpp deleted file mode 100644 index 18a083cb3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_quant8_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 2, 2, 2, 2, 2, 2}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_5.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_5.example.cpp deleted file mode 100644 index 6d2e54121..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/avg_pool_quant8_5.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: avg_pool_quant8_5.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 96, 32, 64, 48, 32, 160, 112}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {44, 92}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/batch_to_space.example.cpp deleted file mode 100644 index 111326298..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: batch_to_space.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_float_1.example.cpp deleted file mode 100644 index 45a758041..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: batch_to_space_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 5, 2, 6, 9, 13, 10, 14, 3, 7, 4, 8, 11, 15, 12, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_quant8_1.example.cpp deleted file mode 100644 index 6d1d6f760..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/batch_to_space_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: batch_to_space_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 5, 2, 6, 9, 13, 10, 14, 3, 7, 4, 8, 11, 15, 12, 16}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/cast_ex_float32_to_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/cast_ex_float32_to_int32.example.cpp deleted file mode 100644 index bf931adee..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/cast_ex_float32_to_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: cast_ex_float32_to_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {100.0f, 20.0f, 3.0f, 0.4f, 0.999f, 1.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {100, 20, 3, 0, 0, 1}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/cast_ex_int32_to_float32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/cast_ex_int32_to_float32.example.cpp deleted file mode 100644 index 7a076e19c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/cast_ex_int32_to_float32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: cast_ex_int32_to_float32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {100, 200, 300, 400, 500, 600}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {100.0f, 200.0f, 300.0f, 400.0f, 500.0f, 600.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/concat_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/concat_float_1.example.cpp deleted file mode 100644 index dc0af926f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/concat_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}, {1, {7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/concat_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/concat_float_2.example.cpp deleted file mode 100644 index 2c07fb933..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/concat_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 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1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 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20974, 20975, 20976, 20977, 20978, 20979, 20980, 20981, 20982, 20983, 20984, 20985, 20986, 20987, 20988, 20989, 20990, 20991, 20992, 20993, 20994, 20995, 20996, 20997, 20998, 20999, 21000, 21001, 21002, 21003, 21004, 21005, 21006, 21007, 21008, 21009, 21010, 21011, 21012, 21013, 21014, 21015, 21016, 21017, 21018, 21019, 21020, 21021, 21022, 21023, 21024, 21025, 21026, 21027, 21028, 21029, 21030, 21031, 21032, 21033, 21034, 21035, 21036, 21037, 21038, 21039, 21040, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21048, 21049, 21050, 21051, 21052, 21053, 21054, 21055, 21056, 21057, 21058, 21059, 21060, 21061, 21062, 21063, 21064, 21065, 21066, 21067, 21068, 21069, 21070, 21071, 21072, 21073, 21074, 21075, 21076, 21077, 21078, 21079, 21080, 21081, 21082, 21083, 21084, 21085, 21086, 21087, 21088, 21089, 21090, 21091, 21092, 21093, 21094, 21095, 21096, 21097, 21098, 21099, 21100, 21101, 21102, 21103, 21104, 21105, 21106, 21107, 21108, 21109, 21110, 21111, 21112, 21113, 21114, 21115, 21116, 21117, 21118, 21119, 21120, 21121, 21122, 21123, 21124, 21125, 21126, 21127, 21128, 21129, 21130, 21131, 21132, 21133, 21134, 21135, 21136, 21137, 21138, 21139, 21140, 21141, 21142, 21143, 21144, 21145, 21146, 21147, 21148, 21149, 21150, 21151, 21152, 21153, 21154, 21155, 21156, 21157, 21158, 21159}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 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a/runtimes/tests/neural_networks_test/generated/examples/concat_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 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12416, 12417, 12418, 12419, -6180, -6181, -6182, -6183, -6184, -6185, -6186, -6187, -6188, -6189, -6190, -6191, -6192, -6193, -6194, -6195, -6196, -6197, -6198, -6199, -6200, -6201, -6202, -6203, -6204, -6205, -6206, -6207, -6208, -6209, 12420, 12421, 12422, 12423, 12424, 12425, 12426, 12427, 12428, 12429, 12430, 12431, 12432, 12433, 12434, 12435, 12436, 12437, 12438, 12439, 12440, 12441, 12442, 12443, 12444, 12445, 12446, 12447, 12448, 12449, 12450, 12451, 12452, 12453, 12454, 12455, 12456, 12457, 12458, 12459, 12460, 12461, 12462, 12463, 12464, 12465, 12466, 12467, 12468, 12469, 12470, 12471, 12472, 12473, 12474, 12475, 12476, 12477, 12478, 12479, -6210, -6211, -6212, -6213, -6214, -6215, -6216, -6217, -6218, -6219, -6220, -6221, -6222, -6223, -6224, -6225, -6226, -6227, -6228, -6229, -6230, -6231, -6232, -6233, -6234, -6235, -6236, -6237, -6238, -6239, 12480, 12481, 12482, 12483, 12484, 12485, 12486, 12487, 12488, 12489, 12490, 12491, 12492, 12493, 12494, 12495, 12496, 12497, 12498, 12499, 12500, 12501, 12502, 12503, 12504, 12505, 12506, 12507, 12508, 12509, 12510, 12511, 12512, 12513, 12514, 12515, 12516, 12517, 12518, 12519, 12520, 12521, 12522, 12523, 12524, 12525, 12526, 12527, 12528, 12529, 12530, 12531, 12532, 12533, 12534, 12535, 12536, 12537, 12538, 12539, -6240, -6241, -6242, -6243, -6244, -6245, -6246, -6247, -6248, -6249, -6250, -6251, -6252, -6253, -6254, -6255, -6256, -6257, -6258, -6259, -6260, -6261, -6262, -6263, -6264, -6265, -6266, -6267, -6268, -6269, 12540, 12541, 12542, 12543, 12544, 12545, 12546, 12547, 12548, 12549, 12550, 12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560, 12561, 12562, 12563, 12564, 12565, 12566, 12567, 12568, 12569, 12570, 12571, 12572, 12573, 12574, 12575, 12576, 12577, 12578, 12579, 12580, 12581, 12582, 12583, 12584, 12585, 12586, 12587, 12588, 12589, 12590, 12591, 12592, 12593, 12594, 12595, 12596, 12597, 12598, 12599, -6270, -6271, -6272, -6273, -6274, -6275, -6276, -6277, -6278, -6279, -6280, -6281, -6282, -6283, -6284, -6285, -6286, -6287, -6288, -6289, -6290, -6291, -6292, -6293, -6294, -6295, -6296, -6297, -6298, -6299, 12600, 12601, 12602, 12603, 12604, 12605, 12606, 12607, 12608, 12609, 12610, 12611, 12612, 12613, 12614, 12615, 12616, 12617, 12618, 12619, 12620, 12621, 12622, 12623, 12624, 12625, 12626, 12627, 12628, 12629, 12630, 12631, 12632, 12633, 12634, 12635, 12636, 12637, 12638, 12639, 12640, 12641, 12642, 12643, 12644, 12645, 12646, 12647, 12648, 12649, 12650, 12651, 12652, 12653, 12654, 12655, 12656, 12657, 12658, 12659, -6300, -6301, -6302, -6303, -6304, -6305, -6306, -6307, -6308, -6309, -6310, -6311, -6312, -6313, -6314, -6315, -6316, -6317, -6318, -6319, -6320, -6321, -6322, -6323, -6324, -6325, -6326, -6327, -6328, -6329, 12660, 12661, 12662, 12663, 12664, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12675, 12676, 12677, 12678, 12679, 12680, 12681, 12682, 12683, 12684, 12685, 12686, 12687, 12688, 12689, 12690, 12691, 12692, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, -6330, -6331, -6332, -6333, -6334, -6335, -6336, -6337, -6338, -6339, -6340, -6341, -6342, -6343, -6344, -6345, -6346, -6347, -6348, -6349, -6350, -6351, -6352, -6353, -6354, -6355, -6356, -6357, -6358, -6359}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/concat_float_4D_axis3_1_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/concat_float_4D_axis3_1_nnfw.example.cpp deleted file mode 100644 index 63ec5c1a8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/concat_float_4D_axis3_1_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_float_4D_axis3_1_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.03203143f, -0.0334147f, -0.02527265f, 0.04576106f, 0.08869292f, 0.06428383f, -0.06473722f, -0.21933985f, -0.05541003f, -0.24157837f, -0.16328812f, -0.04581105f}}, {1, {-0.0569439f, -0.15872048f, 0.02965238f, -0.12761882f, -0.00185435f, -0.03297619f, 0.03581043f, -0.12603407f, 0.05999133f, 0.00290503f, 0.1727029f, 0.03342071f}}, {2, {0.10992613f, 0.09185287f, 0.16433905f, -0.00059073f, -0.01480746f, 0.0135175f, 0.07129054f, -0.15095694f, -0.04579685f, -0.13260484f, -0.10045543f, 0.0647094f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.03203143f, -0.0334147f, -0.0569439f, -0.15872048f, 0.10992613f, 0.09185287f, -0.02527265f, 0.04576106f, 0.02965238f, -0.12761882f, 0.16433905f, -0.00059073f, 0.08869292f, 0.06428383f, -0.00185435f, -0.03297619f, -0.01480746f, 0.0135175f, -0.06473722f, -0.21933985f, 0.03581043f, -0.12603407f, 0.07129054f, -0.15095694f, -0.05541003f, -0.24157837f, 0.05999133f, 0.00290503f, -0.04579685f, -0.13260484f, -0.16328812f, -0.04581105f, 0.1727029f, 0.03342071f, -0.10045543f, 0.0647094f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_1.example.cpp deleted file mode 100644 index fbbeb5f22..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6}}, {1, {7, 8, 9, 10, 11, 12}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_2.example.cpp deleted file mode 100644 index 2fec55a66..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 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175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 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a/runtimes/tests/neural_networks_test/generated/examples/concat_quant8_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: concat_quant8_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 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81, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 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171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 0, 1, 2, 3, 4, 5, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 129, 130, 131, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_SAME.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_SAME.example.cpp deleted file mode 100644 index a75e47a90..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_SAME.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: conv_1_h3_w2_SAME.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.869931f, 0.644628f, -0.918393f, 0.153672f, 0.868562f, -0.358177f, -0.134931f, -0.247565f, 0.22174f, -0.259157f, -0.284296f, -0.538065f, 0.765559f, 0.41986f, -0.556241f, 0.658494f, 0.214355f, -0.850169f, -0.252893f, -0.478935f, 0.530526f, -0.0700663f, -0.988729f, -0.303061f, 0.150845f, 0.829915f, 0.476349f, 0.406537f, -0.355343f, 0.757145f, -0.356362f, 0.800482f, -0.713861f, 0.210483f, -0.634303f, 0.718236f, -0.752038f, 0.457547f, -0.550769f, -0.551178f, 0.446766f, -0.227462f, 0.216348f, -0.852806f, -0.351486f, 0.55906f, -0.668493f, -0.303493f, -0.363763f, -0.162837f, 0.0701012f, 0.756097f, -0.142269f, 0.329724f, -0.656317f, -0.998086f, -0.652949f, -0.40316f, -0.893682f, 0.432744f, 0.612362f, -0.869588f, -0.71327f, -0.398092f, -0.0423559f, 0.436576f, -0.925272f, 0.176549f, 0.822904f, 0.096833f, -0.296802f, -0.427195f, 0.031654f, -0.254479f, 0.244905f, 0.0948254f, 0.643769f, -0.90391f, 0.352665f, -0.901179f, 0.266159f, -0.968068f, -0.615401f, -0.388975f, 0.939052f, -0.116289f, 0.107523f, -0.0582711f, 0.435172f, 0.334675f, 0.459711f, 0.717436f, 0.496627f, -0.680175f, -0.415066f, 0.339848f, 0.506004f, -0.337808f, -0.107218f, -0.172496f, 0.870638f, 0.931872f, -0.953884f, 0.903042f, 0.760078f, 0.209727f, -0.285384f, -0.45514f, 0.113194f, 0.0756611f, 0.0924435f, -0.472863f, 0.960609f, -0.160385f, -0.839445f, 0.457097f, 0.163348f, 0.344867f, -0.131619f, 0.688715f, -0.540827f, 0.571259f, -0.95587f, 0.506164f, -0.155839f, 0.0789621f, 0.756772f, -0.662069f, 0.242908f, 0.460821f, 0.177872f, -0.289839f, -0.640603f, 0.702598f, -0.506406f, -0.568262f, -0.0713716f, 0.413792f, 0.159673f, -0.305208f, 0.133816f, -0.160254f, 0.787323f, -0.753244f, 0.600721f, 0.263186f, -0.162387f, 0.477962f, -0.702951f, -0.731036f, -0.939481f, -0.524519f, 0.934072f, -0.511637f, -0.503499f, 0.106236f, -0.323684f, 0.534444f, -0.843745f, 0.364171f, 0.0370358f, -0.168801f, -0.404559f, -0.814178f, 0.91745f, -0.334276f, 0.66925f, -0.801201f, 0.156511f, -0.427949f, 0.379153f, 0.818597f, -0.649902f, 0.427087f, -0.586015f, -0.559789f, -0.833923f, 0.0892409f, -0.621251f, 0.213826f, 0.465509f, 0.4704f, 0.380261f, 0.413067f, 0.180822f, 0.172866f, 0.59614f, 0.825575f, 0.662916f, -0.704381f, -0.297631f, 0.697778f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.85284f, -0.0393656f, -0.127353f, 1.43115f, -0.302294f, -1.0402f, 0.655023f, -0.587614f, 1.72003f, 1.55816f, 0.667546f, 2.23663f, 0.0661516f, 0.290254f, 0.770222f, -0.346357f, -1.58197f, -0.850595f, -0.484224f, 0.949967f, -0.577263f, -0.871949f, 2.34132f, -0.104506f, -0.135965f, -0.985713f, 0.815147f, 1.03114f, -1.41915f, -0.515534f, -0.373639f, 1.42026f, -1.50604f, 0.673113f, 3.06139f, -0.388578f, -1.76707f, -0.315667f, -1.03815f, -0.343435f, 0.432787f, -1.41643f, 1.12944f, -0.175806f, -0.846415f, 1.40095f, 0.70832f, -1.46717f, 2.19562f, -2.61266f, -0.705383f, 1.26124f, 1.46545f, -2.35761f, 2.04494f, 1.23741f, -0.527402f, -0.39954f, -0.0128623f, 1.3644f, 0.985755f, -0.718118f, -0.1008f, 1.24327f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.295335f, -0.00387601f, -0.552251f, 0.166084f, -0.28482f, -0.152143f, -0.719885f, -0.869386f, -0.745598f, 0.823947f, 0.473183f, -0.331337f, 0.187631f, 0.0426571f, -0.826897f, -0.755085f, -0.472453f, -0.0233656f, 0.0483436f, 0.933418f, -0.961974f, 0.0125783f, 0.219742f, 0.342604f, -0.15166f, 0.0934905f, 0.783221f, 0.129664f, 0.838844f, -0.271388f, 0.924519f, 0.342843f, 0.274418f, 0.350817f, 0.841638f, -0.543993f, -0.00283395f, -0.128467f, -0.682943f, -0.319117f, 0.84634f, 0.283003f, 0.32865f, 0.0293755f, -0.0335696f, 0.591266f, -0.0743476f, -0.741271f, 0.462056f, -0.583625f, -0.590183f, 0.6234f, 0.535269f, -0.670818f, -0.955642f, -0.770173f, 0.479986f, 0.664377f, 0.399445f, -0.968874f, -0.276263f, -0.901951f, 0.544104f, -0.958981f, 0.482658f, -0.807284f, 0.305369f, -0.947818f, 0.827498f, -0.382887f, -0.805741f, -0.796678f, -0.299804f, -0.229828f, 0.818783f, -0.103055f, -0.45568f, -0.227827f, 0.543743f, -0.96073f, 0.946747f, -0.857182f, -0.96426f, -0.292411f, -0.715614f, 0.765278f, -0.475043f, -0.590142f, -0.238507f, 0.673002f, -0.473357f, -0.319626f, 0.936014f, 0.486607f, 0.580844f, 0.425352f, -0.800994f, 0.290763f, -0.494953f, -0.441162f, 0.718677f, -0.828427f, 0.96965f, 7.53637e-05f, -0.699973f, -0.526886f, -0.352682f, 0.799466f, 0.332789f, 0.723389f, 0.407659f, -0.934084f, -0.284705f, 0.961484f, -0.700395f, -0.985808f, -0.595342f, -0.691721f, 0.49448f, -0.0842649f, 0.0390966f, 0.298938f, -0.128094f, -0.97158f, 0.86393f, 0.270606f, -0.468986f, -0.256605f, 0.47215f, -0.273117f, -0.590343f, -0.826529f, -0.725381f, -0.194821f, -0.259661f, -0.0949207f, -0.180302f, 0.0446834f, -0.222133f, -0.40393f, 0.295772f, -0.92949f, 0.580079f, -0.169856f, 0.330311f, 0.0173551f, -0.635823f, 0.475942f, 0.907175f, 0.242777f, -0.512208f, 0.362463f, 0.0496289f, 0.65171f, 0.990057f, 0.690733f, -0.469013f, -0.101311f, -0.68372f, -0.157841f, -0.677711f, -0.708224f, -0.659437f, -0.407607f, 0.677033f, 0.89032f, 0.228307f, -0.749514f, 0.772958f, 0.054701f, 0.551705f, 0.917052f, -0.895022f, -0.702397f, 0.484142f, 0.108648f, 0.833347f, 0.478872f, -0.984112f, 0.387176f, -0.73299f, 0.7526f, 0.443312f, -0.0987856f, 0.125415f, 0.10876f, -0.498108f, 0.43209f, 0.344609f, 0.928941f, -0.130732f, -0.0569167f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.000614278f, -1.21221f, 0.443861f, 0.102117f, -2.52714f, 1.47489f, 0.173474f, -0.237577f, 1.28735f, 1.91315f, 2.51734f, 0.375841f, 0.637563f, 2.653f, 2.72959f, -1.6271f, 1.17389f, -2.12119f, 2.91417f, -2.24246f, 0.0497045f, -0.127107f, -0.144473f, -0.133762f, -0.393284f, -2.02346f, -0.239178f, -0.246508f, 1.29277f, 1.32963f, 0.117521f, 1.22372f, 0.0665713f, 1.09438f, -1.31426f, 2.52594f, -0.969211f, 0.515478f, -1.60926f, -0.838905f, 0.135211f, 0.786415f, -1.14382f, -0.739102f, -1.01731f, 0.281615f, 2.36311f, 0.891823f, 1.93872f, -0.150491f, 3.45217f, 2.28219f, 1.18282f, -2.25086f, 3.05468f, 0.166228f, 0.434554f, -2.57529f, -0.958662f, -2.23978f, 2.66776f, 0.542601f, 1.76107f, -1.08134f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_VALID.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_VALID.example.cpp deleted file mode 100644 index f351f4a0a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_1_h3_w2_VALID.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: conv_1_h3_w2_VALID.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.869931f, 0.644628f, -0.918393f, 0.153672f, 0.868562f, -0.358177f, -0.134931f, -0.247565f, 0.22174f, -0.259157f, -0.284296f, -0.538065f, 0.765559f, 0.41986f, -0.556241f, 0.658494f, 0.214355f, -0.850169f, -0.252893f, -0.478935f, 0.530526f, -0.0700663f, -0.988729f, -0.303061f, 0.150845f, 0.829915f, 0.476349f, 0.406537f, -0.355343f, 0.757145f, -0.356362f, 0.800482f, -0.713861f, 0.210483f, -0.634303f, 0.718236f, -0.752038f, 0.457547f, -0.550769f, -0.551178f, 0.446766f, -0.227462f, 0.216348f, -0.852806f, -0.351486f, 0.55906f, -0.668493f, -0.303493f, -0.363763f, -0.162837f, 0.0701012f, 0.756097f, -0.142269f, 0.329724f, -0.656317f, -0.998086f, -0.652949f, -0.40316f, -0.893682f, 0.432744f, 0.612362f, -0.869588f, -0.71327f, -0.398092f, -0.0423559f, 0.436576f, -0.925272f, 0.176549f, 0.822904f, 0.096833f, -0.296802f, -0.427195f, 0.031654f, -0.254479f, 0.244905f, 0.0948254f, 0.643769f, -0.90391f, 0.352665f, -0.901179f, 0.266159f, -0.968068f, -0.615401f, -0.388975f, 0.939052f, -0.116289f, 0.107523f, -0.0582711f, 0.435172f, 0.334675f, 0.459711f, 0.717436f, 0.496627f, -0.680175f, -0.415066f, 0.339848f, 0.506004f, -0.337808f, -0.107218f, -0.172496f, 0.870638f, 0.931872f, -0.953884f, 0.903042f, 0.760078f, 0.209727f, -0.285384f, -0.45514f, 0.113194f, 0.0756611f, 0.0924435f, -0.472863f, 0.960609f, -0.160385f, -0.839445f, 0.457097f, 0.163348f, 0.344867f, -0.131619f, 0.688715f, -0.540827f, 0.571259f, -0.95587f, 0.506164f, -0.155839f, 0.0789621f, 0.756772f, -0.662069f, 0.242908f, 0.460821f, 0.177872f, -0.289839f, -0.640603f, 0.702598f, -0.506406f, -0.568262f, -0.0713716f, 0.413792f, 0.159673f, -0.305208f, 0.133816f, -0.160254f, 0.787323f, -0.753244f, 0.600721f, 0.263186f, -0.162387f, 0.477962f, -0.702951f, -0.731036f, -0.939481f, -0.524519f, 0.934072f, -0.511637f, -0.503499f, 0.106236f, -0.323684f, 0.534444f, -0.843745f, 0.364171f, 0.0370358f, -0.168801f, -0.404559f, -0.814178f, 0.91745f, -0.334276f, 0.66925f, -0.801201f, 0.156511f, -0.427949f, 0.379153f, 0.818597f, -0.649902f, 0.427087f, -0.586015f, -0.559789f, -0.833923f, 0.0892409f, -0.621251f, 0.213826f, 0.465509f, 0.4704f, 0.380261f, 0.413067f, 0.180822f, 0.172866f, 0.59614f, 0.825575f, 0.662916f, -0.704381f, -0.297631f, 0.697778f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.72003f, 1.55816f, 0.667546f, 2.23663f, 0.0661516f, 0.290254f, 0.770222f, -1.58197f, -0.850595f, -0.484224f, 0.949967f, -0.577263f, -0.871949f, 2.34132f, -0.135965f, -0.985713f, 0.815147f, 1.03114f, -1.41915f, -0.515534f, -0.373639f, -1.50604f, 0.673113f, 3.06139f, -0.388578f, -1.76707f, -0.315667f, -1.03815f, 0.432787f, -1.41643f, 1.12944f, -0.175806f, -0.846415f, 1.40095f, 0.70832f, 2.19562f, -2.61266f, -0.705383f, 1.26124f, 1.46545f, -2.35761f, 2.04494f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.295335f, -0.00387601f, -0.552251f, 0.166084f, -0.28482f, -0.152143f, -0.719885f, -0.869386f, -0.745598f, 0.823947f, 0.473183f, -0.331337f, 0.187631f, 0.0426571f, -0.826897f, -0.755085f, -0.472453f, -0.0233656f, 0.0483436f, 0.933418f, -0.961974f, 0.0125783f, 0.219742f, 0.342604f, -0.15166f, 0.0934905f, 0.783221f, 0.129664f, 0.838844f, -0.271388f, 0.924519f, 0.342843f, 0.274418f, 0.350817f, 0.841638f, -0.543993f, -0.00283395f, -0.128467f, -0.682943f, -0.319117f, 0.84634f, 0.283003f, 0.32865f, 0.0293755f, -0.0335696f, 0.591266f, -0.0743476f, -0.741271f, 0.462056f, -0.583625f, -0.590183f, 0.6234f, 0.535269f, -0.670818f, -0.955642f, -0.770173f, 0.479986f, 0.664377f, 0.399445f, -0.968874f, -0.276263f, -0.901951f, 0.544104f, -0.958981f, 0.482658f, -0.807284f, 0.305369f, -0.947818f, 0.827498f, -0.382887f, -0.805741f, -0.796678f, -0.299804f, -0.229828f, 0.818783f, -0.103055f, -0.45568f, -0.227827f, 0.543743f, -0.96073f, 0.946747f, -0.857182f, -0.96426f, -0.292411f, -0.715614f, 0.765278f, -0.475043f, -0.590142f, -0.238507f, 0.673002f, -0.473357f, -0.319626f, 0.936014f, 0.486607f, 0.580844f, 0.425352f, -0.800994f, 0.290763f, -0.494953f, -0.441162f, 0.718677f, -0.828427f, 0.96965f, 7.53637e-05f, -0.699973f, -0.526886f, -0.352682f, 0.799466f, 0.332789f, 0.723389f, 0.407659f, -0.934084f, -0.284705f, 0.961484f, -0.700395f, -0.985808f, -0.595342f, -0.691721f, 0.49448f, -0.0842649f, 0.0390966f, 0.298938f, -0.128094f, -0.97158f, 0.86393f, 0.270606f, -0.468986f, -0.256605f, 0.47215f, -0.273117f, -0.590343f, -0.826529f, -0.725381f, -0.194821f, -0.259661f, -0.0949207f, -0.180302f, 0.0446834f, -0.222133f, -0.40393f, 0.295772f, -0.92949f, 0.580079f, -0.169856f, 0.330311f, 0.0173551f, -0.635823f, 0.475942f, 0.907175f, 0.242777f, -0.512208f, 0.362463f, 0.0496289f, 0.65171f, 0.990057f, 0.690733f, -0.469013f, -0.101311f, -0.68372f, -0.157841f, -0.677711f, -0.708224f, -0.659437f, -0.407607f, 0.677033f, 0.89032f, 0.228307f, -0.749514f, 0.772958f, 0.054701f, 0.551705f, 0.917052f, -0.895022f, -0.702397f, 0.484142f, 0.108648f, 0.833347f, 0.478872f, -0.984112f, 0.387176f, -0.73299f, 0.7526f, 0.443312f, -0.0987856f, 0.125415f, 0.10876f, -0.498108f, 0.43209f, 0.344609f, 0.928941f, -0.130732f, -0.0569167f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.28735f, 1.91315f, 2.51734f, 0.375841f, 0.637563f, 2.653f, 2.72959f, 1.17389f, -2.12119f, 2.91417f, -2.24246f, 0.0497045f, -0.127107f, -0.144473f, -0.393284f, -2.02346f, -0.239178f, -0.246508f, 1.29277f, 1.32963f, 0.117521f, 0.0665713f, 1.09438f, -1.31426f, 2.52594f, -0.969211f, 0.515478f, -1.60926f, 0.135211f, 0.786415f, -1.14382f, -0.739102f, -1.01731f, 0.281615f, 2.36311f, 1.93872f, -0.150491f, 3.45217f, 2.28219f, 1.18282f, -2.25086f, 3.05468f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_SAME.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_SAME.example.cpp deleted file mode 100644 index b8a15ed54..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_SAME.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: conv_3_h3_w2_SAME.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.869931f, 0.644628f, -0.918393f, 0.153672f, 0.868562f, -0.358177f, -0.134931f, -0.247565f, 0.22174f, -0.259157f, -0.284296f, -0.538065f, 0.765559f, 0.41986f, -0.556241f, 0.658494f, 0.214355f, -0.850169f, -0.252893f, -0.478935f, 0.530526f, -0.0700663f, -0.988729f, -0.303061f, 0.150845f, 0.829915f, 0.476349f, 0.406537f, -0.355343f, 0.757145f, -0.356362f, 0.800482f, -0.713861f, 0.210483f, -0.634303f, 0.718236f, -0.752038f, 0.457547f, -0.550769f, -0.551178f, 0.446766f, -0.227462f, 0.216348f, -0.852806f, -0.351486f, 0.55906f, -0.668493f, -0.303493f, -0.363763f, -0.162837f, 0.0701012f, 0.756097f, -0.142269f, 0.329724f, -0.656317f, -0.998086f, -0.652949f, -0.40316f, -0.893682f, 0.432744f, 0.612362f, -0.869588f, -0.71327f, -0.398092f, -0.0423559f, 0.436576f, -0.925272f, 0.176549f, 0.822904f, 0.096833f, -0.296802f, -0.427195f, 0.031654f, -0.254479f, 0.244905f, 0.0948254f, 0.643769f, -0.90391f, 0.352665f, -0.901179f, 0.266159f, -0.968068f, -0.615401f, -0.388975f, 0.939052f, -0.116289f, 0.107523f, -0.0582711f, 0.435172f, 0.334675f, 0.459711f, 0.717436f, 0.496627f, -0.680175f, -0.415066f, 0.339848f, 0.506004f, -0.337808f, -0.107218f, -0.172496f, 0.870638f, 0.931872f, -0.953884f, 0.903042f, 0.760078f, 0.209727f, -0.285384f, -0.45514f, 0.113194f, 0.0756611f, 0.0924435f, -0.472863f, 0.960609f, -0.160385f, -0.839445f, 0.457097f, 0.163348f, 0.344867f, -0.131619f, 0.688715f, -0.540827f, 0.571259f, -0.95587f, 0.506164f, -0.155839f, 0.0789621f, 0.756772f, -0.662069f, 0.242908f, 0.460821f, 0.177872f, -0.289839f, -0.640603f, 0.702598f, -0.506406f, -0.568262f, -0.0713716f, 0.413792f, 0.159673f, -0.305208f, 0.133816f, -0.160254f, 0.787323f, -0.753244f, 0.600721f, 0.263186f, -0.162387f, 0.477962f, -0.702951f, -0.731036f, -0.939481f, -0.524519f, 0.934072f, -0.511637f, -0.503499f, 0.106236f, -0.323684f, 0.534444f, -0.843745f, 0.364171f, 0.0370358f, -0.168801f, -0.404559f, -0.814178f, 0.91745f, -0.334276f, 0.66925f, -0.801201f, 0.156511f, -0.427949f, 0.379153f, 0.818597f, -0.649902f, 0.427087f, -0.586015f, -0.559789f, -0.833923f, 0.0892409f, -0.621251f, 0.213826f, 0.465509f, 0.4704f, 0.380261f, 0.413067f, 0.180822f, 0.172866f, 0.59614f, 0.825575f, 0.662916f, -0.704381f, -0.297631f, 0.697778f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.27853f, 1.74987f, -0.876718f, 0.989692f, 0.298548f, 0.522103f, -0.536896f, -0.179382f, -0.966914f, 1.33708f, 1.37042f, -0.495494f, 1.43859f, -1.548f, -0.430026f, -0.662793f, -0.0867897f, -0.900658f, -0.524396f, 0.255731f, -0.779081f, 0.12666f, 0.915651f, -0.444765f, -0.186842f, -1.87308f, 1.21135f, -0.385009f, 1.72032f, -1.56036f, -1.23059f, 1.23694f, 0.00200015f, 0.359522f, 1.60084f, 0.434006f, -0.282945f, 2.37292f, -1.28653f, 0.0847837f, -0.352093f, -2.39659f, 0.149246f, 0.920351f, -1.34346f, 0.952311f, -0.35811f, 0.403449f, 0.484796f, -1.19989f, -0.684298f, -1.41301f, 0.103177f, -0.307039f, 1.17741f, 2.58936f, -2.76237f, -1.21565f, -1.09619f, 1.17432f, 0.512143f, 0.771379f, 0.399879f, -0.0533093f, 0.290864f, 0.95563f, 1.16328f, 1.80768f, -1.52564f, -0.126476f, -0.185224f, -0.114779f, 1.2248f, 0.237127f, -0.213297f, -0.619941f, 0.497944f, -1.68688f, 1.59314f, -0.127337f, 0.111419f, 1.13719f, 1.68537f, -0.479644f, 1.18608f, -2.52744f, 1.34136f, 0.548297f, -2.0838f, 2.64585f, -0.993354f, 0.128238f, 1.26092f, 0.318668f, 0.893795f, -0.0600559f, -0.629126f, -0.949229f, 2.25828f, -1.961f, 0.00589599f, -0.187854f, -1.02403f, 0.396121f, 1.3704f, 3.99355f, 0.434221f, 0.274464f, -0.562438f, -0.914871f, 0.539129f, -0.928687f, 0.834954f, 0.844178f, -0.566053f, -0.957341f, 0.933336f, 1.13613f, -1.22109f, 1.4649f, -0.414666f, -0.452821f, -0.706006f, -1.72657f, -0.726574f, -0.0979362f, -0.478669f, 1.78703f, -0.639288f, 1.48565f, -0.179904f, 1.01003f, -0.317118f, -0.675387f, 1.90969f, -1.38343f, 0.697255f, -0.292255f, 1.81634f, 0.717801f, 0.862479f, -0.407478f, -0.343106f, -0.0353232f, -0.481893f, -0.135565f, -2.95941f, 0.247846f, 2.67757f, -2.23999f, -0.519673f, 0.254447f, 0.415283f, -1.01065f, 0.507911f, 0.979926f, -0.184304f, -0.000950437f, -0.734348f, -0.196685f, -0.713241f, 0.594972f, 0.0845042f, 2.48496f, 0.385019f, -0.201145f, 0.533332f, -0.904872f, -0.333518f, -0.581063f, -2.07065f, 0.118687f, -1.86708f, -0.601987f, 0.432037f, 1.73923f, 0.590007f, 0.419788f, 0.314198f, 2.12817f, 0.570793f, -1.15998f, -0.348587f, -1.10231f, -2.13091f, 0.134467f, -0.460382f, 0.138338f, 3.455f, 0.679068f, -0.190282f, -0.0307461f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.295335f, -0.00387601f, -0.552251f, 0.166084f, -0.28482f, -0.152143f, -0.719885f, -0.869386f, -0.745598f, 0.823947f, 0.473183f, -0.331337f, 0.187631f, 0.0426571f, -0.826897f, -0.755085f, -0.472453f, -0.0233656f, 0.0483436f, 0.933418f, -0.961974f, 0.0125783f, 0.219742f, 0.342604f, -0.15166f, 0.0934905f, 0.783221f, 0.129664f, 0.838844f, -0.271388f, 0.924519f, 0.342843f, 0.274418f, 0.350817f, 0.841638f, -0.543993f, -0.00283395f, -0.128467f, -0.682943f, -0.319117f, 0.84634f, 0.283003f, 0.32865f, 0.0293755f, -0.0335696f, 0.591266f, -0.0743476f, -0.741271f, 0.462056f, -0.583625f, -0.590183f, 0.6234f, 0.535269f, -0.670818f, -0.955642f, -0.770173f, 0.479986f, 0.664377f, 0.399445f, -0.968874f, -0.276263f, -0.901951f, 0.544104f, -0.958981f, 0.482658f, -0.807284f, 0.305369f, -0.947818f, 0.827498f, -0.382887f, -0.805741f, -0.796678f, -0.299804f, -0.229828f, 0.818783f, -0.103055f, -0.45568f, -0.227827f, 0.543743f, -0.96073f, 0.946747f, -0.857182f, -0.96426f, -0.292411f, -0.715614f, 0.765278f, -0.475043f, -0.590142f, -0.238507f, 0.673002f, -0.473357f, -0.319626f, 0.936014f, 0.486607f, 0.580844f, 0.425352f, -0.800994f, 0.290763f, -0.494953f, -0.441162f, 0.718677f, -0.828427f, 0.96965f, 7.53637e-05f, -0.699973f, -0.526886f, -0.352682f, 0.799466f, 0.332789f, 0.723389f, 0.407659f, -0.934084f, -0.284705f, 0.961484f, -0.700395f, -0.985808f, -0.595342f, -0.691721f, 0.49448f, -0.0842649f, 0.0390966f, 0.298938f, -0.128094f, -0.97158f, 0.86393f, 0.270606f, -0.468986f, -0.256605f, 0.47215f, -0.273117f, -0.590343f, -0.826529f, -0.725381f, -0.194821f, -0.259661f, -0.0949207f, -0.180302f, 0.0446834f, -0.222133f, -0.40393f, 0.295772f, -0.92949f, 0.580079f, -0.169856f, 0.330311f, 0.0173551f, -0.635823f, 0.475942f, 0.907175f, 0.242777f, -0.512208f, 0.362463f, 0.0496289f, 0.65171f, 0.990057f, 0.690733f, -0.469013f, -0.101311f, -0.68372f, -0.157841f, -0.677711f, -0.708224f, -0.659437f, -0.407607f, 0.677033f, 0.89032f, 0.228307f, -0.749514f, 0.772958f, 0.054701f, 0.551705f, 0.917052f, -0.895022f, -0.702397f, 0.484142f, 0.108648f, 0.833347f, 0.478872f, -0.984112f, 0.387176f, -0.73299f, 0.7526f, 0.443312f, -0.0987856f, 0.125415f, 0.10876f, -0.498108f, 0.43209f, 0.344609f, 0.928941f, -0.130732f, -0.0569167f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.78574f, 0.0700466f, -0.110245f, 0.0141003f, -0.621007f, -0.979104f, 1.24104f, 0.580398f, -0.512997f, 0.900559f, -0.683229f, -1.0162f, 1.0089f, -0.0752488f, 0.110969f, 0.270558f, 0.756819f, -0.10753f, -0.371484f, 0.149005f, 0.0973829f, 0.155766f, -0.476502f, 0.259481f, 1.06709f, -1.16534f, 1.52694f, -0.797245f, 0.802736f, -0.997109f, 2.2661f, -1.45548f, 2.15506f, -1.33682f, 1.15225f, -3.09324f, 0.943457f, 0.885211f, 0.987944f, -0.345875f, -0.114708f, 1.7107f, 0.104745f, 0.828324f, -2.49964f, -0.453742f, -0.288829f, -0.0948694f, -0.489415f, 1.74889f, -0.378257f, -2.10237f, 0.613022f, -2.5225f, -0.746785f, 3.63816f, -1.9287f, 0.774279f, -0.613917f, -0.650011f, 1.03753f, -0.177923f, 0.891815f, -1.00373f, 1.83859f, -1.59239f, -0.0662623f, 0.218806f, -1.088f, 0.280837f, 0.902901f, -1.90127f, 3.04734f, -1.57302f, 1.10881f, -0.980369f, -3.85305f, -0.955859f, 1.64909f, 2.33573f, 0.31144f, -0.594375f, 0.325747f, -0.952566f, -0.613449f, 2.85073f, 1.94692f, 1.12977f, 1.1351f, -0.449652f, 0.118765f, -0.199547f, 2.873f, 1.35182f, -1.85457f, 1.22364f, 1.38049f, 2.38342f, 0.882321f, 1.03795f, -0.321571f, -2.60202f, -1.6372f, 1.09302f, 0.461768f, 1.8485f, -0.158928f, 4.28871f, -0.437375f, -1.5794f, 1.59869f, 0.0811864f, 0.912054f, 0.452176f, 2.01812f, 2.62907f, 1.50304f, -0.840276f, -0.455854f, -0.224913f, 0.609824f, -0.11105f, 3.35635f, 2.02386f, 1.4687f, -0.708365f, -0.508992f, -3.02602f, -0.75725f, 1.85277f, 2.92817f, -0.172997f, -1.13279f, -0.355636f, -0.337669f, -0.588752f, 2.05759f, 1.0651f, 0.884758f, -0.0712112f, 3.81319f, 0.771629f, 0.949634f, 0.0838967f, -2.19264f, 0.114521f, 0.543556f, -1.63197f, -0.267442f, 1.15701f, -2.37862f, 2.57646f, 0.531208f, 0.9499f, -0.231441f, 1.51461f, 1.58888f, 0.895931f, -0.753084f, 0.545251f, 0.746903f, 0.012994f, -0.790398f, -1.1055f, 1.77789f, 0.430923f, 0.818241f, -0.731412f, 0.979546f, -2.48707f, -1.53658f, -1.66798f, -1.04585f, -0.667911f, 1.00299f, -2.20339f, 0.137826f, -2.31281f, 0.755535f, 0.495396f, 0.549629f, 0.713128f, 0.751369f, 0.283996f, -0.814532f, 1.4866f, 1.12105f, 0.927998f, 0.517938f, -0.612661f, -1.47756f, -1.42422f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_VALID.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_VALID.example.cpp deleted file mode 100644 index 627bb6d0b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_3_h3_w2_VALID.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: conv_3_h3_w2_VALID.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.869931f, 0.644628f, -0.918393f, 0.153672f, 0.868562f, -0.358177f, -0.134931f, -0.247565f, 0.22174f, -0.259157f, -0.284296f, -0.538065f, 0.765559f, 0.41986f, -0.556241f, 0.658494f, 0.214355f, -0.850169f, -0.252893f, -0.478935f, 0.530526f, -0.0700663f, -0.988729f, -0.303061f, 0.150845f, 0.829915f, 0.476349f, 0.406537f, -0.355343f, 0.757145f, -0.356362f, 0.800482f, -0.713861f, 0.210483f, -0.634303f, 0.718236f, -0.752038f, 0.457547f, -0.550769f, -0.551178f, 0.446766f, -0.227462f, 0.216348f, -0.852806f, -0.351486f, 0.55906f, -0.668493f, -0.303493f, -0.363763f, -0.162837f, 0.0701012f, 0.756097f, -0.142269f, 0.329724f, -0.656317f, -0.998086f, -0.652949f, -0.40316f, -0.893682f, 0.432744f, 0.612362f, -0.869588f, -0.71327f, -0.398092f, -0.0423559f, 0.436576f, -0.925272f, 0.176549f, 0.822904f, 0.096833f, -0.296802f, -0.427195f, 0.031654f, -0.254479f, 0.244905f, 0.0948254f, 0.643769f, -0.90391f, 0.352665f, -0.901179f, 0.266159f, -0.968068f, -0.615401f, -0.388975f, 0.939052f, -0.116289f, 0.107523f, -0.0582711f, 0.435172f, 0.334675f, 0.459711f, 0.717436f, 0.496627f, -0.680175f, -0.415066f, 0.339848f, 0.506004f, -0.337808f, -0.107218f, -0.172496f, 0.870638f, 0.931872f, -0.953884f, 0.903042f, 0.760078f, 0.209727f, -0.285384f, -0.45514f, 0.113194f, 0.0756611f, 0.0924435f, -0.472863f, 0.960609f, -0.160385f, -0.839445f, 0.457097f, 0.163348f, 0.344867f, -0.131619f, 0.688715f, -0.540827f, 0.571259f, -0.95587f, 0.506164f, -0.155839f, 0.0789621f, 0.756772f, -0.662069f, 0.242908f, 0.460821f, 0.177872f, -0.289839f, -0.640603f, 0.702598f, -0.506406f, -0.568262f, -0.0713716f, 0.413792f, 0.159673f, -0.305208f, 0.133816f, -0.160254f, 0.787323f, -0.753244f, 0.600721f, 0.263186f, -0.162387f, 0.477962f, -0.702951f, -0.731036f, -0.939481f, -0.524519f, 0.934072f, -0.511637f, -0.503499f, 0.106236f, -0.323684f, 0.534444f, -0.843745f, 0.364171f, 0.0370358f, -0.168801f, -0.404559f, -0.814178f, 0.91745f, -0.334276f, 0.66925f, -0.801201f, 0.156511f, -0.427949f, 0.379153f, 0.818597f, -0.649902f, 0.427087f, -0.586015f, -0.559789f, -0.833923f, 0.0892409f, -0.621251f, 0.213826f, 0.465509f, 0.4704f, 0.380261f, 0.413067f, 0.180822f, 0.172866f, 0.59614f, 0.825575f, 0.662916f, -0.704381f, -0.297631f, 0.697778f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.186842f, -1.87308f, 1.21135f, -0.385009f, 1.72032f, -1.56036f, -1.23059f, 1.23694f, 0.00200015f, 0.359522f, 1.60084f, 0.434006f, -0.282945f, 2.37292f, -1.28653f, 0.0847837f, -0.352093f, -2.39659f, 0.149246f, 0.920351f, -1.34346f, 0.484796f, -1.19989f, -0.684298f, -1.41301f, 0.103177f, -0.307039f, 1.17741f, 2.58936f, -2.76237f, -1.21565f, -1.09619f, 1.17432f, 0.512143f, 0.771379f, 0.399879f, -0.0533093f, 0.290864f, 0.95563f, 1.16328f, 1.80768f, -1.52564f, 1.2248f, 0.237127f, -0.213297f, -0.619941f, 0.497944f, -1.68688f, 1.59314f, -0.127337f, 0.111419f, 1.13719f, 1.68537f, -0.479644f, 1.18608f, -2.52744f, 1.34136f, 0.548297f, -2.0838f, 2.64585f, -0.993354f, 0.128238f, 1.26092f, -0.629126f, -0.949229f, 2.25828f, -1.961f, 0.00589599f, -0.187854f, -1.02403f, 0.396121f, 1.3704f, 3.99355f, 0.434221f, 0.274464f, -0.562438f, -0.914871f, 0.539129f, -0.928687f, 0.834954f, 0.844178f, -0.566053f, -0.957341f, 0.933336f, -0.414666f, -0.452821f, -0.706006f, -1.72657f, -0.726574f, -0.0979362f, -0.478669f, 1.78703f, -0.639288f, 1.48565f, -0.179904f, 1.01003f, -0.317118f, -0.675387f, 1.90969f, -1.38343f, 0.697255f, -0.292255f, 1.81634f, 0.717801f, 0.862479f, -0.481893f, -0.135565f, -2.95941f, 0.247846f, 2.67757f, -2.23999f, -0.519673f, 0.254447f, 0.415283f, -1.01065f, 0.507911f, 0.979926f, -0.184304f, -0.000950437f, -0.734348f, -0.196685f, -0.713241f, 0.594972f, 0.0845044f, 2.48496f, 0.385019f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.295335f, -0.00387601f, -0.552251f, 0.166084f, -0.28482f, -0.152143f, -0.719885f, -0.869386f, -0.745598f, 0.823947f, 0.473183f, -0.331337f, 0.187631f, 0.0426571f, -0.826897f, -0.755085f, -0.472453f, -0.0233656f, 0.0483436f, 0.933418f, -0.961974f, 0.0125783f, 0.219742f, 0.342604f, -0.15166f, 0.0934905f, 0.783221f, 0.129664f, 0.838844f, -0.271388f, 0.924519f, 0.342843f, 0.274418f, 0.350817f, 0.841638f, -0.543993f, -0.00283395f, -0.128467f, -0.682943f, -0.319117f, 0.84634f, 0.283003f, 0.32865f, 0.0293755f, -0.0335696f, 0.591266f, -0.0743476f, -0.741271f, 0.462056f, -0.583625f, -0.590183f, 0.6234f, 0.535269f, -0.670818f, -0.955642f, -0.770173f, 0.479986f, 0.664377f, 0.399445f, -0.968874f, -0.276263f, -0.901951f, 0.544104f, -0.958981f, 0.482658f, -0.807284f, 0.305369f, -0.947818f, 0.827498f, -0.382887f, -0.805741f, -0.796678f, -0.299804f, -0.229828f, 0.818783f, -0.103055f, -0.45568f, -0.227827f, 0.543743f, -0.96073f, 0.946747f, -0.857182f, -0.96426f, -0.292411f, -0.715614f, 0.765278f, -0.475043f, -0.590142f, -0.238507f, 0.673002f, -0.473357f, -0.319626f, 0.936014f, 0.486607f, 0.580844f, 0.425352f, -0.800994f, 0.290763f, -0.494953f, -0.441162f, 0.718677f, -0.828427f, 0.96965f, 7.53637e-05f, -0.699973f, -0.526886f, -0.352682f, 0.799466f, 0.332789f, 0.723389f, 0.407659f, -0.934084f, -0.284705f, 0.961484f, -0.700395f, -0.985808f, -0.595342f, -0.691721f, 0.49448f, -0.0842649f, 0.0390966f, 0.298938f, -0.128094f, -0.97158f, 0.86393f, 0.270606f, -0.468986f, -0.256605f, 0.47215f, -0.273117f, -0.590343f, -0.826529f, -0.725381f, -0.194821f, -0.259661f, -0.0949207f, -0.180302f, 0.0446834f, -0.222133f, -0.40393f, 0.295772f, -0.92949f, 0.580079f, -0.169856f, 0.330311f, 0.0173551f, -0.635823f, 0.475942f, 0.907175f, 0.242777f, -0.512208f, 0.362463f, 0.0496289f, 0.65171f, 0.990057f, 0.690733f, -0.469013f, -0.101311f, -0.68372f, -0.157841f, -0.677711f, -0.708224f, -0.659437f, -0.407607f, 0.677033f, 0.89032f, 0.228307f, -0.749514f, 0.772958f, 0.054701f, 0.551705f, 0.917052f, -0.895022f, -0.702397f, 0.484142f, 0.108648f, 0.833347f, 0.478872f, -0.984112f, 0.387176f, -0.73299f, 0.7526f, 0.443312f, -0.0987856f, 0.125415f, 0.10876f, -0.498108f, 0.43209f, 0.344609f, 0.928941f, -0.130732f, -0.0569167f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.06709f, -1.16534f, 1.52694f, -0.797245f, 0.802736f, -0.997109f, 2.2661f, -1.45548f, 2.15506f, -1.33682f, 1.15225f, -3.09324f, 0.943457f, 0.885211f, 0.987944f, -0.345875f, -0.114708f, 1.7107f, 0.104745f, 0.828324f, -2.49964f, -0.489415f, 1.74889f, -0.378257f, -2.10237f, 0.613022f, -2.5225f, -0.746785f, 3.63816f, -1.9287f, 0.774279f, -0.613917f, -0.650011f, 1.03753f, -0.177923f, 0.891815f, -1.00373f, 1.83859f, -1.59239f, -0.0662623f, 0.218806f, -1.088f, 3.04734f, -1.57302f, 1.10881f, -0.980369f, -3.85305f, -0.955859f, 1.64909f, 2.33573f, 0.31144f, -0.594375f, 0.325747f, -0.952566f, -0.613449f, 2.85073f, 1.94692f, 1.12977f, 1.1351f, -0.449652f, 0.118765f, -0.199547f, 2.873f, 1.38049f, 2.38342f, 0.882321f, 1.03795f, -0.321571f, -2.60202f, -1.6372f, 1.09302f, 0.461768f, 1.8485f, -0.158928f, 4.28871f, -0.437375f, -1.5794f, 1.59869f, 0.0811864f, 0.912054f, 0.452176f, 2.01812f, 2.62907f, 1.50304f, 0.609824f, -0.11105f, 3.35635f, 2.02386f, 1.4687f, -0.708365f, -0.508992f, -3.02602f, -0.75725f, 1.85277f, 2.92817f, -0.172997f, -1.13279f, -0.355636f, -0.337669f, -0.588752f, 2.05759f, 1.0651f, 0.884758f, -0.0712112f, 3.81319f, -2.19264f, 0.114521f, 0.543556f, -1.63197f, -0.267442f, 1.15701f, -2.37862f, 2.57646f, 0.531208f, 0.9499f, -0.231441f, 1.51461f, 1.58888f, 0.895931f, -0.753084f, 0.545251f, 0.746904f, 0.0129939f, -0.790398f, -1.1055f, 1.77789f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float.example.cpp deleted file mode 100644 index f3160497e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.875f, 0.875f, 0.875f, 0.875f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_2.example.cpp deleted file mode 100644 index 78d904abd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, 0, 0, 35, 112, 157, 0, 0, 34, 61, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels.example.cpp deleted file mode 100644 index f7c110f2d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_channels.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {99.0f, 99.0f, 99.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {297.0f, 594.0f, 891.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels_weights_as_inputs.example.cpp deleted file mode 100644 index 44b445352..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_channels_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_channels_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {99.0f, 99.0f, 99.0f}}, {1, {1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f}}, {2, {0.0f, 0.0f, 0.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {297.0f, 594.0f, 891.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_large.example.cpp deleted file mode 100644 index 9650e5462..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {30.0f, 36.0f, 42.0f, 66.0f, 81.0f, 96.0f, 102.0f, 126.0f, 150.0f, 138.0f, 171.0f, 204.0f, 174.0f, 216.0f, 258.0f, 210.0f, 261.0f, 312.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_large_weights_as_inputs.example.cpp deleted file mode 100644 index 94441a311..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f}}, {1, {1.0f, 4.0f, 7.0f, 2.0f, 5.0f, 8.0f, 3.0f, 6.0f, 9.0f}}, {2, {0.0f, 0.0f, 0.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {30.0f, 36.0f, 42.0f, 66.0f, 81.0f, 96.0f, 102.0f, 126.0f, 150.0f, 138.0f, 171.0f, 204.0f, 174.0f, 216.0f, 258.0f, 210.0f, 261.0f, 312.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_float_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_float_weights_as_inputs.example.cpp deleted file mode 100644 index 55ec4643a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_float_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_float_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f}}, {1, {0.25f, 0.25f, 0.25f, 0.25f}}, {2, {0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.875f, 0.875f, 0.875f, 0.875f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8.example.cpp deleted file mode 100644 index 34ab5c18f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 8, 8, 8, 4, 8, 8, 8, 8}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {15, 15, 15, 15}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_2.example.cpp deleted file mode 100644 index 319db67e8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {133, 131, 129, 125, 123, 121, 135, 133, 131, 123, 121, 119, 137, 135, 133, 121, 119, 117}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {157, 103, 167, 93}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels.example.cpp deleted file mode 100644 index d65797236..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_channels.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {10, 10, 10}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {15, 38, 60}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels_weights_as_inputs.example.cpp deleted file mode 100644 index bea22100b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_channels_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_channels_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {0, 0, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {10, 10, 10}}, {1, {1, 2, 3, 4, 5, 6, 7, 8, 9}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {15, 38, 60}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large.example.cpp deleted file mode 100644 index cd778a502..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 9, 11, 17, 21, 24, 26, 32, 38, 35, 43, 51, 44, 54, 65, 53, 66, 78}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large_weights_as_inputs.example.cpp deleted file mode 100644 index 12c0ef48a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {0, 0, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}}, {1, {1, 4, 7, 2, 5, 8, 3, 6, 9}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 9, 11, 17, 21, 24, 26, 32, 38, 35, 43, 51, 44, 54, 65, 53, 66, 78}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow.example.cpp deleted file mode 100644 index a9aa2b2f6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_overflow.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {75, 90, 105, 165, 203, 240, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow_weights_as_inputs.example.cpp deleted file mode 100644 index 78c7308d5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_overflow_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_overflow_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {0, 0, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}}, {1, {10, 40, 70, 20, 50, 80, 30, 60, 90}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {75, 90, 105, 165, 203, 240, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_weights_as_inputs.example.cpp deleted file mode 100644 index b7de7fa51..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/conv_quant8_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: conv_quant8_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {4}}}, - // int -> QUANT8_ASYMM map - {{0, {8, 8, 8, 8, 4, 8, 8, 8, 8}}, {1, {2, 2, 2, 2}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {15, 15, 15, 15}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_1.example.cpp deleted file mode 100644 index 57b205735..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depth_to_space_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_2.example.cpp deleted file mode 100644 index 2b3e2519e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depth_to_space_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 5.0f, 6.0f, 3.0f, 4.0f, 7.0f, 8.0f, 9.0f, 10.0f, 13.0f, 14.0f, 11.0f, 12.0f, 15.0f, 16.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_3.example.cpp deleted file mode 100644 index 0124212a6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depth_to_space_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 20, 11, 21, 14, 24, 15, 25, 12, 22, 13, 23, 16, 26, 17, 27, 18, 28, 19, 29, 112, 212, 113, 213, 110, 210, 111, 211, 114, 214, 115, 215}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 20, 11, 21, 12, 22, 13, 23, 14, 24, 15, 25, 16, 26, 17, 27, 18, 28, 19, 29, 110, 210, 111, 211, 112, 212, 113, 213, 114, 214, 115, 215}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_1.example.cpp deleted file mode 100644 index a292e9677..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depth_to_space_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 252, 253, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_2.example.cpp deleted file mode 100644 index 849243c0a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depth_to_space_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depth_to_space_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 4, 5, 2, 3, 6, 7, 248, 249, 252, 253, 250, 251, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 248, 249, 250, 251, 252, 253, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv.example.cpp deleted file mode 100644 index 10f58bfd8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: depthwise_conv.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.869931f, 0.644628f, -0.918393f, 0.153672f, 0.868562f, -0.358177f, -0.134931f, -0.247565f, 0.22174f, -0.259157f, -0.284296f, -0.538065f, 0.765559f, 0.41986f, -0.556241f, 0.658494f, 0.214355f, -0.850169f, -0.252893f, -0.478935f, 0.530526f, -0.0700663f, -0.988729f, -0.303061f, 0.150845f, 0.829915f, 0.476349f, 0.406537f, -0.355343f, 0.757145f, -0.356362f, 0.800482f, -0.713861f, 0.210483f, -0.634303f, 0.718236f, -0.752038f, 0.457547f, -0.550769f, -0.551178f, 0.446766f, -0.227462f, 0.216348f, -0.852806f, -0.351486f, 0.55906f, -0.668493f, -0.303493f, -0.363763f, -0.162837f, 0.0701012f, 0.756097f, -0.142269f, 0.329724f, -0.656317f, -0.998086f, -0.652949f, -0.40316f, -0.893682f, 0.432744f, 0.612362f, -0.869588f, -0.71327f, -0.398092f, -0.0423559f, 0.436576f, -0.925272f, 0.176549f, 0.822904f, 0.096833f, -0.296802f, -0.427195f, 0.031654f, -0.254479f, 0.244905f, 0.0948254f, 0.643769f, -0.90391f, 0.352665f, -0.901179f, 0.266159f, -0.968068f, -0.615401f, -0.388975f, 0.939052f, -0.116289f, 0.107523f, -0.0582711f, 0.435172f, 0.334675f, 0.459711f, 0.717436f, 0.496627f, -0.680175f, -0.415066f, 0.339848f, 0.506004f, -0.337808f, -0.107218f, -0.172496f, 0.870638f, 0.931872f, -0.953884f, 0.903042f, 0.760078f, 0.209727f, -0.285384f, -0.45514f, 0.113194f, 0.0756611f, 0.0924435f, -0.472863f, 0.960609f, -0.160385f, -0.839445f, 0.457097f, 0.163348f, 0.344867f, -0.131619f, 0.688715f, -0.540827f, 0.571259f, -0.95587f, 0.506164f, -0.155839f, 0.0789621f, 0.756772f, -0.662069f, 0.242908f, 0.460821f, 0.177872f, -0.289839f, -0.640603f, 0.702598f, -0.506406f, -0.568262f, -0.0713716f, 0.413792f, 0.159673f, -0.305208f, 0.133816f, -0.160254f, 0.787323f, -0.753244f, 0.600721f, 0.263186f, -0.162387f, 0.477962f, -0.702951f, -0.731036f, -0.939481f, -0.524519f, 0.934072f, -0.511637f, -0.503499f, 0.106236f, -0.323684f, 0.534444f, -0.843745f, 0.364171f, 0.0370358f, -0.168801f, -0.404559f, -0.814178f, 0.91745f, -0.334276f, 0.66925f, -0.801201f, 0.156511f, -0.427949f, 0.379153f, 0.818597f, -0.649902f, 0.427087f, -0.586015f, -0.559789f, -0.833923f, 0.0892409f, -0.621251f, 0.213826f, 0.465509f, 0.4704f, 0.380261f, 0.413067f, 0.180822f, 0.172866f, 0.59614f, 0.825575f, 0.662916f, -0.704381f, -0.297631f, 0.697778f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.840539f, -0.301347f, 0.754947f, -0.14848f, -0.40603f, 0.294432f, 0.130372f, 0.11573f, -0.182277f, 0.2504f, 0.132901f, 0.442306f, -0.739693f, -0.196274f, 0.457246f, -0.636246f, -0.100205f, 0.698864f, 0.244348f, 0.22389f, -0.436108f, 0.067699f, 0.462205f, 0.249125f, -0.145748f, -0.387964f, -0.391573f, -0.392801f, 0.166114f, -0.622396f, 0.344322f, -0.374205f, 0.586815f, -0.203372f, 0.29652f, -0.590411f, 0.726629f, -0.213891f, 0.452749f, 0.532555f, -0.208851f, 0.186981f, -0.209039f, 0.398664f, 0.288932f, -0.540171f, 0.312503f, 0.24948f, 0.351473f, 0.076122f, -0.0576253f, -0.73055f, 0.0665069f, -0.271043f, 0.634142f, 0.466579f, 0.536743f, 0.389538f, 0.417773f, -0.355728f, -0.591672f, 0.40651f, 0.586329f, 0.384641f, 0.0198003f, -0.358878f, 0.894009f, -0.0825318f, -0.676451f, -0.0935613f, 0.138747f, 0.351167f, -0.0305845f, 0.118962f, -0.201319f, -0.0916215f, -0.300945f, 0.743041f, -0.34075f, 0.421278f, -0.218791f, 0.935359f, 0.287684f, 0.319749f, -0.907324f, 0.054362f, -0.0883874f, 0.0563023f, -0.203432f, -0.275113f, -0.444178f, -0.335382f, -0.408242f, 0.657194f, 0.194033f, -0.279365f, -0.488907f, 0.157917f, 0.0881365f, 0.166668f, -0.407001f, -0.766027f, 0.921655f, -0.422149f, -0.624807f, -0.202641f, 0.13341f, 0.374139f, -0.109369f, -0.0353696f, -0.0759913f, 0.456887f, -0.44906f, 0.131841f, 0.811082f, -0.213681f, -0.134277f, -0.333215f, 0.0615286f, -0.566144f, 0.522554f, -0.267049f, 0.785754f, -0.489062f, 0.0728509f, -0.0649092f, -0.731203f, 0.3095f, -0.199677f, -0.445251f, -0.0831503f, 0.238257f, 0.618959f, -0.328446f, 0.416281f, 0.549062f, 0.0333644f, -0.340149f, -0.154278f, 0.142677f, -0.110001f, 0.15484f, -0.368053f, 0.619189f, -0.580424f, -0.123033f, 0.133487f, -0.461813f, 0.328611f, 0.600933f, 0.907739f, 0.245199f, -0.767835f, 0.49435f, 0.235373f, -0.0873295f, 0.312748f, -0.249839f, 0.693584f, -0.351866f, -0.0173133f, 0.13876f, 0.39089f, 0.380607f, -0.754171f, 0.322982f, -0.312857f, 0.658611f, -0.151223f, 0.200055f, -0.311675f, -0.790939f, 0.303812f, -0.351079f, 0.566216f, 0.261687f, 0.68551f, -0.0862257f, 0.290419f, -0.175771f, -0.449781f, -0.2199f, -0.312586f, -0.399111f, -0.0845297f, -0.142101f, -0.575998f, -0.385935f, -0.544937f, 0.680582f, 0.139135f, -0.573594f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.295335f, -0.00387601f, -0.552251f, 0.166084f, -0.28482f, -0.152143f, -0.719885f, -0.869386f, -0.745598f, 0.823947f, 0.473183f, -0.331337f, 0.187631f, 0.0426571f, -0.826897f, -0.755085f, -0.472453f, -0.0233656f, 0.0483436f, 0.933418f, -0.961974f, 0.0125783f, 0.219742f, 0.342604f, -0.15166f, 0.0934905f, 0.783221f, 0.129664f, 0.838844f, -0.271388f, 0.924519f, 0.342843f, 0.274418f, 0.350817f, 0.841638f, -0.543993f, -0.00283395f, -0.128467f, -0.682943f, -0.319117f, 0.84634f, 0.283003f, 0.32865f, 0.0293755f, -0.0335696f, 0.591266f, -0.0743476f, -0.741271f, 0.462056f, -0.583625f, -0.590183f, 0.6234f, 0.535269f, -0.670818f, -0.955642f, -0.770173f, 0.479986f, 0.664377f, 0.399445f, -0.968874f, -0.276263f, -0.901951f, 0.544104f, -0.958981f, 0.482658f, -0.807284f, 0.305369f, -0.947818f, 0.827498f, -0.382887f, -0.805741f, -0.796678f, -0.299804f, -0.229828f, 0.818783f, -0.103055f, -0.45568f, -0.227827f, 0.543743f, -0.96073f, 0.946747f, -0.857182f, -0.96426f, -0.292411f, -0.715614f, 0.765278f, -0.475043f, -0.590142f, -0.238507f, 0.673002f, -0.473357f, -0.319626f, 0.936014f, 0.486607f, 0.580844f, 0.425352f, -0.800994f, 0.290763f, -0.494953f, -0.441162f, 0.718677f, -0.828427f, 0.96965f, 7.53637e-05f, -0.699973f, -0.526886f, -0.352682f, 0.799466f, 0.332789f, 0.723389f, 0.407659f, -0.934084f, -0.284705f, 0.961484f, -0.700395f, -0.985808f, -0.595342f, -0.691721f, 0.49448f, -0.0842649f, 0.0390966f, 0.298938f, -0.128094f, -0.97158f, 0.86393f, 0.270606f, -0.468986f, -0.256605f, 0.47215f, -0.273117f, -0.590343f, -0.826529f, -0.725381f, -0.194821f, -0.259661f, -0.0949207f, -0.180302f, 0.0446834f, -0.222133f, -0.40393f, 0.295772f, -0.92949f, 0.580079f, -0.169856f, 0.330311f, 0.0173551f, -0.635823f, 0.475942f, 0.907175f, 0.242777f, -0.512208f, 0.362463f, 0.0496289f, 0.65171f, 0.990057f, 0.690733f, -0.469013f, -0.101311f, -0.68372f, -0.157841f, -0.677711f, -0.708224f, -0.659437f, -0.407607f, 0.677033f, 0.89032f, 0.228307f, -0.749514f, 0.772958f, 0.054701f, 0.551705f, 0.917052f, -0.895022f, -0.702397f, 0.484142f, 0.108648f, 0.833347f, 0.478872f, -0.984112f, 0.387176f, -0.73299f, 0.7526f, 0.443312f, -0.0987856f, 0.125415f, 0.10876f, -0.498108f, 0.43209f, 0.344609f, 0.928941f, -0.130732f, -0.0569167f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.285357f, 0.00181194f, 0.453967f, -0.160473f, 0.133146f, 0.125066f, 0.695562f, 0.406415f, 0.612903f, -0.796108f, -0.221201f, 0.272369f, -0.181291f, -0.0199411f, 0.679734f, 0.729573f, 0.22086f, 0.0192072f, -0.0467102f, -0.436349f, 0.790771f, -0.0121533f, -0.102724f, -0.281631f, 0.146536f, -0.0437044f, -0.643831f, -0.125283f, -0.392138f, 0.223089f, -0.893282f, -0.16027f, -0.22558f, -0.338964f, -0.393444f, 0.447179f, 0.0027382f, 0.0600548f, 0.5614f, 0.308335f, -0.395642f, -0.232637f, -0.317546f, -0.0137323f, 0.0275952f, -0.571289f, 0.0347555f, 0.609347f, -0.446445f, 0.27283f, 0.485148f, -0.602337f, -0.250224f, 0.551432f, 0.923353f, 0.360036f, -0.394563f, -0.64193f, -0.18673f, 0.796443f, 0.266929f, 0.421638f, -0.44727f, 0.926579f, -0.22563f, 0.663612f, -0.295051f, 0.44308f, -0.680228f, 0.36995f, 0.376663f, 0.654893f, 0.289675f, 0.107439f, -0.673064f, 0.0995729f, 0.213019f, 0.18728f, -0.525372f, 0.449116f, -0.778254f, 0.82822f, 0.450766f, 0.24037f, 0.691436f, -0.357748f, 0.3905f, 0.570203f, 0.111496f, -0.553228f, 0.457363f, 0.149417f, -0.769431f, -0.470166f, -0.271529f, -0.349652f, 0.773931f, -0.135924f, 0.406866f, 0.426256f, -0.335963f, 0.680992f, -0.936889f, -3.52306e-05f, 0.575398f, 0.509084f, 0.16487f, -0.657185f, -0.321545f, -0.338165f, -0.335108f, 0.902524f, 0.133092f, -0.790369f, 0.676731f, 0.46084f, 0.489389f, 0.66835f, -0.231156f, 0.0692682f, -0.0377757f, -0.139746f, 0.105297f, 0.938753f, -0.403865f, -0.222446f, 0.45314f, 0.119956f, -0.388121f, 0.26389f, 0.27597f, 0.679432f, 0.700873f, 0.0910737f, 0.213449f, 0.0917136f, 0.0842865f, -0.0367311f, 0.214628f, 0.188827f, -0.243133f, 0.898085f, -0.271172f, 0.139627f, -0.319151f, -0.00811307f, 0.522665f, -0.459861f, -0.424081f, -0.19957f, 0.494902f, -0.169442f, -0.0407964f, -0.629691f, -0.462826f, -0.567803f, 0.453167f, 0.0473601f, 0.562038f, 0.152508f, 0.316812f, 0.582181f, 0.637157f, 0.190546f, -0.556541f, -0.860239f, -0.106728f, 0.616123f, -0.746842f, -0.0255713f, -0.453518f, -0.886067f, 0.418399f, 0.577391f, -0.467784f, -0.05079f, -0.685036f, -0.462692f, 0.460047f, -0.318271f, 0.708224f, -0.351821f, -0.364416f, 0.0954479f, -0.0586282f, -0.0894044f, 0.481278f, -0.201991f, -0.283279f, -0.897555f, 0.0611137f, 0.0467872f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float.example.cpp deleted file mode 100644 index be9dc5101..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 10, 22, 10, 23, 10, 24, 10, 25, 10, 26, 10, 27, 10, 28, 10, 29}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {11, 3, 7.2f, 10.6f, 11, 3, 7.4f, 10.9f, 11, 3, 7.8f, 11.5f, 11, 3, 8.0f, 11.8f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_2.example.cpp deleted file mode 100644 index 928329e33..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 7, 8, 3, 4, 9, 10, 5, 6, 11, 12}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {71, -34, 99, -20, 91, -26, 127, -4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large.example.cpp deleted file mode 100644 index 074682608..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 10, 22, 10, 23, 10, 24}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {110, 246}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2.example.cpp deleted file mode 100644 index 5716a17af..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 100, 0, 10, 22, 200, 0, 10, 23, 300, 0, 10, 24, 400, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {600010, 700046, 830000, 900000}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2_weights_as_inputs.example.cpp deleted file mode 100644 index 1840bc9cf..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_2_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_2_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 100, 0, 10, 22, 200, 0, 10, 23, 300, 0, 10, 24, 400, 0}}, {1, {0.25f, 0, 10, 100, 0.25f, 1, 20, 100, 0.25f, 0, 30, 100, 0.25f, 1, 40, 100}}, {2, {600000, 700000, 800000, 900000}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {600010, 700046, 830000, 900000}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_weights_as_inputs.example.cpp deleted file mode 100644 index 410b1a397..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 10, 22, 10, 23, 10, 24}}, {1, {0.25f, 0, 0.25f, 1, 0.25f, 0, 0.25f, 1}}, {2, {100, 200}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {110, 246}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_weights_as_inputs.example.cpp deleted file mode 100644 index 6731073fa..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_float_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 21, 10, 22, 10, 23, 10, 24, 10, 25, 10, 26, 10, 27, 10, 28, 10, 29}}, {1, {0.25f, 0, 0.2f, 0, 0.25f, 0, 0, 0.3f, 0.25f, 0, 0, 0, 0.25f, 0.1f, 0, 0}}, {2, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {11, 3, 7.2f, 10.6f, 11, 3, 7.4f, 10.9f, 11, 3, 7.8f, 11.5f, 11, 3, 8.0f, 11.8f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8.example.cpp deleted file mode 100644 index 1079fda6d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {4, 16, 4, 32, 4, 64, 4, 128}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 48}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_2.example.cpp deleted file mode 100644 index 41159326b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {129, 131, 141, 143, 133, 135, 145, 147, 137, 139, 149, 151}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {198, 93, 226, 107, 218, 101, 254, 123}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large.example.cpp deleted file mode 100644 index d8bab860c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {4, 16, 4, 32, 4, 64, 4, 128}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 48}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large_weights_as_inputs.example.cpp deleted file mode 100644 index 4b3905c34..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {0, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {4, 16, 4, 32, 4, 64, 4, 128}}, {1, {2, 4, 2, 0, 2, 2, 2, 0}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 48}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_weights_as_inputs.example.cpp deleted file mode 100644 index b8094ca10..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/depthwise_conv2d_quant8_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {0, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {4, 16, 4, 32, 4, 64, 4, 128}}, {1, {2, 4, 2, 0, 2, 2, 2, 0}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {8, 48}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/dequantize.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/dequantize.example.cpp deleted file mode 100644 index c4648f6dd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/dequantize.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: dequantize.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 32, 128, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, 32.0f, 128.0f, 255.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/div.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/div.example.cpp deleted file mode 100644 index 23ad4b05d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/div.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: div.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, -4.0f, 8.0f, -16.0f}}, {1, {2.0f, -2.0f, -4.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, -2.0f, -4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/div_.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/div_.example.cpp deleted file mode 100644 index cd96fd77c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/div_.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: div_.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, -4.0f, 8.0f, -16.0f}}, {1, {2.0f, -2.0f, -4.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, -2.0f, -4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/div_broadcast_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/div_broadcast_float.example.cpp deleted file mode 100644 index ccbb571e2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/div_broadcast_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: div_broadcast_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2}}, {1, {1, 1, 2, 2}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 0.5f, 1}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup.example.cpp deleted file mode 100644 index 254d6f707..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: embedding_lookup.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0.0f, 0.01f, 0.02f, 0.03f, 0.1f, 0.11f, 0.12000000000000001f, 0.13f, 1.0f, 1.01f, 1.02f, 1.03f, 1.1f, 1.11f, 1.12f, 1.1300000000000001f, 2.0f, 2.01f, 2.02f, 2.03f, 2.1f, 2.11f, 2.12f, 2.13f}}}, - // int -> INT32 map - {{0, {1, 0, 2}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.01f, 1.02f, 1.03f, 1.1f, 1.11f, 1.12f, 1.13f, 0.0f, 0.01f, 0.02f, 0.03f, 0.1f, 0.11f, 0.12f, 0.13f, 2.0f, 2.01f, 2.02f, 2.03f, 2.1f, 2.11f, 2.12f, 2.13f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_2d_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_2d_nnfw.example.cpp deleted file mode 100644 index 14211f5cd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_2d_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: embedding_lookup_2d_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0.0f, 0.1f, 1.0f, 1.1f, 2.0f, 2.1f}}}, - // int -> INT32 map - {{0, {1, 0, 2}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.1f, 0.0f, 0.1f, 2.0f, 2.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_4d_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_4d_nnfw.example.cpp deleted file mode 100644 index 8b144a165..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/embedding_lookup_4d_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: embedding_lookup_4d_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {4, 0, 2}}, {1, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/floor.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/floor.example.cpp deleted file mode 100644 index f15d98488..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/floor.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: floor.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.5f, -1.0f, -0.5f, 0.0f, 0.5f, 1.0f, 1.5f, 10.2f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-2.0f, -1.0f, -1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/floor_.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/floor_.example.cpp deleted file mode 100644 index 7481d112e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/floor_.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: floor_.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.5f, -1.0f, -0.5f, 0.0f, 0.5f, 1.0f, 1.5f, 10.2f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-2.0f, -1.0f, -1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float.example.cpp deleted file mode 100644 index 4af8806ec..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 32, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {8, 68, 36}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_1_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_1_nnfw.example.cpp deleted file mode 100644 index 1a46ccd34..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_1_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_1_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4910057783f, 3.4019672871f, -0.0598693565f, -0.0065411143f, -0.646147728f, 1.9235717058f, 1.0784962177f, 0.176592201f, -2.2495496273f, -1.6010370255f, -2.4747757912f, -0.3825767934f, 2.305898428f, 0.7288306952f, -0.8964791894f, -2.7584488392f, -0.287591964f, 0.1335377693f, 1.8338065147f, -2.6112849712f, 0.9390821457f, 1.984385252f, -1.2190681696f, 1.0274435282f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0375289917f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_2.example.cpp deleted file mode 100644 index 504a345a2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.503691f, 0.196961f, 0.521017f, 0.554248f, 0.288678f, 0.792476f, 0.561653f, 0.46223f, 0.650736f, 0.163132f, 0.029658f, 0.411544f, 0.470539f, 0.57239f, 0.538755f, 0.21203f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0.0732134f, 0, 0, 0, 0.280859f, 0, 0.128927f, 0, 0.0777251f, 0, 0.270268f, 0.271435f, 0.0173503f, 0.335465f, 0.235562f, 0, 0.0745866f, 0, 0.051611f, 0, 0.253876f, 0, 0.0814873f, 0, 0.104104f, 0, 0.248529f, 0.264194f, 0, 0.302973f, 0.166252f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_3.example.cpp deleted file mode 100644 index 14ee46d5f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 2, 1}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {11, 9}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_4d_simple.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_4d_simple.example.cpp deleted file mode 100644 index 4086bc559..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_4d_simple.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_4d_simple.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, -9, -10, 1, 2, 3, 4, 5, 6, 7, -8, 9, -10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {24, 25, 26, 58, 59, 60}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large.example.cpp deleted file mode 100644 index 3ad2caa75..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 10, 100, 1000, 10000}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {965432}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large_weights_as_inputs.example.cpp deleted file mode 100644 index fdc7db12a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 10, 100, 1000, 10000}}, {1, {2, 3, 4, 5, 6}}, {2, {900000}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {965432}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_weights_as_inputs.example.cpp deleted file mode 100644 index 83c552798..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_float_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_float_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 32, 16}}, {1, {2}}, {2, {4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {8, 68, 36}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8.example.cpp deleted file mode 100644 index 18d2098e1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 32, 16}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 17, 9}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_2.example.cpp deleted file mode 100644 index 978cdcb0f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {129, 131, 133, 135, 137, 139, 141, 143, 109, 107, 129, 131, 133, 135, 137, 139, 141, 111, 145, 107}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {151, 152, 153, 185, 186, 187}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large.example.cpp deleted file mode 100644 index c9a5aa196..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_quant8_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {10, 10, 10, 10, 10}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {32}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large_weights_as_inputs.example.cpp deleted file mode 100644 index 14bd00d7c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_large_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_quant8_large_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {10}}}, - // int -> QUANT8_ASYMM map - {{0, {10, 10, 10, 10, 10}}, {1, {10, 20, 20, 20, 10}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {32}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_weights_as_inputs.example.cpp deleted file mode 100644 index 3aca9fdd2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/fully_connected_quant8_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: fully_connected_quant8_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{2, {4}}}, - // int -> QUANT8_ASYMM map - {{0, {2, 32, 16}}, {1, {2}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 17, 9}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_1D_float.example.cpp deleted file mode 100644 index 494968cf0..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_1D_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.0f, 4.0f, 5.0f, 6.0f}}}, - // int -> INT32 map - {{1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {5.0f, 3.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_1D_int32.example.cpp deleted file mode 100644 index 305018add..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_1D_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {40000, 41000, 50000, 60000}}, {1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {50000, 40000}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_1D_quant8.example.cpp deleted file mode 100644 index d633f0c25..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_1D_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_1D_uint8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {7, 9, 11, 13}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {11, 7}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_2D_float.example.cpp deleted file mode 100644 index 0bf46fd4d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_2D_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.123456789123457f, 4.123456789123456f, 5.123456789123456f, 6.123456789123456f, 7.123456789123456f, 8.123456789123457f, 9.123456789123457f, 1.1234567891234568f, 2.123456789123457f, 18.123456789123455f, 19.123456789123455f, 11.123456789123457f}}}, - // int -> INT32 map - {{1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.123456789123457f, 18.123456789123455f, 19.123456789123455f, 11.123456789123457f, 3.123456789123457f, 4.123456789123456f, 5.123456789123456f, 6.123456789123456f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_2D_int32.example.cpp deleted file mode 100644 index 052eff5ee..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_2D_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {40000, 41000, 50000, 60000, 70000, 80000, 90000, 79000, 170000, 180000, 190000, 110000}}, {1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {170000, 180000, 190000, 110000, 40000, 41000, 50000, 60000}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/gather_2D_quant8.example.cpp deleted file mode 100644 index b232d917b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/gather_2D_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: gather_2D_uint8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{1, {2, 0}}}, - // int -> QUANT8_ASYMM map - {{0, {7, 9, 11, 13, 15, 17, 19, 3, 5, 37, 39, 23}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {5, 37, 39, 23, 7, 9, 11, 13}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_float.example.cpp deleted file mode 100644 index cc52e107d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: hashtable_lookup_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{2, {0.0f, 0.1f, 1.0f, 1.1f, 2.0f, 2.1f}}}, - // int -> INT32 map - {{0, {1234, -292, -11, 0}}, {1, {-11, 0, 1234}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, 2.1f, 0, 0, 0.0f, 0.1f, 1.0f, 1.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{1, {1, 0, 1, 1}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_quant8.example.cpp deleted file mode 100644 index b382dc889..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/hashtable_lookup_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: hashtable_lookup_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {123, 250, 255, 0}}, {1, {0, 123, 255}}}, - // int -> QUANT8_ASYMM map - {{2, {0, 1, 10, 11, 20, 21}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {10, 11, 0, 0, 20, 21, 0, 1}}, {1, {1, 0, 1, 1}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_normalization.example.cpp deleted file mode 100644 index d23945bf5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_normalization.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0.6f, 0.8f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_2.example.cpp deleted file mode 100644 index 5975a93f5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_normalization_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.1f, 0.6f, 0.7f, 1.2f, -0.7f, 0.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.55f, 0.3f, 0.35f, 0.6f, -0.35f, 0.05f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_large.example.cpp deleted file mode 100644 index d8f2e37b5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_normalization_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_normalization_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 3, 4, 0, 5, 12, 0, 8, 15, 0, 7, 24}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0.6f, 0.8f, 0, 0.38461539149284363f, 0.9230769872665405f, 0, 0.47058823704719543f, 0.8823529481887817f, 0, 0.28f, 0.96f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float.example.cpp deleted file mode 100644 index 64ed8e176..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_pool_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_2.example.cpp deleted file mode 100644 index 9a5ae1dd7..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_pool_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 6, 2, 4, 3, 2, 10, 7}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.5f, 6.5f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_large.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_large.example.cpp deleted file mode 100644 index 80ab993fd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/l2_pool_float_large.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: l2_pool_float_large.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6.442049503326416f, 7.314369201660156f, 8.215838432312012f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_1.example.cpp deleted file mode 100644 index 25f7590c1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: local_response_norm_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.1f, 0.6f, 0.7f, 1.2f, -0.7f, 0.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.22f, 0.12f, 0.14f, 0.24f, -0.14f, 0.02f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_2.example.cpp deleted file mode 100644 index bef5e6bd3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: local_response_norm_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.1f, 0.6f, 0.7f, 1.2f, -0.7f, 0.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.55f, 0.3f, 0.35f, 0.6f, -0.35f, 0.05f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_3.example.cpp deleted file mode 100644 index 210d6e80a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: local_response_norm_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.1f, 0.6f, 0.7f, 1.2f, -0.7f, 0.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.275f, 0.15f, 0.175f, 0.3f, -0.175f, 0.025f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_4.example.cpp deleted file mode 100644 index 6cdb818cf..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/local_response_norm_float_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: local_response_norm_float_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.1f, 0.6f, 0.7f, 1.2f, -0.7f, 0.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.26492569f, 0.12510864f, 0.14011213f, 0.26726127f, -0.16178755f, 0.0244266f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/logistic_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/logistic_float_1.example.cpp deleted file mode 100644 index 09fb28ee3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/logistic_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: logistic_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 4.0f, 8.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.7310585975646973f, 0.8807970285415649f, 0.9820137619972229f, 0.9996646642684937f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/logistic_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/logistic_float_2.example.cpp deleted file mode 100644 index 7cb4f255a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/logistic_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: logistic_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, 1.0f, 510.0f, 3.0f, 508.0f, 5.0f, 506.0f, 7.0f, 504.0f, 9.0f, 502.0f, 11.0f, 500.0f, 13.0f, 498.0f, 15.0f, 496.0f, 17.0f, 494.0f, 19.0f, 492.0f, 21.0f, 490.0f, 23.0f, 488.0f, 25.0f, 486.0f, 27.0f, 484.0f, 29.0f, 482.0f, 31.0f, 480.0f, 33.0f, 478.0f, 35.0f, 476.0f, 37.0f, 474.0f, 39.0f, 472.0f, 41.0f, 470.0f, 43.0f, 468.0f, 45.0f, 466.0f, 47.0f, 464.0f, 49.0f, 462.0f, 51.0f, 460.0f, 53.0f, 458.0f, 55.0f, 456.0f, 57.0f, 454.0f, 59.0f, 452.0f, 61.0f, 450.0f, 63.0f, 448.0f, 65.0f, 446.0f, 67.0f, 444.0f, 69.0f, 442.0f, 71.0f, 440.0f, 73.0f, 438.0f, 75.0f, 436.0f, 77.0f, 434.0f, 79.0f, 432.0f, 81.0f, 430.0f, 83.0f, 428.0f, 85.0f, 426.0f, 87.0f, 424.0f, 89.0f, 422.0f, 91.0f, 420.0f, 93.0f, 418.0f, 95.0f, 416.0f, 97.0f, 414.0f, 99.0f, 412.0f, 101.0f, 410.0f, 103.0f, 408.0f, 105.0f, 406.0f, 107.0f, 404.0f, 109.0f, 402.0f, 111.0f, 400.0f, 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10.0f, 503.0f, 8.0f, 505.0f, 6.0f, 507.0f, 4.0f, 509.0f, 2.0f, 511.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.5f, 0.7310585786300049f, 1.0f, 0.9525741268224334f, 1.0f, 0.9933071490757153f, 1.0f, 0.9990889488055994f, 1.0f, 0.9998766054240137f, 1.0f, 0.999983298578152f, 1.0f, 0.999997739675702f, 1.0f, 0.999999694097773f, 1.0f, 0.9999999586006244f, 1.0f, 0.9999999943972036f, 1.0f, 0.9999999992417439f, 1.0f, 0.9999999998973812f, 1.0f, 0.999999999986112f, 1.0f, 0.9999999999981204f, 1.0f, 0.9999999999997455f, 1.0f, 0.9999999999999656f, 1.0f, 0.9999999999999953f, 1.0f, 0.9999999999999993f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 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0.9999999999999982f, 1.0f, 0.9999999999999873f, 1.0f, 0.9999999999999065f, 1.0f, 0.9999999999993086f, 1.0f, 0.999999999994891f, 1.0f, 0.9999999999622486f, 1.0f, 0.9999999997210531f, 1.0f, 0.9999999979388463f, 1.0f, 0.9999999847700205f, 1.0f, 0.9999998874648379f, 1.0f, 0.9999991684719722f, 1.0f, 0.9999938558253978f, 1.0f, 0.9999546021312976f, 1.0f, 0.9996646498695336f, 1.0f, 0.9975273768433653f, 1.0f, 0.9820137900379085f, 1.0f, 0.8807970779778823f, 1.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_1.example.cpp deleted file mode 100644 index ce600f709..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: logistic_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 127}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 159, 187, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_2.example.cpp deleted file mode 100644 index 55ece5fa4..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/logistic_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: logistic_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 159, 187, 209, 225, 237, 244, 248, 251, 253, 254, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lsh_projection.example.cpp deleted file mode 100644 index 4996fff12..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lsh_projection.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0.12f, 0.34f, 0.56f}}}, - // int -> INT32 map - {{0, {12345, 54321, 67890, 9876, -12345678, -87654321}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {1, 1, 1, 0, 1, 1, 1, 0}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_2.example.cpp deleted file mode 100644 index aa9de45e6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lsh_projection_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {}}}, - // int -> INT32 map - {{0, {12345, 54321, 67890, 9876, -12345678, -87654321}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {1, 2, 2, 0}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_weights_as_inputs.example.cpp deleted file mode 100644 index 512557d0f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lsh_projection_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lsh_projection_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.123f, 0.456f, -0.321f, -0.654f, 1.234f, 5.678f, -4.321f, -8.765f}}, {2, {0.12f, 0.34f, 0.56f}}}, - // int -> INT32 map - {{1, {12345, 54321, 67890, 9876, -12345678, -87654321}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {1, 1, 1, 0, 1, 1, 1, 0}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm.example.cpp deleted file mode 100644 index 6117f55c0..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, 3.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {0, 0, 0, 0}}, {19, {0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}}, {2, {-0.145439f, 0.157475f, 0.293663f, -0.277353f}}, {3, {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm2.example.cpp deleted file mode 100644 index 167e84106..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, 3.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {0, 0, 0, 0}}, {19, {0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}}, {2, {-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}}, {3, {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm2_state.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm2_state.example.cpp deleted file mode 100644 index 9bc84b4e9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm2_state.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm2_state.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.0f, 4.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}}, {19, {-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.423122f, -0.0121822f, 0.24201f, -0.0812458f}}, {2, {-0.978419f, -0.139203f, 0.338163f, -0.0983904f}}, {3, {-0.42312205f, -0.01218222f, 0.24201041f, -0.08124574f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm2_state2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm2_state2.example.cpp deleted file mode 100644 index 2529e2c7e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm2_state2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm2_state2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.423122f, -0.0121822f, 0.24201f, -0.0812458f}}, {19, {-0.978419f, -0.139203f, 0.338163f, -0.0983904f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.358325f, -0.04621704f, 0.21641694f, -0.06471302f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm3.example.cpp deleted file mode 100644 index 03e5d4a5b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, -0.07224499f, -0.09628791f, 0.045100946f, 0.0012300825f, 0.013964662f, 0.099372394f, 0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f, 0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f, 0.07241905f, 0.018668644f, -0.037377294f, -0.06277783f, -0.08833636f, -0.040120605f, -0.011405586f, -0.007808335f, -0.010301386f, -0.005102167f, 0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f, 0.10939839f, 0.13396506f, -0.08402166f, -0.01901462f, -0.044678304f, -0.07720565f, 0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f, -0.076754324f, -0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f, 0.035839386f, -0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f, 0.05448447f, -0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f, -0.0011980024f, -0.034641717f, -0.026125094f, -0.17582615f, -0.15923657f, -0.27486774f, -0.0006143371f, 0.0001771948f, -8.470171e-05f, 0.02651807f, 0.045790765f, 0.06956496f}}, {3, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f}}, {4, {-0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f, -0.001285394f, 0.10124236f, 0.083122835f, 0.053313006f, -0.062235646f, -0.075637154f, -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, -0.086665764f, -0.037162706f, -0.038880914f, -0.035832845f, -0.014481564f, -0.09825003f, -0.12048569f, -0.097665586f, -0.05287633f, -0.0964047f, -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f, 0.050649304f, 0.05798951f, -0.021852335f, -0.099848844f, 0.014740475f, -0.078897946f, 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f, 0.06945813f, -0.078907564f, -0.06707616f, -0.11844508f, -0.09986688f, -0.07509403f, 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f, 0.14639415f, 0.0015017595f, -0.014267382f, -0.03417257f, 0.012711468f, 0.0028300495f, -0.024758482f, -0.05098548f, -0.0821182f, 0.014225672f, 0.021544158f, 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f, 0.06476295f, -0.022907063f, 0.027562456f, 0.040185735f, 0.019567577f, -0.015598739f, -0.049097303f, -0.017121866f, -0.083368234f, -0.02332002f, -0.0840956f}}, {5, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, -0.10820036f, -0.04666372f, 0.022746278f, 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0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f}}, {9, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f}}, {10, {-0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f}}, {11, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f}}, {12, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f}}, {13, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f}}, {14, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f}}, {15, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}}, {16, {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, 0.027685808f, -0.07675938f, 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0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {19, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f}}, {2, {-0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f}}, {3, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm3_state.example.cpp deleted file mode 100644 index 687daee99..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm3_state.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.596268f, 0.998386f, 0.568695f, 0.864524f, 0.571277f, 0.642421f, 0.52426f, 0.134799f, 0.003639f, 0.162482f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, 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{0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f}}, {13, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f}}, {14, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f}}, {15, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}}, {16, {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f}}, {19, {-0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.0166936f, 0.0381209f, 0.000889684f, 0.0143363f, -0.0328911f, -0.0234288f, 0.0333051f, -0.012229f, 0.0110322f, -0.0457725f, -0.000832209f, -0.0202817f, 0.0327257f, 0.0121309f, 0.0155969f, 0.0312091f, -0.0141913f, 0.0322082f, 0.00227024f, 0.0260507f, -0.0188721f, -0.0296489f, 0.0399134f, -0.0160509f, 0.011604f, -0.0447318f, -0.0150515f, -0.0277406f, 0.0316596f, 0.0118233f, 0.0214762f, 0.0293641f}}, {2, {-0.154022f, -0.124934f, 0.0478463f, 0.0607819f, -0.218727f, -0.111053f, -0.103885f, -0.00447221f, 0.0554757f, -0.0207068f, 0.0595767f, -0.116297f, -0.249466f, -0.0723206f, 0.0794942f, -0.0377107f, 0.124532f, 0.249952f, 0.188641f, 0.411865f, -0.11012f, -0.0694494f, 0.103501f, 0.0428427f, -0.167345f, -0.106061f, -0.0775679f, 0.00936161f, 0.0105526f, -0.0314523f, 0.0243475f, -0.132179f, -0.258763f, -0.0307266f, 0.107047f, -0.0115197f, 0.0995485f, 0.220027f, 0.158355f, 0.436369f}}, {3, {-0.0166936f, 0.0381209f, 0.000889694f, 0.0143363f, -0.0328911f, -0.0234288f, 0.0333051f, -0.012229f, 0.0110322f, -0.0457725f, -0.000832209f, -0.0202817f, 0.0327257f, 0.0121308f, 0.0155969f, 0.0312091f, -0.0141913f, 0.0322082f, 0.00227024f, 0.0260507f, -0.0188721f, -0.0296489f, 0.0399134f, -0.0160509f, 0.0116039f, -0.0447318f, -0.0150515f, -0.0277406f, 0.0316596f, 0.0118233f, 0.0214762f, 0.0293641f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm3_state2.example.cpp deleted file mode 100644 index 230545071..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm3_state2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.073204f, 0.296072f, 0.743333f, 0.069199f, 0.045348f, 0.640394f, 0.930399f, 0.050782f, 0.432485f, 0.988078f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, 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-0.0055694645f, -0.09444123f, 0.0046453946f, 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {-0.0166936f, 0.0381209f, 0.000889684f, 0.0143363f, -0.0328911f, -0.0234288f, 0.0333051f, -0.012229f, 0.0110322f, -0.0457725f, -0.000832209f, -0.0202817f, 0.0327257f, 0.0121309f, 0.0155969f, 0.0312091f, -0.0141913f, 0.0322082f, 0.00227024f, 0.0260507f, -0.0188721f, -0.0296489f, 0.0399134f, -0.0160509f, 0.011604f, -0.0447318f, -0.0150515f, -0.0277406f, 0.0316596f, 0.0118233f, 0.0214762f, 0.0293641f}}, {19, {-0.154022f, -0.124934f, 0.0478463f, 0.0607819f, -0.218727f, -0.111053f, -0.103885f, -0.00447221f, 0.0554757f, -0.0207068f, 0.0595767f, -0.116297f, -0.249466f, -0.0723206f, 0.0794942f, -0.0377107f, 0.124532f, 0.249952f, 0.188641f, 0.411865f, -0.11012f, -0.0694494f, 0.103501f, 0.0428427f, -0.167345f, -0.106061f, -0.0775679f, 0.00936161f, 0.0105526f, -0.0314523f, 0.0243475f, -0.132179f, -0.258763f, -0.0307266f, 0.107047f, -0.0115197f, 0.0995485f, 0.220027f, 0.158355f, 0.436369f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.0213783f, 0.0350169f, 0.000324787f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117379f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.00484469f, -0.0240239f, 0.0325789f, 0.00790064f, 0.0220157f, 0.0333314f}}, {2, {-0.126572f, -0.121882f, 0.121569f, 0.0489971f, -0.240177f, -0.124685f, -0.122565f, 0.0162748f, 0.0317536f, -0.0270355f, 0.0418199f, -0.179755f, -0.327279f, -0.0342741f, 0.133831f, -0.0238279f, 0.122148f, 0.269115f, 0.185989f, 0.525976f, -0.167208f, -0.109612f, 0.0531226f, 0.0695387f, -0.248335f, -0.134123f, -0.108246f, 0.00628498f, 0.0492984f, -0.0264919f, 0.0698144f, -0.0635602f, -0.295363f, -0.0760078f, 0.102725f, -0.0351708f, 0.149804f, 0.259131f, 0.202573f, 0.500664f}}, {3, {-0.0213783f, 0.0350169f, 0.000324794f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117378f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.0048447f, -0.0240239f, 0.0325789f, 0.00790065f, 0.0220157f, 0.0333314f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm3_state3.example.cpp deleted file mode 100644 index f7f1a28e9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm3_state3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm3_state3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.867394f, 0.291279f, 0.013714f, 0.482521f, 0.626339f, 0.082922f, 0.563329f, 0.865614f, 0.333232f, 0.259916f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, -0.07224499f, -0.09628791f, 0.045100946f, 0.0012300825f, 0.013964662f, 0.099372394f, 0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f, 0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f, 0.07241905f, 0.018668644f, -0.037377294f, -0.06277783f, -0.08833636f, -0.040120605f, -0.011405586f, -0.007808335f, -0.010301386f, -0.005102167f, 0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f, 0.10939839f, 0.13396506f, -0.08402166f, -0.01901462f, -0.044678304f, -0.07720565f, 0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f, -0.076754324f, -0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f, 0.035839386f, -0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f, 0.05448447f, -0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f, -0.0011980024f, -0.034641717f, -0.026125094f, -0.17582615f, -0.15923657f, -0.27486774f, 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-0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {-0.0213783f, 0.0350169f, 0.000324787f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117379f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.00484469f, -0.0240239f, 0.0325789f, 0.00790064f, 0.0220157f, 0.0333314f}}, {19, {-0.126572f, -0.121882f, 0.121569f, 0.0489971f, -0.240177f, -0.124685f, -0.122565f, 0.0162748f, 0.0317536f, -0.0270355f, 0.0418199f, -0.179755f, -0.327279f, -0.0342741f, 0.133831f, -0.0238279f, 0.122148f, 0.269115f, 0.185989f, 0.525976f, -0.167208f, -0.109612f, 0.0531226f, 0.0695387f, -0.248335f, -0.134123f, -0.108246f, 0.00628498f, 0.0492984f, -0.0264919f, 0.0698144f, -0.0635602f, -0.295363f, -0.0760078f, 0.102725f, -0.0351708f, 0.149804f, 0.259131f, 0.202573f, 0.500664f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {2, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {3, {-0.0189322f, 0.0464512f, -0.00251373f, 0.0225745f, -0.0308346f, -0.0317124f, 0.0460407f, -0.0189395f, 0.0149363f, -0.0530162f, -0.0150767f, -0.0340193f, 0.0286833f, 0.00824207f, 0.0264887f, 0.0305169f, -0.0264787f, 0.0387855f, -0.000764675f, 0.0217599f, -0.037537f, -0.0335206f, 0.0431679f, -0.0211424f, 0.010203f, -0.062785f, -0.00832363f, -0.025181f, 0.0412031f, 0.0118723f, 0.0239643f, 0.0394009f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm_state.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm_state.example.cpp deleted file mode 100644 index 47cdc7eca..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm_state.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm_state.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.0f, 4.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}}, {19, {-0.145439f, 0.157475f, 0.293663f, -0.277353f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.0371611f, 0.125073f, 0.411934f, -0.208605f}}, {2, {-0.287121f, 0.148115f, 0.556837f, -0.388276f}}, {3, {-0.03716109f, 0.12507336f, 0.41193449f, -0.20860538f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/lstm_state2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/lstm_state2.example.cpp deleted file mode 100644 index aebfba5a2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/lstm_state2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: lstm_state2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.0371611f, 0.125073f, 0.411934f, -0.208605f}}, {19, {-0.287121f, 0.148115f, 0.556837f, -0.388276f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.15053082f, 0.09120187f, 0.24278517f, -0.12222792f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_1.example.cpp deleted file mode 100644 index ba52a11dc..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_2.example.cpp deleted file mode 100644 index 637092254..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 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20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_3.example.cpp deleted file mode 100644 index dcd7a4bd5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 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6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_4.example.cpp deleted file mode 100644 index 4de3ded2e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_float_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_float_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 6, 2, 4, 3, 2, 10, 7}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_1.example.cpp deleted file mode 100644 index d9b23d3e4..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_2.example.cpp deleted file mode 100644 index 3940b7689..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 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20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_3.example.cpp deleted file mode 100644 index 77de3ca2a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_quant8_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 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8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_4.example.cpp deleted file mode 100644 index 4a3fbf290..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/max_pool_quant8_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: max_pool_quant8_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 96, 32, 64, 48, 32, 160, 112}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {96, 160}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean.example.cpp deleted file mode 100644 index 27b8258a9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.5f, 3.5f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_1_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_1_nnfw.example.cpp deleted file mode 100644 index 99d196f17..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_1_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_axis01_1_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_2_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_2_nnfw.example.cpp deleted file mode 100644 index 4ea323074..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_axis01_2_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_axis01_2_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {12.0f, 13.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_float_1.example.cpp deleted file mode 100644 index 4c53d77df..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {12.0f, 13.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_float_2.example.cpp deleted file mode 100644 index 844dd2a7b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10.5f, 12.5f, 14.5f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_1.example.cpp deleted file mode 100644 index 652c84733..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {12, 13}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_2.example.cpp deleted file mode 100644 index 56dec24aa..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mean_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mean_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {10, 12, 14}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mobilenet_224_gender_basic_fixed.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mobilenet_224_gender_basic_fixed.example.cpp deleted file mode 100644 index ce855a9bf..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mobilenet_224_gender_basic_fixed.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mobilenet_224_gender_basic_fixed.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.791841f, 0.131271f, 0.840444f, 0.524133f, 0.301398f, 0.711596f, 8.78908e-05f, 0.358888f, 0.156309f, 0.273055f, 0.143706f, 0.663516f, 0.817875f, 0.0178006f, 0.0663899f, 0.38532f, 0.108224f, 0.120309f, 0.252355f, 0.468634f, 0.535587f, 0.372087f, 0.0191815f, 0.0750463f, 0.896686f, 0.564102f, 0.781927f, 0.138955f, 0.876559f, 0.672544f, 0.47254f, 0.685613f, 0.864712f, 0.678431f, 0.681087f, 0.954624f, 0.707985f, 0.0945955f, 0.663676f, 0.0174337f, 0.514265f, 0.0993437f, 0.837646f, 0.16886f, 0.0206787f, 0.460454f, 0.298217f, 0.0939906f, 0.792089f, 0.145739f, 0.124991f, 0.152928f, 0.18507f, 0.0910606f, 0.886938f, 0.703805f, 0.738545f, 0.0914867f, 0.785625f, 0.255978f, 0.25971f, 0.564347f, 0.496365f, 0.40138f, 0.181351f, 0.229051f, 0.528806f, 0.815946f, 0.723594f, 0.0782805f, 0.417488f, 0.357831f, 0.699987f, 0.110877f, 0.706929f, 0.361355f, 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0.0815982f, 0.91916f, 0.461648f, 0.865653f, 0.983458f, 0.810979f, 0.762103f, 0.384499f, 0.572888f, 0.156534f, 0.281532f, 0.862645f, 0.278199f, 0.337823f, 0.407339f, 0.193315f, 0.384848f, 0.986914f, 0.819812f, 0.828864f, 0.658969f, 0.424426f, 0.0739355f, 0.947452f, 0.368272f, 0.402025f, 0.242601f, 0.693957f, 0.215303f, 0.814995f, 0.265798f, 0.0264865f, 0.286466f, 0.015661f, 0.583045f, 0.291179f, 0.669322f, 0.511267f, 0.229816f, 0.131331f, 0.730827f, 0.74252f, 0.93343f, 0.720619f, 0.612438f, 0.259147f, 0.112357f, 0.586765f, 0.0858606f, 0.153671f, 0.773048f, 0.822479f, 0.443573f, 0.55486f, 0.910416f, 0.85707f, 0.0148852f, 0.853716f, 0.228104f, 0.513679f, 0.951789f, 0.727959f, 0.200735f, 0.210806f, 0.125079f, 0.228391f, 0.209873f, 0.237775f, 0.108994f, 0.446076f, 0.221169f, 0.834191f, 0.98984f, 0.0542308f, 0.58488f, 0.315972f, 0.254531f, 0.828329f, 0.904021f, 0.875414f, 0.150553f, 0.0298306f, 0.215226f, 0.586307f, 0.417627f, 0.479362f, 0.172838f, 0.5259f, 0.908004f, 0.0605249f, 0.859353f, 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0.995379f, 0.970147f, 0.636004f, 0.256138f, 0.263984f, 0.0188201f, 0.836662f, 0.891073f, 0.815278f, 0.213562f, 0.265101f, 0.287762f, 0.853247f, 0.162469f, 0.27606f, 0.396637f, 0.0501615f, 0.519138f, 0.364286f, 0.0357294f, 0.34329f, 0.43058f, 0.134315f, 0.399785f, 0.243176f, 0.897879f, 0.0347835f, 0.977129f, 0.964701f, 0.992374f, 0.5317f, 0.855217f, 0.0626301f, 0.720206f, 0.157749f, 0.331044f, 0.119822f, 0.361034f, 0.657655f, 0.734292f, 0.487953f, 0.878978f, 0.348533f, 0.361729f, 0.655813f, 0.495023f, 0.407889f, 0.922945f, 0.903442f, 0.38402f, 0.512097f, 0.429171f, 0.506999f, 0.895231f, 0.182171f, 0.618858f, 0.88438f, 0.296033f, 0.481725f, 0.858442f, 0.493233f, 0.151025f, 0.16046f, 0.180032f, 0.441936f, 0.211194f, 0.7617f, 0.867114f, 0.199935f, 0.743809f, 0.637003f, 0.808767f, 0.561247f, 0.776601f, 0.43555f, 0.188716f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.605085f, 0.0733608f, 0.0200205f, 0.114568f, 0.178815f, 0.0580576f, 0.123144f, 0.232838f, 0.0916902f, 0.111395f, 0.138349f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mobilenet_quantized.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mobilenet_quantized.example.cpp deleted file mode 100644 index 3130d3039..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mobilenet_quantized.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mobilenet_quantized.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {132, 137, 131, 144, 155, 144, 119, 126, 110, 128, 137, 121, 110, 120, 103, 105, 114, 99, 133, 145, 129, 124, 136, 118, 124, 136, 120, 112, 124, 111, 121, 132, 119, 125, 137, 121, 133, 140, 126, 133, 139, 128, 124, 129, 112, 117, 123, 110, 88, 95, 79, 96, 103, 87, 105, 113, 101, 83, 90, 74, 99, 106, 92, 103, 109, 100, 88, 94, 83, 86, 93, 80, 73, 76, 68, 59, 64, 56, 17, 19, 9, 17, 19, 9, 0, 0, 0, 20, 21, 12, 55, 56, 49, 105, 106, 97, 99, 99, 86, 116, 113, 103, 129, 126, 116, 135, 132, 121, 121, 118, 107, 120, 120, 109, 111, 108, 100, 117, 114, 106, 101, 96, 89, 110, 107, 99, 92, 86, 79, 96, 95, 86, 96, 93, 83, 94, 89, 80, 109, 106, 96, 140, 134, 125, 130, 125, 116, 126, 121, 111, 146, 138, 129, 163, 156, 145, 159, 152, 140, 181, 175, 166, 175, 167, 159, 181, 173, 164, 174, 171, 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255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 245, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul.example.cpp deleted file mode 100644 index 841394843..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, -4, 8, -16}}, {1, {32, -16, -8, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {64, 64, -64, -64}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_1_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_1_nnfw.example.cpp deleted file mode 100644 index c8cd5fdad..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_1_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul_broadcast_3D_1D_1_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.8364028931f, 0.6620308161f, 1.1811592579f, 0.4827561378f, -0.384627074f, -1.7236120701f, 3.5318591595f, 0.2959995866f, 1.6260499954f, -0.7885181308f, -0.8246002197f, -1.1367146969f}}, {1, {0.8364028931f, -0.384627074f, 1.6260499954f, 0.6620308161f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.6995698214f, -0.2546349764f, 1.9206240177f, 0.3195994496f, -0.3217031956f, 0.6629478931f, 5.7429795265f, 0.1959608495f, 1.3600329161f, 0.3032854199f, -1.3408411741f, -0.7525401711f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_2_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_2_nnfw.example.cpp deleted file mode 100644 index 8488edd4f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_3D_1D_2_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul_broadcast_3D_1D_2_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.2774236202f, -2.4773113728f, -0.4044751823f, -0.8101355433f, -1.9691983461f, 2.2676842213f, -2.2757787704f, -0.8289190531f, 0.0121828541f, -1.7484937906f, -0.5269883871f, -0.6346995831f, 2.4886128902f, -1.5107979774f, -0.7372134924f, -0.5374289751f, -1.2039715052f, 1.527836442f, 0.8248311877f, -2.4172706604f, 0.6997106671f, -0.8929677606f, 0.3650484681f, 1.3652951717f}}, {1, {2.2774236202f, 0.0121828541f, -1.2039715052f, -1.9691983461f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {5.1866583824f, -0.0301807225f, 0.4869765937f, 1.5953176022f, -4.4846987724f, 0.0276268665f, 2.7399728298f, 1.6323059797f, 0.0277455188f, -0.0213016439f, 0.6344789863f, 1.2498493195f, 5.6676259041f, -0.0184058305f, 0.8875840306f, 1.0583041906f, -2.7419531345f, 0.0186134093f, -0.993073225f, 4.7600855827f, 1.593537569f, -0.0108788963f, -0.4395079613f, -2.6885368824f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_quant8.example.cpp deleted file mode 100644 index 163a03811..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul_broadcast_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul_broadcast_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 4}}, {1, {1, 2, 4, 8}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 4, 4, 16}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul_quant8.example.cpp deleted file mode 100644 index 838ec5a30..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2}}, {1, {2, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 4}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/mul_relu.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/mul_relu.example.cpp deleted file mode 100644 index 038a8489b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/mul_relu.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: mul_relu.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, -4, 8, -16}}, {1, {32, -16, -8, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {64, 64, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/pad.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/pad.example.cpp deleted file mode 100644 index dbec43395..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/pad.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: pad.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 2.0f, 0.0f, 0.0f, 3.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/pad_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/pad_float_1.example.cpp deleted file mode 100644 index 873149b7f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/pad_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: pad_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu1_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu1_float_1.example.cpp deleted file mode 100644 index 009bc4528..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu1_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu1_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-10.0f, -0.5f, 0.5f, 10.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1.0f, -0.5f, 0.5f, 1.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu1_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu1_float_2.example.cpp deleted file mode 100644 index 4482ee246..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu1_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu1_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.0f, 0.001f, -0.002f, 0.003f, -0.004f, 0.005f, -0.006f, 0.007f, -0.008f, 0.009000000000000001f, -0.01f, 0.011f, -0.012f, 0.013000000000000001f, -0.014f, 0.015f, -0.016f, 0.017f, -0.018000000000000002f, 0.019f, -0.02f, 0.021f, -0.022f, 0.023f, -0.024f, 0.025f, -0.026000000000000002f, 0.027f, -0.028f, 0.029f, -0.03f, 0.031f, -0.032f, 0.033f, -0.034f, 0.035f, -0.036000000000000004f, 0.037f, -0.038f, 0.039f, -0.04f, 0.041f, -0.042f, 0.043000000000000003f, -0.044f, 0.045f, -0.046f, 0.047f, -0.048f, 0.049f, -0.05f, 0.051000000000000004f, -0.052000000000000005f, 0.053f, -0.054f, 0.055f, -0.056f, 0.057f, -0.058f, 0.059000000000000004f, -0.06f, 0.061f, -0.062f, 0.063f, -0.064f, 0.065f, -0.066f, 0.067f, -0.068f, 0.069f, -0.07f, 0.07100000000000001f, -0.07200000000000001f, 0.073f, -0.074f, 0.075f, -0.076f, 0.077f, -0.078f, 0.079f, -0.08f, 0.081f, 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2.629f, -2.63f, 2.6310000000000002f, -2.632f, 2.633f, -2.634f, 2.6350000000000002f, -2.636f, 2.637f, -2.638f, 2.6390000000000002f, -2.64f, 2.641f, -2.642f, 2.6430000000000002f, -2.644f, 2.645f, -2.646f, 2.6470000000000002f, -2.648f, 2.649f, -2.65f, 2.6510000000000002f, -2.652f, 2.653f, -2.654f, 2.6550000000000002f, -2.656f, 2.657f, -2.658f, 2.6590000000000003f, -2.66f, 2.661f, -2.662f, 2.6630000000000003f, -2.664f, 2.665f, -2.666f, 2.6670000000000003f, -2.668f, 2.669f, -2.67f, 2.6710000000000003f, -2.672f, 2.673f, -2.674f, 2.6750000000000003f, -2.676f, 2.677f, -2.678f, 2.6790000000000003f, -2.68f, 2.681f, -2.682f, 2.6830000000000003f, -2.684f, 2.685f, -2.686f, 2.6870000000000003f, -2.688f, 2.689f, -2.69f, 2.691f, -2.692f, 2.693f, -2.694f, 2.695f, -2.696f, 2.697f, -2.698f, 2.699f, -2.7f, 2.701f, -2.702f, 2.703f, -2.704f, 2.705f, -2.706f, 2.707f, -2.708f, 2.709f, -2.71f, 2.711f, -2.712f, 2.713f, -2.714f, 2.715f, -2.716f, 2.717f, -2.718f, 2.719f, -2.72f, 2.721f, -2.722f, 2.723f, -2.724f, 2.725f, -2.726f, 2.727f, -2.728f, 2.729f, -2.73f, 2.731f, -2.732f, 2.733f, -2.734f, 2.735f, -2.736f, 2.737f, -2.738f, 2.739f, -2.74f, 2.741f, -2.742f, 2.743f, -2.744f, 2.745f, -2.746f, 2.747f, -2.748f, 2.749f, -2.75f, 2.751f, -2.7520000000000002f, 2.753f, -2.754f, 2.755f, -2.7560000000000002f, 2.757f, -2.758f, 2.759f, -2.7600000000000002f, 2.761f, -2.762f, 2.763f, -2.7640000000000002f, 2.765f, -2.766f, 2.767f, -2.7680000000000002f, 2.769f, -2.77f, 2.771f, -2.7720000000000002f, 2.773f, -2.774f, 2.775f, -2.7760000000000002f, 2.777f, -2.778f, 2.779f, -2.7800000000000002f, 2.781f, -2.782f, 2.783f, -2.7840000000000003f, 2.785f, -2.786f, 2.787f, -2.7880000000000003f, 2.789f, -2.79f, 2.791f, -2.7920000000000003f, 2.793f, -2.794f, 2.795f, -2.7960000000000003f, 2.797f, -2.798f, 2.799f, -2.8000000000000003f, 2.801f, -2.802f, 2.803f, -2.8040000000000003f, 2.805f, -2.806f, 2.807f, -2.8080000000000003f, 2.809f, -2.81f, 2.811f, -2.8120000000000003f, 2.813f, -2.814f, 2.815f, -2.816f, 2.817f, -2.818f, 2.819f, -2.82f, 2.821f, -2.822f, 2.823f, -2.824f, 2.825f, -2.826f, 2.827f, -2.828f, 2.829f, -2.83f, 2.831f, -2.832f, 2.833f, -2.834f, 2.835f, -2.836f, 2.837f, -2.838f, 2.839f, -2.84f, 2.841f, -2.842f, 2.843f, -2.844f, 2.845f, -2.846f, 2.847f, -2.848f, 2.849f, -2.85f, 2.851f, -2.852f, 2.853f, -2.854f, 2.855f, -2.856f, 2.857f, -2.858f, 2.859f, -2.86f, 2.861f, -2.862f, 2.863f, -2.864f, 2.865f, -2.866f, 2.867f, -2.868f, 2.869f, -2.87f, 2.871f, -2.872f, 2.873f, -2.874f, 2.875f, -2.876f, 2.8770000000000002f, -2.878f, 2.879f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.0f, 0.001f, -0.002f, 0.003f, -0.004f, 0.005f, -0.006f, 0.007f, -0.008f, 0.009000000000000001f, -0.01f, 0.011f, -0.012f, 0.013000000000000001f, -0.014f, 0.015f, -0.016f, 0.017f, -0.018000000000000002f, 0.019f, -0.02f, 0.021f, -0.022f, 0.023f, -0.024f, 0.025f, -0.026000000000000002f, 0.027f, -0.028f, 0.029f, -0.03f, 0.031f, -0.032f, 0.033f, -0.034f, 0.035f, -0.036000000000000004f, 0.037f, -0.038f, 0.039f, -0.04f, 0.041f, -0.042f, 0.043000000000000003f, -0.044f, 0.045f, -0.046f, 0.047f, -0.048f, 0.049f, -0.05f, 0.051000000000000004f, -0.052000000000000005f, 0.053f, -0.054f, 0.055f, -0.056f, 0.057f, -0.058f, 0.059000000000000004f, -0.06f, 0.061f, -0.062f, 0.063f, -0.064f, 0.065f, -0.066f, 0.067f, -0.068f, 0.069f, -0.07f, 0.07100000000000001f, -0.07200000000000001f, 0.073f, -0.074f, 0.075f, -0.076f, 0.077f, -0.078f, 0.079f, -0.08f, 0.081f, -0.082f, 0.083f, -0.084f, 0.085f, -0.08600000000000001f, 0.08700000000000001f, -0.088f, 0.089f, -0.09f, 0.091f, -0.092f, 0.093f, -0.094f, 0.095f, -0.096f, 0.097f, -0.098f, 0.099f, -0.1f, 0.101f, -0.10200000000000001f, 0.10300000000000001f, -0.10400000000000001f, 0.105f, -0.106f, 0.107f, -0.108f, 0.109f, -0.11f, 0.111f, -0.112f, 0.113f, -0.114f, 0.115f, -0.116f, 0.117f, -0.11800000000000001f, 0.11900000000000001f, -0.12f, 0.121f, -0.122f, 0.123f, -0.124f, 0.125f, -0.126f, 0.127f, -0.128f, 0.129f, -0.13f, 0.131f, -0.132f, 0.133f, -0.134f, 0.135f, -0.136f, 0.137f, -0.138f, 0.139f, -0.14f, 0.14100000000000001f, -0.14200000000000002f, 0.14300000000000002f, -0.14400000000000002f, 0.145f, -0.146f, 0.147f, -0.148f, 0.149f, -0.15f, 0.151f, -0.152f, 0.153f, -0.154f, 0.155f, -0.156f, 0.157f, -0.158f, 0.159f, -0.16f, 0.161f, -0.162f, 0.163f, -0.164f, 0.165f, -0.166f, 0.167f, -0.168f, 0.169f, -0.17f, 0.171f, -0.17200000000000001f, 0.17300000000000001f, -0.17400000000000002f, 0.17500000000000002f, -0.176f, 0.177f, -0.178f, 0.179f, -0.18f, 0.181f, -0.182f, 0.183f, -0.184f, 0.185f, -0.186f, 0.187f, -0.188f, 0.189f, -0.19f, 0.191f, -0.192f, 0.193f, -0.194f, 0.195f, -0.196f, 0.197f, -0.198f, 0.199f, -0.2f, 0.201f, -0.202f, 0.203f, -0.20400000000000001f, 0.20500000000000002f, -0.20600000000000002f, 0.20700000000000002f, -0.20800000000000002f, 0.209f, -0.21f, 0.211f, -0.212f, 0.213f, -0.214f, 0.215f, -0.216f, 0.217f, -0.218f, 0.219f, -0.22f, 0.221f, -0.222f, 0.223f, -0.224f, 0.225f, -0.226f, 0.227f, -0.228f, 0.229f, -0.23f, 0.231f, -0.232f, 0.233f, -0.234f, 0.23500000000000001f, -0.23600000000000002f, 0.23700000000000002f, -0.23800000000000002f, 0.23900000000000002f, -0.24f, 0.241f, -0.242f, 0.243f, -0.244f, 0.245f, -0.246f, 0.247f, -0.248f, 0.249f, -0.25f, 0.251f, -0.252f, 0.253f, -0.254f, 0.255f, -0.256f, 0.257f, -0.258f, 0.259f, -0.26f, 0.261f, -0.262f, 0.263f, -0.264f, 0.265f, -0.266f, 0.267f, -0.268f, 0.269f, -0.27f, 0.271f, -0.272f, 0.273f, -0.274f, 0.275f, -0.276f, 0.277f, -0.278f, 0.279f, -0.28f, 0.281f, -0.28200000000000003f, 0.28300000000000003f, -0.28400000000000003f, 0.28500000000000003f, -0.28600000000000003f, 0.28700000000000003f, -0.28800000000000003f, 0.289f, -0.29f, 0.291f, -0.292f, 0.293f, -0.294f, 0.295f, -0.296f, 0.297f, -0.298f, 0.299f, -0.3f, 0.301f, -0.302f, 0.303f, -0.304f, 0.305f, -0.306f, 0.307f, -0.308f, 0.309f, -0.31f, 0.311f, -0.312f, 0.313f, -0.314f, 0.315f, -0.316f, 0.317f, -0.318f, 0.319f, -0.32f, 0.321f, -0.322f, 0.323f, -0.324f, 0.325f, -0.326f, 0.327f, -0.328f, 0.329f, -0.33f, 0.331f, -0.332f, 0.333f, -0.334f, 0.335f, -0.336f, 0.337f, -0.338f, 0.339f, -0.34f, 0.341f, -0.342f, 0.343f, -0.34400000000000003f, 0.34500000000000003f, -0.34600000000000003f, 0.34700000000000003f, -0.34800000000000003f, 0.34900000000000003f, -0.35000000000000003f, 0.35100000000000003f, -0.352f, 0.353f, -0.354f, 0.355f, -0.356f, 0.357f, -0.358f, 0.359f, -0.36f, 0.361f, -0.362f, 0.363f, -0.364f, 0.365f, -0.366f, 0.367f, -0.368f, 0.369f, -0.37f, 0.371f, -0.372f, 0.373f, -0.374f, 0.375f, -0.376f, 0.377f, -0.378f, 0.379f, -0.38f, 0.381f, -0.382f, 0.383f, -0.384f, 0.385f, -0.386f, 0.387f, -0.388f, 0.389f, -0.39f, 0.391f, -0.392f, 0.393f, -0.394f, 0.395f, -0.396f, 0.397f, -0.398f, 0.399f, -0.4f, 0.401f, -0.402f, 0.403f, -0.404f, 0.405f, -0.406f, 0.40700000000000003f, -0.40800000000000003f, 0.40900000000000003f, -0.41000000000000003f, 0.41100000000000003f, -0.41200000000000003f, 0.41300000000000003f, -0.41400000000000003f, 0.41500000000000004f, -0.41600000000000004f, 0.417f, -0.418f, 0.419f, -0.42f, 0.421f, -0.422f, 0.423f, -0.424f, 0.425f, -0.426f, 0.427f, -0.428f, 0.429f, -0.43f, 0.431f, -0.432f, 0.433f, -0.434f, 0.435f, -0.436f, 0.437f, -0.438f, 0.439f, -0.44f, 0.441f, -0.442f, 0.443f, -0.444f, 0.445f, -0.446f, 0.447f, -0.448f, 0.449f, -0.45f, 0.451f, -0.452f, 0.453f, -0.454f, 0.455f, -0.456f, 0.457f, -0.458f, 0.459f, -0.46f, 0.461f, -0.462f, 0.463f, -0.464f, 0.465f, -0.466f, 0.467f, -0.468f, 0.46900000000000003f, -0.47000000000000003f, 0.47100000000000003f, -0.47200000000000003f, 0.47300000000000003f, -0.47400000000000003f, 0.47500000000000003f, -0.47600000000000003f, 0.47700000000000004f, -0.47800000000000004f, 0.47900000000000004f, -0.48f, 0.481f, -0.482f, 0.483f, -0.484f, 0.485f, -0.486f, 0.487f, -0.488f, 0.489f, -0.49f, 0.491f, -0.492f, 0.493f, -0.494f, 0.495f, -0.496f, 0.497f, -0.498f, 0.499f, -0.5f, 0.501f, -0.502f, 0.503f, -0.504f, 0.505f, -0.506f, 0.507f, -0.508f, 0.509f, -0.51f, 0.511f, -0.512f, 0.513f, -0.514f, 0.515f, -0.516f, 0.517f, -0.518f, 0.519f, -0.52f, 0.521f, -0.522f, 0.523f, -0.524f, 0.525f, -0.526f, 0.527f, -0.528f, 0.529f, -0.53f, 0.531f, -0.532f, 0.533f, -0.534f, 0.535f, -0.536f, 0.537f, -0.538f, 0.539f, -0.54f, 0.541f, -0.542f, 0.543f, -0.544f, 0.545f, -0.546f, 0.547f, -0.548f, 0.549f, -0.55f, 0.551f, -0.552f, 0.553f, -0.554f, 0.555f, -0.556f, 0.557f, -0.558f, 0.559f, -0.56f, 0.561f, -0.562f, 0.5630000000000001f, -0.5640000000000001f, 0.5650000000000001f, -0.5660000000000001f, 0.5670000000000001f, -0.5680000000000001f, 0.5690000000000001f, -0.5700000000000001f, 0.5710000000000001f, -0.5720000000000001f, 0.5730000000000001f, -0.5740000000000001f, 0.5750000000000001f, -0.5760000000000001f, 0.577f, -0.578f, 0.579f, -0.58f, 0.581f, -0.582f, 0.583f, -0.584f, 0.585f, -0.586f, 0.587f, -0.588f, 0.589f, -0.59f, 0.591f, -0.592f, 0.593f, -0.594f, 0.595f, -0.596f, 0.597f, -0.598f, 0.599f, -0.6f, 0.601f, -0.602f, 0.603f, -0.604f, 0.605f, -0.606f, 0.607f, -0.608f, 0.609f, -0.61f, 0.611f, -0.612f, 0.613f, -0.614f, 0.615f, -0.616f, 0.617f, -0.618f, 0.619f, -0.62f, 0.621f, -0.622f, 0.623f, -0.624f, 0.625f, -0.626f, 0.627f, -0.628f, 0.629f, -0.63f, 0.631f, -0.632f, 0.633f, -0.634f, 0.635f, -0.636f, 0.637f, -0.638f, 0.639f, -0.64f, 0.641f, -0.642f, 0.643f, -0.644f, 0.645f, -0.646f, 0.647f, -0.648f, 0.649f, -0.65f, 0.651f, -0.652f, 0.653f, -0.654f, 0.655f, -0.656f, 0.657f, -0.658f, 0.659f, -0.66f, 0.661f, -0.662f, 0.663f, -0.664f, 0.665f, -0.666f, 0.667f, -0.668f, 0.669f, -0.67f, 0.671f, -0.672f, 0.673f, -0.674f, 0.675f, -0.676f, 0.677f, -0.678f, 0.679f, -0.68f, 0.681f, -0.682f, 0.683f, -0.684f, 0.685f, -0.686f, 0.687f, -0.6880000000000001f, 0.6890000000000001f, -0.6900000000000001f, 0.6910000000000001f, -0.6920000000000001f, 0.6930000000000001f, -0.6940000000000001f, 0.6950000000000001f, -0.6960000000000001f, 0.6970000000000001f, -0.6980000000000001f, 0.6990000000000001f, -0.7000000000000001f, 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4bb4c4fb6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu1_quant8_1.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: relu1_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 2}}} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {4, 10, 100, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 2, 2, 2}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu1_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu1_quant8_2.example.cpp deleted file mode 100644 index a5dd65890..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu1_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu1_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 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233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 128, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 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129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu6_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu6_float_1.example.cpp deleted file mode 100644 index 2e2ebe423..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu6_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu6_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-10.0f, -0.5f, 0.5f, 10.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, 0.0f, 0.5f, 6.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu6_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu6_float_2.example.cpp deleted file mode 100644 index c6fdc8d93..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu6_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu6_float_2.mod.py). 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See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.0f, 0.001f, 0, 0.003f, 0, 0.005f, 0, 0.007f, 0, 0.009000000000000001f, 0, 0.011f, 0, 0.013000000000000001f, 0, 0.015f, 0, 0.017f, 0, 0.019f, 0, 0.021f, 0, 0.023f, 0, 0.025f, 0, 0.027f, 0, 0.029f, 0, 0.031f, 0, 0.033f, 0, 0.035f, 0, 0.037f, 0, 0.039f, 0, 0.041f, 0, 0.043000000000000003f, 0, 0.045f, 0, 0.047f, 0, 0.049f, 0, 0.051000000000000004f, 0, 0.053f, 0, 0.055f, 0, 0.057f, 0, 0.059000000000000004f, 0, 0.061f, 0, 0.063f, 0, 0.065f, 0, 0.067f, 0, 0.069f, 0, 0.07100000000000001f, 0, 0.073f, 0, 0.075f, 0, 0.077f, 0, 0.079f, 0, 0.081f, 0, 0.083f, 0, 0.085f, 0, 0.08700000000000001f, 0, 0.089f, 0, 0.091f, 0, 0.093f, 0, 0.095f, 0, 0.097f, 0, 0.099f, 0, 0.101f, 0, 0.10300000000000001f, 0, 0.105f, 0, 0.107f, 0, 0.109f, 0, 0.111f, 0, 0.113f, 0, 0.115f, 0, 0.117f, 0, 0.11900000000000001f, 0, 0.121f, 0, 0.123f, 0, 0.125f, 0, 0.127f, 0, 0.129f, 0, 0.131f, 0, 0.133f, 0, 0.135f, 0, 0.137f, 0, 0.139f, 0, 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4.023f, 0, 4.025f, 0, 4.027f, 0, 4.029f, 0, 4.031f, 0, 4.033f, 0, 4.035f, 0, 4.037f, 0, 4.039f, 0, 4.041f, 0, 4.043f, 0, 4.045f, 0, 4.047f, 0, 4.049f, 0, 4.051f, 0, 4.053f, 0, 4.055f, 0, 4.057f, 0, 4.059f, 0, 4.061f, 0, 4.063f, 0, 4.065f, 0, 4.067f, 0, 4.069f, 0, 4.071f, 0, 4.073f, 0, 4.075f, 0, 4.077f, 0, 4.079f, 0, 4.081f, 0, 4.083f, 0, 4.085f, 0, 4.087f, 0, 4.089f, 0, 4.091f, 0, 4.093f, 0, 4.095f, 0, 4.097f, 0, 4.099f, 0, 4.101f, 0, 4.103f, 0, 4.105f, 0, 4.107f, 0, 4.109f, 0, 4.111f, 0, 4.113f, 0, 4.115f, 0, 4.117f, 0, 4.119f, 0, 4.121f, 0, 4.123f, 0, 4.125f, 0, 4.127f, 0, 4.1290000000000004f, 0, 4.131f, 0, 4.133f, 0, 4.135f, 0, 4.1370000000000005f, 0, 4.139f, 0, 4.141f, 0, 4.143f, 0, 4.1450000000000005f, 0, 4.147f, 0, 4.149f, 0, 4.151f, 0, 4.1530000000000005f, 0, 4.155f, 0, 4.157f, 0, 4.159f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_1.example.cpp deleted file mode 100644 index cc99a692d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_1.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: relu6_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 11, 12}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 11, 12}}} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {13, 14, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {12, 12, 12, 12}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_2.example.cpp deleted file mode 100644 index 1620cc7c1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu6_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu6_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 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249, 250, 251, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 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134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu_float_1.example.cpp deleted file mode 100644 index ead346c45..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-10.0f, -0.5f, 0.5f, 10.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, 0.0f, 0.5f, 10.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu_float_2.example.cpp deleted file mode 100644 index b983355cf..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.0f, 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f, 9.0f, -10.0f, 11.0f, -12.0f, 13.0f, -14.0f, 15.0f, -16.0f, 17.0f, -18.0f, 19.0f, -20.0f, 21.0f, -22.0f, 23.0f, -24.0f, 25.0f, -26.0f, 27.0f, -28.0f, 29.0f, -30.0f, 31.0f, -32.0f, 33.0f, -34.0f, 35.0f, -36.0f, 37.0f, -38.0f, 39.0f, -40.0f, 41.0f, -42.0f, 43.0f, -44.0f, 45.0f, -46.0f, 47.0f, -48.0f, 49.0f, -50.0f, 51.0f, -52.0f, 53.0f, -54.0f, 55.0f, -56.0f, 57.0f, -58.0f, 59.0f, -60.0f, 61.0f, -62.0f, 63.0f, -64.0f, 65.0f, -66.0f, 67.0f, -68.0f, 69.0f, -70.0f, 71.0f, -72.0f, 73.0f, -74.0f, 75.0f, -76.0f, 77.0f, -78.0f, 79.0f, -80.0f, 81.0f, -82.0f, 83.0f, -84.0f, 85.0f, -86.0f, 87.0f, -88.0f, 89.0f, -90.0f, 91.0f, -92.0f, 93.0f, -94.0f, 95.0f, -96.0f, 97.0f, -98.0f, 99.0f, -100.0f, 101.0f, -102.0f, 103.0f, -104.0f, 105.0f, -106.0f, 107.0f, -108.0f, 109.0f, -110.0f, 111.0f, -112.0f, 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1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 11, 0, 13, 0, 15, 0, 17, 0, 19, 0, 21, 0, 23, 0, 25, 0, 27, 0, 29, 0, 31, 0, 33, 0, 35, 0, 37, 0, 39, 0, 41, 0, 43, 0, 45, 0, 47, 0, 49, 0, 51, 0, 53, 0, 55, 0, 57, 0, 59, 0, 61, 0, 63, 0, 65, 0, 67, 0, 69, 0, 71, 0, 73, 0, 75, 0, 77, 0, 79, 0, 81, 0, 83, 0, 85, 0, 87, 0, 89, 0, 91, 0, 93, 0, 95, 0, 97, 0, 99, 0, 101, 0, 103, 0, 105, 0, 107, 0, 109, 0, 111, 0, 113, 0, 115, 0, 117, 0, 119, 0, 121, 0, 123, 0, 125, 0, 127, 0, 129, 0, 131, 0, 133, 0, 135, 0, 137, 0, 139, 0, 141, 0, 143, 0, 145, 0, 147, 0, 149, 0, 151, 0, 153, 0, 155, 0, 157, 0, 159, 0, 161, 0, 163, 0, 165, 0, 167, 0, 169, 0, 171, 0, 173, 0, 175, 0, 177, 0, 179, 0, 181, 0, 183, 0, 185, 0, 187, 0, 189, 0, 191, 0, 193, 0, 195, 0, 197, 0, 199, 0, 201, 0, 203, 0, 205, 0, 207, 0, 209, 0, 211, 0, 213, 0, 215, 0, 217, 0, 219, 0, 221, 0, 223, 0, 225, 0, 227, 0, 229, 0, 231, 0, 233, 0, 235, 0, 237, 0, 239, 0, 241, 0, 243, 0, 245, 0, 247, 0, 249, 0, 251, 0, 253, 0, 255, 0, 257, 0, 259, 0, 261, 0, 263, 0, 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515b0671c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu_quant8_1.example.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: relu_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 126, 127}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 128, 128, 128}}} -} -}, // End of an example -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 129, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {128, 129, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/relu_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/relu_quant8_2.example.cpp deleted file mode 100644 index a59fcf068..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/relu_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: relu_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/reshape.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/reshape.example.cpp deleted file mode 100644 index 054b2f45f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/reshape.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8.example.cpp deleted file mode 100644 index e959af7ce..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8_weights_as_inputs.example.cpp deleted file mode 100644 index 40260cfe1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/reshape_quant8_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape_quant8_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{1, {-1}}}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/reshape_weights_as_inputs.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/reshape_weights_as_inputs.example.cpp deleted file mode 100644 index d97b03ee9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/reshape_weights_as_inputs.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape_weights_as_inputs.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}}, - // int -> INT32 map - {{1, {-1}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear.example.cpp deleted file mode 100644 index 1b2bce5eb..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: resize_bilinear.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 2.0f, 2.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 1.0f, 1.0f, 1.666666667f, 1.666666667f, 1.666666667f, 2.0f, 2.0f, 2.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear_2.example.cpp deleted file mode 100644 index df5d4bfea..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/resize_bilinear_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: resize_bilinear_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3, 4, 6, 10, 9, 10, 12, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3, 4, 5, 8, 6, 10, 7, 8, 9, 12, 10, 14, 9, 10, 11, 14, 12, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/rnn.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/rnn.example.cpp deleted file mode 100644 index 0697fb122..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/rnn.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: rnn.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f, 0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f}}, {1, {0.461459f, 0.153381f, 0.529743f, -0.00371218f, 0.676267f, -0.211346f, 0.317493f, 0.969689f, -0.343251f, 0.186423f, 0.398151f, 0.152399f, 0.448504f, 0.317662f, 0.523556f, -0.323514f, 0.480877f, 0.333113f, -0.757714f, -0.674487f, -0.643585f, 0.217766f, -0.0251462f, 0.79512f, -0.595574f, -0.422444f, 0.371572f, -0.452178f, -0.556069f, -0.482188f, -0.685456f, -0.727851f, 0.841829f, 0.551535f, -0.232336f, 0.729158f, -0.00294906f, -0.69754f, 0.766073f, -0.178424f, 0.369513f, -0.423241f, 0.548547f, -0.0152023f, -0.757482f, -0.85491f, 0.251331f, -0.989183f, 0.306261f, -0.340716f, 0.886103f, -0.0726757f, -0.723523f, -0.784303f, 0.0354295f, 0.566564f, -0.485469f, -0.620498f, 0.832546f, 0.697884f, -0.279115f, 0.294415f, -0.584313f, 0.548772f, 0.0648819f, 0.968726f, 0.723834f, -0.0080452f, -0.350386f, -0.272803f, 0.115121f, -0.412644f, -0.824713f, -0.992843f, -0.592904f, -0.417893f, 0.863791f, -0.423461f, -0.147601f, -0.770664f, -0.479006f, 0.654782f, 0.587314f, -0.639158f, 0.816969f, -0.337228f, 0.659878f, 0.73107f, 0.754768f, -0.337042f, 0.0960841f, 0.368357f, 0.244191f, -0.817703f, -0.211223f, 0.442012f, 0.37225f, -0.623598f, -0.405423f, 0.455101f, 0.673656f, -0.145345f, -0.511346f, -0.901675f, -0.81252f, -0.127006f, 0.809865f, -0.721884f, 0.636255f, 0.868989f, -0.347973f, -0.10179f, -0.777449f, 0.917274f, 0.819286f, 0.206218f, -0.00785118f, 0.167141f, 0.45872f, 0.972934f, -0.276798f, 0.837861f, 0.747958f, -0.0151566f, -0.330057f, -0.469077f, 0.277308f, 0.415818f}}, {2, {0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f}}, {3, {0.065691948f, -0.69055247f, 0.1107955f, -0.97084129f, -0.23957068f, -0.23566568f, -0.389184f, 0.47481549f, -0.4791103f, 0.29931796f, 0.10463274f, 0.83918178f, 0.37197268f, 0.61957061f, 0.3956964f, -0.37609905f}}, {4, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {1, {0.496726f, 0, 0.965996f, 0, 0.0584254f, 0, 0, 0.12315f, 0, 0, 0.612266f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291232f, 0.496726f, 0, 0.965996f, 0, 0.0584254f, 0, 0, 0.12315f, 0, 0, 0.612266f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291232f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/rnn_state.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/rnn_state.example.cpp deleted file mode 100644 index 3766691f2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/rnn_state.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: rnn_state.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.69424844f, -0.93421471f, -0.87287879f, 0.37144363f, -0.62476718f, 0.23791671f, 0.40060222f, 0.1356622f, -0.69424844f, -0.93421471f, -0.87287879f, 0.37144363f, -0.62476718f, 0.23791671f, 0.40060222f, 0.1356622f}}, {1, {0.461459f, 0.153381f, 0.529743f, -0.00371218f, 0.676267f, -0.211346f, 0.317493f, 0.969689f, -0.343251f, 0.186423f, 0.398151f, 0.152399f, 0.448504f, 0.317662f, 0.523556f, -0.323514f, 0.480877f, 0.333113f, -0.757714f, -0.674487f, -0.643585f, 0.217766f, -0.0251462f, 0.79512f, -0.595574f, -0.422444f, 0.371572f, -0.452178f, -0.556069f, -0.482188f, -0.685456f, -0.727851f, 0.841829f, 0.551535f, -0.232336f, 0.729158f, -0.00294906f, -0.69754f, 0.766073f, -0.178424f, 0.369513f, -0.423241f, 0.548547f, -0.0152023f, -0.757482f, -0.85491f, 0.251331f, -0.989183f, 0.306261f, -0.340716f, 0.886103f, -0.0726757f, -0.723523f, -0.784303f, 0.0354295f, 0.566564f, -0.485469f, -0.620498f, 0.832546f, 0.697884f, -0.279115f, 0.294415f, -0.584313f, 0.548772f, 0.0648819f, 0.968726f, 0.723834f, -0.0080452f, -0.350386f, -0.272803f, 0.115121f, -0.412644f, -0.824713f, -0.992843f, -0.592904f, -0.417893f, 0.863791f, -0.423461f, -0.147601f, -0.770664f, -0.479006f, 0.654782f, 0.587314f, -0.639158f, 0.816969f, -0.337228f, 0.659878f, 0.73107f, 0.754768f, -0.337042f, 0.0960841f, 0.368357f, 0.244191f, -0.817703f, -0.211223f, 0.442012f, 0.37225f, -0.623598f, -0.405423f, 0.455101f, 0.673656f, -0.145345f, -0.511346f, -0.901675f, -0.81252f, -0.127006f, 0.809865f, -0.721884f, 0.636255f, 0.868989f, -0.347973f, -0.10179f, -0.777449f, 0.917274f, 0.819286f, 0.206218f, -0.00785118f, 0.167141f, 0.45872f, 0.972934f, -0.276798f, 0.837861f, 0.747958f, -0.0151566f, -0.330057f, -0.469077f, 0.277308f, 0.415818f}}, {2, {0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f}}, {3, {0.065691948f, -0.69055247f, 0.1107955f, -0.97084129f, -0.23957068f, -0.23566568f, -0.389184f, 0.47481549f, -0.4791103f, 0.29931796f, 0.10463274f, 0.83918178f, 0.37197268f, 0.61957061f, 0.3956964f, -0.37609905f}}, {4, {0.496726f, 0, 0.965996f, 0, 0.0584256f, 0, 0, 0.12315f, 0, 0, 0.612267f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291233f, 0.496726f, 0, 0.965996f, 0, 0.0584256f, 0, 0, 0.12315f, 0, 0, 0.612267f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291233f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, 0.524902f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355731f, 0, 0, 0, 0, 0.524902f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355731f, 0, 0}}, {1, {0, 0, 0.524901f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355727f, 0, 0, 0, 0, 0.524901f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355727f, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/softmax_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/softmax_float_1.example.cpp deleted file mode 100644 index d2cf06af9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/softmax_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: softmax_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 10.0f, 20.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.25f, 0.25f, 0.25f, 0.25f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/softmax_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/softmax_float_2.example.cpp deleted file mode 100644 index deba11aec..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/softmax_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: softmax_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, -1.0f, -2.0f, -3.0f, -4.0f, -5.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.011656231f, 0.031684921f, 0.086128544f, 0.234121657f, 0.636408647f, 0.636408647f, 0.234121657f, 0.086128544f, 0.031684921f, 0.011656231f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_1.example.cpp deleted file mode 100644 index 7b7506ed3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: softmax_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 10, 20}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {64, 64, 64, 64}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_2.example.cpp deleted file mode 100644 index 8152c89a9..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/softmax_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: softmax_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 255, 254, 253, 252, 251}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {15, 24, 40, 67, 110, 110, 67, 40, 24, 15}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch.example.cpp deleted file mode 100644 index e226e3634..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_1.example.cpp deleted file mode 100644 index 06d0ff34d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_2.example.cpp deleted file mode 100644 index a7b001022..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, 0, 5, 0, 0, 0, 6, 0, 1, 0, 7, 0, 2, 0, 8, 0, 3, 0, 9, 0, 4, 0, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_3.example.cpp deleted file mode 100644 index 5198bae1e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_1.example.cpp deleted file mode 100644 index 1c86710b0..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_2.example.cpp deleted file mode 100644 index 4e615d0e5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 0, 0, 5, 0, 0, 0, 6, 0, 1, 0, 7, 0, 2, 0, 8, 0, 3, 0, 9, 0, 4, 0, 10}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_3.example.cpp deleted file mode 100644 index 13745acdd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_batch_quant8_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_batch_quant8_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_1.example.cpp deleted file mode 100644 index d76f8e799..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_depth_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_2.example.cpp deleted file mode 100644 index 452df7a6f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_depth_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 5.0f, 6.0f, 3.0f, 4.0f, 7.0f, 8.0f, 9.0f, 10.0f, 13.0f, 14.0f, 11.0f, 12.0f, 15.0f, 16.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_3.example.cpp deleted file mode 100644 index ef6928b25..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_depth_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 20, 11, 21, 12, 22, 13, 23, 14, 24, 15, 25, 16, 26, 17, 27, 18, 28, 19, 29, 110, 210, 111, 211, 112, 212, 113, 213, 114, 214, 115, 215}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {10, 20, 11, 21, 14, 24, 15, 25, 12, 22, 13, 23, 16, 26, 17, 27, 18, 28, 19, 29, 112, 212, 113, 213, 110, 210, 111, 211, 114, 214, 115, 215}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_1.example.cpp deleted file mode 100644 index 5c82f5d4e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_depth_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 252, 253, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_2.example.cpp deleted file mode 100644 index 9cc30e773..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/space_to_depth_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: space_to_depth_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 248, 249, 250, 251, 252, 253, 254, 255}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 4, 5, 2, 3, 6, 7, 248, 249, 252, 253, 250, 251, 254, 255}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/squeeze.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/squeeze.example.cpp deleted file mode 100644 index bcbc54f6c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/squeeze.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: squeeze.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/squeeze_2D_float_1_nnfw.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/squeeze_2D_float_1_nnfw.example.cpp deleted file mode 100644 index 31a82efa8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/squeeze_2D_float_1_nnfw.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: squeeze_2D_float_1_nnfw.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.4f, 2.3f, 3.2f, 4.1f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/squeeze_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/squeeze_float_1.example.cpp deleted file mode 100644 index 2616d6587..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/squeeze_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: squeeze_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/squeeze_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/squeeze_quant8_1.example.cpp deleted file mode 100644 index 53bb0a832..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/squeeze_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: squeeze_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice.example.cpp deleted file mode 100644 index 6f93529e2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 3.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_1.example.cpp deleted file mode 100644 index fe389a455..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_10.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_10.example.cpp deleted file mode 100644 index cbd0a4911..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_10.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_10.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_2.example.cpp deleted file mode 100644 index 91dfc5f07..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_3.example.cpp deleted file mode 100644 index 1a12e90f0..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_4.example.cpp deleted file mode 100644 index 1e1d0005d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_5.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_5.example.cpp deleted file mode 100644 index d3aa972f7..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_5.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_5.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_6.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_6.example.cpp deleted file mode 100644 index 958e561a8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_6.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_6.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_7.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_7.example.cpp deleted file mode 100644 index 2a2861ae2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_7.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_7.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3, 2, 1}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_8.example.cpp deleted file mode 100644 index 63a9b4ce1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6, 5, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_9.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_9.example.cpp deleted file mode 100644 index ae7f829bd..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_ex_float_9.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_ex_float_9.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 4, 5}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_1.example.cpp deleted file mode 100644 index 9e996dd6c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_10.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_10.example.cpp deleted file mode 100644 index 5350c61cf..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_10.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_10.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_11.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_11.example.cpp deleted file mode 100644 index 19bb573a6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_11.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_11.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_2.example.cpp deleted file mode 100644 index b1c0011f5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_3.example.cpp deleted file mode 100644 index fbd8616b1..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_4.example.cpp deleted file mode 100644 index 0ec32c310..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_5.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_5.example.cpp deleted file mode 100644 index a2b8b66bc..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_5.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_5.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_6.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_6.example.cpp deleted file mode 100644 index 0a318b0d2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_6.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_6.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_7.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_7.example.cpp deleted file mode 100644 index 72cdcd373..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_7.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_7.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3, 2, 1}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_8.example.cpp deleted file mode 100644 index 528e2d607..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6, 5, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_9.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_9.example.cpp deleted file mode 100644 index 2920d8e56..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_float_9.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_float_9.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 3, 4, 5, 6}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2, 4, 5}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_10.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_10.example.cpp deleted file mode 100644 index 091aa0623..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_10.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_qaunt8_10.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {4, 5, 6}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_11.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_11.example.cpp deleted file mode 100644 index 8e8c23eb8..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_qaunt8_11.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_qaunt8_11.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_1.example.cpp deleted file mode 100644 index 6eb2d951a..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_2.example.cpp deleted file mode 100644 index 481fe2e12..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_3.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_3.example.cpp deleted file mode 100644 index a68e88219..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_3.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_3.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_4.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_4.example.cpp deleted file mode 100644 index aa486a4e3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_4.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_4.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_5.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_5.example.cpp deleted file mode 100644 index db8458096..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_5.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_5.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_6.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_6.example.cpp deleted file mode 100644 index 232e8c4b0..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_6.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_6.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {2, 3, 4}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_7.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_7.example.cpp deleted file mode 100644 index 86f32fea2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_7.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_7.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {3, 2, 1}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_8.example.cpp deleted file mode 100644 index fe5026d1e..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {6, 5, 4}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_9.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_9.example.cpp deleted file mode 100644 index dd590e482..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/strided_slice_quant8_9.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: strided_slice_quant8_9.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 3, 4, 5, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {1, 2, 4, 5}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/sub.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/sub.example.cpp deleted file mode 100644 index 282dbe941..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/sub.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: sub.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {2.0f, -4.0f, 8.0f, -16.0f}}, {1, {2.0f, -2.0f, -4.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.0f, -2.0f, 12.0f, -20.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/sub_broadcast_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/sub_broadcast_float.example.cpp deleted file mode 100644 index 4888c76ee..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/sub_broadcast_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: sub_broadcast_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1, 2}}, {1, {1, 2, 3, 4}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 0, -2, -2}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/svdf.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/svdf.example.cpp deleted file mode 100644 index 1e2b54db6..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/svdf.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: svdf.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {}}, {4, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0.014899f, -0.0517661f, -0.143725f, -0.00271883f, 0.014899f, -0.0517661f, -0.143725f, -0.00271883f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/svdf2.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/svdf2.example.cpp deleted file mode 100644 index 602cddf75..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/svdf2.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: svdf2.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.35867718f, 0.36897406f, 0.73463392f}}, {1, {-0.31930989f, 0.0079667f, 0.39296314f, 0.37613347f, 0.12416199f, 0.15785322f, 0.27901134f, 0.3905206f, 0.21931258f, -0.36137494f, -0.10640851f, 0.31053296f, -0.36118156f, -0.0976817f, -0.36916667f, 0.22197971f, 0.15294972f, 0.38031587f, 0.27557442f, 0.39635518f, -0.21580373f, -0.06634006f, -0.02702999f, 0.27072677f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f, -0.14884081f, 0.19931212f, -0.36002168f, 0.34663299f, -0.11405486f, 0.12672701f, 0.39463779f, -0.07886535f, -0.06384811f, 0.08249187f, -0.26816407f, -0.19905911f, 0.29211238f, 0.31264046f, -0.28664589f, 0.05698794f, 0.11613581f, 0.14078894f, 0.02187902f, -0.21781836f, -0.15567942f, 0.08693647f, -0.38256618f, 0.36580828f, -0.22922277f, -0.0226903f, 0.12878349f, -0.28122205f, -0.10850525f, -0.11955214f, 0.27179423f, -0.04710215f, 0.31069002f, 0.22672787f, 0.09580326f, 0.08682203f, 0.1258215f, 0.1851041f, 0.29228821f, 0.12366763f}}, {3, {}}, {4, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {-0.09623547f, -0.10193135f, 0.11083051f, -0.0347917f, 0.1141196f, 0.12965347f, -0.12652366f, 0.01007236f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/svdf_state.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/svdf_state.example.cpp deleted file mode 100644 index 99604a4ba..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/svdf_state.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: svdf_state.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0.14278367f, -1.64410412f, -0.75222826f, 0.14278367f, -1.64410412f, -0.75222826f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {}}, {4, {0, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{1, {0.068281f, -0.162217f, -0.152268f, 0.00323521f, 0.068281f, -0.162217f, -0.152268f, 0.00323521f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0.119996f, 0.542235f, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, -0.40465f, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, -0.706995f, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0.137515f, 0, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0.542235f, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, -0.40465f, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, -0.706995f, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0.137515f, 0}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/tanh.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/tanh.example.cpp deleted file mode 100644 index 5c12ca093..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/tanh.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: tanh.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1, 0, 1, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.761594156f, 0, 0.761594156f, 0.999999996f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/tanh_.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/tanh_.example.cpp deleted file mode 100644 index 36fbb3715..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/tanh_.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: tanh_.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-1, 0, 1, 10}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {-0.761594156f, 0, 0.761594156f, 0.999999996f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_float.example.cpp deleted file mode 100644 index 650eecd78..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: tensorflowmax_ex_2D_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.2f, 11.47f, 3.8f, 5.76f, 28.2f, 0.999f, -1.3f, -13.5f, -3.4f, -22.1f, -2.2f, -49.7f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {11.47f, 28.2f, -2.2f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_int32.example.cpp deleted file mode 100644 index 3ce040497..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/tensorflowmax_ex_2D_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: tensorflowmax_ex_2D_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {3, 11, 3, 5, 28, 0, -1, -13, -4, -22, -2, -49}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {11, 28, -2}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_float.example.cpp deleted file mode 100644 index 44d438cf5..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_1D_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {5.123456789123456f, 3.123456789123457f, 4.123456789123456f, 6.123456789123456f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6.123456789123456f, 5.123456789123456f}}}, - // int -> INT32 map - {{1, {3, 0}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_int32.example.cpp deleted file mode 100644 index 3702e5a82..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_1D_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {50000, 40000, 41000, 60000}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {60000, 50000}}, {1, {3, 0}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_quant8.example.cpp deleted file mode 100644 index ce173e97d..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_1D_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_1D_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {7, 4, 5, 6}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{1, {0, 3}}}, - // int -> QUANT8_ASYMM map - {{0, {7, 6}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_float.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_float.example.cpp deleted file mode 100644 index 393a7a8b3..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_float.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_2D_float.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {3.123456789123457f, 4.123456789123456f, 5.123456789123456f, 6.123456789123456f, 7.123456789123456f, 8.123456789123457f, 9.123456789123457f, 1.1234567891234568f, 2.123456789123457f, 18.123456789123455f, 19.123456789123455f, 11.123456789123457f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {6.123456789123456f, 5.123456789123456f, 9.123456789123457f, 8.123456789123457f, 19.123456789123455f, 18.123456789123455f}}}, - // int -> INT32 map - {{1, {3, 2, 2, 1, 2, 1}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_int32.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_int32.example.cpp deleted file mode 100644 index 62045728b..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_int32.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_2D_int32.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {40000, 41000, 50000, 60000, 70000, 80000, 90000, 79000, 170000, 180000, 190000, 110000}}}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{0, {60000, 50000, 90000, 80000, 190000, 180000}}, {1, {3, 2, 2, 1, 2, 1}}}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_quant8.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_quant8.example.cpp deleted file mode 100644 index 5a192ce8f..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/topk_v2_2D_quant8.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: topk_v2_2D_quant8.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {3, 4, 5, 6, 7, 8, 9, 1, 2, 18, 19, 11}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {{1, {3, 2, 2, 1, 2, 1}}}, - // int -> QUANT8_ASYMM map - {{0, {6, 5, 9, 8, 19, 18}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/transpose.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/transpose.example.cpp deleted file mode 100644 index 790923cc2..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/transpose.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: transpose.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 2.0f, 3.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {1.0f, 3.0f, 2.0f, 4.0f}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/transpose_float_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/transpose_float_1.example.cpp deleted file mode 100644 index 31f67994c..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/transpose_float_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: transpose_float_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {{0, {0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119}}}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/examples/transpose_quant8_1.example.cpp b/runtimes/tests/neural_networks_test/generated/examples/transpose_quant8_1.example.cpp deleted file mode 100644 index f1bb2fa16..000000000 --- a/runtimes/tests/neural_networks_test/generated/examples/transpose_quant8_1.example.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: transpose_quant8_1.mod.py). Do not edit -// Begin of an example -{ -//Input(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}}} -}, -//Output(s) -{ // See tools/test_generator/include/TestHarness.h:MixedTyped - // int -> FLOAT32 map - {}, - // int -> INT32 map - {}, - // int -> QUANT8_ASYMM map - {{0, {0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119}}} -} -}, // End of an example diff --git a/runtimes/tests/neural_networks_test/generated/models/add.model.cpp b/runtimes/tests/neural_networks_test/generated/models/add.model.cpp deleted file mode 100644 index e8b3aa130..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/add.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: add.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/add_broadcast_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/add_broadcast_quant8.model.cpp deleted file mode 100644 index 3dd6f3fe6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/add_broadcast_quant8.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: add_broadcast_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 2.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 1.0, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/add_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/add_quant8.model.cpp deleted file mode 100644 index 57c7c5708..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/add_quant8.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: add_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2}, 2.0, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_ADD, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_1.model.cpp deleted file mode 100644 index 9f3b02e7e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_1.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: avg_pool_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_2.model.cpp deleted file mode 100644 index f6f2e5b90..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_2.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: avg_pool_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {5, 11, 13, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {5, 52, 60, 3}); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {5}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {100}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {50}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_3.model.cpp deleted file mode 100644 index c056c61c3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_3.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: avg_pool_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 200, 180, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 96, 86, 1}); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {2}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {10}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_4.model.cpp deleted file mode 100644 index d3b613391..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_4.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: avg_pool_float_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {5, 11, 13, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {5, 52, 60, 3}); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto relu6_activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {5}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {100}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {50}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t relu6_activation_init[] = {3}; - model->setOperandValue(relu6_activation, relu6_activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_5.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_5.model.cpp deleted file mode 100644 index 1e0aa941b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_float_5.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: avg_pool_float_5.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 4, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons2 = model->addOperand(&type1); - auto pad_same = model->addOperand(&type1); - auto act_none = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t cons2_init[] = {2}; - model->setOperandValue(cons2, cons2_init, sizeof(int32_t) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad_same, cons2, cons2, cons2, cons2, act_none}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_1.model.cpp deleted file mode 100644 index c14c38324..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_1.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: avg_pool_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_2.model.cpp deleted file mode 100644 index 0d346d48d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_2.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: avg_pool_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {5, 11, 13, 3}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {5, 52, 60, 3}, 0.5f, 0); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {5}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {10}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {5}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_3.model.cpp deleted file mode 100644 index 4bcab8fd4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_3.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: avg_pool_quant8_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 100, 100, 1}, 0.5f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 23, 23, 1}, 0.5f, 0); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {4}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {10}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_4.model.cpp deleted file mode 100644 index 998138778..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_4.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: avg_pool_quant8_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto relu1_activitation = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t relu1_activitation_init[] = {2}; - model->setOperandValue(relu1_activitation, relu1_activitation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, relu1_activitation}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_5.model.cpp b/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_5.model.cpp deleted file mode 100644 index 31b6c1834..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/avg_pool_quant8_5.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: avg_pool_quant8_5.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1, 2, 1}, 0.0625f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 4, 1}, 0.0625f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons2 = model->addOperand(&type1); - auto pad_same = model->addOperand(&type1); - auto act_none = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t cons2_init[] = {2}; - model->setOperandValue(cons2, cons2_init, sizeof(int32_t) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op1, pad_same, cons2, cons2, cons2, cons2, act_none}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/batch_to_space.model.cpp b/runtimes/tests/neural_networks_test/generated/models/batch_to_space.model.cpp deleted file mode 100644 index 6c6d5900c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/batch_to_space.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: batch_to_space.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 1, 1, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/batch_to_space_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/batch_to_space_float_1.model.cpp deleted file mode 100644 index e074783e5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/batch_to_space_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: batch_to_space_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 4, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 2, 2, 1}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/batch_to_space_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/batch_to_space_quant8_1.model.cpp deleted file mode 100644 index 892274029..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/batch_to_space_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: batch_to_space_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 1}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - model->addOperation(ANEURALNETWORKS_BATCH_TO_SPACE_ND, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/cast_ex_float32_to_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/cast_ex_float32_to_int32.model.cpp deleted file mode 100644 index d28671b22..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/cast_ex_float32_to_int32.model.cpp +++ /dev/null @@ -1,20 +0,0 @@ -// Generated file (from: cast_ex_float32_to_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - // Phase 2, operations - model->addOperationEx(ANEURALNETWORKS_CAST_EX, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/cast_ex_int32_to_float32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/cast_ex_int32_to_float32.model.cpp deleted file mode 100644 index af435bff8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/cast_ex_int32_to_float32.model.cpp +++ /dev/null @@ -1,20 +0,0 @@ -// Generated file (from: cast_ex_int32_to_float32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type0(Type::TENSOR_INT32, {2, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - // Phase 2, operations - model->addOperationEx(ANEURALNETWORKS_CAST_EX, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_float_1.model.cpp deleted file mode 100644 index a40506dcd..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_float_1.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: concat_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto axis0 = model->addOperand(&type1); - auto result = model->addOperand(&type2); - // Phase 2, operations - static int32_t axis0_init[] = {0}; - model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis0}, {result}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {result}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_float_2.model.cpp deleted file mode 100644 index 2938a1aec..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_float_2.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: concat_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {40, 230}); - OperandType type0(Type::TENSOR_FLOAT32, {52, 230}); - OperandType type3(Type::TENSOR_FLOAT32, {92, 230}); - // Phase 1, operands - auto input1 = model->addOperand(&type0); - auto input2 = model->addOperand(&type1); - auto axis0 = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis0_init[] = {0}; - model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input1, input2}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_float_3.model.cpp deleted file mode 100644 index ac5cc8f2a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_float_3.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: concat_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {212, 30}); - OperandType type0(Type::TENSOR_FLOAT32, {212, 60}); - OperandType type3(Type::TENSOR_FLOAT32, {212, 90}); - // Phase 1, operands - auto input1 = model->addOperand(&type0); - auto input2 = model->addOperand(&type1); - auto axis1 = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis1_init[] = {1}; - model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input1, input2}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_float_4D_axis3_1_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_float_4D_axis3_1_nnfw.model.cpp deleted file mode 100644 index 82ef41dc9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_float_4D_axis3_1_nnfw.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: concat_float_4D_axis3_1_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 3, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 2, 3, 6}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type0); - auto axis0 = model->addOperand(&type1); - auto result = model->addOperand(&type2); - // Phase 2, operations - static int32_t axis0_init[] = {3}; - model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, op3, axis0}, {result}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {result}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_quant8_1.model.cpp deleted file mode 100644 index ab377d6a4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_1.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: concat_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 0.5f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 6}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto axis1 = model->addOperand(&type1); - auto result = model->addOperand(&type2); - // Phase 2, operations - static int32_t axis1_init[] = {1}; - model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {op1, op2, axis1}, {result}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {result}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_quant8_2.model.cpp deleted file mode 100644 index 286b5d82c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_2.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: concat_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {40, 300}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {52, 300}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {92, 300}, 0.5f, 0); - // Phase 1, operands - auto input1 = model->addOperand(&type0); - auto input2 = model->addOperand(&type1); - auto axis0 = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis0_init[] = {0}; - model->setOperandValue(axis0, axis0_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis0}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input1, input2}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/concat_quant8_3.model.cpp deleted file mode 100644 index 085c0d930..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/concat_quant8_3.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: concat_quant8_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {400, 30}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {400, 60}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {400, 90}, 0.5f, 0); - // Phase 1, operands - auto input1 = model->addOperand(&type0); - auto input2 = model->addOperand(&type1); - auto axis1 = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis1_init[] = {1}; - model->setOperandValue(axis1, axis1_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONCATENATION, {input1, input2, axis1}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input1, input2}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_SAME.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_SAME.model.cpp deleted file mode 100644 index 47d92b6b8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_SAME.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: conv_1_h3_w2_SAME.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 8, 8, 1}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto b4 = model->addOperand(&type0); - auto b5 = model->addOperand(&type0); - auto b6 = model->addOperand(&type0); - auto b7 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto op0 = model->addOperand(&type3); - auto op1 = model->addOperand(&type4); - // Phase 2, operations - static int32_t b4_init[] = {1}; - model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); - static int32_t b5_init[] = {1}; - model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); - static int32_t b6_init[] = {1}; - model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1); - static int32_t b7_init[] = {0}; - model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1); - static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f}; - model->setOperandValue(op0, op0_init, sizeof(float) * 18); - static float op1_init[] = {0.0f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_VALID.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_VALID.model.cpp deleted file mode 100644 index 3a7294684..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_1_h3_w2_VALID.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: conv_1_h3_w2_VALID.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 2, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 6, 7, 1}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto b4 = model->addOperand(&type0); - auto b5 = model->addOperand(&type0); - auto b6 = model->addOperand(&type0); - auto b7 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto op0 = model->addOperand(&type3); - auto op1 = model->addOperand(&type4); - // Phase 2, operations - static int32_t b4_init[] = {2}; - model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); - static int32_t b5_init[] = {1}; - model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); - static int32_t b6_init[] = {1}; - model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1); - static int32_t b7_init[] = {0}; - model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1); - static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f, -0.579455f, 0.0278809f, -0.79946f, -0.684259f, 0.563238f, 0.37289f, 0.738216f, 0.386045f, -0.917775f, 0.184325f, -0.270568f, 0.82236f, 0.0973683f, -0.941308f, -0.144706f}; - model->setOperandValue(op0, op0_init, sizeof(float) * 18); - static float op1_init[] = {0.0f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_SAME.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_SAME.model.cpp deleted file mode 100644 index e3104ee1b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_SAME.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: conv_3_h3_w2_SAME.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {3, 3, 2, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto b4 = model->addOperand(&type0); - auto b5 = model->addOperand(&type0); - auto b6 = model->addOperand(&type0); - auto b7 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type1); - auto op0 = model->addOperand(&type2); - auto op1 = model->addOperand(&type3); - // Phase 2, operations - static int32_t b4_init[] = {1}; - model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); - static int32_t b5_init[] = {1}; - model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); - static int32_t b6_init[] = {1}; - model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1); - static int32_t b7_init[] = {0}; - model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1); - static float op0_init[] = {-0.966213f, -0.579455f, -0.684259f, 0.738216f, 0.184325f, 0.0973683f, -0.176863f, -0.23936f, -0.000233404f, 0.055546f, -0.232658f, -0.316404f, -0.012904f, 0.320705f, -0.326657f, -0.919674f, 0.868081f, -0.824608f, -0.467474f, 0.0278809f, 0.563238f, 0.386045f, -0.270568f, -0.941308f, -0.779227f, -0.261492f, -0.774804f, -0.79665f, 0.22473f, -0.414312f, 0.685897f, -0.327792f, 0.77395f, -0.714578f, -0.972365f, 0.0696099f, -0.82203f, -0.79946f, 0.37289f, -0.917775f, 0.82236f, -0.144706f, -0.167188f, 0.268062f, 0.702641f, -0.412223f, 0.755759f, 0.721547f, -0.43637f, -0.274905f, -0.269165f, 0.16102f, 0.819857f, -0.312008f}; - model->setOperandValue(op0, op0_init, sizeof(float) * 54); - static float op1_init[] = {0.0f, 0.0f, 0.0f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 3); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_VALID.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_VALID.model.cpp deleted file mode 100644 index d5860f4cd..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_3_h3_w2_VALID.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: conv_3_h3_w2_VALID.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 6, 7, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {3, 3, 2, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto b4 = model->addOperand(&type0); - auto b5 = model->addOperand(&type0); - auto b6 = model->addOperand(&type0); - auto b7 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto op0 = model->addOperand(&type3); - auto op1 = model->addOperand(&type4); - // Phase 2, operations - static int32_t b4_init[] = {2}; - model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); - static int32_t b5_init[] = {1}; - model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); - static int32_t b6_init[] = {1}; - model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1); - static int32_t b7_init[] = {0}; - model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1); - static float op0_init[] = {-0.966213f, -0.579455f, -0.684259f, 0.738216f, 0.184325f, 0.0973683f, -0.176863f, -0.23936f, -0.000233404f, 0.055546f, -0.232658f, -0.316404f, -0.012904f, 0.320705f, -0.326657f, -0.919674f, 0.868081f, -0.824608f, -0.467474f, 0.0278809f, 0.563238f, 0.386045f, -0.270568f, -0.941308f, -0.779227f, -0.261492f, -0.774804f, -0.79665f, 0.22473f, -0.414312f, 0.685897f, -0.327792f, 0.77395f, -0.714578f, -0.972365f, 0.0696099f, -0.82203f, -0.79946f, 0.37289f, -0.917775f, 0.82236f, -0.144706f, -0.167188f, 0.268062f, 0.702641f, -0.412223f, 0.755759f, 0.721547f, -0.43637f, -0.274905f, -0.269165f, 0.16102f, 0.819857f, -0.312008f}; - model->setOperandValue(op0, op0_init, sizeof(float) * 54); - static float op1_init[] = {0.0f, 0.0f, 0.0f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 3); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op2, op0, op1, b4, b5, b6, b7}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float.model.cpp deleted file mode 100644 index 0b97e31a9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float.model.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// Generated file (from: conv_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type1); - // Phase 2, operations - static float op2_init[] = {0.25f, 0.25f, 0.25f, 0.25f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 4); - static float op3_init[] = {0.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_2.model.cpp deleted file mode 100644 index a94c550ac..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_2.model.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// Generated file (from: conv_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 4, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad_same = model->addOperand(&type3); - auto act_relu = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type0); - // Phase 2, operations - static float op2_init[] = {1.0f, 4.0f, 7.0f, 2.0f, 5.0f, 8.0f, 3.0f, 6.0f, 9.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 9); - static float op3_init[] = {-200.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_relu_init[] = {1}; - model->setOperandValue(act_relu, act_relu_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_same, stride, stride, act_relu}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_channels.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_channels.model.cpp deleted file mode 100644 index 05321fb31..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_channels.model.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// Generated file (from: conv_float_channels.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {3, 1, 1, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type0); - // Phase 2, operations - static float op2_init[] = {1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 9); - static float op3_init[] = {0.0f, 0.0f, 0.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 3); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_channels_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_channels_weights_as_inputs.model.cpp deleted file mode 100644 index b3e7da0d7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_channels_weights_as_inputs.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: conv_float_channels_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {3, 1, 1, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type0); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_large.model.cpp deleted file mode 100644 index 165b3bb6f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_large.model.cpp +++ /dev/null @@ -1,37 +0,0 @@ -// Generated file (from: conv_float_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 3, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {3, 1, 1, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type0); - // Phase 2, operations - static float op2_init[] = {1.0f, 4.0f, 7.0f, 2.0f, 5.0f, 8.0f, 3.0f, 6.0f, 9.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 9); - static float op3_init[] = {0.0f, 0.0f, 0.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 3); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_large_weights_as_inputs.model.cpp deleted file mode 100644 index d3a2b443e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: conv_float_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 3, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {3, 1, 1, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type0); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_float_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_float_weights_as_inputs.model.cpp deleted file mode 100644 index f4a1e2d0d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_float_weights_as_inputs.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: conv_float_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type1); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8.model.cpp deleted file mode 100644 index 81e31be5d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8.model.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// Generated file (from: conv_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {2, 2, 2, 2}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); - static int32_t op3_init[] = {4}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_2.model.cpp deleted file mode 100644 index bae45e35a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_2.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: conv_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.f, 127); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad_valid = model->addOperand(&type3); - auto act_none = model->addOperand(&type3); - auto stride1 = model->addOperand(&type3); - auto stride3 = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {129, 131, 133, 135}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); - static int32_t op3_init[] = {-4}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); - static int32_t pad_valid_init[] = {2}; - model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - static int32_t stride1_init[] = {1}; - model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); - static int32_t stride3_init[] = {3}; - model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels.model.cpp deleted file mode 100644 index cbd3927ef..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels.model.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// Generated file (from: conv_quant8_channels.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 3}, 0.5f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {1, 2, 3, 4, 5, 6, 7, 8, 9}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 9); - static int32_t op3_init[] = {0, 0, 0}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 3); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels_weights_as_inputs.model.cpp deleted file mode 100644 index 56f5fbd61..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_channels_weights_as_inputs.model.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// Generated file (from: conv_quant8_channels_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 3}, 0.5f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large.model.cpp deleted file mode 100644 index d1a97ca99..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large.model.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// Generated file (from: conv_quant8_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {1, 4, 7, 2, 5, 8, 3, 6, 9}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 9); - static int32_t op3_init[] = {0, 0, 0}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 3); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large_weights_as_inputs.model.cpp deleted file mode 100644 index e98c4ca51..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// Generated file (from: conv_quant8_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow.model.cpp deleted file mode 100644 index dca8a0e7b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow.model.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// Generated file (from: conv_quant8_overflow.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {10, 40, 70, 20, 50, 80, 30, 60, 90}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 9); - static int32_t op3_init[] = {0, 0, 0}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 3); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow_weights_as_inputs.model.cpp deleted file mode 100644 index 8a3155f23..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_overflow_weights_as_inputs.model.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// Generated file (from: conv_quant8_overflow_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 0.5, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 3}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 1, 1, 3}, 0.5, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/conv_quant8_weights_as_inputs.model.cpp deleted file mode 100644 index 76d13a604..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/conv_quant8_weights_as_inputs.model.cpp +++ /dev/null @@ -1,34 +0,0 @@ -// Generated file (from: conv_quant8_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_1.model.cpp deleted file mode 100644 index d45c9bc9f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: depth_to_space_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 8}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_2.model.cpp deleted file mode 100644 index 8392d15e9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_2.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: depth_to_space_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 4, 1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_3.model.cpp deleted file mode 100644 index 3df413b5b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_float_3.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: depth_to_space_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 8}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 4, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_1.model.cpp deleted file mode 100644 index 4d8aae883..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: depth_to_space_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 8}, 0.5f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t radius_init[] = {2}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, radius}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_2.model.cpp deleted file mode 100644 index dcabb12ad..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depth_to_space_quant8_2.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: depth_to_space_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 0.5f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t radius_init[] = {2}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTH_TO_SPACE, {input, radius}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv.model.cpp deleted file mode 100644 index 2d2cfcc13..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv.model.cpp +++ /dev/null @@ -1,43 +0,0 @@ -// Generated file (from: depthwise_conv.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 1, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 8, 8, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto b4 = model->addOperand(&type0); - auto b5 = model->addOperand(&type0); - auto b6 = model->addOperand(&type0); - auto b7 = model->addOperand(&type0); - auto b8 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type1); - auto op0 = model->addOperand(&type2); - auto op1 = model->addOperand(&type3); - // Phase 2, operations - static int32_t b4_init[] = {1}; - model->setOperandValue(b4, b4_init, sizeof(int32_t) * 1); - static int32_t b5_init[] = {1}; - model->setOperandValue(b5, b5_init, sizeof(int32_t) * 1); - static int32_t b6_init[] = {1}; - model->setOperandValue(b6, b6_init, sizeof(int32_t) * 1); - static int32_t b7_init[] = {1}; - model->setOperandValue(b7, b7_init, sizeof(int32_t) * 1); - static int32_t b8_init[] = {0}; - model->setOperandValue(b8, b8_init, sizeof(int32_t) * 1); - static float op0_init[] = {-0.966213f, -0.467474f, -0.82203f}; - model->setOperandValue(op0, op0_init, sizeof(float) * 3); - static float op1_init[] = {0.0f, 0.0f, 0.0f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 3); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op2, op0, op1, b4, b5, b6, b7, b8}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float.model.cpp deleted file mode 100644 index ace293dbf..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: depthwise_conv2d_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto channelMultiplier = model->addOperand(&type3); - auto op4 = model->addOperand(&type1); - // Phase 2, operations - static float op2_init[] = {0.25f, 0.0f, 0.2f, 0.0f, 0.25f, 0.0f, 0.0f, 0.3f, 0.25f, 0.0f, 0.0f, 0.0f, 0.25f, 0.1f, 0.0f, 0.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 16); - static float op3_init[] = {1.0f, 2.0f, 3.0f, 4.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 4); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {2}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_2.model.cpp deleted file mode 100644 index 58850edae..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_2.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type4(Type::TENSOR_FLOAT32, {1, 2, 1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 2, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad_valid = model->addOperand(&type3); - auto act_none = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto channelMultiplier = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, -9.0f, 10.0f, -11.0f, 12.0f, 5.0f, 6.0f, 7.0f, 8.0f, 13.0f, -14.0f, 15.0f, -16.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 16); - static float op3_init[] = {1.0f, 2.0f, 3.0f, 4.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 4); - static int32_t pad_valid_init[] = {2}; - model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {2}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad_valid, stride, stride, channelMultiplier, act_none}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large.model.cpp deleted file mode 100644 index 98f40e3a7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - OperandType type1(Type::TENSOR_FLOAT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static float op2_init[] = {0.25f, 0.0f, 0.25f, 1.0f, 0.25f, 0.0f, 0.25f, 1.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 8); - static float op3_init[] = {100.0f, 200.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 2); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2.model.cpp deleted file mode 100644 index 521eb3ffc..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static float op2_init[] = {0.25f, 0.0f, 10.0f, 100.0f, 0.25f, 1.0f, 20.0f, 100.0f, 0.25f, 0.0f, 30.0f, 100.0f, 0.25f, 1.0f, 40.0f, 100.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 16); - static float op3_init[] = {600000.0f, 700000.0f, 800000.0f, 900000.0f}; - model->setOperandValue(op3, op3_init, sizeof(float) * 4); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2_weights_as_inputs.model.cpp deleted file mode 100644 index 38e56eaad..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_2_weights_as_inputs.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_2_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_weights_as_inputs.model.cpp deleted file mode 100644 index 0500e0f7d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - OperandType type1(Type::TENSOR_FLOAT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_weights_as_inputs.model.cpp deleted file mode 100644 index d45063c55..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_float_weights_as_inputs.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: depthwise_conv2d_float_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 3, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad0 = model->addOperand(&type3); - auto act = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto channelMultiplier = model->addOperand(&type3); - auto op4 = model->addOperand(&type1); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {2}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8.model.cpp deleted file mode 100644 index 5a6932fc0..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}, 0.25f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1,1,1,2}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static uint8_t op2_init[] = {2, 4, 2, 0, 2, 2, 2, 0}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 8); - static int32_t op3_init[] = {0, 0}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 2); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_2.model.cpp deleted file mode 100644 index 095036ddd..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_2.model.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {4}, 0.25f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 1, 4}, 1.f, 127); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 0.5f, 127); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 2, 2}, 0.5f, 127); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto pad_valid = model->addOperand(&type3); - auto act_none = model->addOperand(&type3); - auto stride = model->addOperand(&type3); - auto channelMultiplier = model->addOperand(&type3); - auto op4 = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {129, 131, 133, 135, 109, 147, 105, 151, 137, 139, 141, 143, 153, 99, 157, 95}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); - static int32_t op3_init[] = {4, 8, 12, 16}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 4); - static int32_t pad_valid_init[] = {2}; - model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {2}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad_valid, stride, stride, channelMultiplier, act_none}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large.model.cpp deleted file mode 100644 index dbabdf55c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}, 0.25f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static uint8_t op2_init[] = {2, 4, 2, 0, 2, 2, 2, 0}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 8); - static int32_t op3_init[] = {0, 0}; - model->setOperandValue(op3, op3_init, sizeof(int32_t) * 2); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large_weights_as_inputs.model.cpp deleted file mode 100644 index b965949a4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}, 0.25f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_weights_as_inputs.model.cpp deleted file mode 100644 index 959fed1f8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/depthwise_conv2d_quant8_weights_as_inputs.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: depthwise_conv2d_quant8_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}, 0.25f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1,1,1,2}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type2); - auto act = model->addOperand(&type2); - auto stride = model->addOperand(&type2); - auto channelMultiplier = model->addOperand(&type2); - auto op4 = model->addOperand(&type3); - // Phase 2, operations - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - static int32_t stride_init[] = {1}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t channelMultiplier_init[] = {1}; - model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, op3}, - {op4}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/dequantize.model.cpp b/runtimes/tests/neural_networks_test/generated/models/dequantize.model.cpp deleted file mode 100644 index 248830738..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/dequantize.model.cpp +++ /dev/null @@ -1,20 +0,0 @@ -// Generated file (from: dequantize.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_DEQUANTIZE, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/div.model.cpp b/runtimes/tests/neural_networks_test/generated/models/div.model.cpp deleted file mode 100644 index 31213de0f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/div.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: div.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DIV, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/div_.model.cpp b/runtimes/tests/neural_networks_test/generated/models/div_.model.cpp deleted file mode 100644 index 137a8b90b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/div_.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: div_.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DIV, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/div_broadcast_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/div_broadcast_float.model.cpp deleted file mode 100644 index e6f442d09..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/div_broadcast_float.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: div_broadcast_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type1(Type::TENSOR_FLOAT32, {2, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_DIV, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup.model.cpp b/runtimes/tests/neural_networks_test/generated/models/embedding_lookup.model.cpp deleted file mode 100644 index a7b82c79d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup.model.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// Generated file (from: embedding_lookup.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_FLOAT32, {3, 2, 4}); - OperandType type0(Type::TENSOR_INT32, {3}); - // Phase 1, operands - auto index = model->addOperand(&type0); - auto value = model->addOperand(&type1); - auto output = model->addOperand(&type1); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_EMBEDDING_LOOKUP, {index, value}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {index, value}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_2d_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_2d_nnfw.model.cpp deleted file mode 100644 index 0234e403f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_2d_nnfw.model.cpp +++ /dev/null @@ -1,21 +0,0 @@ -// Generated file (from: embedding_lookup_2d_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_FLOAT32, {3, 2}); - OperandType type0(Type::TENSOR_INT32, {3}); - // Phase 1, operands - auto index = model->addOperand(&type0); - auto value = model->addOperand(&type1); - auto output = model->addOperand(&type1); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_EMBEDDING_LOOKUP, {index, value}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {index, value}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_4d_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_4d_nnfw.model.cpp deleted file mode 100644 index 2acd291ae..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/embedding_lookup_4d_nnfw.model.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: embedding_lookup_4d_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_INT32, {3, 2, 4, 2}); - OperandType type0(Type::TENSOR_INT32, {3}); - OperandType type1(Type::TENSOR_INT32, {5, 2, 4, 2}); - // Phase 1, operands - auto index = model->addOperand(&type0); - auto value = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_EMBEDDING_LOOKUP, {index, value}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {index, value}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/floor.model.cpp b/runtimes/tests/neural_networks_test/generated/models/floor.model.cpp deleted file mode 100644 index 2425f4757..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/floor.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: floor.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_FLOOR, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/floor_.model.cpp b/runtimes/tests/neural_networks_test/generated/models/floor_.model.cpp deleted file mode 100644 index b54e9fc8f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/floor_.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: floor_.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_FLOOR, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float.model.cpp deleted file mode 100644 index 598c4cfad..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float.model.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// Generated file (from: fully_connected_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - OperandType type0(Type::TENSOR_FLOAT32, {3, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type0); - auto act = model->addOperand(&type3); - // Phase 2, operations - static float op2_init[] = {2.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 1); - static float b0_init[] = {4.0f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_1_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_1_nnfw.model.cpp deleted file mode 100644 index 04c4efece..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_1_nnfw.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_float_1_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 24}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 3, 4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act_relu = model->addOperand(&type4); - // Phase 2, operations - static float op2_init[] = {-0.25449711f, 0.0f, -2.1247749f, 0.0f, -1.143796f, 0.0f, -1.0299346f, 0.0f, -2.2373879f, 0.0f, -0.083096743f, 0.0f, -1.3230739f, 0.0f, 0.15294921f, 0.0f, -0.53045893f, 0.0f, -0.46075189f, 0.0f, -1.4482396f, 0.0f, -1.609534f, 0.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 24); - static float b0_init[] = {0.70098364f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 1); - static int32_t act_relu_init[] = {0}; - model->setOperandValue(act_relu, act_relu_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act_relu}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_2.model.cpp deleted file mode 100644 index b0d5bd758..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_2.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); - OperandType type2(Type::TENSOR_FLOAT32, {16}); - OperandType type3(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act_relu = model->addOperand(&type4); - // Phase 2, operations - static float op2_init[] = {0.091327f, 0.103366f, -0.316505f, -0.08312f, 0.149366f, -0.196636f, -0.123672f, 0.0628f, 0.063031f, 0.19167f, -0.062001f, -0.061504f, -0.275581f, 0.059388f, -0.118497f, -0.079224f, 0.109758f, 0.008307f, -0.062657f, -0.060962f, -0.049782f, -0.106719f, -0.319482f, -0.10365f, 0.266455f, 0.051517f, -0.123448f, 0.322464f, 0.043282f, -0.173782f, -0.190381f, 0.002013f, 0.096086f, 0.131157f, 0.031164f, 0.100638f, -0.312191f, -0.080923f, -0.101318f, -0.116614f, 0.142238f, 0.08654f, -0.139154f, 0.174268f, -0.073161f, 0.080072f, 0.006874f, 0.229382f, -0.104321f, -0.176035f, -0.208587f, -0.001019f, -0.162032f, 0.080824f, -0.025021f, 0.07446f, -0.252595f, -0.16175f, -0.136403f, 0.008308f, 0.00571f, 0.0966f, 0.289839f, 0.218816f, -0.304651f, -0.070958f, 0.054598f, 0.147113f, -0.139112f, -0.072798f, -0.163335f, -0.167863f, -0.128762f, -0.03578f, 0.117262f, 0.017177f, 0.263335f, -0.176612f, 0.262961f, -0.093654f, -0.339283f, 0.333071f, 0.180827f, 0.287583f, 0.06635f, -0.197947f, -0.114449f, -0.236035f, 0.103532f, -0.034284f, 0.093299f, -0.145361f, 0.054001f, 0.25057f, 0.15701f, -0.14348f, -0.139061f, -0.048873f, 0.067557f, 0.139038f, 0.324106f, 0.227041f, 0.037793f, -0.225747f, -0.241619f, 0.357835f, 0.135762f, -0.306764f, -0.125982f, 0.091916f, 0.266587f, 0.030135f, 0.265148f, 0.141627f, 0.02012f, 0.083815f, -0.124556f, -0.100124f, -0.048159f, 0.181172f, 0.302309f, -0.041084f, 0.146334f, -0.061511f, -0.232605f, 0.281324f, 0.145408f, -0.221897f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 128); - static float b0_init[] = {-0.160594f, 0.20577f, -0.078307f, -0.077984f, 0.001937f, 0.01586f, 0.03681f, 0.012346f, 0.001028f, 0.038551f, 0.075415f, 0.020804f, 0.048478f, -0.03227f, 0.175688f, -0.085662f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 16); - static int32_t act_relu_init[] = {1}; - model->setOperandValue(act_relu, act_relu_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act_relu}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_3.model.cpp deleted file mode 100644 index 15275251f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_3.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - OperandType type3(Type::TENSOR_FLOAT32, {2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act = model->addOperand(&type4); - // Phase 2, operations - static float op2_init[] = {2.0f, 4.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 2); - static float b0_init[] = {1.0f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_4d_simple.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_4d_simple.model.cpp deleted file mode 100644 index aa645d966..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_4d_simple.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_float_4d_simple.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_FLOAT32, {3, 10}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 1, 5, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act = model->addOperand(&type4); - // Phase 2, operations - static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 30); - static float b0_init[] = {1.0f, 2.0f, 3.0f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 3); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large.model.cpp deleted file mode 100644 index cb17f7bb6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large.model.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// Generated file (from: fully_connected_float_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 5}); - OperandType type1(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto b0 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto act = model->addOperand(&type3); - // Phase 2, operations - static float op2_init[] = {2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 5); - static float b0_init[] = {900000.0f}; - model->setOperandValue(b0, b0_init, sizeof(float) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large_weights_as_inputs.model.cpp deleted file mode 100644 index 2b1f58697..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// Generated file (from: fully_connected_float_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 5}); - OperandType type1(Type::TENSOR_FLOAT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto b0 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto act = model->addOperand(&type3); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, b0}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_weights_as_inputs.model.cpp deleted file mode 100644 index 745eafac9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_float_weights_as_inputs.model.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// Generated file (from: fully_connected_float_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1}); - OperandType type0(Type::TENSOR_FLOAT32, {3, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type0); - auto act = model->addOperand(&type3); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, b0}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8.model.cpp deleted file mode 100644 index cdb9119d0..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 1}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {3, 1}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3, 1}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {2}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 1); - static int32_t b0_init[] = {4}; - model->setOperandValue(b0, b0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_2.model.cpp deleted file mode 100644 index 06df5a545..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_2.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: fully_connected_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3}, 0.25f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 1.f, 127); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {3, 10}, 0.5f, 127); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4, 1, 5, 1}, 0.5f, 127); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act_relu = model->addOperand(&type4); - // Phase 2, operations - static uint8_t op2_init[] = {129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 30); - static int32_t b0_init[] = {4, 8, 12}; - model->setOperandValue(b0, b0_init, sizeof(int32_t) * 3); - static int32_t act_relu_init[] = {1}; - model->setOperandValue(act_relu, act_relu_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act_relu}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large.model.cpp deleted file mode 100644 index 761f25734..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large.model.cpp +++ /dev/null @@ -1,31 +0,0 @@ -// Generated file (from: fully_connected_quant8_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}, 0.04, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 5}, 0.2, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto b0 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto act = model->addOperand(&type3); - // Phase 2, operations - static uint8_t op2_init[] = {10, 20, 20, 20, 10}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 5); - static int32_t b0_init[] = {10}; - model->setOperandValue(b0, b0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large_weights_as_inputs.model.cpp deleted file mode 100644 index 5d70dc9be..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_large_weights_as_inputs.model.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// Generated file (from: fully_connected_quant8_large_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}, 0.04, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1}, 1.f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 5}, 0.2, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto b0 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - auto act = model->addOperand(&type3); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, b0}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_weights_as_inputs.model.cpp deleted file mode 100644 index ae0b2e13e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/fully_connected_quant8_weights_as_inputs.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: fully_connected_quant8_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type4(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 1}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {3, 1}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3, 1}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto b0 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - auto act = model->addOperand(&type4); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_FULLY_CONNECTED, {op1, op2, b0, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2, b0}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_1D_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_1D_float.model.cpp deleted file mode 100644 index 8ad160990..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_1D_float.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: gather_1D_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_1D_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_1D_int32.model.cpp deleted file mode 100644 index ae7fa6685..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_1D_int32.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: gather_1D_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_1D_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_1D_quant8.model.cpp deleted file mode 100644 index 4984c167b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_1D_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: gather_1D_uint8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2}, 0.5f, 1); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 0.5f, 1); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_2D_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_2D_float.model.cpp deleted file mode 100644 index 3d80c4496..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_2D_float.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: gather_2D_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2,4}); - OperandType type0(Type::TENSOR_FLOAT32, {3,4}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_2D_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_2D_int32.model.cpp deleted file mode 100644 index 50411d5a7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_2D_int32.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: gather_2D_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_INT32, {2,4}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_INT32, {3,4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/gather_2D_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/gather_2D_quant8.model.cpp deleted file mode 100644 index d7aa0aba7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/gather_2D_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: gather_2D_uint8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2,4}, 0.5f, 1); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {3,4}, 0.5f, 1); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto axis = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_GATHER_EX, {op1, op2, axis}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_float.model.cpp deleted file mode 100644 index 8c5970d1c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_float.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: hashtable_lookup_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_FLOAT32, {3, 2}); - OperandType type3(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type1(Type::TENSOR_INT32, {3}); - OperandType type0(Type::TENSOR_INT32, {4}); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {4}, 1.f, 0); - // Phase 1, operands - auto lookup = model->addOperand(&type0); - auto key = model->addOperand(&type1); - auto value = model->addOperand(&type2); - auto output = model->addOperand(&type3); - auto hits = model->addOperand(&type4); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_HASHTABLE_LOOKUP, {lookup, key, value}, {output, hits}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {lookup, key, value}, - {output, hits}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_quant8.model.cpp deleted file mode 100644 index 043f2d377..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/hashtable_lookup_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: hashtable_lookup_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {3}); - OperandType type0(Type::TENSOR_INT32, {4}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {3, 2}, 0.5f, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {4, 2}, 0.5f, 0); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {4}, 1.f, 0); - // Phase 1, operands - auto lookup = model->addOperand(&type0); - auto key = model->addOperand(&type1); - auto value = model->addOperand(&type2); - auto output = model->addOperand(&type3); - auto hits = model->addOperand(&type4); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_HASHTABLE_LOOKUP, {lookup, key, value}, {output, hits}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {lookup, key, value}, - {output, hits}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_normalization.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_normalization.model.cpp deleted file mode 100644 index a207ba4e7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_normalization.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: l2_normalization.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_L2_NORMALIZATION, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_normalization_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_normalization_2.model.cpp deleted file mode 100644 index ebce2bb47..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_normalization_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: l2_normalization_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_L2_NORMALIZATION, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_normalization_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_normalization_large.model.cpp deleted file mode 100644 index cadfa7b45..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_normalization_large.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: l2_normalization_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_L2_NORMALIZATION, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_pool_float.model.cpp deleted file mode 100644 index d3a7bf338..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: l2_pool_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_L2_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_2.model.cpp deleted file mode 100644 index e01a9a55b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_2.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: l2_pool_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 4, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons2 = model->addOperand(&type1); - auto pad_same = model->addOperand(&type1); - auto act_none = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t cons2_init[] = {2}; - model->setOperandValue(cons2, cons2_init, sizeof(int32_t) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_L2_POOL_2D, {op1, pad_same, cons2, cons2, cons2, cons2, act_none}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_large.model.cpp b/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_large.model.cpp deleted file mode 100644 index 9c3c562c3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/l2_pool_float_large.model.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// Generated file (from: l2_pool_float_large.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 1, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 3}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto filter_width = model->addOperand(&type1); - auto filter_height = model->addOperand(&type1); - auto stride_width = model->addOperand(&type1); - auto stride_height = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t filter_width_init[] = {2}; - model->setOperandValue(filter_width, filter_width_init, sizeof(int32_t) * 1); - static int32_t filter_height_init[] = {2}; - model->setOperandValue(filter_height, filter_height_init, sizeof(int32_t) * 1); - static int32_t stride_width_init[] = {1}; - model->setOperandValue(stride_width, stride_width_init, sizeof(int32_t) * 1); - static int32_t stride_height_init[] = {1}; - model->setOperandValue(stride_height, stride_height_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_L2_POOL_2D, {op1, pad0, pad0, pad0, pad0, stride_width, stride_height, filter_width, filter_height, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_1.model.cpp deleted file mode 100644 index 7543bbe3d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_1.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: local_response_norm_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::FLOAT32, {}); - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto bias = model->addOperand(&type2); - auto alpha = model->addOperand(&type2); - auto beta = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t radius_init[] = {20}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - static float bias_init[] = {9.0f}; - model->setOperandValue(bias, bias_init, sizeof(float) * 1); - static float alpha_init[] = {4.0f}; - model->setOperandValue(alpha, alpha_init, sizeof(float) * 1); - static float beta_init[] = {0.5f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_2.model.cpp deleted file mode 100644 index 5aae9a67b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_2.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: local_response_norm_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::FLOAT32, {}); - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto bias = model->addOperand(&type2); - auto alpha = model->addOperand(&type2); - auto beta = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t radius_init[] = {20}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - static float bias_init[] = {0.0f}; - model->setOperandValue(bias, bias_init, sizeof(float) * 1); - static float alpha_init[] = {1.0f}; - model->setOperandValue(alpha, alpha_init, sizeof(float) * 1); - static float beta_init[] = {0.5f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_3.model.cpp deleted file mode 100644 index 016da49fe..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_3.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: local_response_norm_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::FLOAT32, {}); - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto bias = model->addOperand(&type2); - auto alpha = model->addOperand(&type2); - auto beta = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t radius_init[] = {20}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - static float bias_init[] = {0.0f}; - model->setOperandValue(bias, bias_init, sizeof(float) * 1); - static float alpha_init[] = {4.0f}; - model->setOperandValue(alpha, alpha_init, sizeof(float) * 1); - static float beta_init[] = {0.5f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_4.model.cpp deleted file mode 100644 index 8311f8f80..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/local_response_norm_float_4.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: local_response_norm_float_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::FLOAT32, {}); - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto bias = model->addOperand(&type2); - auto alpha = model->addOperand(&type2); - auto beta = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t radius_init[] = {2}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - static float bias_init[] = {9.0f}; - model->setOperandValue(bias, bias_init, sizeof(float) * 1); - static float alpha_init[] = {4.0f}; - model->setOperandValue(alpha, alpha_init, sizeof(float) * 1); - static float beta_init[] = {0.5f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/logistic_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/logistic_float_1.model.cpp deleted file mode 100644 index 2ca725fc6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/logistic_float_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: logistic_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_LOGISTIC, {op1}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/logistic_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/logistic_float_2.model.cpp deleted file mode 100644 index 18d52ccfc..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/logistic_float_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: logistic_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 32, 40, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_1.model.cpp deleted file mode 100644 index b0f97a95d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_1.model.cpp +++ /dev/null @@ -1,20 +0,0 @@ -// Generated file (from: logistic_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.00390625f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_LOGISTIC, {op1}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_2.model.cpp deleted file mode 100644 index b14418301..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/logistic_quant8_2.model.cpp +++ /dev/null @@ -1,20 +0,0 @@ -// Generated file (from: logistic_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, .5f, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.00390625f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type1); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lsh_projection.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lsh_projection.model.cpp deleted file mode 100644 index d5c135775..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lsh_projection.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: lsh_projection.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type1(Type::TENSOR_INT32, {3, 2}); - OperandType type4(Type::TENSOR_INT32, {8}); - // Phase 1, operands - auto hash = model->addOperand(&type0); - auto lookup = model->addOperand(&type1); - auto weight = model->addOperand(&type2); - auto type_param = model->addOperand(&type3); - auto output = model->addOperand(&type4); - // Phase 2, operations - static float hash_init[] = {0.123f, 0.456f, -0.321f, -0.654f, 1.234f, 5.678f, -4.321f, -8.765f}; - model->setOperandValue(hash, hash_init, sizeof(float) * 8); - static int32_t type_param_init[] = {2}; - model->setOperandValue(type_param, type_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_LSH_PROJECTION, {hash, lookup, weight, type_param}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {lookup, weight}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lsh_projection_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lsh_projection_2.model.cpp deleted file mode 100644 index 25e9d8b42..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lsh_projection_2.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: lsh_projection_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type1(Type::TENSOR_INT32, {3, 2}); - OperandType type4(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto hash = model->addOperand(&type0); - auto lookup = model->addOperand(&type1); - auto weight = model->addOperand(&type2); - auto type_param = model->addOperand(&type3); - auto output = model->addOperand(&type4); - // Phase 2, operations - static float hash_init[] = {0.123f, 0.456f, -0.321f, -0.654f, 1.234f, 5.678f, -4.321f, -8.765f}; - model->setOperandValue(hash, hash_init, sizeof(float) * 8); - static int32_t type_param_init[] = {1}; - model->setOperandValue(type_param, type_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_LSH_PROJECTION, {hash, lookup, weight, type_param}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {lookup, weight}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lsh_projection_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lsh_projection_weights_as_inputs.model.cpp deleted file mode 100644 index 4abfc18e2..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lsh_projection_weights_as_inputs.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: lsh_projection_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type3(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type1(Type::TENSOR_INT32, {3, 2}); - OperandType type4(Type::TENSOR_INT32, {8}); - // Phase 1, operands - auto hash = model->addOperand(&type0); - auto lookup = model->addOperand(&type1); - auto weight = model->addOperand(&type2); - auto type_param = model->addOperand(&type3); - auto output = model->addOperand(&type4); - // Phase 2, operations - static int32_t type_param_init[] = {2}; - model->setOperandValue(type_param, type_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_LSH_PROJECTION, {hash, lookup, weight, type_param}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {hash, lookup, weight}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm.model.cpp deleted file mode 100644 index c1772806a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm2.model.cpp deleted file mode 100644 index 7c3d95a88..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm2.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 12}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type4); - auto cell_to_output_weights = model->addOperand(&type4); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm2_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm2_state.model.cpp deleted file mode 100644 index 74c328bec..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm2_state.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm2_state.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 12}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type4); - auto cell_to_output_weights = model->addOperand(&type4); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm2_state2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm2_state2.model.cpp deleted file mode 100644 index 885cc34e3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm2_state2.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm2_state2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 12}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type4); - auto cell_to_output_weights = model->addOperand(&type4); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {1, 2, 0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp deleted file mode 100644 index 32f2d012d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm3.model.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Generated file (from: lstm3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type9(Type::FLOAT32, {}); - OperandType type8(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0}); - OperandType type4(Type::TENSOR_FLOAT32, {16,20}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); - OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); - OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); - OperandType type3(Type::TENSOR_FLOAT32, {20}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type3); - auto forget_gate_bias = model->addOperand(&type3); - auto cell_gate_bias = model->addOperand(&type3); - auto output_gate_bias = model->addOperand(&type3); - auto projection_weights = model->addOperand(&type4); - auto projection_bias = model->addOperand(&type5); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type7); - auto activation_param = model->addOperand(&type8); - auto cell_clip_param = model->addOperand(&type9); - auto proj_clip_param = model->addOperand(&type9); - auto scratch_buffer = model->addOperand(&type10); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type7); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3_state.model.cpp deleted file mode 100644 index ce5a70509..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm3_state.model.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Generated file (from: lstm3_state.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type9(Type::FLOAT32, {}); - OperandType type8(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0}); - OperandType type4(Type::TENSOR_FLOAT32, {16,20}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); - OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); - OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); - OperandType type3(Type::TENSOR_FLOAT32, {20}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type3); - auto forget_gate_bias = model->addOperand(&type3); - auto cell_gate_bias = model->addOperand(&type3); - auto output_gate_bias = model->addOperand(&type3); - auto projection_weights = model->addOperand(&type4); - auto projection_bias = model->addOperand(&type5); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type7); - auto activation_param = model->addOperand(&type8); - auto cell_clip_param = model->addOperand(&type9); - auto proj_clip_param = model->addOperand(&type9); - auto scratch_buffer = model->addOperand(&type10); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type7); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp deleted file mode 100644 index 0484ac4b5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Generated file (from: lstm3_state2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type9(Type::FLOAT32, {}); - OperandType type8(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0}); - OperandType type4(Type::TENSOR_FLOAT32, {16,20}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); - OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); - OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); - OperandType type3(Type::TENSOR_FLOAT32, {20}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type3); - auto forget_gate_bias = model->addOperand(&type3); - auto cell_gate_bias = model->addOperand(&type3); - auto output_gate_bias = model->addOperand(&type3); - auto projection_weights = model->addOperand(&type4); - auto projection_bias = model->addOperand(&type5); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type7); - auto activation_param = model->addOperand(&type8); - auto cell_clip_param = model->addOperand(&type9); - auto proj_clip_param = model->addOperand(&type9); - auto scratch_buffer = model->addOperand(&type10); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type7); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3_state3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3_state3.model.cpp deleted file mode 100644 index cf00f1ce4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm3_state3.model.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Generated file (from: lstm3_state3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type9(Type::FLOAT32, {}); - OperandType type8(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0}); - OperandType type4(Type::TENSOR_FLOAT32, {16,20}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); - OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); - OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); - OperandType type3(Type::TENSOR_FLOAT32, {20}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type3); - auto forget_gate_bias = model->addOperand(&type3); - auto cell_gate_bias = model->addOperand(&type3); - auto output_gate_bias = model->addOperand(&type3); - auto projection_weights = model->addOperand(&type4); - auto projection_bias = model->addOperand(&type5); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type7); - auto activation_param = model->addOperand(&type8); - auto cell_clip_param = model->addOperand(&type9); - auto proj_clip_param = model->addOperand(&type9); - auto scratch_buffer = model->addOperand(&type10); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type7); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {1, 2, 0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm_state.model.cpp deleted file mode 100644 index 7f67b2399..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm_state.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm_state.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp deleted file mode 100644 index 7952e34bd..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/lstm_state2.model.cpp +++ /dev/null @@ -1,59 +0,0 @@ -// Generated file (from: lstm_state2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type8(Type::FLOAT32, {}); - OperandType type7(Type::INT32, {}); - OperandType type5(Type::TENSOR_FLOAT32, {0,0}); - OperandType type3(Type::TENSOR_FLOAT32, {0}); - OperandType type9(Type::TENSOR_FLOAT32, {1, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto input_to_input_weights = model->addOperand(&type1); - auto input_to_forget_weights = model->addOperand(&type1); - auto input_to_cell_weights = model->addOperand(&type1); - auto input_to_output_weights = model->addOperand(&type1); - auto recurrent_to_intput_weights = model->addOperand(&type2); - auto recurrent_to_forget_weights = model->addOperand(&type2); - auto recurrent_to_cell_weights = model->addOperand(&type2); - auto recurrent_to_output_weights = model->addOperand(&type2); - auto cell_to_input_weights = model->addOperand(&type3); - auto cell_to_forget_weights = model->addOperand(&type3); - auto cell_to_output_weights = model->addOperand(&type3); - auto input_gate_bias = model->addOperand(&type4); - auto forget_gate_bias = model->addOperand(&type4); - auto cell_gate_bias = model->addOperand(&type4); - auto output_gate_bias = model->addOperand(&type4); - auto projection_weights = model->addOperand(&type5); - auto projection_bias = model->addOperand(&type3); - auto output_state_in = model->addOperand(&type6); - auto cell_state_in = model->addOperand(&type6); - auto activation_param = model->addOperand(&type7); - auto cell_clip_param = model->addOperand(&type8); - auto proj_clip_param = model->addOperand(&type8); - auto scratch_buffer = model->addOperand(&type9); - auto output_state_out = model->addOperand(&type6); - auto cell_state_out = model->addOperand(&type6); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t activation_param_init[] = {4}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - static float cell_clip_param_init[] = {0.0f}; - model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); - static float proj_clip_param_init[] = {0.0f}; - model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, - {scratch_buffer, output_state_out, cell_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {1, 2, 0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_float_1.model.cpp deleted file mode 100644 index b37ce65e4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_1.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: max_pool_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_float_2.model.cpp deleted file mode 100644 index a0b89e167..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_2.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: max_pool_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {5, 2, 3, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {5, 50, 70, 3}); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {20}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {20}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_float_3.model.cpp deleted file mode 100644 index 3d7e231a2..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_3.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: max_pool_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {5, 2, 3, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {5, 50, 70, 3}); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto relu6_activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {20}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {20}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t relu6_activation_init[] = {3}; - model->setOperandValue(relu6_activation, relu6_activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_float_4.model.cpp deleted file mode 100644 index 2734236d3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_float_4.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: max_pool_float_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 4, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons2 = model->addOperand(&type1); - auto pad_same = model->addOperand(&type1); - auto act_none = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t cons2_init[] = {2}; - model->setOperandValue(cons2, cons2_init, sizeof(int32_t) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad_same, cons2, cons2, cons2, cons2, act_none}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_1.model.cpp deleted file mode 100644 index 978dcbea0..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_1.model.cpp +++ /dev/null @@ -1,29 +0,0 @@ -// Generated file (from: max_pool_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons1 = model->addOperand(&type1); - auto pad0 = model->addOperand(&type1); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t cons1_init[] = {1}; - model->setOperandValue(cons1, cons1_init, sizeof(int32_t) * 1); - static int32_t pad0_init[] = {0}; - model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad0, pad0, pad0, pad0, cons1, cons1, cons1, cons1, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_2.model.cpp deleted file mode 100644 index 2ce943466..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_2.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: max_pool_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {5, 2, 3, 3}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {5, 50, 70, 3}, 0.5f, 0); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {20}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {20}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t activation_init[] = {0}; - model->setOperandValue(activation, activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_3.model.cpp deleted file mode 100644 index f8c39322d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_3.model.cpp +++ /dev/null @@ -1,33 +0,0 @@ -// Generated file (from: max_pool_quant8_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {5, 2, 3, 3}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {5, 50, 70, 3}, 0.5f, 0); - // Phase 1, operands - auto i0 = model->addOperand(&type0); - auto stride = model->addOperand(&type1); - auto filter = model->addOperand(&type1); - auto padding = model->addOperand(&type1); - auto relu1_activation = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t stride_init[] = {20}; - model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); - static int32_t filter_init[] = {20}; - model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); - static int32_t padding_init[] = {0}; - model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); - static int32_t relu1_activation_init[] = {2}; - model->setOperandValue(relu1_activation, relu1_activation_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu1_activation}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {i0}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_4.model.cpp deleted file mode 100644 index 461297b4c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/max_pool_quant8_4.model.cpp +++ /dev/null @@ -1,30 +0,0 @@ -// Generated file (from: max_pool_quant8_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1, 2, 1}, 0.0625f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 4, 1}, 0.0625f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto cons2 = model->addOperand(&type1); - auto pad_same = model->addOperand(&type1); - auto act_none = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t cons2_init[] = {2}; - model->setOperandValue(cons2, cons2_init, sizeof(int32_t) * 1); - static int32_t pad_same_init[] = {1}; - model->setOperandValue(pad_same, pad_same_init, sizeof(int32_t) * 1); - static int32_t act_none_init[] = {0}; - model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {op1, pad_same, cons2, cons2, cons2, cons2, act_none}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean.model.cpp deleted file mode 100644 index 7d26f9fa8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 2, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {2}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - static int32_t keepDims_init[] = {0}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_axis01_1_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_axis01_1_nnfw.model.cpp deleted file mode 100644 index e14f6888a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_axis01_1_nnfw.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_axis01_1_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 3, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {1, 2}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 2); - static int32_t keepDims_init[] = {1}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_axis01_2_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_axis01_2_nnfw.model.cpp deleted file mode 100644 index 26afa5aa5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_axis01_2_nnfw.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_axis01_2_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 3, 2}); - OperandType type1(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {1, 2, -3, -3}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 4); - static int32_t keepDims_init[] = {1}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_float_1.model.cpp deleted file mode 100644 index 7a3ce25a5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_float_1.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 3, 2}); - OperandType type1(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {1, 0, -3, -3}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 4); - static int32_t keepDims_init[] = {0}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_float_2.model.cpp deleted file mode 100644 index 9838db48b..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_float_2.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3, 1}); - OperandType type0(Type::TENSOR_FLOAT32, {4, 3, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0, 2}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 2); - static int32_t keepDims_init[] = {1}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_quant8_1.model.cpp deleted file mode 100644 index bbc6c101e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_quant8_1.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {4}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2}, 0.8, 5); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4, 3, 2}, 0.8, 5); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {1, 0, -3, -3}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 4); - static int32_t keepDims_init[] = {0}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mean_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mean_quant8_2.model.cpp deleted file mode 100644 index dec9d81ca..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mean_quant8_2.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: mean_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1}, 0.8, 5); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4, 3, 2}, 0.8, 5); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto keepDims = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t axis_init[] = {0, 2}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 2); - static int32_t keepDims_init[] = {1}; - model->setOperandValue(keepDims, keepDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MEAN, {input, axis, keepDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mobilenet_224_gender_basic_fixed.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mobilenet_224_gender_basic_fixed.model.cpp deleted file mode 100644 index 9e0fcd5b8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mobilenet_224_gender_basic_fixed.model.cpp +++ /dev/null @@ -1,672 +0,0 @@ -// Generated file (from: mobilenet_224_gender_basic_fixed.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 1, 11}); - OperandType type32(Type::TENSOR_FLOAT32, {1, 1, 1, 256}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 112, 112, 16}); - OperandType type6(Type::TENSOR_FLOAT32, {1, 14, 14, 128}); - OperandType type30(Type::TENSOR_FLOAT32, {1, 14, 14, 64}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 224, 224, 3}); - OperandType type24(Type::TENSOR_FLOAT32, {1, 28, 28, 32}); - OperandType type25(Type::TENSOR_FLOAT32, {1, 28, 28, 64}); - OperandType type8(Type::TENSOR_FLOAT32, {1, 3, 3, 128}); - OperandType type16(Type::TENSOR_FLOAT32, {1, 3, 3, 16}); - OperandType type14(Type::TENSOR_FLOAT32, {1, 3, 3, 256}); - OperandType type22(Type::TENSOR_FLOAT32, {1, 3, 3, 32}); - OperandType type28(Type::TENSOR_FLOAT32, {1, 3, 3, 64}); - OperandType type18(Type::TENSOR_FLOAT32, {1, 56, 56, 16}); - OperandType type19(Type::TENSOR_FLOAT32, {1, 56, 56, 32}); - OperandType type10(Type::TENSOR_FLOAT32, {1, 7, 7, 128}); - OperandType type11(Type::TENSOR_FLOAT32, {1, 7, 7, 256}); - OperandType type34(Type::TENSOR_FLOAT32, {11, 1, 1, 256}); - OperandType type33(Type::TENSOR_FLOAT32, {11}); - OperandType type9(Type::TENSOR_FLOAT32, {128, 1, 1, 128}); - OperandType type31(Type::TENSOR_FLOAT32, {128, 1, 1, 64}); - OperandType type7(Type::TENSOR_FLOAT32, {128}); - OperandType type17(Type::TENSOR_FLOAT32, {16, 1, 1, 16}); - OperandType type5(Type::TENSOR_FLOAT32, {16, 3, 3, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {16}); - OperandType type13(Type::TENSOR_FLOAT32, {256, 1, 1, 128}); - OperandType type15(Type::TENSOR_FLOAT32, {256, 1, 1, 256}); - OperandType type12(Type::TENSOR_FLOAT32, {256}); - OperandType type21(Type::TENSOR_FLOAT32, {32, 1, 1, 16}); - OperandType type23(Type::TENSOR_FLOAT32, {32, 1, 1, 32}); - OperandType type20(Type::TENSOR_FLOAT32, {32}); - OperandType type27(Type::TENSOR_FLOAT32, {64, 1, 1, 32}); - OperandType type29(Type::TENSOR_FLOAT32, {64, 1, 1, 64}); - OperandType type26(Type::TENSOR_FLOAT32, {64}); - // Phase 1, operands - auto b87 = model->addOperand(&type0); - auto b88 = model->addOperand(&type0); - auto b89 = model->addOperand(&type0); - auto b90 = model->addOperand(&type0); - auto b91 = model->addOperand(&type0); - auto b92 = model->addOperand(&type0); - auto b93 = model->addOperand(&type0); - auto b94 = model->addOperand(&type0); - auto b95 = model->addOperand(&type0); - auto b96 = model->addOperand(&type0); - auto b97 = model->addOperand(&type0); - auto b98 = model->addOperand(&type0); - auto b99 = model->addOperand(&type0); - auto b100 = model->addOperand(&type0); - auto b101 = model->addOperand(&type0); - auto b102 = model->addOperand(&type0); - auto b103 = model->addOperand(&type0); - auto b104 = model->addOperand(&type0); - auto b105 = model->addOperand(&type0); - auto b106 = model->addOperand(&type0); - auto b107 = model->addOperand(&type0); - auto b108 = model->addOperand(&type0); - auto b109 = model->addOperand(&type0); - auto b110 = model->addOperand(&type0); - auto b111 = model->addOperand(&type0); - auto b112 = model->addOperand(&type0); - auto b113 = model->addOperand(&type0); - auto b114 = model->addOperand(&type0); - auto b115 = model->addOperand(&type0); - auto b116 = model->addOperand(&type0); - auto b117 = model->addOperand(&type0); - auto b118 = model->addOperand(&type0); - auto b119 = model->addOperand(&type0); - auto b120 = model->addOperand(&type0); - auto b121 = model->addOperand(&type0); - auto b122 = model->addOperand(&type0); - auto b123 = model->addOperand(&type0); - auto b124 = model->addOperand(&type0); - auto b125 = model->addOperand(&type0); - auto b126 = model->addOperand(&type0); - auto b127 = model->addOperand(&type0); - auto b128 = model->addOperand(&type0); - auto b129 = model->addOperand(&type0); - auto b130 = model->addOperand(&type0); - auto b131 = model->addOperand(&type0); - auto b132 = model->addOperand(&type0); - auto b133 = model->addOperand(&type0); - auto b134 = model->addOperand(&type0); - auto b135 = model->addOperand(&type0); - auto b136 = model->addOperand(&type0); - auto b137 = model->addOperand(&type0); - auto b138 = model->addOperand(&type0); - auto b139 = model->addOperand(&type0); - auto b140 = model->addOperand(&type0); - auto b141 = model->addOperand(&type0); - auto b142 = model->addOperand(&type0); - auto b143 = model->addOperand(&type0); - auto b144 = model->addOperand(&type0); - auto b145 = model->addOperand(&type0); - auto b146 = model->addOperand(&type0); - auto b147 = model->addOperand(&type0); - auto b148 = model->addOperand(&type0); - auto b149 = model->addOperand(&type0); - auto b150 = model->addOperand(&type0); - auto b151 = model->addOperand(&type0); - auto b152 = model->addOperand(&type0); - auto b153 = model->addOperand(&type0); - auto b154 = model->addOperand(&type0); - auto b155 = model->addOperand(&type0); - auto b156 = model->addOperand(&type0); - auto b157 = model->addOperand(&type0); - auto b158 = model->addOperand(&type0); - auto b159 = model->addOperand(&type0); - auto b160 = model->addOperand(&type0); - auto b161 = model->addOperand(&type0); - auto b162 = model->addOperand(&type0); - auto b163 = model->addOperand(&type0); - auto b164 = model->addOperand(&type0); - auto b165 = model->addOperand(&type0); - auto b166 = model->addOperand(&type0); - auto b167 = model->addOperand(&type0); - auto b168 = model->addOperand(&type0); - auto b169 = model->addOperand(&type0); - auto b170 = model->addOperand(&type0); - auto b171 = model->addOperand(&type0); - auto b172 = model->addOperand(&type0); - auto b173 = model->addOperand(&type0); - auto b174 = model->addOperand(&type0); - auto b175 = model->addOperand(&type0); - auto b176 = model->addOperand(&type0); - auto b177 = model->addOperand(&type0); - auto b178 = model->addOperand(&type0); - auto b179 = model->addOperand(&type0); - auto b180 = model->addOperand(&type0); - auto b181 = model->addOperand(&type0); - auto b182 = model->addOperand(&type0); - auto b183 = model->addOperand(&type0); - auto b184 = model->addOperand(&type0); - auto b185 = model->addOperand(&type0); - auto b186 = model->addOperand(&type0); - auto b187 = model->addOperand(&type0); - auto b188 = model->addOperand(&type0); - auto b189 = model->addOperand(&type0); - auto b190 = model->addOperand(&type0); - auto b191 = model->addOperand(&type0); - auto b192 = model->addOperand(&type0); - auto b193 = model->addOperand(&type0); - auto b194 = model->addOperand(&type0); - auto b195 = model->addOperand(&type0); - auto b196 = model->addOperand(&type0); - auto b197 = model->addOperand(&type0); - auto b198 = model->addOperand(&type0); - auto b199 = model->addOperand(&type0); - auto b200 = model->addOperand(&type0); - auto b201 = model->addOperand(&type0); - auto b202 = model->addOperand(&type0); - auto b203 = model->addOperand(&type0); - auto b204 = model->addOperand(&type0); - auto b205 = model->addOperand(&type0); - auto b206 = model->addOperand(&type0); - auto b207 = model->addOperand(&type0); - auto b208 = model->addOperand(&type0); - auto b209 = model->addOperand(&type0); - auto b210 = model->addOperand(&type0); - auto b211 = model->addOperand(&type0); - auto b212 = model->addOperand(&type0); - auto b213 = model->addOperand(&type0); - auto b214 = model->addOperand(&type0); - auto b215 = model->addOperand(&type0); - auto b216 = model->addOperand(&type0); - auto b217 = model->addOperand(&type0); - auto op86 = model->addOperand(&type1); - auto op85 = model->addOperand(&type2); - auto op0 = model->addOperand(&type3); - auto op1 = model->addOperand(&type4); - auto op2 = model->addOperand(&type5); - auto op3 = model->addOperand(&type6); - auto op4 = model->addOperand(&type7); - auto op5 = model->addOperand(&type8); - auto op6 = model->addOperand(&type6); - auto op7 = model->addOperand(&type7); - auto op8 = model->addOperand(&type9); - auto op9 = model->addOperand(&type6); - auto op10 = model->addOperand(&type7); - auto op11 = model->addOperand(&type8); - auto op12 = model->addOperand(&type6); - auto op13 = model->addOperand(&type7); - auto op14 = model->addOperand(&type9); - auto op15 = model->addOperand(&type10); - auto op16 = model->addOperand(&type7); - auto op17 = model->addOperand(&type8); - auto op18 = model->addOperand(&type11); - auto op19 = model->addOperand(&type12); - auto op20 = model->addOperand(&type13); - auto op21 = model->addOperand(&type11); - auto op22 = model->addOperand(&type12); - auto op23 = model->addOperand(&type14); - auto op24 = model->addOperand(&type11); - auto op25 = model->addOperand(&type12); - auto op26 = model->addOperand(&type15); - auto op27 = model->addOperand(&type3); - auto op28 = model->addOperand(&type4); - auto op29 = model->addOperand(&type16); - auto op30 = model->addOperand(&type3); - auto op31 = model->addOperand(&type4); - auto op32 = model->addOperand(&type17); - auto op33 = model->addOperand(&type18); - auto op34 = model->addOperand(&type4); - auto op35 = model->addOperand(&type16); - auto op36 = model->addOperand(&type19); - auto op37 = model->addOperand(&type20); - auto op38 = model->addOperand(&type21); - auto op39 = model->addOperand(&type19); - auto op40 = model->addOperand(&type20); - auto op41 = model->addOperand(&type22); - auto op42 = model->addOperand(&type19); - auto op43 = model->addOperand(&type20); - auto op44 = model->addOperand(&type23); - auto op45 = model->addOperand(&type24); - auto op46 = model->addOperand(&type20); - auto op47 = model->addOperand(&type22); - auto op48 = model->addOperand(&type25); - auto op49 = model->addOperand(&type26); - auto op50 = model->addOperand(&type27); - auto op51 = model->addOperand(&type25); - auto op52 = model->addOperand(&type26); - auto op53 = model->addOperand(&type28); - auto op54 = model->addOperand(&type25); - auto op55 = model->addOperand(&type26); - auto op56 = model->addOperand(&type29); - auto op57 = model->addOperand(&type30); - auto op58 = model->addOperand(&type26); - auto op59 = model->addOperand(&type28); - auto op60 = model->addOperand(&type6); - auto op61 = model->addOperand(&type7); - auto op62 = model->addOperand(&type31); - auto op63 = model->addOperand(&type6); - auto op64 = model->addOperand(&type7); - auto op65 = model->addOperand(&type8); - auto op66 = model->addOperand(&type6); - auto op67 = model->addOperand(&type7); - auto op68 = model->addOperand(&type9); - auto op69 = model->addOperand(&type6); - auto op70 = model->addOperand(&type7); - auto op71 = model->addOperand(&type8); - auto op72 = model->addOperand(&type6); - auto op73 = model->addOperand(&type7); - auto op74 = model->addOperand(&type9); - auto op75 = model->addOperand(&type6); - auto op76 = model->addOperand(&type7); - auto op77 = model->addOperand(&type8); - auto op78 = model->addOperand(&type6); - auto op79 = model->addOperand(&type7); - auto op80 = model->addOperand(&type9); - auto op81 = model->addOperand(&type32); - auto op82 = model->addOperand(&type2); - auto op83 = model->addOperand(&type33); - auto op84 = model->addOperand(&type34); - // Phase 2, operations - static int32_t b87_init[] = {1}; - model->setOperandValue(b87, b87_init, sizeof(int32_t) * 1); - static int32_t b88_init[] = {2}; - model->setOperandValue(b88, b88_init, sizeof(int32_t) * 1); - static int32_t b89_init[] = {2}; - model->setOperandValue(b89, b89_init, sizeof(int32_t) * 1); - static int32_t b90_init[] = {3}; - model->setOperandValue(b90, b90_init, sizeof(int32_t) * 1); - static int32_t b91_init[] = {1}; - model->setOperandValue(b91, b91_init, sizeof(int32_t) * 1); - static int32_t b92_init[] = {1}; - model->setOperandValue(b92, b92_init, sizeof(int32_t) * 1); - static int32_t b93_init[] = {1}; - model->setOperandValue(b93, b93_init, sizeof(int32_t) * 1); - static int32_t b94_init[] = {1}; - model->setOperandValue(b94, b94_init, sizeof(int32_t) * 1); - static int32_t b95_init[] = {3}; - model->setOperandValue(b95, b95_init, sizeof(int32_t) * 1); - static int32_t b96_init[] = {1}; - model->setOperandValue(b96, b96_init, sizeof(int32_t) * 1); - static int32_t b97_init[] = {1}; - model->setOperandValue(b97, b97_init, sizeof(int32_t) * 1); - static int32_t b98_init[] = {1}; - model->setOperandValue(b98, b98_init, sizeof(int32_t) * 1); - static int32_t b99_init[] = {3}; - model->setOperandValue(b99, b99_init, sizeof(int32_t) * 1); - static int32_t b100_init[] = {1}; - model->setOperandValue(b100, b100_init, sizeof(int32_t) * 1); - static int32_t b101_init[] = {2}; - model->setOperandValue(b101, b101_init, sizeof(int32_t) * 1); - static int32_t b102_init[] = {2}; - model->setOperandValue(b102, b102_init, sizeof(int32_t) * 1); - static int32_t b103_init[] = {1}; - model->setOperandValue(b103, b103_init, sizeof(int32_t) * 1); - static int32_t b104_init[] = {3}; - model->setOperandValue(b104, b104_init, sizeof(int32_t) * 1); - static int32_t b105_init[] = {1}; - model->setOperandValue(b105, b105_init, sizeof(int32_t) * 1); - static int32_t b106_init[] = {1}; - model->setOperandValue(b106, b106_init, sizeof(int32_t) * 1); - static int32_t b107_init[] = {1}; - model->setOperandValue(b107, b107_init, sizeof(int32_t) * 1); - static int32_t b108_init[] = {3}; - model->setOperandValue(b108, b108_init, sizeof(int32_t) * 1); - static int32_t b109_init[] = {1}; - model->setOperandValue(b109, b109_init, sizeof(int32_t) * 1); - static int32_t b110_init[] = {1}; - model->setOperandValue(b110, b110_init, sizeof(int32_t) * 1); - static int32_t b111_init[] = {1}; - model->setOperandValue(b111, b111_init, sizeof(int32_t) * 1); - static int32_t b112_init[] = {1}; - model->setOperandValue(b112, b112_init, sizeof(int32_t) * 1); - static int32_t b113_init[] = {3}; - model->setOperandValue(b113, b113_init, sizeof(int32_t) * 1); - static int32_t b114_init[] = {1}; - model->setOperandValue(b114, b114_init, sizeof(int32_t) * 1); - static int32_t b115_init[] = {1}; - model->setOperandValue(b115, b115_init, sizeof(int32_t) * 1); - static int32_t b116_init[] = {1}; - model->setOperandValue(b116, b116_init, sizeof(int32_t) * 1); - static int32_t b117_init[] = {3}; - model->setOperandValue(b117, b117_init, sizeof(int32_t) * 1); - static int32_t b118_init[] = {1}; - model->setOperandValue(b118, b118_init, sizeof(int32_t) * 1); - static int32_t b119_init[] = {2}; - model->setOperandValue(b119, b119_init, sizeof(int32_t) * 1); - static int32_t b120_init[] = {2}; - model->setOperandValue(b120, b120_init, sizeof(int32_t) * 1); - static int32_t b121_init[] = {1}; - model->setOperandValue(b121, b121_init, sizeof(int32_t) * 1); - static int32_t b122_init[] = {3}; - model->setOperandValue(b122, b122_init, sizeof(int32_t) * 1); - static int32_t b123_init[] = {1}; - model->setOperandValue(b123, b123_init, sizeof(int32_t) * 1); - static int32_t b124_init[] = {1}; - model->setOperandValue(b124, b124_init, sizeof(int32_t) * 1); - static int32_t b125_init[] = {1}; - model->setOperandValue(b125, b125_init, sizeof(int32_t) * 1); - static int32_t b126_init[] = {3}; - model->setOperandValue(b126, b126_init, sizeof(int32_t) * 1); - static int32_t b127_init[] = {1}; - model->setOperandValue(b127, b127_init, sizeof(int32_t) * 1); - static int32_t b128_init[] = {1}; - model->setOperandValue(b128, b128_init, sizeof(int32_t) * 1); - static int32_t b129_init[] = {1}; - model->setOperandValue(b129, b129_init, sizeof(int32_t) * 1); - static int32_t b130_init[] = {1}; - model->setOperandValue(b130, b130_init, sizeof(int32_t) * 1); - static int32_t b131_init[] = {3}; - model->setOperandValue(b131, b131_init, sizeof(int32_t) * 1); - static int32_t b132_init[] = {1}; - model->setOperandValue(b132, b132_init, sizeof(int32_t) * 1); - static int32_t b133_init[] = {1}; - model->setOperandValue(b133, b133_init, sizeof(int32_t) * 1); - static int32_t b134_init[] = {1}; - model->setOperandValue(b134, b134_init, sizeof(int32_t) * 1); - static int32_t b135_init[] = {3}; - model->setOperandValue(b135, b135_init, sizeof(int32_t) * 1); - static int32_t b136_init[] = {1}; - model->setOperandValue(b136, b136_init, sizeof(int32_t) * 1); - static int32_t b137_init[] = {2}; - model->setOperandValue(b137, b137_init, sizeof(int32_t) * 1); - static int32_t b138_init[] = {2}; - model->setOperandValue(b138, b138_init, sizeof(int32_t) * 1); - static int32_t b139_init[] = {1}; - model->setOperandValue(b139, b139_init, sizeof(int32_t) * 1); - static int32_t b140_init[] = {3}; - model->setOperandValue(b140, b140_init, sizeof(int32_t) * 1); - static int32_t b141_init[] = {1}; - model->setOperandValue(b141, b141_init, sizeof(int32_t) * 1); - static int32_t b142_init[] = {1}; - model->setOperandValue(b142, b142_init, sizeof(int32_t) * 1); - static int32_t b143_init[] = {1}; - model->setOperandValue(b143, b143_init, sizeof(int32_t) * 1); - static int32_t b144_init[] = {3}; - model->setOperandValue(b144, b144_init, sizeof(int32_t) * 1); - static int32_t b145_init[] = {1}; - model->setOperandValue(b145, b145_init, sizeof(int32_t) * 1); - static int32_t b146_init[] = {1}; - model->setOperandValue(b146, b146_init, sizeof(int32_t) * 1); - static int32_t b147_init[] = {1}; - model->setOperandValue(b147, b147_init, sizeof(int32_t) * 1); - static int32_t b148_init[] = {1}; - model->setOperandValue(b148, b148_init, sizeof(int32_t) * 1); - static int32_t b149_init[] = {3}; - model->setOperandValue(b149, b149_init, sizeof(int32_t) * 1); - static int32_t b150_init[] = {1}; - model->setOperandValue(b150, b150_init, sizeof(int32_t) * 1); - static int32_t b151_init[] = {1}; - model->setOperandValue(b151, b151_init, sizeof(int32_t) * 1); - static int32_t b152_init[] = {1}; - model->setOperandValue(b152, b152_init, sizeof(int32_t) * 1); - static int32_t b153_init[] = {3}; - model->setOperandValue(b153, b153_init, sizeof(int32_t) * 1); - static int32_t b154_init[] = {1}; - model->setOperandValue(b154, b154_init, sizeof(int32_t) * 1); - static int32_t b155_init[] = {1}; - model->setOperandValue(b155, b155_init, sizeof(int32_t) * 1); - static int32_t b156_init[] = {1}; - model->setOperandValue(b156, b156_init, sizeof(int32_t) * 1); - static int32_t b157_init[] = {1}; - model->setOperandValue(b157, b157_init, sizeof(int32_t) * 1); - static int32_t b158_init[] = {3}; - model->setOperandValue(b158, b158_init, sizeof(int32_t) * 1); - static int32_t b159_init[] = {1}; - model->setOperandValue(b159, b159_init, sizeof(int32_t) * 1); - static int32_t b160_init[] = {1}; - model->setOperandValue(b160, b160_init, sizeof(int32_t) * 1); - static int32_t b161_init[] = {1}; - model->setOperandValue(b161, b161_init, sizeof(int32_t) * 1); - static int32_t b162_init[] = {3}; - model->setOperandValue(b162, b162_init, sizeof(int32_t) * 1); - static int32_t b163_init[] = {1}; - model->setOperandValue(b163, b163_init, sizeof(int32_t) * 1); - static int32_t b164_init[] = {1}; - model->setOperandValue(b164, b164_init, sizeof(int32_t) * 1); - static int32_t b165_init[] = {1}; - model->setOperandValue(b165, b165_init, sizeof(int32_t) * 1); - static int32_t b166_init[] = {1}; - model->setOperandValue(b166, b166_init, sizeof(int32_t) * 1); - static int32_t b167_init[] = {3}; - model->setOperandValue(b167, b167_init, sizeof(int32_t) * 1); - static int32_t b168_init[] = {1}; - model->setOperandValue(b168, b168_init, sizeof(int32_t) * 1); - static int32_t b169_init[] = {1}; - model->setOperandValue(b169, b169_init, sizeof(int32_t) * 1); - static int32_t b170_init[] = {1}; - model->setOperandValue(b170, b170_init, sizeof(int32_t) * 1); - static int32_t b171_init[] = {3}; - model->setOperandValue(b171, b171_init, sizeof(int32_t) * 1); - static int32_t b172_init[] = {1}; - model->setOperandValue(b172, b172_init, sizeof(int32_t) * 1); - static int32_t b173_init[] = {1}; - model->setOperandValue(b173, b173_init, sizeof(int32_t) * 1); - static int32_t b174_init[] = {1}; - model->setOperandValue(b174, b174_init, sizeof(int32_t) * 1); - static int32_t b175_init[] = {1}; - model->setOperandValue(b175, b175_init, sizeof(int32_t) * 1); - static int32_t b176_init[] = {3}; - model->setOperandValue(b176, b176_init, sizeof(int32_t) * 1); - static int32_t b177_init[] = {1}; - model->setOperandValue(b177, b177_init, sizeof(int32_t) * 1); - static int32_t b178_init[] = {1}; - model->setOperandValue(b178, b178_init, sizeof(int32_t) * 1); - static int32_t b179_init[] = {1}; - model->setOperandValue(b179, b179_init, sizeof(int32_t) * 1); - static int32_t b180_init[] = {3}; - model->setOperandValue(b180, b180_init, sizeof(int32_t) * 1); - static int32_t b181_init[] = {1}; - model->setOperandValue(b181, b181_init, sizeof(int32_t) * 1); - static int32_t b182_init[] = {1}; - model->setOperandValue(b182, b182_init, sizeof(int32_t) * 1); - static int32_t b183_init[] = {1}; - model->setOperandValue(b183, b183_init, sizeof(int32_t) * 1); - static int32_t b184_init[] = {1}; - model->setOperandValue(b184, b184_init, sizeof(int32_t) * 1); - static int32_t b185_init[] = {3}; - model->setOperandValue(b185, b185_init, sizeof(int32_t) * 1); - static int32_t b186_init[] = {1}; - model->setOperandValue(b186, b186_init, sizeof(int32_t) * 1); - static int32_t b187_init[] = {1}; - model->setOperandValue(b187, b187_init, sizeof(int32_t) * 1); - static int32_t b188_init[] = {1}; - model->setOperandValue(b188, b188_init, sizeof(int32_t) * 1); - static int32_t b189_init[] = {3}; - model->setOperandValue(b189, b189_init, sizeof(int32_t) * 1); - static int32_t b190_init[] = {1}; - model->setOperandValue(b190, b190_init, sizeof(int32_t) * 1); - static int32_t b191_init[] = {2}; - model->setOperandValue(b191, b191_init, sizeof(int32_t) * 1); - static int32_t b192_init[] = {2}; - model->setOperandValue(b192, b192_init, sizeof(int32_t) * 1); - static int32_t b193_init[] = {1}; - model->setOperandValue(b193, b193_init, sizeof(int32_t) * 1); - static int32_t b194_init[] = {3}; - model->setOperandValue(b194, b194_init, sizeof(int32_t) * 1); - static int32_t b195_init[] = {1}; - model->setOperandValue(b195, b195_init, sizeof(int32_t) * 1); - static int32_t b196_init[] = {1}; - model->setOperandValue(b196, b196_init, sizeof(int32_t) * 1); - static int32_t b197_init[] = {1}; - model->setOperandValue(b197, b197_init, sizeof(int32_t) * 1); - static int32_t b198_init[] = {3}; - model->setOperandValue(b198, b198_init, sizeof(int32_t) * 1); - static int32_t b199_init[] = {1}; - model->setOperandValue(b199, b199_init, sizeof(int32_t) * 1); - static int32_t b200_init[] = {1}; - model->setOperandValue(b200, b200_init, sizeof(int32_t) * 1); - static int32_t b201_init[] = {1}; - model->setOperandValue(b201, b201_init, sizeof(int32_t) * 1); - static int32_t b202_init[] = {1}; - model->setOperandValue(b202, b202_init, sizeof(int32_t) * 1); - static int32_t b203_init[] = {3}; - model->setOperandValue(b203, b203_init, sizeof(int32_t) * 1); - static int32_t b204_init[] = {1}; - model->setOperandValue(b204, b204_init, sizeof(int32_t) * 1); - static int32_t b205_init[] = {1}; - model->setOperandValue(b205, b205_init, sizeof(int32_t) * 1); - static int32_t b206_init[] = {1}; - model->setOperandValue(b206, b206_init, sizeof(int32_t) * 1); - static int32_t b207_init[] = {3}; - model->setOperandValue(b207, b207_init, sizeof(int32_t) * 1); - static int32_t b208_init[] = {2}; - model->setOperandValue(b208, b208_init, sizeof(int32_t) * 1); - static int32_t b209_init[] = {2}; - model->setOperandValue(b209, b209_init, sizeof(int32_t) * 1); - static int32_t b210_init[] = {2}; - model->setOperandValue(b210, b210_init, sizeof(int32_t) * 1); - static int32_t b211_init[] = {7}; - model->setOperandValue(b211, b211_init, sizeof(int32_t) * 1); - static int32_t b212_init[] = {7}; - model->setOperandValue(b212, b212_init, sizeof(int32_t) * 1); - static int32_t b213_init[] = {0}; - model->setOperandValue(b213, b213_init, sizeof(int32_t) * 1); - static int32_t b214_init[] = {1}; - model->setOperandValue(b214, b214_init, sizeof(int32_t) * 1); - static int32_t b215_init[] = {1}; - model->setOperandValue(b215, b215_init, sizeof(int32_t) * 1); - static int32_t b216_init[] = {1}; - model->setOperandValue(b216, b216_init, sizeof(int32_t) * 1); - static int32_t b217_init[] = {0}; - model->setOperandValue(b217, b217_init, sizeof(int32_t) * 1); - static float op1_init[] = {0.247857f, 0.75021f, 0.741359f, 1.36951f, -0.799518f, 0.835508f, 1.00795f, 0.758906f, 0.872059f, -0.442585f, -0.724606f, 0.347115f, 0.0888976f, 0.718811f, 1.06249f, -0.285038f}; - model->setOperandValue(op1, op1_init, sizeof(float) * 16); - static float op2_init[] = {0.652068f, -0.440708f, -0.370313f, 0.0371835f, -0.200534f, 0.187246f, 0.608945f, 0.0446021f, -0.328933f, 0.608707f, -0.458118f, -0.338548f, 0.544501f, -0.100406f, -0.552947f, 0.264365f, 0.00542196f, -0.236679f, 0.00718806f, -0.39411f, 0.566754f, -0.0512145f, -0.355038f, 0.474691f, -0.0822169f, -0.646847f, 0.576151f, -0.0225515f, -0.311586f, 0.520931f, 0.0131213f, -0.0204246f, 0.122719f, 0.0407416f, -0.00254109f, -0.169118f, -0.733753f, 0.488922f, 0.210584f, 0.529329f, 0.35191f, -0.070027f, 0.939339f, 0.246604f, 0.309091f, -0.772781f, -0.0317189f, 0.184809f, 0.217716f, 0.123726f, -0.581245f, 0.753962f, -0.237803f, -0.534238f, 0.066889f, 0.205891f, 0.0796765f, -0.0730868f, 0.247787f, -0.0795177f, -0.153247f, -0.141179f, -0.107013f, 0.347468f, 0.159756f, 0.202276f, 0.333447f, 0.282742f, 0.174085f, 0.0931139f, -0.0784908f, 0.124231f, -0.0355699f, -0.0750061f, -0.0602844f, 0.153779f, 0.190586f, -0.0436086f, 0.127754f, -0.0132479f, -0.0590352f, -0.236779f, 0.100819f, 0.0578522f, -0.110667f, 0.0517516f, 0.28305f, -0.36037f, -0.0109951f, 0.258977f, -0.171027f, 0.182065f, 0.342808f, -0.0865413f, -0.120913f, 0.0876433f, -0.321358f, 0.182235f, 0.182115f, -0.102917f, 0.124579f, 0.266657f, -0.107564f, -0.0623865f, 0.107621f, -0.404334f, 0.0282603f, 0.331853f, -0.0646019f, -0.0455715f, -0.217516f, -0.0037875f, -0.235336f, -0.0671481f, 0.0357478f, -0.0844078f, 0.0935384f, -0.0619307f, -0.0631019f, 0.0568289f, -0.165787f, -0.147547f, -0.12507f, -0.16594f, -0.131169f, -0.0705661f, -0.0969777f, 0.0446729f, 0.245831f, 0.016168f, -0.0636066f, 0.00757261f, -0.241471f, -0.201184f, 0.0612182f, -0.235653f, -0.0647185f, -0.266401f, 0.140807f, 0.253679f, 0.201186f, 0.376029f, 0.55602f, 0.0176408f, -0.34448f, -0.105594f, -0.0830195f, -0.073044f, 0.332442f, 0.189069f, 0.579356f, 0.500409f, 0.081007f, 0.0224194f, -0.418452f, 0.117647f, 0.102363f, -0.308967f, -0.130247f, 0.0744453f, 0.0937165f, 0.129678f, -0.206706f, 0.355731f, -0.183979f, 0.0930233f, 0.376961f, -0.496447f, 0.0753673f, 0.550546f, -0.530313f, -0.119834f, 0.633561f, -0.350414f, 0.17257f, 0.363476f, -0.326057f, -0.117922f, 0.439519f, -0.361867f, -0.587992f, 0.540487f, -0.0493363f, -0.423359f, 0.39267f, -0.0985205f, -0.361801f, 0.406216f, 0.312372f, 0.19384f, 0.38476f, 0.0793438f, -0.0243019f, 0.0973445f, 0.34309f, -0.336955f, 0.047901f, -0.142791f, 0.111938f, -0.0117197f, -0.140463f, 0.134465f, 0.238205f, 0.133603f, 0.28098f, -0.0365245f, 0.0201062f, 0.0713887f, -0.201147f, -0.352104f, 0.122226f, -0.092246f, 0.159098f, 0.282097f, 0.418289f, -0.108599f, -0.294907f, -0.46983f, -0.238307f, -0.442626f, -0.572947f, -0.247161f, 0.0933571f, -0.387562f, -0.0395712f, -0.0761291f, -0.0795069f, 0.228746f, -0.110803f, 0.041032f, -0.00155868f, -0.0828624f, -0.224932f, 0.0649534f, 0.307345f, 0.528949f, -0.0949103f, 0.390639f, 0.54659f, 0.404815f, 0.342257f, 0.489076f, -0.121948f, 0.530045f, -0.476237f, -0.529727f, 0.262048f, -0.0304337f, -0.117802f, -0.0623434f, -0.00425207f, -0.238349f, -0.113178f, -0.259427f, -0.202885f, -0.600946f, -0.225316f, -0.0887756f, -0.29406f, -0.0257875f, 0.129552f, 0.600505f, 0.195343f, 0.693125f, -0.417967f, -0.359057f, 0.428299f, -0.633522f, -0.0536405f, -0.0580457f, -0.237309f, -0.267921f, -0.0486694f, -0.192913f, -0.344927f, -0.085713f, 0.0203166f, 0.0355307f, 0.270309f, 0.0918823f, -0.010063f, -0.135408f, -0.141108f, -0.496817f, -0.290794f, 0.224278f, -0.249464f, -0.0518539f, 0.0756295f, 0.171187f, 0.182209f, -0.195592f, 0.0958233f, -0.0921988f, -0.367889f, 0.134003f, 0.0687944f, 0.308343f, -0.0118914f, 0.331651f, -0.430663f, 0.706435f, 0.141785f, -0.554641f, 0.588355f, -0.750443f, -0.669483f, -0.996132f, -0.457539f, 0.493816f, -0.817835f, 0.120477f, 0.303217f, 0.46568f, 0.630415f, -0.92812f, -0.261296f, -0.702809f, -0.383505f, -0.48721f, 0.299454f, 1.24191f, 0.41616f, 0.162217f, 0.296053f, 0.251336f, 0.0753915f, 0.0408618f, -0.108743f, -0.0320105f, -0.296446f, -0.22903f, 0.277229f, -0.0633153f, 0.121183f, -0.0197022f, -0.284926f, -0.35553f, -0.0807346f, -0.334026f, -0.584447f, -0.200952f, 0.274273f, 0.255205f, 0.298184f, 0.135537f, 0.128646f, -0.0911873f, -0.345975f, -0.51712f, -0.331822f, 0.541729f, 0.790656f, -0.363103f, 0.9478f, 1.07197f, 0.00359358f, -0.397684f, -0.204973f, -0.592952f, 1.0766f, 0.92906f, 0.670814f, -0.0147435f, 0.112532f, 0.0736042f, -1.07179f, -1.08754f, -0.629355f, 0.68915f, 0.12786f, 0.522442f, -0.973202f, -0.867001f, 0.019969f, -0.654796f, -0.849004f, 0.197318f, -0.0344764f, 0.0267163f, -0.0116256f, 0.00203475f, 0.0356207f, 0.000402505f, -0.0853754f, 0.100378f, 0.0482042f, -0.0503914f, 0.0231722f, -0.00244786f, -0.033307f, 0.0556787f, -0.0229831f, -0.0586009f, 0.0728227f, 0.0432203f, 0.0261995f, 0.0298879f, -0.0300803f, -0.0232209f, 0.0302866f, -0.0522305f, -0.0859193f, 0.0553994f, -0.0223054f, 0.0875387f, -0.0641483f, -0.0985766f, -0.300987f, -0.185793f, 0.0301473f, -0.171482f, -0.0271402f, 0.0324297f, -0.104419f, -0.0167332f, -0.201047f, -0.012749f, -0.414162f, 0.0129281f, -0.126695f, -0.177922f, -0.158758f, 0.109131f, 0.15325f, 0.279018f, -0.144136f, -0.247177f, 0.166079f, -0.247908f, -0.251021f, 0.136724f}; - model->setOperandValue(op2, op2_init, sizeof(float) * 432); - static float op4_init[] = {-0.604559f, 0.937928f, -0.974893f, -0.53343f, 1.28805f, 1.10148f, -0.0531712f, 0.645655f, -0.739696f, 0.761929f, -0.768367f, 0.947203f, -0.0259644f, -0.0806925f, 0.826225f, 0.0647016f, 1.1949f, -0.590577f, -0.48047f, 1.0571f, -0.531508f, -0.145154f, -0.85109f, -0.428907f, -0.990977f, -0.21766f, -0.831976f, 0.807643f, -0.883484f, -0.101848f, -0.633951f, -0.934549f, -0.637571f, 0.917919f, -0.616174f, 0.638678f, -0.522414f, 0.164932f, 0.735401f, 0.388233f, -0.54634f, 0.663964f, -0.714432f, -0.770424f, -0.506502f, -0.00663996f, 1.20178f, -0.658902f, 0.202565f, -0.772786f, -0.908852f, -0.753642f, 0.299485f, -0.64437f, -0.258062f, -0.647217f, 1.16684f, -0.601416f, -0.581131f, -0.604002f, 0.303339f, 0.99038f, -0.252728f, 0.835258f, -0.479581f, 1.05239f, 0.0875528f, 0.436033f, 1.14433f, -0.755638f, 1.2817f, -0.110367f, -0.055392f, -0.327483f, 0.960676f, -0.268293f, 0.632768f, -0.77256f, -0.821636f, 0.0321576f, 0.777652f, -0.50267f, 0.998972f, 0.684318f, -0.592786f, -0.105259f, -0.183601f, -0.83277f, -0.584282f, -0.548963f, -0.186272f, -0.573955f, 0.637204f, -0.455254f, -0.0298786f, 0.0313744f, -0.835853f, 1.1131f, 1.13073f, -0.527641f, 0.6035f, 1.09069f, -0.727978f, -0.957797f, 0.795879f, 0.0702782f, 0.992534f, 0.447629f, 1.15544f, 1.02855f, -0.760967f, -0.552526f, 1.16415f, -0.720886f, -0.704716f, -0.918078f, -0.495209f, 0.79913f, -0.579286f, 1.12242f, 1.24057f, 0.868452f, 0.422623f, 1.26555f, -0.85856f, 0.536015f, 0.277351f, -0.375872f}; - model->setOperandValue(op4, op4_init, sizeof(float) * 128); - static float op5_init[] = {-0.371999f, -0.346798f, 0.0479857f, 0.357291f, 0.0781941f, -0.468469f, -0.189905f, -0.330524f, 0.149986f, -0.160447f, -0.0118703f, -0.550328f, -0.791003f, 0.40966f, -0.504578f, -0.59432f, -0.388201f, 0.599258f, 0.765913f, 0.0550903f, 0.687241f, 0.416152f, 0.45043f, 0.799941f, 0.183287f, -0.0806467f, 0.40882f, 0.038171f, 0.623295f, 0.334548f, -0.0286598f, 0.625486f, -0.0592684f, -0.44542f, 0.877997f, -0.37489f, 0.0650874f, 0.523281f, -0.299677f, -0.547368f, -0.273818f, 0.507475f, 0.0302324f, 0.628441f, 0.00878996f, 0.106071f, -0.590101f, 0.00397916f, 0.74002f, 0.656696f, 0.610534f, 0.469231f, 0.788127f, -0.0209616f, 0.695851f, -0.258907f, -0.104862f, 0.419681f, 0.288236f, 0.614652f, 0.567888f, 0.144987f, 0.180178f, 0.0734783f, 0.322781f, -0.568792f, 0.724734f, 0.733f, -0.118631f, -0.267416f, -0.560893f, 1.09369f, -0.33351f, -0.177277f, 0.451596f, -0.409597f, 0.758123f, -0.107428f, 0.446403f, -0.339231f, 0.715291f, 0.726725f, 0.104168f, 0.113386f, 0.801029f, -0.49758f, -0.403269f, 0.104698f, 0.390301f, 0.0562365f, -0.660356f, 0.726264f, -0.382822f, -0.502128f, 0.373597f, -0.327868f, -0.140265f, -0.263564f, -0.00081655f, -0.205775f, -0.216874f, -0.287382f, 0.754078f, 0.568083f, -0.580143f, -0.797676f, -0.193579f, -0.0638705f, -0.42645f, -0.218093f, 0.131062f, 0.366589f, -0.318487f, 0.400512f, 0.0918075f, 0.219986f, -0.191933f, -0.695975f, 0.376914f, -0.629351f, -0.305271f, -0.273703f, -0.539896f, -0.24429f, 0.738327f, -0.0349007f, -0.588518f, 0.656455f, 0.210178f, -0.410348f, -0.0931675f, 0.140479f, -0.455182f, -0.267616f, -0.319923f, -0.409643f, 0.288601f, -0.285263f, 0.194517f, 0.144924f, -0.52604f, 0.517934f, -0.158135f, -0.375607f, -0.417484f, 0.654575f, 0.209492f, -0.630293f, 0.152386f, 0.727502f, 0.418395f, 0.877609f, 0.213141f, -0.608543f, 0.141474f, -0.394892f, 0.584262f, 0.163144f, -0.145766f, 0.297692f, -0.126787f, -0.375714f, 0.374215f, -0.435386f, 0.489997f, 0.967364f, -0.518685f, -0.0186661f, -0.199576f, -1.02716f, 0.0914696f, -0.293028f, -0.342356f, 0.253988f, -0.345911f, -0.0787308f, 0.893786f, 0.0658312f, 0.328602f, 0.471665f, -0.454748f, 0.4051f, 0.42156f, -0.0440271f, -0.250534f, 0.309111f, 0.495076f, 0.453569f, 0.55799f, -0.0140493f, 0.52114f, 0.0349987f, 0.703054f, -0.387149f, 0.105532f, 0.0556309f, -0.403943f, 0.224924f, -0.602211f, -0.126065f, -0.189629f, -0.324051f, 0.0385442f, 0.142331f, 0.399993f, 0.541429f, 0.099055f, -0.748623f, 0.108785f, 0.604047f, -0.0259912f, -0.450731f, 0.310894f, -0.301575f, -0.302417f, -0.35221f, 0.0479682f, -0.168233f, 0.00968778f, -0.263735f, -0.248705f, 0.0826444f, 0.696327f, -0.232563f, 0.281257f, -0.243034f, -0.977377f, 0.260563f, -0.184195f, 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-0.0416337f, 0.227834f, -0.493132f, 0.392436f, -0.580179f, 0.297359f, -0.534621f, 0.499555f, -0.142822f, 0.176653f, -0.00980859f, 0.16683f, -0.472059f, -0.523987f, -0.376091f, -0.710018f, 0.79939f, 0.165761f, -0.348652f, 0.535135f, 0.375553f, 0.22469f, -0.0912158f, -0.370138f, 0.0500168f, 0.023739f, -0.172477f, 0.119806f, 0.383176f, 0.231837f, 0.0605284f, -0.100968f, 0.870541f, -0.0866504f, 0.409598f, 0.346036f, -0.289824f, -0.0732644f, -0.155742f, 0.0287473f, -0.752854f, -0.30823f, -0.408025f, -0.897713f, -0.29823f, -0.392733f, -0.165728f, -0.452032f, -0.0737648f, 0.693609f, 0.770581f, -0.697199f, 0.0877333f, -0.0932719f, 0.816154f, 0.688962f, -0.0667026f, -0.884026f, 0.342806f, -0.422542f, 0.100861f, -0.142711f, 0.417825f, 0.840715f, 0.115279f, 0.0247498f, 0.276853f, -0.149863f, -0.476889f, 0.912344f, 0.616008f, -0.432487f, 0.584148f, 1.13549f, -0.218403f, -0.218826f, 0.359245f, 0.12474f, -0.0657246f, 0.275225f, 0.00851578f, -0.0880628f, -0.0945963f, -0.179661f, -0.492065f, -0.482909f, -0.857491f, 0.039062f, 0.355535f, -0.282645f, 0.177667f, 0.779713f, 0.475427f, 0.224803f, -0.453928f, 0.376146f, 0.363905f, -0.211716f, -0.350152f, 0.533777f, -0.0899128f, -0.114365f, 0.88236f, -0.354094f, 0.200705f, 0.649566f, -0.385433f, 0.477843f, 0.31851f, -0.753048f, -0.11764f, 0.333795f, -0.0543095f, 0.167575f, 0.347282f, 0.324299f, -0.0835072f, 0.479318f, -0.388574f, -0.471185f, 0.480752f, -0.275772f, 0.301711f, -0.411163f, -0.489759f, 0.0623785f, -0.192641f, 0.0569767f, 0.111328f, 0.142228f, 0.0686482f, 0.0796447f, -0.263096f, 0.378977f, -0.0194688f, 0.30457f, 0.0137224f, 0.304379f, -0.50259f, -0.344538f, -0.294819f, 0.558693f, -0.357826f, 0.49133f, 0.506093f, -0.143837f, -0.25083f, 0.666564f, 0.437864f, 0.344516f, -0.437348f, -0.350776f, 0.778032f, -0.284674f, 0.527345f, 0.493619f, -0.19436f, 1.00847f, 0.361624f, -0.400407f, 0.314858f, -0.346484f, -0.508386f, -0.0496384f, -0.189277f, -0.0508045f, -0.19384f, -0.277062f, -0.586299f, 0.0188307f, -0.32649f, 0.195243f, -0.691762f, -0.399693f, 0.572609f, -0.508129f, -0.379947f, -0.172318f, 0.150124f, -0.00221663f, 0.61593f, -0.600549f, -0.562772f, 0.208896f, 0.611838f, 0.268238f, -0.213357f, -0.737937f, 0.699431f, -0.143642f, 0.514483f, 0.132155f, 0.51063f, 0.789351f, 0.520512f, 0.187771f, -0.330414f, -0.175605f, -0.361545f, -0.0891978f, 0.671198f, 0.142142f, -0.352542f, -0.233065f, 0.257493f, -0.0495003f, -0.191727f, -0.0484265f, 0.482953f, 0.193174f, -0.528268f, -0.246402f, -0.000374761f, -0.0302073f, 0.0310964f, -0.213433f, -0.460854f, -0.481105f, -0.60283f, 0.434087f, 0.142084f, 0.705319f, -0.171898f, -0.0783328f, -0.198899f, -0.469111f, -0.513186f, 0.258557f, -0.493187f, 0.157313f, 0.250994f, 0.220334f, 0.0393204f}; - model->setOperandValue(op5, op5_init, sizeof(float) * 1152); - static float op7_init[] = {-0.239844f, 0.432217f, -0.153807f, 0.0767933f, -0.275068f, 0.0063897f, 0.0516512f, 0.422338f, 0.0895534f, -0.416501f, 0.240182f, 0.0289379f, 0.302629f, -0.0327263f, -0.580243f, 0.86227f, 0.517094f, -0.23759f, 0.536654f, 0.597244f, 0.667096f, -0.507551f, -0.255663f, 0.61003f, 0.976404f, 0.232841f, 0.358804f, 0.348653f, 0.47637f, 0.230082f, 0.458516f, -0.664486f, -0.279672f, 0.192337f, -0.179005f, 0.656821f, -1.05123f, 0.0301599f, -0.677498f, -0.646811f, 0.100461f, 0.898003f, 0.12403f, 1.04799f, 0.960812f, -0.00393687f, -0.175513f, 0.242039f, 0.163904f, -0.022697f, 0.296417f, 0.225485f, 0.650953f, 0.257254f, 0.358139f, -0.39075f, 0.627265f, 0.0965187f, -0.232091f, -0.25681f, 0.651693f, 0.631126f, 0.499249f, 0.956892f, 0.710567f, -0.384283f, 0.920582f, 0.525813f, 0.0432827f, 0.0850359f, 1.35225f, -0.463563f, -0.61526f, 1.34561f, 2.03387f, -1.1429f, 0.108398f, 0.14919f, 0.387328f, 0.335212f, -0.33643f, 0.481889f, 0.742792f, 0.416105f, 0.267452f, 1.12879f, -0.692795f, -0.116663f, -0.651768f, 0.815917f, 0.150634f, -1.101f, -0.0903521f, 0.642167f, 0.636696f, 0.31574f, -0.432929f, 0.332695f, 0.571667f, -0.927278f, -0.330166f, 0.728891f, 0.120788f, 0.798598f, 0.215431f, 0.672161f, -0.256499f, -0.0800854f, 0.435699f, 0.64343f, -0.333017f, 0.271489f, 0.429805f, 0.21972f, 0.286028f, -0.512005f, -0.14715f, -0.265395f, 0.543187f, -0.288313f, 0.233415f, 0.571179f, -0.218133f, 0.990613f, 0.0603392f, 0.353106f, 0.610102f, 0.899427f}; - model->setOperandValue(op7, op7_init, sizeof(float) * 128); - static float op8_init[] = {-0.172699f, 0.0449607f, -0.00371321f, 0.0898063f, -0.0704206f, -0.0275239f, 0.041895f, -0.00635676f, -0.0417219f, -0.0280383f, -0.173593f, 0.135424f, -0.0752411f, 0.11462f, -0.124894f, 0.000502583f, -0.216759f, -0.0630591f, -0.110061f, 0.12233f, -0.0932574f, -0.161026f, 0.186579f, 0.130671f, -0.0936801f, -0.0569374f, 0.357974f, 0.0954326f, 0.101225f, -0.0409947f, -0.0455288f, -0.0440348f, 0.123336f, 0.0231551f, -0.0408172f, -0.00361653f, -0.00148246f, 0.119164f, 0.0527698f, 0.0506977f, 0.256418f, -0.0326844f, -0.0874518f, 0.067015f, -0.0604058f, 0.0366124f, 0.00296697f, 0.116773f, 0.218754f, 0.00682259f, 0.126256f, -0.142779f, -0.0523525f, 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0.141655f, 0.220536f, 0.0749074f, -0.0933725f, 0.118923f, 0.0410347f, -0.0161365f, -0.0723201f, -0.262448f, -0.0502746f, 0.044515f, -0.0685405f, 0.0791582f, 0.0344833f, 0.0611067f, 0.0951519f, -0.0523037f, -0.00838804f, -0.184084f, -0.0895456f, -0.0011999f, 0.0849984f, 0.0106403f, -0.0544982f, -0.154347f, 0.112921f, -0.0589115f, -0.157886f, 0.035458f, 0.106848f, 0.0371984f, 0.0239643f, 0.0421104f, -0.0259203f, 0.180176f, -0.212496f, -0.0366865f, -0.0723261f, 0.031068f, -0.0449782f, -0.115249f, -0.176421f, 0.128785f, 0.0496948f, 0.000222186f, -0.0294286f, -0.0897237f, 0.00569609f, 0.116698f, 0.285006f, -0.156228f, 0.127624f, 0.148411f, 0.0609212f, -0.135154f, -0.00909233f, 0.147309f, -0.169101f, -0.0343822f, 0.0851516f, -0.133019f, 0.201457f, -0.130316f, -0.104724f, 0.177934f, -0.0438326f, -0.372659f, 0.0286109f, -0.000941961f, -0.14556f, 0.0897591f, 0.231051f, -0.0873842f, 0.0141118f, -0.0139249f, -0.0289038f, 0.138774f, -0.0571027f, -0.0705663f, -0.01782f, -0.0442669f, -0.11826f}; - model->setOperandValue(op8, op8_init, sizeof(float) * 16384); - static float op10_init[] = {0.870905f, 0.732366f, -0.669046f, 1.08174f, 0.916159f, -0.294452f, 0.592262f, 0.301429f, -0.422655f, -0.625296f, -0.536564f, -0.667137f, -0.583286f, -0.72358f, -0.568471f, -0.57524f, -0.62291f, -0.911421f, -0.501374f, -0.00290018f, -0.878221f, -0.051163f, -0.74821f, 0.888223f, 0.813446f, 0.55647f, -0.622013f, 0.836827f, 0.841449f, -0.816436f, -0.759883f, -0.658268f, -0.625168f, 0.871381f, -0.78675f, -0.608052f, -0.0327976f, -0.379691f, -0.654795f, -0.0660218f, -0.676479f, -0.664595f, 0.265069f, -0.625888f, 0.169051f, -0.675591f, 0.299855f, -0.703214f, -0.134523f, -0.59296f, 0.937484f, -0.710908f, 0.845609f, -0.475655f, -0.636727f, 0.533551f, 0.124115f, 0.695435f, 0.879939f, -0.377836f, -0.816544f, 0.277795f, -0.607381f, -0.656329f, -0.667769f, 1.03546f, -0.725715f, -0.620013f, -0.527745f, -0.646767f, -0.275308f, -0.620535f, -0.637898f, -0.210308f, 1.20405f, 0.595138f, 0.794743f, 0.230801f, -0.840184f, -0.533846f, 0.487095f, -0.708161f, -0.744136f, -0.818685f, 1.0441f, -0.865297f, -0.666535f, 0.501392f, 0.186682f, -0.305148f, 0.56623f, 0.904152f, 0.799276f, 0.318903f, -0.21392f, -0.759348f, 1.00587f, -0.924452f, -0.69364f, 0.775983f, 0.740833f, -1.03741f, 0.567915f, -0.742716f, -0.793044f, 1.04104f, 1.06893f, -0.932649f, -0.672023f, -0.674185f, -0.697389f, 0.243591f, -0.875875f, -0.175759f, 0.0657881f, 0.764382f, 0.616322f, 0.697396f, -0.48311f, -0.592917f, -0.368845f, 1.1508f, -0.615879f, 1.07167f, 0.983414f, -0.53735f, 0.828645f, -0.713031f}; - model->setOperandValue(op10, op10_init, sizeof(float) * 128); - static float op11_init[] = {-0.0338548f, -0.209956f, 0.299f, -0.587087f, -0.155182f, -0.215745f, -0.457096f, 0.354364f, -0.0881957f, 0.448084f, 0.448586f, 0.265426f, -0.588544f, 0.100833f, 0.472292f, 0.884849f, 0.231727f, 0.277485f, -0.347502f, 0.294058f, 0.429232f, 0.856874f, 0.414706f, -0.696934f, 0.0382086f, -0.235027f, -0.208378f, -0.267724f, -0.500691f, 0.574836f, 0.0970658f, 0.392687f, -0.180284f, -0.126758f, -0.357164f, 0.569162f, 0.53462f, 0.83247f, -0.139077f, 0.291479f, 0.201703f, -0.0439143f, 0.560568f, -0.288697f, 0.579956f, 0.462056f, 0.707969f, 0.331557f, 0.71481f, 0.380786f, -0.150362f, 0.654365f, -0.318957f, -0.337636f, 0.57301f, 0.474281f, 0.565656f, 0.509379f, 0.245357f, 0.326285f, -0.651439f, 0.345691f, -0.116152f, -0.302133f, 0.780393f, -0.490086f, 0.247162f, 0.383248f, 0.0440418f, -0.177182f, 0.254644f, 0.296667f, 0.359842f, -0.278091f, -0.277828f, -0.595406f, -0.179306f, 0.431211f, 0.440019f, 0.310423f, 0.208222f, 0.540036f, 0.643585f, -0.139872f, -0.521879f, -0.122953f, 0.218204f, 0.158868f, -1.03219f, 0.557886f, 0.0889677f, -0.235379f, -0.392137f, -0.642783f, 0.328969f, 0.0884135f, -0.309692f, 0.763224f, 0.628604f, 0.00669528f, -0.0988385f, 0.79442f, 0.132674f, 0.250137f, 0.914897f, -0.107312f, -0.588339f, 0.473188f, 0.0187198f, -0.174877f, -0.0569728f, 0.0701776f, 0.735162f, 0.154904f, 0.688248f, -0.374409f, -0.556647f, 0.340098f, 0.888618f, 0.0980508f, -0.569228f, -0.595296f, -0.170562f, -0.0317408f, -0.395618f, -0.0171476f, 0.0485352f, 0.43209f, -0.00638226f, -0.0261422f, 0.0718189f, -0.075711f, -0.293662f, -0.319053f, -0.403627f, 0.174707f, -0.448796f, 0.392557f, 0.181877f, 0.370088f, 0.324692f, 0.177263f, 0.018459f, -0.210587f, 0.176304f, 0.420158f, 0.178299f, -0.805978f, 0.0792364f, -0.0386405f, 0.124362f, -0.792909f, -0.122755f, -0.0688359f, 0.159819f, -0.0211371f, -0.122289f, 0.360688f, 0.341697f, 0.0625401f, 0.0971241f, -0.478006f, 0.908729f, 0.189923f, 0.510477f, 0.20677f, 0.236684f, 0.776365f, 0.442561f, 0.090511f, 0.177896f, -0.236025f, 0.0264561f, 0.118261f, -0.809493f, 0.238168f, 0.112513f, 0.338984f, -0.110895f, 0.305834f, -0.621152f, 0.00687668f, 0.447304f, -0.0398545f, 0.370995f, -0.283421f, -0.213979f, 0.113023f, 0.342538f, 0.0130436f, -0.037166f, -0.22709f, 0.772525f, -0.174775f, 0.234323f, 0.159163f, -0.42859f, 0.277611f, -0.632671f, 0.565038f, 0.103225f, -0.305878f, 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-0.53722f, 0.383588f, 1.04688f, -0.223798f, 0.468294f, -0.220214f, 0.405335f, -0.280534f, 0.286776f, -0.179761f, 0.178508f, -0.297183f, 0.182498f, -0.685467f, 0.580719f, 0.19707f, -0.128853f, -0.819698f, -0.270809f, -0.832294f, -0.152185f, -0.393175f, -0.750241f, 0.529206f, 0.762727f, -0.407887f, -0.244154f, 0.572863f, 0.466904f, 0.957789f, 0.686932f, 0.133498f, 0.18891f, 0.850856f, 0.510167f, -0.172768f, 0.14671f, -1.13979f, -0.742267f, 0.393743f, 0.444338f, -0.33688f, -0.0438455f, 0.314722f, 0.306787f, -0.405244f, 0.441624f, 0.505547f, -0.291027f, 0.414324f, -0.252424f, 0.570165f, -0.324644f, -0.521873f, -0.0440274f, -0.176604f, 0.866987f, -0.429828f, 0.280288f, -0.0479095f, -0.246395f, -0.581399f, 1.1762f, -0.870758f, 0.643341f, 0.721751f, -0.412596f, -0.406957f, 0.610477f, 0.501528f, 0.41953f, 0.71976f, -0.913314f, 0.638418f, -0.444716f, -0.425936f, -0.0714459f, -0.656934f, -0.436864f, -0.208498f, 0.283547f, 0.403649f, -0.0865038f, 0.157091f, -0.676679f, -0.0651144f, 0.710754f, -0.689077f, 0.528029f}; - model->setOperandValue(op11, op11_init, sizeof(float) * 1152); - static float op13_init[] = {0.546519f, 0.196527f, 1.33245f, 0.223313f, -0.0686019f, 0.29652f, 0.373173f, 0.536225f, 0.675113f, -0.632758f, -0.340469f, 0.693325f, 0.451477f, -0.38477f, 0.163996f, -0.0238434f, -0.480369f, 0.486697f, -0.157606f, -0.453389f, -0.0613977f, 0.148022f, 0.210699f, 0.305469f, -0.719091f, -0.98168f, -0.729431f, 0.384724f, 0.170649f, 0.102487f, -0.177468f, -0.112562f, 0.977599f, 0.843605f, -0.160566f, -0.0826119f, -1.01795f, 0.327381f, -0.311044f, 0.148776f, 0.458008f, 0.712339f, -0.627031f, -0.0185205f, -0.220114f, -0.254519f, 0.157792f, -0.0902074f, -0.40048f, 0.0213905f, -0.106975f, 0.0542978f, -0.259207f, -0.529396f, -0.023698f, 0.0370436f, 0.175882f, -0.231802f, -0.755031f, -0.635971f, -0.210382f, 0.263938f, 0.644176f, -0.407618f, -0.815028f, 0.719426f, -0.215798f, -0.770013f, -0.190236f, -0.594785f, -0.0741564f, 0.436073f, -0.0194503f, 0.253288f, 0.257328f, -0.475578f, 0.323997f, 0.351296f, 0.0860487f, -0.836167f, 0.220064f, -0.593962f, -0.319109f, 0.20685f, -0.474809f, 0.0252552f, 0.137135f, 0.44559f, 0.241269f, -0.0192984f, 0.139146f, -0.431877f, 0.618441f, 0.246926f, -0.749076f, -0.602428f, -0.0262464f, -0.727089f, 0.614319f, 0.119553f, -0.658924f, 0.177904f, 0.520725f, -0.24752f, 0.539497f, -0.346223f, 1.01883f, -0.375712f, 0.150706f, -0.0695844f, 0.291091f, 0.231353f, -0.302372f, -0.159618f, 1.3969f, 0.838611f, -0.0641228f, -0.112692f, -0.443575f, 0.839417f, 0.531622f, 0.328611f, -0.124474f, 0.493831f, 0.0234903f, 0.0202693f, -0.531455f, 0.58728f}; - model->setOperandValue(op13, op13_init, sizeof(float) * 128); - static float op14_init[] = {0.0492167f, -0.0080753f, 0.106292f, 0.0105276f, -0.0331312f, 0.114954f, 0.170489f, -0.12154f, 0.0805692f, -0.167832f, 0.034986f, -0.296966f, -0.0655937f, -0.111175f, 0.0439435f, -0.018318f, -0.134041f, -0.0846412f, 0.092653f, -0.0790839f, 0.179607f, 0.0128861f, 0.170719f, -0.00102839f, -0.000371042f, 0.187442f, 0.367099f, 0.155459f, 0.127148f, 0.109095f, -0.0116157f, -0.0649709f, 0.00564519f, -0.220081f, 0.0481978f, -0.158649f, -0.00169554f, -0.189301f, 0.0927649f, -0.0162247f, -0.146722f, 0.0395212f, -0.119687f, -0.0263079f, -0.0115938f, -0.00645318f, -0.00155876f, 0.201296f, 0.00391423f, 0.0928273f, -0.0865713f, 0.0358025f, 0.0465153f, -0.192458f, -0.0337003f, 0.0168396f, -0.105297f, -0.052292f, -0.200595f, 0.0113962f, 0.0109133f, -0.0220112f, 0.055337f, -0.177347f, 0.0508988f, 0.0870318f, -0.132901f, -0.0590428f, -0.0393112f, 0.109377f, -0.0827402f, 0.112023f, -0.0645175f, 0.159355f, -0.0984026f, -0.18086f, 0.15466f, 0.0725992f, 0.0280992f, 0.0225695f, 0.00691936f, -0.0502526f, -0.128364f, -0.145571f, 0.0579586f, -0.0274957f, -0.16697f, -0.0250412f, 0.0943546f, 0.0787729f, 0.0940538f, -0.119917f, 0.0964136f, 0.113825f, -0.152387f, 0.311643f, -0.0707457f, -0.0727547f, -0.173086f, -0.113407f, -0.0740743f, -0.00903834f, -0.204262f, -0.0651392f, 0.00987691f, -0.129447f, -0.149183f, 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-0.0330365f, -0.191473f, 0.0398691f, 0.118263f, 0.0205686f, -0.0784466f, -0.161596f, 0.00894492f, 0.0908254f, 0.0254452f, 0.0771828f, -0.271077f, 0.0428394f, 0.083752f, 0.0356295f, -0.201973f, 0.0980716f, 0.166446f, -0.121299f, 0.2049f, -0.143584f, -0.00551848f, 0.173327f, 0.183502f, 0.233481f, -0.0436572f, 0.032631f, -0.173408f, 0.149638f, -0.0951374f, 0.0468391f}; - model->setOperandValue(op14, op14_init, sizeof(float) * 16384); - static float op16_init[] = {0.8739f, 0.830384f, -0.767024f, -0.878567f, 0.767678f, -0.558645f, -0.618775f, -0.612598f, 0.910247f, 0.796888f, 0.563224f, 0.630585f, -0.716768f, 0.630871f, 0.734415f, -0.211464f, 0.613973f, -0.504694f, -1.0123f, -0.0627354f, 1.00681f, -0.42962f, 0.466991f, -0.450799f, 0.803035f, 0.693793f, 0.732683f, -0.692463f, 0.886803f, -0.565427f, -0.576628f, -0.652566f, -0.782693f, 0.674996f, 0.221864f, 0.572101f, 0.803257f, -0.52347f, -0.486955f, 0.727345f, 0.589734f, 1.15092f, -0.769234f, -0.0774833f, -0.462587f, 0.74472f, 1.08679f, 1.08325f, 0.383234f, -0.845903f, 1.26034f, 0.813296f, 0.771082f, 0.776552f, 0.846018f, -0.685205f, 0.695407f, 0.797641f, 0.626686f, 0.817206f, 0.515052f, 0.50643f, -0.598648f, -0.46153f, -0.452983f, 0.583775f, 0.640815f, 0.539884f, -0.435857f, -0.513433f, -0.485565f, -0.195357f, 0.652978f, -0.33663f, 0.661144f, -0.898781f, 0.287976f, 0.635894f, -0.62165f, 1.0629f, -0.681778f, -0.185328f, 0.487961f, 0.872278f, 0.395395f, -0.740133f, 0.817477f, -0.57104f, -0.664395f, 0.906709f, -0.0626727f, -0.587757f, 0.951452f, -0.61133f, 0.566099f, -0.653262f, 0.896796f, 0.510386f, -0.00755904f, -0.520829f, 0.699523f, -0.51857f, -0.71965f, 0.323285f, -0.547704f, -0.916064f, -0.617135f, -0.584734f, -0.566928f, -0.680113f, 0.935143f, -0.548582f, 0.577092f, 0.969824f, -0.473696f, -0.53212f, 0.666168f, 0.797083f, 0.329202f, 0.815607f, -0.826578f, 0.890131f, 0.866098f, 0.759649f, -0.554823f, -0.791157f, 0.559045f, -0.747694f}; - model->setOperandValue(op16, op16_init, sizeof(float) * 128); - static float op17_init[] = {-0.177647f, -0.0871816f, 0.0601548f, 0.116089f, -0.183163f, 0.578973f, 0.0811623f, 0.372188f, -0.292915f, -0.251623f, -0.133364f, 0.0249744f, 0.209747f, -0.151377f, -0.274688f, 0.10489f, -0.346888f, 0.0908122f, 0.251565f, 0.102152f, -0.659776f, 0.27186f, -0.0203186f, 0.0910226f, 0.0232463f, 0.0918937f, -0.227945f, 0.220206f, -0.129868f, 0.0454901f, 0.00159753f, 0.277318f, 0.341466f, -0.0645028f, 0.29646f, -0.302511f, -0.225254f, 0.181877f, -0.003094f, -0.368428f, 0.0974374f, -0.200103f, 0.0589677f, 0.277546f, 0.111057f, 0.0328741f, -0.426973f, -0.199579f, -0.0472897f, 0.12313f, -0.242737f, -0.122301f, -0.0727011f, -0.235287f, -0.198718f, 0.324696f, -0.0974758f, -0.0987441f, -0.0489072f, -0.460212f, -0.244398f, -0.723718f, 0.0412443f, 0.267359f, -0.464882f, -0.32003f, -0.303246f, -0.329555f, 0.0122901f, 0.167574f, -0.0176612f, 0.299886f, -0.180337f, 0.293521f, -0.276708f, 0.277298f, 0.0906849f, 0.0211019f, 0.0815586f, -0.148871f, 0.0734914f, 0.428517f, 0.163658f, -0.40818f, 0.113258f, 0.304425f, -0.152113f, 0.105986f, 0.133363f, -0.362861f, -0.0816546f, 0.0946013f, 0.00721106f, -0.408414f, -0.484878f, 0.161902f, -0.0793248f, -0.336323f, -0.460177f, 0.307582f, 0.0354122f, -0.0519718f, 0.00674454f, 0.245323f, 0.033865f, 0.415952f, 0.20131f, 0.0334114f, 0.0995454f, 0.0562219f, -0.142446f, 0.256265f, 0.0640284f, -0.129566f, 0.244361f, 0.197299f, 0.0670241f, -0.309337f, -0.176682f, -0.363202f, 0.0597816f, 0.00494263f, -0.272968f, -0.0303995f, 0.193418f, 0.348788f, 0.139601f, 0.404337f, -0.274467f, -0.220471f, 0.234827f, 0.141957f, -0.205693f, 0.177229f, 0.31607f, 0.435521f, -0.378783f, -0.318383f, -0.0414879f, -0.110089f, 0.329408f, -0.132554f, -0.331081f, 0.223624f, -0.0905046f, 0.168813f, 0.348085f, 0.37548f, -0.0922745f, 0.245665f, -0.0927419f, 0.197676f, -0.102639f, -0.232749f, -0.375181f, 0.379403f, -0.250526f, 0.275713f, 0.124628f, 0.320001f, 0.481393f, -0.269458f, 0.179667f, -0.21777f, -0.0993835f, 0.188987f, 0.12176f, -0.470666f, -0.133013f, -0.44663f, 0.314893f, 0.255007f, 0.144322f, -0.133561f, -0.424778f, -0.344765f, -0.136315f, 0.269317f, -0.287856f, -0.233998f, -0.1886f, -0.241606f, -0.320311f, 0.204107f, -0.16412f, -0.294065f, 0.0965721f, -0.470172f, -0.303492f, -0.560968f, 0.202298f, 0.316824f, 0.174251f, -0.208489f, -0.215164f, -0.491982f, 0.245413f, 0.1279f, 0.177035f, 0.207553f, -0.233947f, 0.229277f, -0.270925f, 0.324056f, 0.309045f, 0.231493f, 0.147565f, -0.302136f, 0.273794f, 0.365701f, -0.111206f, -0.278091f, 0.250966f, 0.383613f, -0.131385f, 0.293494f, 0.0994267f, -0.185249f, -0.405195f, 0.282423f, -0.26585f, 0.660062f, -0.318764f, 0.249091f, -0.192755f, -0.248749f, -0.402543f, 0.307286f, -0.0854098f, 0.00795549f, 0.410958f, 0.162823f, 0.114975f, 0.375128f, 0.328851f, 0.163193f, 0.169105f, 0.360847f, -0.18493f, 0.231475f, -0.202768f, -0.304476f, 0.221781f, 0.252247f, -0.162048f, -0.326926f, 0.072488f, -0.293284f, 0.414543f, -0.301267f, -0.278129f, -0.176203f, 0.249626f, 0.528465f, -0.0376272f, 0.2294f, -0.290052f, -0.125104f, 0.300564f, 0.221424f, -0.196515f, -0.132026f, 0.211947f, 0.0255294f, -0.225691f, -0.1778f, -0.129375f, -0.182211f, 0.355858f, -0.0431843f, -0.0592856f, 0.112769f, -0.247296f, 0.134681f, 0.200602f, 0.359153f, -0.0533255f, -0.0650103f, -0.482401f, 0.124266f, -0.0882049f, -0.150976f, -0.225175f, 0.241558f, -0.223492f, 0.117191f, 0.110726f, 0.0700387f, 0.348165f, -0.218468f, 0.255051f, -0.216326f, -0.0982582f, 0.0521327f, 0.164097f, -0.0305243f, -0.224774f, -0.122026f, 0.22847f, -0.305519f, 0.0611823f, -0.132229f, -0.114884f, -0.322513f, -0.0835829f, 0.178658f, -0.0630996f, -0.210751f, -0.321467f, -0.166589f, -0.279502f, -0.239058f, -0.0959481f, -0.11015f, -0.245431f, -0.247913f, -0.146589f, 0.0946597f, -0.0442946f, 0.164044f, 0.657227f, -0.00436399f, -0.0244688f, -0.287218f, 0.202973f, -0.0169252f, 0.24289f, 0.193422f, -0.11275f, -0.0277141f, -0.386446f, 0.396187f, 0.273171f, 0.456946f, 0.10829f, -0.111849f, 0.0602555f, 0.27488f, 0.219216f, -0.168014f, 0.177434f, 0.158263f, -0.0659201f, 0.21938f, 0.0174049f, 0.0117175f, -0.331309f, 0.212254f, -0.203833f, -0.276827f, -0.12618f, 0.16201f, -0.0375803f, -0.203265f, -0.405161f, -0.0383014f, -0.436336f, 0.139065f, 0.400452f, 0.0355814f, -0.125888f, 0.058507f, 0.321675f, 0.145888f, 0.103469f, 0.405551f, -0.23371f, 0.06158f, -0.180621f, -0.191603f, 0.0476887f, 0.0602744f, -0.119576f, -0.074028f, -0.0118865f, -0.16039f, 0.113066f, -0.339936f, -0.217272f, -0.196035f, 0.050938f, -0.0251069f, -0.235276f, -0.000757713f, -0.296079f, -0.110058f, 0.258742f, 0.420009f, -0.245554f, 0.453115f, 0.317986f, 0.299668f, -0.277939f, -0.39049f, -0.257227f, -0.0856895f, 0.228484f, 0.0342279f, -0.131498f, 0.101379f, -0.0877639f, 0.231183f, 0.326838f, 0.0749738f, -0.283428f, 0.228858f, -0.049279f, 0.160293f, -0.189703f, -0.0250891f, -0.372925f, 0.24678f, -0.22283f, 0.271814f, 0.264571f, 0.228567f, 0.413428f, -0.123231f, -0.292158f, -0.271049f, -0.186574f, 0.152277f, 0.195322f, -0.266342f, -0.0173473f, -0.383596f, 0.309367f, 0.403505f, 0.188349f, -0.120478f, -0.298738f, -0.415039f, 0.214563f, 0.332686f, -0.412697f, -0.045258f, -0.251678f, -0.151431f, -0.238026f, 0.43075f, -0.186537f, -0.144898f, -0.0849375f, -0.0673f, -0.0303413f, 0.143291f, 0.271016f, 0.482529f, -0.299016f, -0.213718f, -0.309961f, -0.0617735f, 0.14388f, 0.155023f, 0.23866f, 0.545315f, -0.0913158f, 0.241437f, -0.221745f, 0.27077f, 0.167114f, -0.345467f, 0.251814f, -0.307936f, 0.205636f, 0.481653f, 0.107034f, -0.125381f, -0.345654f, 0.452588f, -0.154499f, 0.238078f, 0.262978f, -0.549896f, 0.149393f, 0.310907f, -0.347523f, -0.42861f, -0.287733f, 0.407821f, -0.246214f, -0.13513f, -0.000567387f, 0.416311f, -0.155481f, 0.256069f, 0.447515f, 0.267273f, 0.366993f, 0.409352f, 0.367687f, 0.262309f, 0.18948f, 0.0406171f, -0.266851f, 0.31281f, -0.0729797f, -0.228566f, 0.119522f, 0.229003f, -0.150106f, -0.484836f, -0.226986f, -0.149434f, 0.29663f, -0.091025f, -0.2921f, -0.220726f, 0.266003f, 0.365309f, -0.0456341f, 0.266656f, -0.279605f, -0.345084f, 0.370643f, 0.37088f, -0.0268642f, 0.243782f, 0.405799f, 0.278391f, -0.538491f, -0.421344f, -0.358549f, -0.311298f, 0.331689f, -0.0540822f, -0.30822f, 0.289189f, -0.272308f, 0.344655f, 0.500284f, 0.272903f, -0.427396f, 0.4165f, -0.27431f, 0.306682f, -0.198429f, -0.328838f, -0.473414f, 0.39285f, -0.303793f, 0.320321f, 0.328035f, 0.387076f, 0.627329f, -0.288295f, -0.0605434f, -0.319832f, -0.151415f, 0.224896f, 0.299414f, -0.259801f, -0.177669f, -0.626195f, 0.453454f, -0.0100813f, 0.295044f, -0.208736f, -0.647739f, -0.550532f, 0.382869f, 0.45022f, -0.590547f, -0.410671f, -0.279767f, -0.274314f, -0.19359f, 0.319678f, -0.334927f, -0.481704f, -0.246078f, -0.224691f, -0.0542857f, 0.0167961f, 0.282006f, 0.330704f, 0.215266f, -0.356376f, 0.107614f, -0.0754958f, 0.306347f, 0.296404f, 0.353815f, 0.134206f, -0.134798f, 0.368807f, -0.358746f, 0.278237f, 0.350032f, -0.227183f, 0.25473f, -0.442747f, 0.227231f, 0.193296f, -0.490342f, -0.379623f, -0.203105f, 0.411301f, -0.504094f, 0.362848f, 0.367674f, -0.17126f, 0.247855f, 0.428379f, -0.564512f, 0.688888f, -0.121817f, 0.327344f, -0.324932f, -0.321873f, 0.111342f, 0.466117f, -0.114591f, 0.25745f, 0.599431f, 0.188409f, 0.731075f, 0.617758f, 0.365941f, 0.371005f, 0.28158f, 0.325367f, -0.47852f, 0.357208f, -0.406809f, -0.398635f, 0.298849f, 0.388177f, -0.192094f, -0.246657f, -0.0421371f, -0.310092f, 0.474442f, -0.547938f, -0.378277f, -0.445861f, 0.379655f, 0.371314f, 0.0761212f, 0.313204f, -0.392242f, -0.375087f, 0.26996f, 0.213625f, -0.311669f, -0.304637f, 0.314832f, 0.176197f, -0.365741f, -0.173783f, -0.0874835f, -0.587745f, 0.251719f, -0.189482f, -0.394076f, 0.227509f, -0.211751f, 0.190396f, 0.352448f, 0.052262f, -0.264675f, 0.265481f, -0.386231f, 0.215168f, -0.280036f, -0.209575f, -0.0565344f, 0.216523f, -0.219674f, 0.198369f, 0.067544f, 0.210373f, 0.438628f, -0.34531f, 0.269559f, -0.112518f, -0.548147f, 0.256189f, 0.208292f, -0.155067f, -0.249604f, -0.22687f, 0.281386f, -0.390824f, 0.206864f, -0.255331f, -0.288741f, -0.24127f, -0.126734f, 0.276143f, -0.264786f, -0.474847f, -0.111699f, -0.271605f, -0.150584f, -0.0785853f, -0.22858f, -0.406254f, -0.282864f, -0.175679f, -0.165192f, 0.147815f, 0.219002f, -0.0444022f, 0.531143f, -0.281989f, -0.267387f, -0.232064f, 0.191775f, 0.231901f, 0.11466f, -0.265283f, -0.470041f, 0.138806f, -0.16848f, 0.270328f, 0.299141f, -0.0753426f, 0.15565f, -0.139846f, 0.297359f, 0.100413f, -0.341507f, -0.316434f, 0.000290225f, 0.107806f, -0.355047f, 0.305206f, 0.16506f, -0.368997f, 0.0740634f, 0.263219f, -0.187163f, -0.10158f, -0.111327f, 0.139576f, -0.247992f, -0.0548272f, 0.136212f, 0.119239f, -0.320333f, 0.12343f, 0.376278f, -0.553465f, 0.426086f, 0.536895f, 0.197861f, 0.28211f, 0.202422f, 0.391186f, -0.452794f, 0.125706f, -0.319177f, -0.270248f, 0.183954f, 0.176344f, -0.397669f, -0.146993f, -0.000925186f, -0.233553f, 0.326648f, -0.309439f, -0.314187f, -0.275544f, 0.167025f, 0.0392552f, -0.411068f, 0.164177f, -0.108162f, -0.00573354f, 0.251166f, 0.558204f, -0.315271f, 0.283163f, 0.261653f, 0.0670132f, -0.168622f, -0.155348f, -0.0474235f, -0.127982f, 0.0712446f, -0.364687f, 0.0897745f, 0.00733073f, -0.0612411f, 0.175381f, 0.365922f, -0.130426f, -0.049202f, 0.0616472f, -0.109447f, 0.0280195f, -0.223873f, -0.425023f, -0.127319f, 0.101025f, -0.0944779f, 0.22706f, 0.226776f, 0.0947206f, 0.3393f, -0.153124f, -0.639028f, -0.108781f, -0.140769f, 0.257962f, 0.221979f, -0.0850165f, -0.352863f, -0.238736f, 0.275487f, 0.222169f, 0.059329f, -0.483214f, -0.190109f, -0.379134f, -0.917725f, 0.317487f, -0.276374f, -0.089726f, -0.0552475f, -0.0356918f, -0.130264f, 0.348185f, -0.332862f, -0.0269239f, -0.371905f, -0.23288f, 0.0573066f, 0.0850446f, 0.365517f, 0.169593f, -0.0717293f, -0.112743f, -0.141701f, 0.11976f, 0.144278f, 0.234456f, 0.00789795f, 0.00279787f, -0.167253f, -0.0863365f, -0.0916999f, 0.385244f, -0.669646f, -0.562956f, 0.402339f, -0.487651f, 0.183208f, -0.284185f, -0.180969f, -0.00513625f, -0.405246f, 0.155039f, -0.113185f, 0.161695f, 0.352399f, -0.387996f, 0.160007f, 0.135512f, -0.194359f, -0.273423f, -0.0515216f, 0.313761f, -0.267454f, -0.0671176f, 0.204648f, 0.0968436f, -0.211575f, 0.340844f, 0.501942f, -0.322834f, -0.571688f, 0.233282f, 0.197781f, 0.134457f, 0.200076f, 0.0231796f, -0.0233646f, 0.136335f, -0.0475553f, -0.0154011f, -0.05394f, 0.14276f, -0.452707f, -0.301241f, -0.26561f, 0.0193491f, 0.425522f, -0.266067f, -0.0760103f, -0.23328f, 0.169324f, 0.0762291f, -0.222994f, 0.0854677f, -0.281563f, -0.358938f, 0.236145f, 0.410882f, -0.255268f, 0.257027f, 0.269625f, 0.081258f, -0.207181f, -0.129883f, -0.3676f, -0.284548f, 0.22034f, -0.322737f, -0.200545f, 0.0944379f, -0.296793f, 0.30017f, 0.418536f, -0.728572f, -0.298464f, 0.271853f, -0.263083f, 0.181573f, -0.377288f, -0.200982f, -0.0957494f, 0.204865f, -0.27232f, 0.214701f, 0.367255f, 0.179368f, 0.352101f, -0.161102f, -0.495211f, -0.114128f, -0.202276f, 0.22617f, 0.271564f, -0.0971297f, -0.208229f, -0.501904f, 0.290799f, -0.249978f, 0.240916f, -0.451848f, -0.258969f, -0.317451f, -0.48139f, 0.415029f, -0.355259f, -0.309499f, -0.186237f, -0.176257f, -0.222657f, 0.296689f, -0.225151f, -0.211715f, -0.0798991f, -0.149355f, -0.269878f, -0.00542135f, 0.395065f, -0.0506057f, 0.223675f, -0.140136f, -0.321944f, 0.0552234f, 0.13866f, 0.286808f, 0.317716f, -0.418532f, -0.0381384f, 0.172643f, 0.0674257f, 0.512635f, -1.01998f, -0.770424f, 0.280475f, -0.23201f, 0.270747f, -0.902413f, -0.477235f, -0.189689f, -0.492929f, 0.269974f, -0.213427f, 0.210412f, 0.440219f, -0.1266f, 0.413074f, 0.321902f, -0.36647f, 0.407438f, -0.153076f, 0.23766f, -0.42057f, -0.191747f, 0.393705f, 0.310401f, -0.415272f, 0.325159f, 0.532648f, -0.293704f, -0.175539f, 0.351968f, 0.213235f, 0.213214f, 0.218929f, 0.120335f, -0.228709f, 0.187762f, -0.247334f, -0.2135f, 0.168559f, 0.249568f, -0.589931f, -0.127207f, -0.324903f, -0.161224f, 0.217231f, -0.395982f, -0.130205f, -0.301879f, 0.250213f, 0.41101f, -0.249774f, 0.361477f, -0.0715028f, -0.506833f, 0.0712546f, 0.203802f, -0.089808f, -0.369859f, 0.245312f, 0.150604f, -0.027467f, 0.029619f, -0.130162f, -0.291405f, 0.0780199f, -0.327593f, -0.257673f, 0.136259f, -0.126371f, 0.177277f, 0.26127f, -0.730241f, -0.333356f, 0.318555f, 0.164954f, 0.183569f, -0.317724f, -0.22966f, 0.0234902f, 0.164969f, -0.163785f, 0.213277f, 0.123644f, 0.206416f, 0.0728148f, -0.13989f, -0.246562f, 0.0543395f, -0.170864f, 0.0696319f, 0.197585f, -0.0722028f, -0.3203f, -0.186663f, 0.236492f, -0.214728f, 0.222345f, -0.191442f, -0.12056f, 0.135759f, -0.213994f, 0.269784f, -0.210303f, -0.148635f, -0.286331f, -0.242858f, 0.00784691f, 0.0133885f, -0.100079f, -0.202f, -0.295953f, 0.12847f, -0.345867f, -0.0751299f, 0.180716f, -0.221087f, 0.196135f, 0.0130223f, -0.156671f, -0.0242788f, 0.0520482f, 0.166427f, 0.288313f, -0.447982f, -0.209238f, 0.240116f, 0.0410266f, 0.191869f, -0.662093f, -0.238494f, 0.020257f, -0.071556f, 0.284502f, -0.404911f, -0.226362f, -0.0850094f, -0.204616f, 0.106331f, -0.158366f, 0.104914f, 0.109703f, -0.0281585f, 0.330799f, 0.196422f, -0.158644f, 0.0104495f, 0.153696f, 0.154121f, -0.293512f, 0.0318065f, 0.615847f, 0.319811f, -0.196002f, 0.0428157f, 0.274682f, -0.716382f, -0.518719f, 0.572651f, 0.0312116f, 0.189001f, 0.114644f, 0.216293f, -0.141647f, 0.101355f, -0.189668f, -0.160207f, 0.202959f, 0.112228f, -0.201573f, -0.0583956f, -0.441833f, -0.140472f, 0.136687f, -0.0816845f, -0.0523801f, -0.0820299f, 0.0932448f, 0.0421082f, -0.505471f, 0.299505f}; - model->setOperandValue(op17, op17_init, sizeof(float) * 1152); - static float op19_init[] = {0.380364f, 0.737516f, 0.474086f, 0.849658f, 0.804377f, -0.080858f, -0.10305f, 0.536719f, -1.1622f, -0.342834f, 0.402781f, 0.115227f, 0.24287f, -0.695056f, -0.0436921f, 0.222036f, 0.996912f, 0.176891f, -0.0650841f, 0.269952f, -0.00177294f, -0.0444905f, -0.431159f, 0.182715f, -0.0429493f, 1.03982f, -1.14416f, -0.334853f, -0.319257f, 0.250542f, 0.427705f, 1.00408f, 0.319165f, 0.338126f, 1.06596f, -0.130311f, 0.1664f, -0.91822f, -0.482969f, 0.379555f, 0.931786f, -0.259521f, -0.26676f, -0.511134f, -0.161333f, 0.407205f, 0.127215f, -0.115874f, -0.74857f, -0.614097f, -1.24632f, 0.438902f, -0.010038f, -0.131985f, -0.290398f, -0.303011f, 0.362789f, 0.295431f, 0.486214f, 0.48313f, 0.473817f, -0.260674f, -0.160361f, 0.720992f, 0.004341f, -0.0257513f, -0.544244f, -0.197532f, -0.0570926f, -0.0470323f, -0.358175f, 0.309724f, -0.121827f, -0.451902f, 0.654175f, -0.379612f, 0.425311f, 0.64248f, 0.4375f, -0.608515f, 0.183475f, -0.59398f, 0.735809f, -0.911337f, -0.50899f, 0.477327f, 0.424055f, -1.43826f, 0.624114f, 0.084826f, -0.97254f, -1.32759f, 0.622444f, 1.64941f, -0.44358f, -0.345241f, 0.272338f, 0.318131f, -0.716748f, 0.360981f, -0.0467942f, -0.517288f, 0.874968f, 0.893388f, -0.269836f, 1.12224f, -0.0204914f, -0.0885676f, -0.31172f, -0.76596f, -0.00294207f, -1.0415f, -1.26892f, -0.614158f, -0.893911f, 0.585932f, -0.60524f, -0.365573f, 0.793604f, 0.154101f, 0.602275f, 0.0436358f, 1.52191f, -0.525973f, 0.601713f, 0.7219f, -0.308903f, -0.316313f, -0.198256f, 0.560813f, -1.91373f, 1.20156f, 0.412508f, 0.354517f, 0.949946f, -1.29522f, -0.712151f, -1.15794f, 0.91844f, -0.524767f, 0.732848f, -0.513226f, 1.0062f, -0.260316f, 0.256711f, -0.754329f, 0.287398f, 1.30026f, -0.375037f, 1.20769f, -0.0969743f, 0.499185f, 0.925531f, 0.234913f, -0.153141f, -0.403909f, 0.0366042f, 0.169739f, 0.116354f, -0.264196f, -1.46265f, -0.785626f, -0.301059f, -0.890582f, -0.369919f, -0.121138f, -0.894905f, 0.298203f, 1.05391f, 0.365703f, -0.698288f, 0.568782f, -0.420118f, -0.243541f, -0.510473f, -0.485931f, 0.14858f, 0.716983f, -0.561495f, -0.274852f, -0.0997454f, 0.799118f, 0.5632f, 0.3f, -0.143994f, 0.167208f, -0.405972f, -0.611116f, 0.592297f, 0.0573383f, -0.302561f, 0.397041f, -0.208663f, 0.105459f, 0.208787f, -0.28467f, -0.53325f, -0.289965f, -0.46143f, -0.0343105f, 0.454569f, -0.986058f, -0.528081f, -0.484903f, -0.313444f, -0.159601f, -0.057389f, -0.414452f, 1.10135f, 0.310185f, -0.201504f, -0.383645f, 0.358057f, 0.418672f, -0.0795512f, 0.262256f, 0.050103f, 0.106968f, 0.0782813f, -0.328971f, 0.0522201f, 0.331822f, 0.42769f, 0.085807f, 0.317563f, -0.0834861f, -0.964089f, -0.674804f, -0.763733f, 1.20728f, -0.38138f, 0.567048f, 0.257946f, -0.790288f, 0.503187f, 0.637437f, 0.0358286f, 0.573902f, -0.955088f, -0.526057f, -0.427204f, 0.251301f, -0.0395128f, -0.954701f, 0.364203f, 1.39881f, 0.198054f, 0.481997f, -0.56449f, -0.328474f, -0.499845f, -1.18902f, 0.387863f, 0.154186f, 0.213897f, 0.0501174f}; - model->setOperandValue(op19, op19_init, sizeof(float) * 256); - static float op20_init[] = {-0.121763f, -0.00146586f, -0.152926f, -0.0166871f, -0.0651794f, -0.0883152f, 0.226854f, 0.0820697f, -0.0387004f, -0.0709573f, -0.0293144f, 0.0838588f, 0.0277707f, 0.0109299f, -0.160442f, 0.0837343f, 0.0469172f, -0.00955172f, 0.082859f, -0.0517241f, 0.00340798f, -0.187592f, -0.0247601f, -0.185591f, -0.0532604f, 0.105719f, -0.0966383f, -0.0199812f, -0.00629264f, -0.294315f, 0.0403008f, 0.0803754f, 0.0908397f, 0.219608f, -0.0440038f, 0.189251f, -0.291901f, 0.1426f, -0.134149f, 0.0519352f, 0.0551092f, -0.0487455f, -0.153575f, -0.228557f, -0.208332f, 0.0684225f, -0.0891165f, 0.0469084f, -0.0286844f, -0.0918755f, -0.0639394f, -0.233065f, 0.103416f, -0.0636482f, -0.0807054f, 0.0911907f, 0.162369f, -0.0989415f, 0.0365839f, -0.22269f, 0.0418927f, -0.0636223f, -0.000230235f, 0.117136f, -0.0605703f, 0.127913f, -0.0568488f, 0.0117915f, -0.0931372f, 0.0390826f, -0.22523f, -0.26497f, 0.151425f, -0.0157661f, 0.00381962f, 0.0363325f, -0.159047f, 0.0596872f, 0.16343f, -0.136773f, 0.150644f, -0.144535f, -0.186384f, -0.0577443f, 0.250243f, 0.0107071f, 0.139932f, 0.0679555f, -0.17552f, 0.16454f, -0.218745f, 0.0534704f, -0.133623f, -0.0147184f, -0.201297f, -0.0154522f, 0.265836f, 0.227862f, 0.0136758f, 0.112423f, -0.116164f, 0.0440703f, -0.00406094f, 0.122091f, 0.303093f, -0.0224877f, -0.0710294f, -0.0362077f, 0.0404482f, -0.0223894f, -0.240027f, -0.0128031f, -0.209106f, 0.0263517f, 0.162145f, 0.161153f, -0.0253479f, 0.0410495f, 0.00849734f, 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0.0912971f, -0.122214f, 0.160509f, 0.0219858f, 0.107816f, 0.0999836f, 0.198589f, 0.18158f, 0.126099f, 0.0183922f, 0.0470495f, 0.00337156f, -0.0911492f, 0.167851f, -0.12535f, 0.170912f, -0.0221781f, 0.0244123f, -0.0408453f, 0.0977436f, -0.018356f, -0.256684f, -0.132659f, 0.138018f, 0.167459f, -0.0300068f, -0.0859621f, -0.11962f, 0.161279f, -0.128255f, 0.118447f, -0.11943f, -0.0799545f, -0.298379f, -0.097287f, 0.120704f, 0.169731f, -0.185681f, -0.269999f, -0.244908f, 0.0999901f, 0.200081f, 0.183987f, 0.429091f, 0.0778352f, -0.263344f, -0.0471771f, 0.296332f, 0.172708f, 0.206192f, -0.13921f, 0.0510251f, -0.125251f, 0.131075f, 0.102271f, -0.0467462f, 0.0257676f, -0.127603f, -0.114564f, 0.00889627f, -0.114112f, 0.0823589f, -0.0514956f, -0.178806f, -0.0337608f, -0.119296f, -0.00738485f, 0.0928288f, 0.0769336f, -0.0976911f, 0.0787971f, -0.078196f, -0.201005f, -0.0696651f, 0.0954467f, -0.163542f, -0.0303046f, 0.158927f, 0.201786f, -0.0513393f, 0.104259f, -0.179513f, -0.0779392f, -0.00327828f, -0.165715f, -0.188637f, -0.137166f, 0.0781553f, 0.103884f, 0.332258f, -0.016888f, 0.156441f, -0.0388447f, 0.0632115f, -0.0582905f, -0.00186296f, -0.0495149f, 0.225687f, -0.153448f, -0.10367f, -0.124781f, 0.0737913f, 0.0862624f, 0.206727f, 0.196715f, -0.0284487f, -0.00988739f, 0.220012f, -0.0252623f, 0.168786f, 0.127867f, -0.0185743f, -0.0307794f, -0.204029f, 0.141954f, -0.211531f, -0.332643f, 0.139616f, -0.0401154f, -0.0643476f, 0.0273888f, 0.218811f, -0.109139f, -0.00338293f, 0.0826068f, 0.0258077f, -0.0032594f, 0.332864f, -0.103928f, 0.0196971f, -0.156372f, -0.0798499f, 0.296399f, 0.0271506f, 0.091495f, 0.241478f, 0.222023f, -0.0387302f, -0.0299842f, 0.0579832f, -0.182791f, 0.0985393f, -0.00523261f, -0.138758f, -0.107541f, -0.0948399f, -0.147055f, 0.184178f, -0.133554f, -0.131978f, -0.296845f, 0.0325436f, 0.199941f, 0.175093f, -0.00230138f, -0.102818f, -0.0420201f, 0.129457f, 0.068017f, -0.0332648f, -0.00894236f, -0.098845f, -0.00753575f, -0.0673654f, -0.145263f, 0.113437f, 0.0914055f, -0.00143512f, 0.0942811f, -0.0305817f, 0.282929f, -0.18672f, -0.112662f, -0.220805f, -0.066148f, 0.199075f, 0.0782547f, 0.114651f, 0.0937889f, 0.205614f, -0.115569f, -0.0613862f, -0.207668f, -0.11476f, 0.153937f, -0.151151f, -0.346595f, 0.0833564f, -0.0359258f, -0.115247f, -0.0581681f, -0.09493f, 0.103946f, 0.25415f, -0.150323f, -0.0380579f, -0.0330787f, -0.00525024f, 0.0186346f, -0.0305149f, -0.186087f, -0.0999572f, -0.115797f, 0.00276318f, 0.0112095f, -0.101085f, -0.106808f, 0.107935f, 0.00961863f, -0.00667402f, 0.00801296f, -0.156995f, 0.181634f, 0.197861f, -0.0630275f, -0.277353f, -0.115226f, -0.36412f, -0.0266432f, 0.0206991f, 0.15612f, 0.0255977f, 0.201208f, 0.139746f, 0.0557096f, 0.0973374f, -0.0266156f, 0.0175912f, 0.134545f, 0.0101333f, -0.160581f, 0.0372828f, -0.0839715f, -0.183419f, -0.00429197f, -0.132434f, -0.120953f, 0.148075f, -0.255095f, 0.0394775f, -0.016211f, 0.191533f, 0.14964f, 0.141708f, 0.115694f, -0.0246513f, 0.0293724f, -0.0883495f, 0.246417f, -0.117415f, 0.0597688f, -0.110295f, -0.00520283f, -0.182618f, 0.0625962f, 0.0081863f, -0.0142607f, 0.0541194f, -0.0554157f, -0.00591652f, -0.0022292f, 0.03706f, 0.214832f, -0.0560465f, -0.131744f, -0.138133f, -0.0986632f, -0.229274f, -0.136698f, 0.0995877f, 0.43432f, -0.160847f, 0.204996f, 0.0603452f, 0.053275f, -0.164842f, -0.0665397f, 0.0256539f, -0.103223f, -0.197584f, -0.0329347f, 0.0701767f, -0.0415869f, 0.0392298f, 0.0519155f, 0.152607f, -0.103417f, 0.0263919f, -0.203595f, -0.038185f, -0.0368837f, 0.092794f, 0.0911869f, 0.0672124f, -0.101674f, 0.247117f, 0.104358f, -0.0781367f, 0.0259994f, -0.0368053f, -0.106514f, 0.224546f, -0.108995f, 0.112747f, -0.366657f, 0.0341756f, -0.041221f, -0.0731361f, 0.308381f, 0.193964f, -0.149423f, -0.0224257f, -0.278178f, 0.0550206f, 0.0494453f, 0.0740552f, -0.140144f, -0.120898f, 0.0805589f, 0.0484794f, 0.0413425f, 0.337922f, 0.0334329f, 0.250736f, 0.0264221f, 0.0346671f, -0.112495f, -0.0637923f, 0.224169f, -0.0160768f, -0.141467f, 0.0300025f, -0.0421491f, 0.189176f, -0.11531f, -0.038429f, -0.188034f, -0.107783f, -0.12626f, -0.159933f, -0.137536f, -0.110569f, -0.182712f, 0.321717f, -0.0170232f, -0.17529f, 0.147837f, -0.0675082f, 0.0675104f, 0.136173f, 0.132304f, 0.0529975f, 0.505866f, -0.255341f, 0.0331028f, -0.120566f, -0.0390507f, -0.0885224f, -0.360095f, 0.0694635f, -0.066831f, -0.212694f, 0.270353f, -0.282276f, -0.237955f, -0.0265897f, 0.415486f, -0.314802f, -0.295329f, -0.0937045f, 0.0339239f, -0.216374f, -0.103194f, -0.120421f, -0.23692f, 0.053388f, 0.396106f, -0.0410978f, 0.00122743f, -0.141285f, -0.0194634f, 0.240345f, 0.0563302f, 0.0557607f, 0.216166f, -0.018195f, -0.0587413f, 0.0335691f, -0.0811946f, 0.0310523f, -0.071152f, 0.00116798f, -0.248101f, -0.233317f, 0.00207474f, 0.211031f, -0.182801f, -0.23281f, -0.0803899f, 0.0796748f, -0.373119f, -0.0236714f, 0.298858f, -0.210002f, -0.123173f, 0.056211f, 0.0710101f, -0.000420267f, 0.222125f, -0.0806963f, -0.177015f, -0.293845f, -0.0810273f, 0.312525f, 0.0173415f, 0.0854641f, -0.0657604f, 0.246809f, 0.18022f, 0.261195f, -0.0449006f, -0.0299894f, 0.125036f, -0.167513f, -0.0828896f, 0.294635f, 0.147186f, 0.172629f, -0.16528f, 0.082555f, -0.130248f, -0.139386f, 0.355252f, 0.17594f, 0.178713f, -0.121214f, -0.0727996f, -0.113729f, -0.0205094f, 0.405982f, -0.060515f, 0.0601772f, 0.144893f, -0.0382382f, -0.103134f, -0.107361f, 0.142849f, 0.245431f, 0.238787f, -0.0994103f, 0.0760072f, 0.14f, 0.154757f}; - model->setOperandValue(op20, op20_init, sizeof(float) * 32768); - static float op22_init[] = {1.08059f, 0.613529f, -0.951719f, -0.861612f, 0.867697f, 0.776146f, 0.886088f, 0.0360881f, -0.228725f, 0.99308f, 0.772503f, 0.13115f, 0.444418f, 0.753733f, 0.806567f, 0.957842f, -1.40967f, -0.29546f, -0.59267f, 0.666514f, -1.10016f, -0.646609f, 0.673707f, 0.953951f, 0.393974f, 0.98207f, -0.644093f, 0.89083f, -0.145055f, -0.821302f, 0.0106507f, -1.09542f, 0.705736f, -0.0362283f, -0.649667f, -0.401635f, -0.00998712f, 0.739409f, -0.187072f, 0.690995f, -0.175346f, -0.465088f, 0.753494f, -1.065f, 1.0958f, -0.684324f, -0.946476f, 0.273813f, 0.795813f, 0.986562f, -0.739017f, -0.449237f, 1.0614f, 0.819235f, 0.557115f, 0.0782367f, 0.00833844f, 0.804665f, 0.954324f, 0.631661f, 0.942427f, -0.650992f, -0.797254f, -0.283754f, 0.497299f, -1.00708f, 0.869227f, -0.574946f, -1.04143f, 0.826134f, 0.710585f, 0.76314f, 0.477792f, 0.930909f, -0.913406f, -1.28013f, 1.40116f, -0.587567f, 0.0107547f, 0.895997f, -0.702578f, -0.681548f, -1.42103f, 0.439222f, 0.629061f, -0.661517f, 0.650495f, 1.01512f, 0.304494f, 0.471469f, 0.70978f, 0.520241f, -1.12464f, 0.789097f, 0.486918f, -0.564991f, -1.16599f, 0.908221f, 0.900087f, 0.804052f, -0.912126f, 1.16053f, -0.964254f, -1.47969f, 0.75153f, -1.10504f, 0.643193f, 0.654283f, -0.774442f, 0.859534f, 1.08754f, 0.894428f, -0.349845f, 0.717952f, -0.818105f, -0.685272f, 0.719653f, 0.221689f, 0.580021f, -0.868784f, -0.765754f, 0.335712f, -1.05373f, -0.736756f, -1.34203f, 0.840665f, -0.783447f, -0.76105f, 0.736778f, -0.714565f, -0.757179f, 0.502076f, -0.429628f, -0.815403f, 0.305288f, 0.511788f, 0.639633f, 0.833758f, -0.881914f, 0.670985f, -0.759582f, 0.477742f, -1.97672f, 0.0150744f, 0.892992f, -0.513419f, -0.636598f, -0.391863f, 0.714185f, -0.271203f, 0.50773f, -0.725078f, -1.09182f, -0.00830997f, 0.800519f, -0.790665f, 0.011206f, 0.648068f, 0.314541f, 0.833785f, 0.743304f, -0.729218f, 0.69944f, 0.716866f, -0.0155263f, 0.236963f, -0.750609f, -0.764535f, -0.974223f, 0.907331f, 0.766474f, -0.902966f, 0.503484f, -0.00109834f, 0.83286f, -0.568512f, -0.30153f, -0.555918f, 0.772897f, -0.910785f, -0.0350145f, -0.973868f, 0.404545f, -0.0615412f, 0.00991651f, -1.01877f, 0.676516f, -0.74005f, -0.598321f, -0.80478f, -1.03634f, -0.874505f, 1.21163f, 1.10213f, -0.00699455f, -0.072837f, -0.449261f, 0.976919f, 0.0676252f, 0.695134f, -0.812793f, 0.759372f, 0.768298f, -0.855527f, 0.614652f, -0.903347f, -1.3134f, 0.884192f, 1.05967f, 0.478357f, -1.11453f, -0.234713f, -0.723932f, 0.288597f, 0.0341888f, -0.4238f, 0.0638279f, 0.793826f, -0.591246f, 0.95119f, 0.88514f, -0.191094f, -0.677312f, -0.934787f, 0.553466f, 0.570809f, 0.292745f, -0.427575f, -0.881358f, -0.879608f, 0.395251f, 0.48285f, -0.605223f, 0.904051f, -0.953032f, -0.500631f, -0.0687485f, 0.0676696f, 0.62603f, 0.0224478f, 0.419152f, 0.835996f, 0.0583883f, -0.620254f, -1.18509f, -0.968522f, -0.298168f, 0.805448f, -0.797511f, 0.413079f, 0.754239f, -0.934051f, 0.800133f, 1.00281f, -0.730008f, 0.2403f}; - model->setOperandValue(op22, op22_init, sizeof(float) * 256); - static float op23_init[] = {-0.28458f, 0.0472449f, 0.32551f, 0.661396f, -0.529311f, -0.109768f, -0.191594f, -0.174226f, -0.677528f, -0.400651f, -0.468258f, 0.465917f, -0.318087f, -0.764231f, -0.291188f, -0.284253f, 0.586599f, -0.0226678f, 0.390401f, -0.471706f, 0.438813f, 0.453643f, -0.36546f, -0.301168f, -0.271752f, -0.334392f, 0.482651f, -0.289412f, -0.793406f, 0.300859f, 0.224297f, 0.203824f, -0.288313f, 0.350108f, -0.368492f, 0.803083f, 0.379972f, -0.337444f, -0.283055f, 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-0.453082f, -0.592571f, -0.606518f, -0.0694814f, 0.49124f, 0.435993f, 0.0573165f, -0.0463377f, -0.0815331f, 0.171534f, 0.523446f, -0.45542f, -0.224177f, -0.237075f, -0.0303856f, -0.281409f, 0.161822f, 0.640442f, -0.188442f, 0.582021f, 0.188833f, -0.441974f, 0.401265f, -0.256714f, 1.07948f, 0.0952702f, -0.205754f, 0.38561f, -0.0153532f, -0.324404f, -0.201656f, -0.49744f, -0.622206f, -0.614933f, 0.466828f, -0.407296f, -0.0402541f, 0.226478f, 1.38546f, -0.195908f, -0.377207f, -0.465495f, 0.64496f, -0.696121f, 0.368228f, 0.487155f, -0.675746f, 0.369155f, -0.135017f, 0.201887f, 0.196515f, -0.211104f, -0.528813f, -0.573743f, -0.658933f, -0.210996f, 0.374055f, -0.342353f, -0.112863f, 0.436295f, -0.537892f, 0.773281f, 0.319618f, -0.607668f, 0.125097f, 0.452643f, 0.3964f, -0.45951f, 0.0684969f, 0.331722f, 0.0313258f, 0.232967f, 0.433636f, 0.0453946f, 0.696013f, 0.305709f, -0.00556975f, -1.04627f, -0.182575f, -0.312805f, 0.289384f, -0.209719f, 0.498444f, -0.0556132f, 0.618527f, -0.489828f, -0.27455f, 0.454784f, 0.38062f, -0.053773f, -0.197319f, -0.0958428f, 0.0976471f, 0.391348f, 0.691822f, -0.928015f, -0.180371f, 0.28998f, 0.53853f, -0.928278f, 0.152375f, -0.0323293f, -0.235519f, 0.242977f, -0.253792f, 0.0648444f, 0.273537f, -1.09638f, 0.494502f, 0.260978f, -0.119055f, -0.505667f, -0.420982f, 0.721884f, 0.342437f, -0.584703f, -0.316703f, 0.314904f, 0.482382f, 0.800729f, -0.197054f, 0.318495f, 0.472762f, 0.660303f, -0.34121f, 0.682947f, 0.423539f, 0.360519f, -0.127974f, 0.336235f, 0.454418f, 0.123031f, -0.421285f, 0.257301f, -0.147292f, -0.372451f, -0.217667f, -0.770868f, 0.173788f, -0.292246f, 0.37707f, -0.51294f, 0.246741f, -0.250596f, -0.102101f, 0.529798f, -0.168518f, 0.40385f, 0.731781f, -0.305084f, -0.329997f, -0.294573f, 0.45172f, 0.320347f, 0.444778f, -0.845154f, 0.503533f, 0.204295f, -0.625158f, -0.256043f, 0.172196f, -0.528596f, -0.1252f, -0.8008f, -0.00855475f, -0.116112f, 0.484289f, 0.114892f, -0.387584f, 0.476534f, 0.357803f, 0.321582f, -0.794938f, -0.401772f, 0.636876f, -0.310624f, 0.42546f, 0.680463f, 0.216965f, -0.74392f, -0.922457f, -0.699813f, -0.129799f, -0.16734f, -0.859169f, 0.328387f, 0.688874f, 0.669709f, -0.228886f, -0.469983f, 0.728589f, 0.27625f, -0.0885543f, 0.415512f, -0.284111f, -0.807751f, 0.0895664f, -0.474001f}; - model->setOperandValue(op23, op23_init, sizeof(float) * 2304); - static float op25_init[] = {0.059552f, -0.776265f, 0.586331f, -0.19955f, -1.15878f, -0.623411f, 0.482733f, 0.620705f, -1.42987f, 0.599004f, 0.296987f, 0.243854f, -0.326452f, -1.66933f, 0.114657f, -0.912619f, -1.40799f, 0.873297f, -2.02128f, 0.349338f, 1.03578f, -0.584717f, 0.68394f, 0.12814f, 0.766933f, -0.348055f, 0.426793f, -0.0519259f, -0.31104f, -0.70917f, -1.98005f, 0.466914f, -1.22813f, 0.922352f, 0.803956f, -0.758258f, -0.0178442f, 0.159704f, -0.693864f, 0.541313f, -0.61248f, 0.140332f, -0.732719f, -0.061292f, -1.21815f, -1.36358f, 1.55065f, -0.867879f, -0.118565f, -1.429f, -0.24872f, 0.756503f, -0.276562f, 1.01472f, 0.613279f, 0.252628f, -0.154586f, 0.213353f, -0.259666f, -0.0845054f, 1.48898f, -0.78227f, 0.475328f, -0.149806f, -0.126194f, -0.504444f, -0.156527f, -0.485018f, -0.398986f, -0.390188f, 0.805819f, 1.24142f, 1.92392f, -0.0204732f, -0.34912f, -0.821812f, 0.714334f, 1.01406f, -0.827258f, 1.56897f, 0.435821f, 0.00175438f, -0.196037f, -1.00622f, -0.934045f, -0.814433f, -0.00923873f, 0.67536f, -0.183227f, 0.118676f, 1.29346f, -0.292178f, -0.172461f, 0.696343f, -0.603604f, -0.135618f, 0.911071f, 0.863372f, 1.14968f, 0.495017f, 0.525449f, 0.94923f, -0.203282f, -1.86435f, 0.334763f, -0.803913f, -0.151671f, 0.61989f, -0.661122f, 0.621632f, -0.835182f, 0.0620014f, 0.110495f, 0.10499f, 0.867545f, -0.117217f, -0.450633f, 0.805698f, 0.270723f, 0.080994f, -0.234987f, -0.834794f, 0.946942f, -0.149305f, -0.994457f, 0.0162253f, -0.229257f, 0.749062f, -0.160174f, -0.192936f, -0.562147f, -0.481395f, 0.0400992f, 0.707402f, 1.25304f, 0.0850877f, -0.387467f, -0.719366f, 0.425257f, 0.663763f, -0.312089f, 0.084609f, -0.127238f, 0.0892191f, 0.452648f, -0.180974f, 0.200651f, -1.02692f, 0.723075f, -0.819408f, 0.61707f, 0.638342f, -1.42357f, 0.182241f, -1.5596f, -0.504548f, 0.99742f, 2.21562f, -0.450749f, 0.899844f, -0.779047f, -0.0130739f, 0.932655f, -0.301747f, -0.302013f, 1.12503f, -1.0337f, 0.157642f, -0.390012f, -0.439374f, 0.319801f, -0.420911f, 0.207037f, -0.415911f, 0.133631f, 0.65813f, -0.103256f, 1.28002f, -0.722025f, 0.198662f, 0.668599f, -0.573971f, -0.503427f, 0.327037f, 0.382418f, 0.452662f, -0.326031f, -0.109258f, 0.634596f, -0.626296f, -0.882987f, -0.975486f, -0.198001f, -0.736511f, -0.401334f, 0.675054f, -0.0783199f, 0.820609f, -0.213448f, -0.517507f, 0.424629f, 0.708728f, 0.375385f, -0.204245f, 1.46808f, 1.40466f, -0.615909f, -0.109114f, 1.3006f, -0.314086f, -0.640962f, 0.597668f, -1.2886f, -0.942914f, -0.615433f, -0.267436f, -0.234165f, 0.180395f, -0.505358f, 0.389795f, -0.418978f, 0.16119f, 0.701265f, 1.801f, 0.797389f, 1.04949f, 0.470902f, 0.794846f, 1.20815f, 0.639917f, 0.1663f, 0.715599f, 0.227951f, -1.18309f, 0.619096f, 0.799573f, 0.666824f, -0.205099f, -0.0918158f, 0.922449f, -0.674654f, 0.607058f, -1.33098f, 0.303168f, -0.158435f, 0.493879f, 0.64636f, 0.76502f, 0.398199f, -0.708121f, 0.245043f, -0.573639f, 1.09403f, 0.477721f, -0.794756f, 0.0482809f}; - model->setOperandValue(op25, op25_init, sizeof(float) * 256); - static float op26_init[] = {0.00876338f, 0.159478f, 0.311471f, -0.00469625f, -0.242645f, -0.283327f, 0.117541f, 0.154311f, 0.016744f, 0.0241657f, 0.166488f, 0.0644902f, -0.0694389f, 0.0201139f, -0.0289717f, -0.113529f, -0.149222f, -0.0559651f, 0.0669082f, -0.0953956f, 0.115591f, -0.0932612f, 0.198847f, 0.258349f, 0.0286179f, 0.0536193f, -0.0161622f, 0.0876863f, 0.0223337f, -0.0558078f, -0.015007f, 0.0117489f, -0.123187f, -0.00973389f, -0.0112027f, -0.0269762f, 0.216264f, 0.0934549f, 0.0489395f, -0.0667397f, -0.048408f, -0.201219f, -0.051415f, 0.217466f, 0.00516112f, -0.0248904f, 0.0924763f, -0.0488527f, 0.02581f, -0.0741177f, 0.0257687f, 0.0631479f, -0.135362f, -0.0443659f, -0.112051f, -0.00969363f, 0.176092f, -0.160745f, 0.0324613f, 0.143151f, -0.281107f, 0.119342f, 0.0919042f, 0.0855942f, 0.0613969f, 0.0313948f, -0.0784912f, 0.270995f, -0.0791038f, 0.0612157f, 0.149409f, 0.0454657f, 0.206511f, -0.0189417f, 0.123541f, -0.0517153f, 0.0294248f, 0.111004f, 0.186126f, -0.135645f, -0.0811628f, -0.0798263f, 0.0706075f, -0.175273f, 0.173768f, 0.14998f, 0.138094f, 0.0740594f, 0.204021f, 0.0880566f, -0.139851f, -0.177713f, -0.0423176f, 0.0368738f, 0.0833688f, -0.0332293f, 0.266283f, -0.12446f, -0.272446f, 0.0747981f, -0.137697f, 0.0238475f, -0.0551994f, -0.0104084f, 0.134573f, -0.0983598f, 0.0826753f, -0.0760464f, -0.206143f, 0.03409f, 0.153575f, -0.0931692f, 0.128078f, -0.204306f, -0.0243301f, 0.164837f, -0.0402115f, -0.167248f, 0.0258381f, -0.0858297f, -0.0882213f, -0.0250546f, -0.0438364f, -0.0132724f, -0.0246596f, 0.200127f, 0.0307405f, -0.169237f, 0.0827933f, -0.0836252f, 0.0653367f, 0.0873026f, -0.0222144f, 0.163728f, -0.0919097f, 0.0278681f, 0.0965614f, -0.188887f, 0.231546f, 0.135525f, 0.0622755f, 0.0741135f, 0.22767f, 0.130422f, 0.0304746f, 0.0694859f, -0.210291f, -0.11043f, -0.233902f, -0.0784602f, -0.155348f, 0.016333f, -0.0206643f, 0.0427296f, 0.0238507f, -0.11085f, 0.0384003f, -0.0580579f, 0.186813f, 0.049836f, -0.12948f, -0.124773f, 0.17535f, -0.0860361f, 0.0401083f, -0.0891493f, -0.00699356f, -0.173003f, -0.0128182f, -0.202517f, -0.0820098f, 0.122637f, 0.17859f, 0.106333f, 0.0623388f, -0.173419f, -0.231867f, 0.207386f, -0.128314f, 0.0830676f, 0.168943f, -0.16992f, 0.0947031f, 0.156151f, 0.0470067f, -0.179337f, -0.025086f, -0.138531f, -0.0102241f, 0.179236f, 0.0613753f, -0.111747f, 0.0190405f, 0.0472922f, -0.0298265f, 0.242461f, 0.0606838f, -0.190629f, 0.0569852f, -0.0506247f, -0.0122446f, 0.00488097f, 0.011793f, 0.00413108f, -0.0100453f, -0.0393225f, -0.108041f, -0.105423f, 0.0817237f, -0.121646f, -0.134984f, -0.0706075f, 0.123754f, 0.037536f, 0.0315421f, -0.0168049f, -0.0360235f, -0.165986f, -0.0976946f, -0.0792321f, 0.00785608f, 0.0641992f, 0.145496f, -0.097555f, 0.149636f, 0.0787423f, -0.0974546f, -0.0579778f, -0.144506f, 0.143188f, -0.149776f, 0.0156335f, -0.0903146f, -0.0917537f, -0.0808573f, -0.0416986f, 0.0487195f, -0.118266f, 0.0489898f, 0.122683f, 0.000334909f, -0.0596077f, -0.138331f, 0.00820342f, -0.278814f, 0.078717f, 0.143128f, 0.0283292f, 0.0222995f, 0.0665451f, -0.290689f, 0.0607143f, 0.191095f, -0.105912f, 0.0540141f, -0.0986289f, -0.0190708f, 0.196634f, -0.126051f, -0.123402f, -0.0311565f, 0.00364957f, -0.103171f, -0.0656316f, 0.0876046f, 0.173918f, -0.121705f, 0.109689f, 0.106514f, -0.0118686f, -0.0102367f, 0.199663f, -0.0169068f, 0.00247418f, 0.0407632f, -0.0477548f, 0.178778f, 0.0247504f, 0.0478882f, 0.150284f, -0.0568678f, 0.210097f, -0.0419008f, 0.00926958f, 0.0957613f, -0.146762f, 0.0863339f, 0.0103325f, 0.0430991f, -0.0163946f, -0.00966456f, -0.047376f, -0.0544308f, -0.102422f, -0.0468923f, 0.0723415f, 0.00513594f, -0.0128337f, 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-0.229077f, 0.156335f, 0.0772177f, -0.079883f, -0.0419373f, 0.0863525f, 0.0248776f, -0.178254f, 0.0379421f, 0.133932f, -0.14642f, 0.0785913f, -0.0496133f, 0.198256f, 0.0112049f, -0.0590842f, 0.218723f, -0.147393f, 0.181167f, 0.0947072f, -0.214306f, -0.246591f, 0.12883f, -0.00173171f, 0.182896f, -0.107363f, 0.123074f, -0.14054f, 0.209795f, 0.116346f, -0.0617875f, 0.110168f, -0.0232458f, -0.172889f, 0.0595888f, -0.192809f, 0.00841598f, -0.199597f, -0.0586655f, -0.00795562f, -0.0956537f, 0.00128222f, 0.0421622f, -0.152675f, 0.181584f, -0.0704876f, 0.0858321f, -0.0567543f, 0.11328f, 0.152461f, -0.208114f, -0.167482f, -0.0419129f, 0.0908803f, -0.140871f, 0.0105671f, 0.160085f, 0.207388f, 0.199671f, -0.0255306f, -0.0943408f, 0.111921f, -0.180809f, 0.0473658f, 0.0100724f, -0.216183f, 0.0654583f, -0.0243085f, 0.0737685f, -0.0502344f, -0.205512f, -0.0139682f, 0.109137f, 0.105571f, 0.0552158f, -0.125576f, 0.0620585f, 0.068879f, -0.0253168f, 0.133263f, 0.0400752f, -0.0424402f, -0.0872983f, -0.107545f, -0.0277247f, -0.0578045f, 0.0362632f, 0.140196f, -0.0261901f, 0.187779f, -0.0635921f, -0.0523621f, 0.229485f, -0.0190439f, -0.0718016f, 0.043292f, 0.0299828f, 0.123821f, -0.158516f, 0.153005f, -0.214138f, 0.131521f, 0.0891609f, 0.193635f, -0.0886758f, -0.0056219f, -0.0073096f, 0.140728f, -0.0284736f, -0.186328f, 0.00946189f, -0.0481822f, 0.145028f, -0.0471732f, 0.142449f, 0.0480657f, -0.0927644f, 0.017843f, 0.0131457f, -0.03732f, -0.0260419f, 0.0918974f, -0.0431395f, -0.118994f, 0.231523f, -0.318429f, 0.0397127f, 0.0492337f, 0.0726929f, 0.10699f, -0.35274f, 0.164078f, 0.149988f, 0.131295f, -0.00388833f, -0.0601893f, -0.0853431f, 0.267183f, -0.0624809f, 0.0213025f, -0.109965f, -0.00912624f, 0.0275681f, -0.256799f, 0.0402505f, 0.199534f, -0.00599506f, 0.14945f, -0.0331971f, 0.0865041f, -0.0730954f, 0.0564954f, 0.270481f, -0.149565f, -0.0988167f, 0.00997729f, 0.0891173f, -0.0959777f, 0.0471534f, 0.0116906f, -0.02657f, 0.0191297f, 0.0985174f, -0.0396014f, 0.014939f, -0.142408f, -0.188645f, 0.145756f, 0.12443f, -0.0477664f, 0.106668f, 0.118081f, 0.0601337f, 0.0923126f, -0.0981329f, 0.0247064f, 0.189113f, 0.184961f, 0.0779132f, -0.0683965f, 0.0574355f, 0.010815f, 0.128234f, -0.0222933f, -0.104797f, -0.273139f, -0.0875092f, -0.0713455f, -0.0670424f, -0.0397861f, -0.0757338f, -0.0321257f, 0.0375769f, 0.187379f, -0.106943f, 0.127953f, 0.270204f, -0.0822088f, -0.0242656f, 0.0990913f, 0.227184f, 0.0255154f, 0.156065f, -0.0162164f, -0.175027f, -0.0536166f, 0.350152f, -0.0863701f, 0.108102f, 0.0131009f, 0.0558025f, -0.22567f, 0.2056f, -0.295193f, -0.14584f, 0.0661884f, -0.0893963f, 0.11974f, -0.0382503f, 0.0285192f, -0.100242f, 0.113722f, 0.0184857f, -0.00448416f, -0.0413476f, -0.0891821f, -0.177578f, 0.0180004f, -0.165026f, -0.0337959f, -0.236752f, 0.180452f, -0.085643f, 0.108829f, 0.0491221f, 0.117443f, -0.234701f, 0.138727f, -0.025024f, 0.0401201f, 0.0987451f, -0.066132f, -0.16346f, -0.100028f, -0.186702f, -0.110143f, 0.0575087f, -0.130745f, -0.0705247f, 0.0110047f, 0.198597f, 0.1569f, -0.187375f, -0.0377752f, -0.0192336f, 0.0893955f, -0.104773f, -0.0441842f, 0.0238164f, 0.0458868f, 0.061032f, -0.187736f, -0.0753091f, -0.0973642f, -0.00944946f, 0.0210427f, -0.0883291f, 0.0215373f, 0.0569074f, -0.0131094f, -0.190012f, -0.0206695f}; - model->setOperandValue(op26, op26_init, sizeof(float) * 65536); - static float op28_init[] = {0.950459f, 0.0296315f, -0.0123494f, 1.19466f, 0.255425f, -0.197264f, 1.6053f, 0.809934f, 0.513879f, 0.820272f, 0.23914f, -0.194486f, -0.458706f, 0.368204f, -3.87869f, -0.0187326f}; - model->setOperandValue(op28, op28_init, sizeof(float) * 16); - static float op29_init[] = {1.23719f, -0.67217f, 1.08832f, 0.484643f, -1.00873f, -0.653809f, 0.661964f, 1.06108f, 0.184015f, -0.878391f, -2.61659f, 1.83468f, -1.24167f, -0.334045f, -1.42114f, -1.4081f, 0.743121f, -1.58702f, 1.60977f, 0.541011f, 1.03177f, -0.777374f, 0.170171f, -0.410803f, -2.08614f, -0.253479f, -2.48095f, 0.450924f, -1.17839f, 1.02977f, 0.632391f, 0.683954f, 1.24468f, -1.03801f, 0.808721f, 0.503261f, 1.52073f, -0.299188f, 0.0812176f, -0.914902f, -1.96872f, -0.236636f, -1.64942f, -0.512883f, -0.442176f, -0.0897904f, -1.55935f, 2.44719f, -0.321642f, 0.045825f, -0.0790507f, -2.15146f, -0.670395f, 0.376227f, 0.796031f, 0.631427f, -0.277739f, 0.175722f, 0.0270909f, 0.811911f, 0.761613f, 0.639926f, 3.61268f, -1.81263f, -0.688285f, 0.315179f, 0.18691f, -1.5542f, 1.12532f, 0.312383f, 0.0806777f, -0.642157f, 1.22573f, 0.183517f, -0.756625f, -0.332537f, 0.916962f, 1.02378f, 4.7186f, -1.10432f, -0.0695368f, 0.18656f, 0.428627f, -1.33377f, 3.06145f, -0.561649f, -0.496272f, -1.75449f, 1.9422f, -0.33641f, -0.0531178f, -0.696678f, -0.0305814f, -1.75068f, -1.7157f, 1.20486f, -0.779788f, 0.937587f, -0.774572f, 1.5068f, -1.08521f, 1.30134f, -0.970244f, 0.509486f, -0.322837f, -0.513111f, 2.5801f, -0.221533f, 1.53355f, -0.0568376f, 0.26331f, 0.0692347f, -0.911869f, 0.874316f, -1.37498f, 0.116868f, -0.563794f, 1.08106f, -0.737648f, 1.09901f, 0.941713f, 0.319754f, 3.2568f, -0.13864f, 0.882837f, -0.194686f, 2.77097f, -0.19543f, -0.54728f, 0.935008f, -1.22157f, 0.670149f, -1.28085f, 0.137126f, -0.665442f, 0.262963f, 0.34245f, -0.106122f, 2.05506f, -0.604292f, -0.217629f, -0.123072f, -3.16904f, 0.02477f}; - model->setOperandValue(op29, op29_init, sizeof(float) * 144); - static float op31_init[] = {-0.517417f, 0.0426414f, -0.357654f, 0.645831f, -0.182669f, -1.11405f, 0.516978f, 0.0161366f, -0.438554f, 1.18839f, -1.07079f, 0.0316377f, 0.552099f, 1.03371f, 1.01932f, 0.267632f}; - model->setOperandValue(op31, op31_init, sizeof(float) * 16); - static float op32_init[] = {0.0442092f, 0.100294f, -0.165622f, -0.0254995f, -0.000117605f, 0.108391f, 0.103699f, -0.0864047f, 1.14539f, -0.0113399f, -0.0835518f, -0.0363124f, 0.0108043f, -0.0282255f, 0.00364833f, -0.0477529f, 0.136618f, -0.24613f, 0.137167f, 0.000881128f, 0.123369f, 0.697108f, 0.223616f, 0.022898f, -0.159309f, -0.374391f, 0.165748f, 0.376668f, -0.570377f, -0.0785476f, 0.560518f, 0.186954f, 0.8159f, 0.0888002f, 0.406594f, 0.0370246f, -0.127239f, -0.517474f, 0.0261465f, 0.229986f, 0.0585454f, 0.166348f, 0.304265f, 0.120066f, 0.354865f, -0.224302f, -0.735672f, -0.0550739f, 0.404087f, 0.0294423f, 0.0200603f, 0.0209366f, -0.000457059f, -0.0117939f, -0.30404f, 0.00396221f, -0.0269691f, -0.0334725f, -0.0343909f, -0.0246526f, -0.0922904f, 0.0158384f, 0.575937f, 0.0346629f, -0.101794f, 0.254893f, -0.00422475f, -1.21483f, 0.10141f, -0.191787f, 0.473363f, 0.0594516f, 0.141317f, 0.0189096f, 0.120227f, 0.0256014f, 0.0587697f, 0.018237f, 0.818418f, -0.162319f, 0.0492727f, -0.277837f, 0.0923082f, -0.0693218f, 1.13724f, -0.436901f, 0.134505f, 0.165517f, 0.216349f, 0.521773f, 0.133342f, -0.538622f, -0.00520006f, -0.352552f, 0.186707f, -0.563571f, 0.157166f, -0.203963f, 0.615195f, -0.0157265f, -0.184236f, -0.127932f, 0.1663f, -0.280368f, -0.659668f, 0.118125f, 0.440214f, 0.0015294f, 0.0927123f, -0.197986f, 0.0760051f, 0.08768f, 0.0432116f, -0.7727f, -0.39429f, 0.121467f, 0.290871f, 1.06573f, -0.0100696f, -0.3215f, -0.605128f, -0.365822f, 0.0282545f, 0.0354838f, 0.233038f, -0.479717f, 0.179297f, 0.23784f, -0.0593619f, -0.0224619f, 0.0558388f, -0.026943f, 0.080387f, -0.0668255f, 0.0346286f, 0.0982081f, -0.0113279f, -0.0573998f, 0.0346731f, -0.276434f, 0.029451f, 0.978238f, 0.046547f, -0.0428502f, -0.069273f, -0.112573f, 0.0353035f, 0.0760213f, -0.229825f, 0.256784f, -0.0865124f, -0.117515f, -0.0190794f, 0.053253f, -0.0527775f, 0.0563685f, -0.216214f, -0.0908149f, 0.0196348f, -0.0147699f, 0.000646807f, 0.519219f, 0.553983f, -0.0162967f, 0.180212f, -0.158552f, 0.103136f, -0.0657329f, 0.716499f, 0.00303961f, 0.383627f, -0.0865072f, 0.159668f, -0.10951f, 0.504002f, -0.237362f, -0.018895f, 0.402239f, -0.253648f, -0.042068f, 0.632025f, 0.28811f, 0.0143706f, 0.365129f, 0.110419f, -0.292138f, -0.40216f, 0.0356688f, -0.803869f, -0.0731997f, 0.0585228f, 0.116289f, -0.00771922f, 0.14662f, -0.151595f, 0.0332509f, 0.422503f, -0.0735451f, -0.022096f, 0.419504f, 0.0316678f, -0.108478f, -0.0858874f, -0.117858f, -0.0308824f, -0.0150928f, -0.135797f, -0.00134129f, -0.0363398f, -0.133177f, -0.329766f, -0.0157401f, -0.0585963f, 0.418158f, -0.0203871f, -0.0345592f, -0.869422f, -0.0642393f, -0.248801f, 0.310259f, -0.0907119f, -0.428003f, 0.0452463f, 0.0290068f, -0.40103f, 0.130153f, 0.180791f, -0.0258097f, -0.141686f, -0.323571f, -0.395809f, -0.0476737f, 0.27939f, 0.161304f, 0.238462f, -0.0476686f, 0.453612f, 0.02649f, 0.129983f, 0.0522385f, 0.0297746f, 0.213469f, 0.11375f, 0.0878489f, 0.199765f, -0.0642271f, -0.271912f, 0.00243141f, 0.835912f, 0.104512f, -0.0144645f, 0.0659315f, -0.0166996f, -0.0624446f, -0.627624f, -0.104059f}; - model->setOperandValue(op32, op32_init, sizeof(float) * 256); - static float op34_init[] = {1.19974f, -0.185076f, 1.32092f, 0.0976943f, -0.187869f, -0.601424f, 0.109655f, -0.824136f, 1.76613f, 1.92313f, 0.0470319f, 0.0441899f, 1.04743f, 1.48342f, 1.92262f, 0.417516f}; - model->setOperandValue(op34, op34_init, sizeof(float) * 16); - static float op35_init[] = {-0.192145f, 0.606418f, -0.0568535f, -0.174212f, -0.220749f, 0.294504f, 0.250408f, 0.0870216f, -0.106517f, -0.0128364f, 0.190258f, 0.458306f, 0.936822f, -0.362511f, -0.0127489f, -0.0133373f, -0.232533f, -0.442404f, -0.128353f, 0.140481f, 0.354678f, 0.165237f, 0.0561957f, 0.540504f, -0.169096f, -0.31736f, 0.124375f, 0.145857f, -0.286506f, -0.333099f, -0.451718f, -0.691901f, -0.0416297f, -0.195466f, -0.0223783f, 0.427847f, -0.0137003f, -0.172101f, -0.0535766f, 0.271151f, -0.0675339f, -0.429987f, 0.108888f, -0.110792f, -0.921419f, -0.121298f, -0.258522f, -0.52257f, -0.344404f, 0.519004f, 0.167952f, -0.327749f, -0.400563f, 0.765311f, 0.285217f, 0.367853f, -0.200799f, -0.290995f, 0.27534f, 0.530624f, 0.955898f, -0.176768f, -0.445762f, 0.444517f, -0.357274f, 0.324649f, -0.107971f, 0.150048f, 0.494237f, 0.30383f, 0.324753f, 0.567321f, -0.301667f, -0.296505f, 0.264912f, 0.250856f, 0.279213f, -0.466188f, -0.337701f, 0.291899f, -0.064519f, -0.745488f, -0.305469f, 0.61913f, 0.0724054f, -0.444646f, -0.0926981f, 0.161211f, -0.146827f, 0.00351782f, 0.130313f, -0.132444f, -1.32228f, -0.323326f, -0.0250575f, -0.701916f, -0.207874f, 0.0825065f, -0.10657f, -0.341674f, -0.223069f, 0.791148f, 0.0434073f, 0.322624f, -0.0867508f, -0.227108f, 0.121276f, 0.139853f, 0.0622248f, 0.00764603f, -0.313398f, 0.101314f, -0.24079f, 0.620351f, -0.251547f, 0.0892458f, 0.370692f, 0.201349f, 0.233192f, 0.221301f, -0.164076f, -0.111546f, 0.0542954f, 0.223996f, 0.370248f, -0.315324f, -0.155129f, 0.476494f, -0.00596379f, -0.131588f, -0.276947f, 0.468515f, 0.00297447f, -0.529512f, 0.0695675f, 0.0265823f, -0.0858685f, 0.310371f, 0.0143207f, -0.0321677f, -0.515507f, -0.335209f, 0.171599f, 0.021023f}; - model->setOperandValue(op35, op35_init, sizeof(float) * 144); - static float op37_init[] = {1.01203f, 0.729789f, 1.05009f, 2.45074f, -0.428937f, -0.168061f, 0.883948f, -0.463319f, 0.716813f, 0.896726f, -1.56615f, -1.25198f, -0.865042f, 1.70229f, 1.22468f, 0.840712f, 0.992703f, 0.409845f, 1.88565f, 0.918815f, 0.842341f, -1.1515f, -1.97437f, 1.05457f, 0.111851f, 0.675341f, 0.645972f, -0.579008f, -0.226735f, 0.758494f, -1.2271f, 0.945416f}; - model->setOperandValue(op37, op37_init, sizeof(float) * 32); - static float op38_init[] = {0.0270471f, 0.00919901f, -0.0193453f, 0.0103695f, -0.0430962f, -0.0132642f, 0.0137244f, 0.00528576f, 0.0410232f, -0.0444378f, 0.00887553f, -0.0248847f, -0.000360784f, 0.00789876f, 0.00872832f, 0.00384528f, 0.0841045f, 0.049066f, -0.0429076f, -0.000282252f, 0.0454269f, 0.20789f, -0.0372577f, -0.130965f, -0.0968065f, 0.447734f, 8.29139e-05f, -0.0827644f, 0.0259536f, 0.067688f, -0.0901188f, -0.00476768f, -0.812498f, -0.0419137f, -0.404739f, -0.481643f, -0.364882f, 0.0809418f, 0.61269f, -0.0632456f, -0.0305597f, 0.334748f, 0.4738f, 0.316527f, 0.132221f, -0.574053f, 0.0378586f, 0.47517f, 0.404207f, 0.370556f, -0.0152144f, -0.0397815f, -0.245993f, -0.0494612f, -0.302243f, -0.132276f, -1.62311f, 0.112993f, -0.0164078f, -0.333526f, 0.228636f, 0.142801f, 0.304721f, -0.116069f, 0.093896f, 0.6867f, -0.143538f, -0.0901391f, 0.43879f, 0.0573514f, -0.328247f, -0.0460923f, -0.0681418f, 0.0378803f, -0.0743326f, 0.186537f, 0.336441f, 0.343403f, 0.0384152f, 0.180381f, 0.230074f, -0.403994f, 0.0168588f, -0.133206f, 0.161728f, 0.6601f, -0.112798f, -0.379202f, -0.228558f, 0.615611f, -0.0327493f, 0.594717f, 0.318287f, 0.0584771f, 0.0113252f, -0.144715f, -0.301842f, -0.0705235f, 0.0115849f, 0.131159f, 0.670893f, 0.168075f, -0.0718182f, 0.0903675f, -0.353166f, 0.161096f, -0.0640814f, 0.362689f, 0.175512f, -0.473472f, 0.270516f, -0.0792066f, -0.0256601f, -0.0171877f, -0.120534f, -0.113002f, 0.357678f, 0.161182f, -0.0263743f, -0.923133f, 0.463269f, 0.772355f, 0.0742514f, 0.00882856f, 0.063145f, 0.109518f, -0.0389513f, 0.0758492f, -0.0484083f, -0.14761f, -0.122375f, 0.0561956f, -0.150992f, -0.0506205f, 0.522672f, -0.075867f, 0.156564f, 0.120401f, -0.370186f, -0.450635f, -0.100374f, -0.0106076f, -0.0834781f, 0.0691193f, -0.0671956f, 0.11691f, 0.0368327f, 0.0432756f, -0.205644f, 0.0410307f, -0.0199087f, -0.427719f, -0.293893f, 0.468435f, -0.0095345f, -0.198647f, -0.144639f, 0.468013f, -0.247251f, 0.0817541f, -0.216556f, 0.138428f, -0.134626f, 0.798388f, 1.2151f, 0.0627166f, 0.0323023f, -0.468217f, 0.213002f, 0.482848f, 0.0785823f, -0.155659f, 0.0957937f, 0.175202f, 0.53568f, -0.0522815f, 0.199507f, 0.438776f, 0.0555042f, -0.186707f, 0.738733f, 0.198786f, -0.379305f, -0.21661f, 0.276172f, 0.111102f, -0.187843f, 0.00667934f, 0.479794f, 0.417874f, -0.0321797f, 0.113586f, 0.149764f, 0.344401f, 0.0596981f, -0.0722722f, -0.144758f, 0.216975f, -0.273642f, -0.05668f, -0.174795f, 0.315556f, -0.0698253f, -0.0174929f, 1.22012f, 0.298247f, -0.0437427f, 0.0571144f, -0.0448819f, -0.035783f, 0.341813f, -1.44398f, -0.932115f, 0.0554499f, 0.139797f, 0.0454919f, 0.101146f, -0.0365573f, 0.0181533f, -0.072497f, 0.00469406f, 0.114721f, -0.0307393f, -0.0595061f, -0.107221f, -0.0190126f, -0.066591f, 0.0274221f, -0.220164f, 0.04476f, 0.0132432f, -0.033681f, -0.122918f, 0.129631f, 0.0465355f, 0.00490838f, -0.0404164f, 0.085287f, -0.19016f, -0.00529878f, 0.0815777f, 0.214508f, -0.0097201f, -0.014709f, 0.175969f, -0.284429f, -0.138555f, 0.24701f, -0.0639662f, -0.627076f, -0.192857f, 0.721998f, -0.226272f, -0.287441f, 0.163892f, -0.434394f, 0.134582f, 0.346779f, 0.139164f, 0.0617441f, 0.15026f, 0.0976386f, 0.0772374f, -0.241325f, -1.20489f, 0.231982f, -0.324956f, -0.00869559f, 0.629038f, 0.00517482f, -0.000377918f, -0.0204305f, 0.210397f, 0.106963f, -0.231252f, -0.226723f, -0.0227848f, -0.049826f, 1.16369f, -0.050116f, -0.271526f, 0.328031f, -0.205096f, -0.363005f, -0.214108f, 0.225625f, -0.539605f, 0.0983158f, -0.619605f, -0.0497222f, -0.251876f, 0.0436864f, -0.160641f, 0.0571772f, 0.272138f, 0.0668712f, -0.430683f, -0.0100436f, 0.295768f, 0.298574f, -0.106221f, -0.260428f, -0.177709f, 0.0896013f, 0.053705f, 0.0500741f, 0.131009f, -0.181435f, -0.416209f, -0.257817f, 0.266792f, 0.113934f, 0.289465f, -0.150348f, 0.156032f, -0.194724f, -0.663587f, -0.254988f, 0.0849075f, -0.0868606f, -0.444052f, -0.223751f, -0.276668f, 0.0910332f, -0.210636f, 0.129256f, 0.258805f, 0.10269f, -0.286526f, -0.00178165f, 0.563636f, 0.429938f, -0.0224727f, 0.0574613f, 0.05257f, 0.0964023f, -0.0647819f, 0.00682808f, 1.0809f, -0.843355f, -0.764512f, 0.568327f, -0.154132f, 0.299537f, 0.299945f, 0.544647f, 0.208732f, 0.429021f, 0.692963f, -0.0132254f, -0.315425f, -0.0306844f, 0.454138f, 0.0640088f, -0.0526967f, -0.0886315f, -0.353569f, -0.0445053f, 0.419271f, 0.0554374f, 1.36378f, 0.0733646f, -0.150825f, -0.0180057f, -0.497634f, 0.0108371f, -0.0605194f, 0.196103f, -0.413485f, 0.104696f, -0.150669f, 0.125607f, 0.66632f, 0.105853f, 0.603983f, -0.0469635f, -0.536611f, 0.324353f, 0.0555507f, -0.0131337f, -0.187258f, -0.111389f, -0.122898f, 0.137639f, 0.183434f, -0.012268f, 0.343272f, -0.128094f, 0.901428f, -0.159127f, 0.281001f, 0.201204f, -0.383114f, -0.204803f, -0.02333f, 0.0352839f, 0.118129f, -0.282447f, -0.0108675f, -0.0391744f, -0.229019f, 0.0577521f, 0.116441f, 0.0644183f, 0.427821f, -0.704257f, 0.524999f, 0.186882f, -0.127407f, -0.35676f, 0.0595068f, 0.115227f, 0.0461664f, -0.142496f, -0.00631152f, -0.0108409f, -0.481236f, -0.0852718f, -0.376636f, 0.525184f, -0.273659f, 0.121326f, 0.472309f, -0.261966f, 0.0891272f, 0.0111846f, 0.253588f, -0.597467f, 0.0274354f, -0.116376f, 0.18483f, -0.381528f, -1.08966f, 0.220949f, 0.0125376f, -0.159113f, -0.607749f, -0.117129f, 0.0208151f, -0.0192488f, 0.965594f, 0.0103654f, 0.122626f, 0.27669f, 0.308212f, 0.41214f, -0.17695f, 0.457152f, -0.186629f, -0.811885f, -0.450484f, 0.326845f, -0.313783f, -0.0318016f, -0.0703636f, -0.348998f, 0.401331f, -0.0972392f, -0.0138799f, 0.941508f, 0.183815f, -0.368143f, 0.348649f, 0.34287f, -0.329698f, 0.971174f, -0.38763f, 0.270437f, -0.177012f, -0.503141f, -0.464137f, -0.166893f, -0.128612f, -0.233088f, -0.447929f, 0.283954f, 0.091494f, 0.468883f, -0.17161f, 0.414808f, -0.156788f, -0.0546122f, 0.149743f, 0.149753f, 0.164002f, 0.338386f, 0.306504f, -0.636032f, 0.220797f, 1.17419f, 0.228991f, 0.207466f, 0.34877f, -0.215867f, -0.11421f, 0.0348584f, -0.292257f, -0.929841f, -0.0867478f, 0.343753f, -0.313036f, 0.0911876f, -0.065675f, 0.277021f, 0.174759f, -0.0565729f, 0.20129f, -0.183456f, 0.0117971f, -0.288494f, -0.102646f, -0.242777f}; - model->setOperandValue(op38, op38_init, sizeof(float) * 512); - static float op40_init[] = {6.42744f, 0.0321568f, -0.000620484f, 1.38238f, 0.80865f, -0.00817357f, -0.0552231f, 0.722048f, -0.0648024f, 0.00133108f, -0.0215043f, -0.0571425f, 1.02867f, 1.70436f, -0.3152f, 0.00116053f, 0.952789f, -0.00285661f, 0.684438f, -0.342237f, 0.47185f, -0.382995f, -1.09887f, 0.927754f, -0.395105f, 0.399458f, 1.70644f, 1.26431f, -0.628364f, -0.0923156f, 0.128607f, 0.136128f}; - model->setOperandValue(op40, op40_init, sizeof(float) * 32); - static float op41_init[] = {-0.213624f, 0.138209f, 0.0335167f, 0.0765658f, -0.469466f, -0.273326f, -0.00221819f, -0.295019f, 0.0829199f, -0.0548812f, -0.490404f, -0.00631009f, 0.14766f, -0.322987f, -1.15481f, 0.0922192f, -0.518827f, 0.0107877f, -0.141691f, -0.281104f, -0.444652f, -0.0437552f, -0.282668f, -0.0346714f, -0.304982f, 0.655019f, 0.0312901f, 0.236608f, -0.0684982f, -0.0444543f, 0.948018f, 0.33508f, -1.01091f, -0.96846f, 0.358376f, -0.314516f, -0.607375f, 0.582129f, 1.09556f, 0.493909f, 1.29421f, 1.0163f, 0.364732f, 0.341571f, 0.0880636f, -0.351738f, -2.30092f, -0.150112f, -0.960678f, 0.297181f, -0.0706625f, -0.19208f, -0.0957771f, 0.309638f, 0.0750161f, -0.780806f, 0.511076f, 0.773651f, -0.396476f, -0.540966f, 0.0747744f, 0.438967f, 0.0576069f, -0.240702f, 0.965804f, 0.444225f, -0.236452f, -0.0359711f, -0.576393f, 0.191653f, 0.25384f, 0.403406f, 0.785365f, 0.791804f, -0.236619f, -0.159129f, -0.0397893f, -0.0192526f, 0.639548f, -0.151068f, -0.708892f, 0.0186311f, -0.169363f, 0.641973f, 0.835608f, -0.0062986f, -0.0722358f, -0.280815f, 0.528226f, 0.234705f, -0.0855388f, 0.0502671f, -0.0663052f, -0.0660385f, -1.09458f, -0.30802f, -0.352286f, -0.541121f, 0.0738992f, -0.600995f, -0.85345f, -0.640068f, 0.35932f, -0.94734f, -0.750374f, 0.0843853f, 0.533934f, 0.342629f, -0.285891f, -0.362343f, 2.27695f, -0.178586f, -0.146698f, 0.051864f, -1.63582f, -0.498046f, 0.439183f, 0.25438f, 0.860595f, 0.605293f, -0.487595f, 0.693431f, 0.0504463f, 0.0676578f, 0.491436f, 0.00781199f, 0.970881f, 1.24158f, 0.522268f, -1.66232f, 0.596904f, -0.771637f, -0.314527f, -0.0736619f, 0.477234f, -0.26977f, -1.32936f, 0.571956f, 0.401798f, 0.163323f, -0.48078f, -0.199079f, 3.68406f, 0.0363556f, -0.0116286f, 0.683029f, -0.411191f, -0.223925f, -2.16462f, 0.863175f, -0.0633758f, -0.104411f, 0.697775f, -0.0682223f, -0.170274f, -0.798971f, 0.543116f, -0.577803f, -0.264083f, -1.29701f, 1.3298f, 2.19506f, 0.129616f, -0.0882273f, 0.387708f, 0.835795f, -0.44719f, 0.727769f, -0.154647f, -1.36135f, -0.1804f, -0.972617f, -0.102816f, 0.0446513f, 0.00496158f, 0.254847f, -0.289563f, -0.1219f, 0.715903f, 1.20232f, 0.50676f, -0.160549f, 0.0658346f, -0.500259f, -0.18733f, -0.553927f, -0.327592f, -0.0115506f, -0.0648266f, 1.02016f, -0.445415f, 0.147632f, -2.72305f, -1.02403f, 0.091488f, 0.389223f, -0.340593f, 0.0441604f, -0.0813384f, -1.26204f, 0.451759f, 0.109403f, 0.163282f, -0.108921f, -0.513226f, 0.00951998f, -0.962222f, -0.0764701f, 0.417888f, -0.0341903f, -0.215405f, -0.135682f, 0.448615f, -0.0672329f, 0.319827f, 0.36181f, -0.0126959f, 0.120521f, -0.410612f, -0.41408f, 0.0858084f, -0.0372158f, 0.354041f, 0.407449f, -3.48641f, 0.715232f, -0.0997449f, -0.011738f, 0.39694f, -0.472036f, -0.731723f, 0.364184f, -0.032735f, -0.651235f, 0.316316f, 0.617327f, -0.563622f, -0.31476f, -1.77474f, 0.888013f, 0.460156f, 0.208053f, 0.928476f, -0.262615f, -0.0164628f, -0.125161f, 1.41364f, 0.104717f, -0.105201f, -0.949609f, -0.225559f, -0.34806f, 0.382115f, -0.0615256f, -0.466405f, -0.549317f, -0.837217f, 0.954736f, -0.138345f, -0.148199f, 0.526336f, 0.0140706f, -0.162999f, 0.511944f, -0.274986f, -0.340369f, -0.046259f, 0.346976f, -0.0313568f, 0.228732f, -0.0489806f, 0.223879f, 0.562803f, 0.0103503f, 0.0799935f, 0.558369f, -0.171246f, 0.171886f, 0.166322f, 0.238082f, 0.673778f, -0.77636f, -0.163403f, -0.0436278f, 0.141954f, -0.0472412f, -0.204982f, -0.0389803f}; - model->setOperandValue(op41, op41_init, sizeof(float) * 288); - static float op43_init[] = {0.3436f, -1.04269f, 0.225925f, -0.537982f, 1.08172f, -0.204524f, 0.172497f, 0.674492f, 0.456061f, 0.668595f, -0.481863f, -0.133324f, 1.32533f, 0.993317f, 0.748185f, -0.195914f, -0.0134926f, 0.688711f, 0.511972f, 0.636836f, 0.279939f, 0.321013f, 0.876996f, -0.260606f, -0.451471f, 0.190377f, 0.252418f, -0.148638f, -0.0716072f, -0.423855f, -0.435216f, 0.208755f}; - model->setOperandValue(op43, op43_init, sizeof(float) * 32); - static float op44_init[] = {-0.997767f, -0.138253f, 0.257102f, 0.198314f, -0.288036f, 0.000366378f, 0.179375f, 0.0428916f, 0.37398f, 0.200809f, 0.0708428f, -0.0552363f, 0.2561f, 0.0673435f, 0.214471f, -0.273985f, 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0.0490718f, 0.0615049f, 0.105296f, -0.108085f, -0.313966f, 0.426299f, 0.124506f, -0.0795267f, 0.174412f, 0.272431f, 0.124504f, 0.0739427f, -0.0700362f, 0.111758f, 0.00671678f, -0.107815f, -0.0662027f, -0.0918283f, -0.102847f, -0.0980065f, -0.0483559f, -0.120665f, 0.0151454f, 0.00351556f, -0.488797f, 0.0444841f, -0.253477f, -0.28651f, 0.00837357f, 0.0809181f, -0.0199791f, -0.0799819f, 0.0997407f, 0.235679f, 0.97399f, -0.167017f, -0.122257f, 0.597685f, 0.0827836f, 0.119421f, 0.243396f, -0.0572792f, -0.0470654f, 0.120889f, -0.0969786f, -0.0506503f, 4.19253e-05f, -0.0744475f, 0.135971f, -0.0525192f, 0.056829f, -0.148527f, 0.191463f, 0.116223f, -0.0730916f, 0.0129901f, 0.350111f, 0.135758f, 0.152902f, 0.485041f, 0.0448233f, -0.111131f, -0.233994f, -0.410416f, 0.243623f, 0.138663f, -0.00277319f, 0.227577f, 0.0683518f, -0.210476f, 0.226312f, 0.21227f, -0.0362238f, 0.206224f, -0.0785877f, -0.20799f, -0.0422812f, 0.225806f, -0.347121f, -0.154161f, -0.15515f, 0.109949f, 0.386982f, -0.399289f, -0.374705f, -0.193131f, 0.123094f, -0.198874f}; - model->setOperandValue(op44, op44_init, sizeof(float) * 1024); - static float op46_init[] = {0.0353964f, -0.498098f, -0.0637909f, 1.46605f, 0.0985285f, 1.55767f, 1.67486f, 2.08488f, 1.24002f, 1.53329f, -0.0350761f, 0.185034f, 3.21054f, 0.0545383f, 0.137788f, 1.5124f, -0.0263243f, 1.91751f, 1.16072f, 1.34599f, 0.0323791f, -0.516612f, 3.40556f, -0.203818f, 0.916529f, 1.36208f, 1.47077f, 1.58338f, 0.0124303f, 0.116432f, -0.00318241f, -0.000725091f}; - model->setOperandValue(op46, op46_init, sizeof(float) * 32); - static float op47_init[] = {0.156179f, 0.740114f, 0.146084f, -0.180304f, 0.135386f, -0.112967f, -0.159752f, -0.0912054f, -0.16292f, -0.170387f, 0.184984f, 0.534837f, -0.222242f, 0.0663174f, 0.322167f, -0.246094f, 0.225455f, 0.0679039f, -0.139034f, 0.0039652f, 0.096747f, 0.0668452f, -0.101501f, 0.184103f, -0.189683f, -0.181256f, -0.226854f, -0.115267f, 0.117309f, 0.212836f, 0.0102671f, -0.00837151f, 0.205307f, -0.118807f, 0.473089f, -0.27765f, 0.0257295f, -0.295094f, -0.306755f, -0.196187f, -0.151386f, -0.190512f, 0.279854f, 0.368561f, -0.512608f, 0.123368f, 0.785377f, -0.338917f, 0.246306f, -0.39264f, -0.164611f, -0.108249f, 0.0665555f, 0.271457f, -0.442514f, 0.309919f, -0.24764f, -0.256974f, -0.244039f, -0.130222f, 0.144196f, 0.288034f, 0.16275f, -0.0276356f, 0.0973298f, 0.172178f, 0.361618f, -0.159499f, -0.147958f, -0.236342f, -0.197888f, -0.139265f, -0.0194431f, -0.0795726f, 0.154615f, -0.0446899f, -0.209686f, 0.102243f, 0.751226f, -0.128102f, 0.17202f, -0.496175f, -0.0630421f, -0.141689f, -0.0395602f, 0.209026f, -0.399518f, 0.229802f, -0.11026f, -0.0416616f, -0.0478338f, -0.0866013f, 0.0788821f, 0.145776f, 0.163885f, -0.00528089f, 0.322996f, 0.674345f, -0.250062f, -0.266435f, 0.300283f, -0.245216f, -0.171486f, -0.218911f, -0.278993f, -0.238749f, 0.188046f, 0.398108f, -0.367571f, 0.161006f, -0.251809f, -0.283983f, 0.308162f, -0.147899f, -0.256196f, -0.028727f, 0.148012f, 0.237726f, -0.543712f, 0.184632f, -0.229882f, -0.0937904f, -0.23355f, -0.230001f, 0.159108f, 0.186345f, 0.195609f, 0.129094f, 0.489674f, -0.0359821f, 0.287801f, -0.349692f, 0.310358f, -0.550261f, -0.432363f, -0.390494f, -0.310232f, -0.378422f, 0.330258f, 0.210456f, -0.7278f, 0.343291f, -0.112808f, -0.449118f, 0.422889f, -0.56646f, -0.363238f, -0.285837f, 0.386119f, 0.402224f, -0.728247f, 0.29205f, -0.396501f, -0.402005f, -0.351576f, -0.278841f, 0.310083f, 0.328826f, 0.322681f, 0.242448f, 0.230535f, -0.205397f, 0.441373f, -0.201717f, 0.0573488f, -0.362298f, -0.335419f, -0.231367f, -0.0660249f, -0.192796f, 0.201418f, -0.0941352f, -0.462079f, 0.230986f, 0.0567214f, -0.278781f, 0.272637f, -0.405269f, -0.192166f, -0.325699f, 0.228205f, 0.318716f, -0.363493f, 0.188267f, -0.245576f, -0.301783f, -0.107653f, -0.160302f, 0.208713f, 0.167456f, 0.19856f, 0.195301f, 0.167139f, 0.639249f, -0.396651f, -0.173979f, 0.170304f, -0.206958f, -0.0727969f, -0.147468f, -0.12681f, -0.162747f, -0.00979657f, -0.0850251f, -0.12852f, 0.13592f, -0.436702f, -0.143019f, 0.110177f, -0.261806f, -0.2219f, -0.0652627f, 0.0486492f, 0.267457f, -0.252875f, 0.0509038f, -0.162644f, 0.0361367f, -0.100097f, -0.111549f, 0.0648925f, -0.0166337f, 0.217277f, 0.200447f, 0.341306f, -0.348518f, -0.0825517f, -0.17013f, 0.328561f, -0.346055f, -0.274162f, -0.227547f, -0.221071f, -0.312126f, 0.0529024f, -0.446766f, -0.346986f, 0.294348f, -0.709363f, -0.258129f, 0.174978f, -0.334403f, -0.354706f, -0.246383f, 0.300609f, 0.287097f, -0.326242f, -0.0250746f, -0.290678f, -0.221031f, -0.200902f, -0.185348f, 0.197853f, 0.0334783f, 0.239879f, 0.349207f, 0.202404f, -0.169038f, 0.219634f, -0.0465216f, 0.240188f, -0.12988f, -0.230147f, -0.162086f, -0.0892766f, -0.219808f, 0.0623033f, -0.560078f, -0.357664f, 0.18296f, -0.540818f, -0.205105f, 0.0497816f, -0.0818515f, -0.224175f, -0.22876f, 0.27044f, 0.203257f, -0.0265541f, -0.0891287f, -0.109839f, -0.326671f, -0.0904553f, -0.116456f, 0.158232f, 0.00285579f, 0.0707352f, 0.215775f}; - model->setOperandValue(op47, op47_init, sizeof(float) * 288); - static float op49_init[] = {0.473197f, -1.08994f, 0.157816f, 0.475708f, 1.16729f, 1.19871f, 0.434978f, 0.260128f, 0.362141f, 0.81847f, 0.155825f, 1.22273f, -0.382775f, 0.24913f, 0.492578f, -0.541874f, 2.08853f, 2.6003f, -0.14665f, 1.43908f, -0.740456f, -0.0744309f, -0.208456f, 0.235227f, 1.44235f, 1.09803f, -0.430158f, 0.810813f, 0.336378f, 0.0121858f, 0.65212f, 0.865938f, 0.137012f, 0.389252f, -0.54846f, -0.951355f, 1.09939f, 0.120903f, 0.0200256f, 0.277682f, -0.0387516f, 0.193967f, 0.248117f, 1.22538f, 0.930871f, -0.169939f, -0.747988f, 0.66065f, -0.23336f, 1.11278f, 1.44047f, -1.61965f, 0.535887f, 2.11026f, 0.840902f, 0.677272f, 1.36815f, 0.758101f, -0.292219f, 0.202975f, -2.03518f, 1.01007f, 0.902706f, -0.218632f}; - model->setOperandValue(op49, op49_init, sizeof(float) * 64); - static float op50_init[] = {0.0613606f, -0.553296f, 0.195442f, 0.267107f, 0.555671f, -0.10669f, -0.145497f, 0.0230295f, 0.0348993f, -0.179045f, 0.0174281f, -0.010889f, -0.0904074f, -0.0220914f, 0.0483935f, -0.0840004f, 0.0468793f, -0.0856841f, -0.00308835f, -0.301048f, -0.188984f, -0.135143f, 0.249993f, -0.0918953f, 0.0902932f, 0.0467749f, -0.00621833f, -0.0903459f, 0.186926f, -0.00039472f, -0.0259989f, 0.0783119f, -0.042534f, 0.215926f, 0.0965445f, 0.12098f, -0.0839443f, -0.0667839f, 0.163285f, 0.0395507f, 0.162628f, 0.0585461f, 0.0800772f, 0.212197f, 0.12528f, -0.223154f, 0.107762f, 0.145602f, 0.456506f, 0.0179339f, 0.250854f, -0.0810258f, 0.286478f, -0.222802f, 0.233478f, -0.151214f, 0.106238f, -0.0293863f, 0.158757f, -0.106969f, 0.0623353f, 0.0613231f, -0.195503f, -0.063429f, -0.437771f, -0.000534805f, 0.162869f, 0.168638f, -0.052143f, 0.176157f, 0.213678f, -0.247235f, -0.0893308f, 0.152861f, 0.000336537f, 0.339348f, 0.225011f, 0.0203482f, -0.160058f, 0.488881f, -0.20454f, 0.166462f, 0.00362215f, 0.231823f, 0.029677f, 0.108874f, 0.108483f, -0.373634f, -0.0598272f, 0.014483f, -0.0458149f, -0.100886f, 0.0769476f, -0.248573f, 0.0777195f, -0.167846f, -0.00647958f, -0.518774f, 0.0129852f, -0.193443f, 0.095585f, -0.0166189f, 0.33796f, -0.105717f, 0.264887f, 0.14487f, -0.279174f, -0.100932f, -0.197915f, -0.0108937f, 0.111626f, 0.199351f, -0.175863f, 0.0949413f, 0.0559241f, -0.161725f, -0.0733558f, 0.120563f, -0.238592f, -0.176971f, 0.210153f, -0.0585696f, -0.399517f, 0.0162901f, -0.149515f, 0.733276f, 0.193254f, 0.0962554f, -0.0427208f, -0.430693f, 0.0589916f, -0.0103642f, -0.216882f, -0.24326f, -0.120194f, 0.0294264f, 0.00550552f, 0.0405278f, -0.0812524f, 0.0401351f, -0.0489547f, 0.00637494f, 0.0824973f, 0.0130646f, -0.00663252f, 0.0683121f, -0.179022f, 0.00282102f, -0.0349031f, -0.0273978f, -0.0273364f, -0.0127886f, -0.076215f, -0.119346f, -0.0320238f, 0.0791878f, 0.203465f, -0.131939f, -0.0518493f, 0.0874591f, 0.0202136f, -0.38524f, 0.0343551f, 0.109279f, 0.18277f, -0.306352f, -0.0952819f, 0.213221f, -0.425617f, 0.208708f, -0.125334f, -0.042471f, 0.0776871f, -0.0403078f, -0.0676745f, 0.0199275f, -0.183478f, -0.0769372f, -0.202255f, -0.103242f, -0.186987f, 0.0234087f, -0.167436f, -0.20559f, -0.21246f, 0.0408609f, 0.0994809f, 0.308898f, -0.10407f, 0.0804366f, -0.143496f, -0.0976051f, 0.0771549f, 0.0874504f, 0.0660832f, 0.400231f, -0.0107677f, -0.0201709f, -0.00538637f, -0.14317f, 0.098519f, 0.0432682f, -0.161831f, -0.0702254f, 0.237993f, -0.240798f, 0.0160579f, -0.0558574f, -0.155444f, -0.0860663f, -0.247103f, 0.505568f, -0.218629f, 0.0653454f, 0.169408f, -0.134756f, 0.152116f, 0.442444f, 0.0330443f, -0.0523724f, -0.054462f, -0.161614f, -0.261135f, -0.268574f, -0.139154f, -0.260528f, -0.120727f, 0.00851199f, -0.225665f, 0.127361f, -0.00853388f, -0.067299f, 0.260902f, -0.0950226f, -0.11589f, 0.0530933f, -0.0651117f, 0.133556f, 0.0623896f, 0.035837f, 0.155237f, -0.00146263f, 0.0291835f, -0.0617668f, 0.0308919f, 0.0810174f, -0.162716f, 0.122673f, 0.264901f, 0.622652f, 0.0293035f, -0.0149803f, 0.0276966f, -0.23522f, -0.123944f, -0.200096f, -0.1039f, 0.0265614f, -0.0836676f, 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0.595705f, -0.00355858f, -0.280972f, 0.288604f, -0.246517f, 0.104765f, 0.23599f, 0.0626923f, 0.431966f, -0.0706402f, 0.444929f, -0.0391279f, 0.214596f, 0.0142286f, -0.127732f, 0.101488f, 0.167395f, 0.189494f, 0.111607f, 0.0246908f, 0.0359995f, 0.120173f, -0.045409f, 0.256838f, -0.0781464f, 0.00970439f, -0.0484562f, 0.247528f, 0.0649018f, -0.158475f, 0.139769f, -0.0900704f, -0.371826f, 0.0790552f, -0.37067f, -0.208308f, -0.211739f, -0.282507f, -0.270256f, 0.205481f, -0.503264f, 0.270588f, -0.0385014f, -0.26717f, 0.128288f, 0.362188f, 0.19137f, -0.247913f, -0.153628f, -0.167465f, 0.814905f, -0.0979974f, 0.0865845f, 0.386866f, -0.37493f, -0.129166f, 0.120307f, 0.435242f, -0.218038f, 0.0716689f, -0.34914f, -0.152382f, 0.062478f, -0.114676f, -0.23435f, 0.0159545f, 0.171189f, -0.00842011f, 0.131103f, -0.262238f, -0.215891f, -0.102031f, 0.0864728f, 0.0985599f, 0.164302f, -0.0653579f, 0.320503f, -0.556148f, 0.536547f, -0.0820048f, 0.14663f, 0.397948f, 0.0787573f, -0.748766f, -0.129899f, -0.474231f, 0.123753f, 0.27761f, 0.0410578f, -0.543769f, 0.339079f, 0.0367555f, -0.2571f, -0.0964757f, -0.0154511f, 0.00377123f, -0.113003f, 0.0291767f, -0.257889f, 0.124925f, 0.0266814f, 0.114631f, 0.256503f, -0.0293197f, 0.0422625f, -0.00998103f, 0.0881326f, 0.104533f, -0.125819f, -0.243805f, -0.0148122f, 0.51277f, -0.197339f, 0.257117f, 0.144041f, 0.245811f, -0.160956f, 0.305854f, 0.0214668f, 0.308474f}; - model->setOperandValue(op50, op50_init, sizeof(float) * 2048); - static float op52_init[] = {2.09374f, -0.224081f, -0.26393f, -0.213004f, 0.148951f, 1.95099f, 0.00966659f, 0.00670771f, 0.751565f, 0.15019f, -0.259461f, -0.210099f, 0.114971f, 0.522198f, 0.00564572f, 0.242789f, 1.04283f, 0.983166f, -0.110847f, -0.112012f, -0.707376f, 0.0605569f, 0.967749f, 1.54696f, 2.39179f, -0.0660475f, -1.02395f, 0.663784f, 0.0684345f, 0.835432f, -0.0555699f, -0.595563f, 1.63423f, -0.275387f, -0.57632f, -0.448239f, 1.57884f, -0.854438f, -0.00365815f, 0.157436f, -0.458565f, 0.175284f, -0.240444f, -0.955042f, -0.158954f, 0.0995562f, 1.09967f, -0.109549f, 1.181f, -0.130396f, -0.246582f, -0.121111f, -0.0830782f, -0.801972f, -0.0843711f, -0.0243731f, -0.21979f, 0.114646f, -0.358143f, 0.00306739f, -0.00465024f, 1.03416f, -0.229731f, 1.37862f}; - model->setOperandValue(op52, op52_init, sizeof(float) * 64); - static float op53_init[] = {-0.12698f, -0.238352f, 0.244215f, -0.458646f, 1.27555f, -0.421724f, 0.0884984f, -0.250955f, -0.158853f, -0.176467f, -0.0758706f, -1.31031f, -0.776091f, -0.753093f, -0.0744585f, 0.458402f, -0.386373f, -0.379819f, -0.205575f, -0.434537f, -0.528748f, 0.667563f, 0.197939f, -0.00334295f, -0.674948f, 0.0583558f, 0.349504f, 0.0669012f, -0.62573f, 0.0202494f, -0.851811f, 0.0796548f, -0.349216f, 0.336193f, 0.242802f, 0.410446f, 0.127221f, 0.337145f, -0.671162f, 0.550955f, -0.047319f, 0.60033f, 0.342804f, 0.128321f, -0.329242f, -0.581331f, -0.757822f, 0.260636f, 0.183208f, 0.0755782f, -0.0630638f, -0.328286f, 0.71131f, -0.747697f, -0.114446f, 0.878907f, -0.362007f, 0.567657f, -0.0426601f, -0.120681f, -0.158017f, -0.132928f, -0.0400564f, -0.119035f, -0.347841f, 0.27945f, 0.522869f, -0.318377f, -0.617688f, 0.00215388f, -0.288244f, -0.0643205f, -0.125579f, 0.0714863f, 0.491909f, -1.17593f, -0.790597f, 0.237053f, 1.10418f, 1.01839f, -0.588406f, -0.163287f, -0.00948245f, 0.0295395f, 0.0913539f, -0.597769f, -0.199139f, -0.736147f, -1.13063f, -0.966132f, 0.670015f, -0.384273f, -0.35121f, -0.452121f, 0.139313f, -0.752194f, -0.581573f, 1.20926f, 0.468125f, 0.0024432f, -0.864836f, 1.40619f, 0.248723f, -0.0319902f, 0.243259f, 0.719603f, 1.56375f, 0.582131f, 0.517324f, -0.558173f, -0.486479f, 0.240216f, -0.10745f, -0.600757f, -0.407625f, -0.026077f, 2.22213f, 0.964383f, -0.283648f, 0.242633f, -0.671491f, 0.247282f, 0.246633f, 0.569997f, -0.442304f, 0.711694f, 0.689518f, -0.270639f, -0.0341924f, 0.299518f, -0.221812f, 0.0267754f, -0.701543f, -0.256183f, -0.464542f, 0.533444f, 0.551868f, -0.156954f, -0.522106f, -0.947321f, -0.370692f, 0.302054f, -0.157298f, 0.220857f, -0.485616f, -0.304364f, 0.451741f, 0.226862f, 0.0889601f, -0.72402f, 0.0010362f, -0.0768618f, -0.602799f, -0.202045f, -0.731567f, 0.1843f, -0.019595f, -0.44772f, 1.08144f, -0.0849737f, -0.0383033f, 0.0770167f, 0.00544352f, -0.0513838f, 0.354596f, 0.803038f, 0.14348f, -0.586081f, 0.350211f, 0.445668f, -0.142114f, 0.559075f, -0.197881f, -0.485669f, 0.374767f, -0.529465f, -0.433189f, -0.381117f, 0.303766f, -0.0149781f, 0.259829f, 0.058601f, -0.273908f, -0.600653f, -0.189578f, -0.437372f, -0.0137133f, 0.325366f, -0.269418f, 0.337198f, 0.263951f, -0.216138f, -0.620022f, -0.112354f, 0.262422f, -0.0179674f, 0.942104f, 0.421509f, 0.458286f, -1.16175f, -0.0139678f, -0.103121f, -0.210449f, 1.87519f, 0.0331067f, -0.751383f, 0.222364f, 0.106374f, 0.0638171f, -0.167853f, 0.139778f, 0.38963f, 0.274968f, 0.155153f, -0.34655f, -0.0563739f, -0.261732f, 0.414308f, -0.593183f, -0.306091f, 0.042063f, 0.0501084f, -1.38946f, 0.40545f, -0.226405f, 0.00702765f, -0.194214f, 0.428538f, -0.455647f, 0.72176f, -0.497389f, 1.40647f, -0.173328f, 0.0971494f, 0.144964f, -0.0565212f, -0.0767172f, 0.505409f, 0.466856f, 0.226217f, -0.298469f, 0.587705f, -1.19975f, 0.451319f, -0.245376f, 0.0492834f, -0.227796f, 1.13696f, 0.20848f, 1.05685f, 0.305445f, -0.358505f, -0.297229f, -0.174603f, 0.0937863f, -0.118832f, -0.547926f, -0.233037f, 0.670064f, 0.432594f, 1.08319f, -0.0648255f, -0.242524f, -0.551126f, -0.997542f, -0.612246f, 0.784354f, 0.924871f, 0.0868394f, 0.00189777f, 0.451893f, -0.200028f, -0.456986f, 0.202478f, 0.358685f, 0.479158f, 0.923011f, 2.25015f, -0.577736f, -0.680415f, -0.405968f, 2.08862f, 1.7708f, -0.221745f, -0.668693f, -0.386576f, -0.0936857f, 0.880579f, -0.200297f, -0.182471f, 0.915206f, 0.561029f, -1.09806f, 0.738976f, 1.01242f, -0.84413f, 0.888704f, -0.0140932f, 0.683439f, 0.69737f, 0.909058f, -0.424791f, -0.723923f, 2.54434f, -0.459792f, 0.214831f, 0.538751f, 0.406171f, -0.164808f, 1.96384f, 0.426688f, -0.884862f, 1.96028f, 0.437135f, 1.31552f, 0.295632f, 1.20595f, -0.33993f, 0.41051f, -0.742935f, -0.528444f, -0.256337f, -0.165411f, -0.0731374f, -1.95086f, -1.16248f, -0.510701f, 1.78344f, 0.62322f, -0.397011f, -0.296941f, 0.519863f, 0.145054f, 0.773714f, 0.261809f, -0.121355f, -0.109046f, -0.255428f, -0.124878f, -0.544119f, 0.301598f, -1.87278f, -0.352883f, -0.0320303f, -0.502195f, 0.0743574f, 0.0359745f, 0.0942341f, -0.00728066f, 0.063997f, 0.687835f, 0.0469159f, -0.220356f, 0.361827f, 0.100022f, -0.168349f, -0.177127f, -0.83667f, 0.191707f, -0.584849f, -0.150654f, -0.402803f, -0.255429f, 0.212771f, 0.901497f, -0.422539f, -0.33554f, 0.158121f, -0.0110873f, -0.70106f, 0.84911f, -0.48385f, -0.34909f, -0.756153f, -0.127089f, -0.54675f, 0.233831f, -0.275361f, -0.0572444f, 0.524687f, 0.152529f, 0.0550876f, -0.103777f, -0.17179f, 0.0959539f, 0.336758f, 0.0242661f, 0.209466f, -0.532314f, -0.381342f, 0.881586f, 0.262059f, -0.239896f, -0.112729f, -0.240745f, 0.524678f, 0.635937f, 0.0749855f, -0.404162f, -0.219252f, 0.298954f, -0.362545f, -0.0425563f, 1.14806f, -0.497664f, -0.489604f, -0.541267f, 0.0938686f, -0.0829758f, -0.302335f, -0.354915f, -0.0567779f, 0.548323f, 0.0893113f, -0.171931f, 0.221819f, -0.444963f, -0.13688f, 0.0294968f, 0.61855f, 0.0477813f, -0.647976f, 0.134058f, 1.16525f, -0.0321262f, -0.360732f, -0.268031f, 0.0867808f, -0.28502f, 0.954255f, -0.158386f, -0.695564f, -0.244045f, 0.650121f, -0.532843f, 0.225883f, -1.0656f, 0.730367f, -0.108003f, 0.475861f, 0.427236f, -0.0965219f, 0.131015f, -0.93574f, -0.0239511f, -0.571369f, 0.214945f, 0.668269f, -0.264419f, 0.574199f, 0.209625f, 1.06012f, 0.479062f, -0.550203f, 0.786737f, -0.917411f, -0.802023f, 1.57227f, 0.643995f, -0.626247f, 1.3826f, -0.643308f, -0.296662f, -0.631958f, 0.125903f, -0.561165f, -0.354695f, -0.645927f, 0.529553f, 0.714097f, -0.208208f, -0.307469f, -0.0649088f, -0.672144f, 0.0743959f, -0.580105f, 1.32175f, -0.34053f, 0.213644f, 0.960989f, -0.369211f, -0.700574f, 0.542504f, -0.0307876f, -0.52194f, -1.04995f, 0.166939f, -0.644977f, 0.373263f, -0.293433f, -0.259692f, 0.662439f, -0.339301f, 1.1388f, -0.0593778f, -0.706105f, -0.789209f, 0.808238f, 0.749022f, 1.10028f, -1.14159f, -0.0990618f, 1.79632f, -0.906117f, -0.0451775f, -0.855292f, -0.272456f, -0.144439f, 0.49774f, -0.336441f, 0.198619f, -0.990211f, -0.152451f, 0.780138f, -0.237703f, 0.203953f, -0.109738f, -0.0133946f, -0.343127f, 0.644481f, 0.0697042f, 0.0927609f, 0.102066f, 0.488014f, 0.18753f, 0.160318f, -0.214566f, -0.428027f, 0.085759f, -0.0918024f, 1.19273f, -0.210594f, 0.283777f, -0.176787f, -0.253484f, 0.0706566f, 0.0444775f, -0.0274145f, 0.572465f, -0.245561f, 0.108852f, 0.262295f, 0.43359f, -0.312667f, -0.0226221f, -0.197633f, 0.383403f, -0.0244389f, -0.208731f, 0.202447f, 0.0624711f, -0.616221f, -0.140103f, -0.249181f, -0.389501f, -0.614766f, -0.362506f, 0.2265f, -0.330135f, -1.07509f, 0.146874f, -0.145657f, -0.0291293f, -0.642817f, -0.246684f, -0.630452f, 0.0672653f, 0.269267f, -0.940307f, -0.158987f, -0.305785f, 0.0163865f, 0.376522f, -0.472504f, -0.561235f, -0.436323f}; - model->setOperandValue(op53, op53_init, sizeof(float) * 576); - static float op55_init[] = {1.07345f, -0.394695f, -0.0742894f, -0.383227f, 0.324794f, 0.418869f, 0.934568f, -1.90608f, 0.209722f, -0.224193f, -0.195225f, -0.0252866f, 0.404565f, 0.300348f, 0.357196f, 0.0298129f, 0.337042f, 0.475936f, -0.631903f, 0.0679197f, 0.65726f, 0.140373f, 0.508893f, 0.775208f, 0.445816f, -1.23974f, 0.0313992f, 0.169123f, 0.112667f, -0.581175f, -0.376066f, 0.885236f, 0.215963f, 0.438986f, -0.157146f, 0.0287784f, -0.757865f, 0.0841731f, 1.03121f, -0.271182f, 0.589192f, 0.976112f, 0.145576f, 0.092291f, 1.06837f, 0.119318f, 0.147453f, -0.525313f, 0.302458f, 0.984562f, 0.142083f, -0.513634f, -0.498019f, 1.29289f, -0.676165f, -0.777563f, 0.0133348f, -1.3991f, 0.416201f, 1.29946f, 0.735901f, -0.407577f, 0.0395284f, -0.244576f}; - model->setOperandValue(op55, op55_init, sizeof(float) * 64); - static float op56_init[] = {0.113075f, -0.0541261f, -0.00254935f, 0.0902875f, -0.32416f, -0.010111f, -0.221566f, -0.0446467f, 0.025913f, 0.0487722f, -0.180431f, -0.0793264f, 0.129554f, -0.0131898f, -0.246616f, 0.0261182f, 0.195068f, 0.0110834f, -0.268252f, -0.0556206f, 0.152303f, -0.159758f, 0.00937392f, 0.0980935f, 0.325152f, -0.115004f, -0.350504f, 0.0197425f, -0.11301f, -0.0753603f, -0.175782f, -0.134789f, 0.00631809f, -0.0891922f, -0.239375f, 0.160609f, -0.0435589f, -0.0562327f, -0.350046f, -0.0121122f, -0.140865f, 0.0352316f, -0.0174294f, 0.110653f, -0.139081f, -0.0948299f, -0.0264269f, -0.029115f, 0.137921f, 0.13543f, -0.0189765f, -0.0165422f, -0.0823839f, -0.256189f, -0.121788f, -0.216157f, 0.00363343f, -0.151778f, 0.252971f, -0.173452f, 0.182134f, -0.156715f, 0.14355f, 0.121809f, -0.0304927f, 0.599508f, 0.041677f, 0.097244f, 0.125037f, -0.133955f, 0.173185f, 0.0350644f, -0.145842f, 0.0234546f, -0.0706621f, -0.132305f, -0.0380816f, -0.16649f, -0.105998f, 0.0139125f, -0.0896661f, 0.149856f, 0.132628f, -0.0480307f, 0.138173f, 0.0411969f, 0.148886f, -0.00950225f, -0.0722613f, 0.0126559f, 0.107946f, -0.09147f, 0.0730902f, 0.125814f, 0.281345f, -0.128981f, -0.0151615f, 0.114928f, 0.0109224f, 0.0134429f, 0.0731687f, -0.014135f, 0.357557f, 0.0469398f, 0.0442583f, 0.0772733f, 0.0690856f, 0.17982f, -0.133452f, 0.0745796f, 0.122212f, -0.0208794f, 0.020065f, 0.0295544f, -0.171575f, 0.0622467f, -0.0769533f, -0.00222684f, -0.0404518f, -0.0317308f, -0.331273f, -0.16373f, 0.156179f, 0.00540106f, -0.0322326f, 0.0850205f, 0.0480989f, -0.152385f, 0.0694411f, 0.229565f, -0.0611579f, 0.0585534f, 0.095432f, -0.023594f, 0.079468f, -0.118917f, -0.0132471f, -0.00255374f, 0.0104798f, 0.584729f, 0.14753f, -0.0907179f, -0.163356f, -0.111535f, 0.14899f, -0.0385063f, 0.0277369f, -0.190452f, -0.0012917f, 0.1646f, 0.0322249f, 0.246906f, 0.123279f, -0.0258971f, 0.00786738f, 0.0542629f, 0.0694776f, 0.150078f, -0.16412f, 0.191585f, 0.049951f, -0.159397f, -0.0137556f, 0.174186f, -0.0435317f, -0.307162f, 0.182426f, -0.177937f, -0.115525f, 0.0298391f, -0.368494f, -0.221122f, -0.0168894f, -0.0401771f, 0.109005f, -0.0733102f, -0.025999f, 0.00781864f, 0.00232855f, 0.00959035f, 0.0469935f, -0.299108f, -0.171356f, -0.0674005f, 0.149625f, -0.0230295f, -0.0455517f, 0.0233079f, 0.205671f, 0.0393789f, -0.287905f, 0.141024f, 0.0859168f, -0.179896f, 0.0601555f, -0.121133f, 0.159868f, 0.0623252f, 0.104785f, -0.188308f, -0.146959f, -0.134704f, -0.319101f, 0.0808484f, 0.344271f, 0.0113916f, 0.175252f, 0.0849976f, -0.0352785f, 0.0743872f, -0.165385f, -0.0677743f, 0.0751335f, -0.00489914f, -0.0963297f, -0.0482092f, -0.135487f, -0.0115389f, -0.0197858f, -0.0754902f, 0.194723f, -0.0186476f, -0.0349434f, 0.189749f, 0.0227529f, 0.0180564f, 0.129332f, -0.0349466f, -0.106948f, 0.0585846f, -0.131004f, -0.0161526f, 0.0149128f, 0.0490677f, 0.0892119f, 0.0442311f, -0.102199f, 0.172912f, -0.153624f, 0.143834f, -0.0421306f, 0.142092f, 0.0772596f, 0.0888918f, 0.102791f, -0.0272839f, -0.00127824f, -0.0746725f, 0.116443f, 0.01735f, 0.00530435f, 0.00146042f, 0.152961f, 0.0888051f, 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-0.0167416f, -0.0360491f, -0.0325039f, 0.0518329f, 0.115549f, 0.130139f, 0.152888f, -0.170668f, -0.143866f, 0.0448703f, -0.0423743f, -0.118023f, 0.226404f, 0.00380555f, -0.0556225f, 0.111643f, -0.000808296f, 0.221888f, -0.0465153f, -0.0175943f, -0.222517f, -0.156542f, 0.162187f, -0.000673403f, 0.183668f, 0.0201649f, -0.0564224f, 0.0668854f}; - model->setOperandValue(op56, op56_init, sizeof(float) * 4096); - static float op58_init[] = {-0.123297f, 1.03112f, -0.605606f, -0.00193548f, 0.930903f, 0.00530672f, 0.0289674f, 0.0371349f, 0.0378613f, 0.0165883f, 1.03057f, 1.32473f, 0.00546759f, 1.61818f, 1.17308f, 1.15808f, 0.121822f, -0.000576973f, -0.754154f, -0.0371096f, -0.64391f, 0.00522935f, 1.5031f, -0.151665f, 0.0318197f, 1.04132f, -0.000814885f, 0.00560421f, 0.00324315f, -0.665056f, 1.39725f, 0.895453f, 0.43012f, -0.19119f, 0.00455064f, -0.0945014f, -0.00135079f, 1.15827f, 1.27354f, 0.064009f, -0.00115699f, 1.73396f, 1.1265f, -0.000266075f, -0.0288338f, 0.00843227f, 1.43842f, -0.0304834f, 1.27166f, -0.0468991f, 0.00850764f, 0.00357342f, 0.0125746f, 1.28928f, 0.0119823f, -0.734399f, -0.59924f, -0.61645f, 0.0474129f, 0.996462f, 1.90151f, 0.0524316f, 1.28534f, 0.120804f}; - model->setOperandValue(op58, op58_init, sizeof(float) * 64); - static float op59_init[] = {0.156719f, -0.365274f, 0.46557f, 0.194803f, -0.196279f, 0.140896f, 0.048415f, 0.0850183f, 0.121988f, 0.113677f, -0.0442027f, -0.301051f, 0.127761f, -0.242727f, -0.0352292f, -0.174421f, 0.129519f, 0.0561737f, 0.512661f, 0.169123f, 0.0472558f, -0.0649848f, -0.114959f, 0.0635479f, 0.172206f, -0.0733962f, 0.27192f, 0.0156798f, -0.188786f, 0.418895f, -0.197189f, -0.132645f, -0.239554f, 0.0477091f, 0.0892152f, 0.217369f, 0.196861f, -0.223356f, -0.0580539f, 0.0847171f, 0.12474f, -0.239668f, -0.132419f, 0.188901f, 0.134479f, 0.153927f, -0.267787f, 0.0921442f, -0.253014f, 0.166386f, 0.161665f, 0.130119f, 0.0325234f, -0.0883673f, 0.141165f, -0.114373f, 0.337908f, 0.422624f, 0.120521f, -0.272987f, -0.0879027f, 0.39587f, -0.0924423f, 0.383058f, 0.236604f, -0.296603f, 0.606555f, 0.15836f, -0.218761f, 0.217377f, 0.250057f, 0.190977f, 0.265866f, 0.184472f, -0.0895751f, -0.31142f, 0.302541f, -0.426601f, -0.205388f, -0.319032f, 0.176047f, 0.108955f, 0.47963f, 0.217736f, 0.169026f, -0.0729668f, -0.310251f, 0.134645f, 0.279504f, -0.255052f, 0.408038f, 0.122761f, 0.0185433f, 0.409245f, -0.410897f, -0.195616f, -0.0545787f, 0.212299f, 0.16841f, 0.449203f, 0.313798f, -0.43426f, -0.244359f, 0.162991f, 0.162584f, -0.218486f, -0.331835f, 0.31621f, 0.246989f, 0.283238f, -0.546161f, 0.247583f, -0.212179f, 0.275593f, 0.468498f, 0.318836f, 0.206248f, -0.341647f, 0.209406f, 0.529377f, 0.514118f, 0.679407f, 0.232351f, -0.173954f, -0.291855f, 0.601301f, -0.230597f, 0.295756f, 0.148746f, -0.0206303f, 0.30146f, -0.00565797f, -0.134681f, 0.139712f, 0.203158f, 0.166793f, 0.167439f, 0.0876717f, 0.0390046f, -0.0658044f, 0.202852f, -0.434664f, -0.216976f, -0.159045f, 0.0890226f, 0.0870474f, 0.101252f, 0.0657062f, 0.0714723f, -0.0286613f, -0.233553f, 0.0811676f, 0.156996f, -0.149108f, 0.155671f, 0.130916f, 0.0883327f, 0.372619f, -0.300563f, -0.165772f, 0.131113f, 0.160236f, 0.115812f, 0.382637f, 0.176239f, -0.295282f, -0.241622f, 0.122368f, 0.0595133f, -0.053039f, -0.159197f, 0.166583f, 0.136762f, 0.136108f, -0.346738f, 0.198406f, -0.0226704f, 0.140314f, 0.332062f, 0.209204f, 0.200125f, -0.319958f, 0.127433f, -0.307969f, 0.373432f, 0.42893f, 0.159448f, 0.043322f, -0.237465f, 0.418199f, -0.135938f, -0.120479f, 0.250778f, -0.337925f, 0.097704f, 0.35875f, -0.213009f, 0.228708f, 0.0488628f, 0.125034f, 0.199651f, 0.210063f, -0.343646f, -0.400311f, 0.191584f, -0.203307f, -0.157494f, -0.308041f, 0.15821f, 0.174559f, 0.468329f, 0.309438f, 0.247248f, 0.130968f, -0.0867318f, 0.206511f, 0.30064f, -0.171905f, 0.435259f, 0.123804f, -0.253817f, 0.342408f, -0.184797f, -0.255919f, -0.396232f, 0.194991f, 0.218345f, 0.111341f, 0.2816f, -0.204434f, -0.221971f, 0.143186f, 0.24028f, -0.221973f, -0.291024f, 0.283368f, 0.214397f, 0.266395f, -0.118249f, 0.188255f, -0.265134f, 0.262492f, 0.209908f, 0.3002f, 0.169594f, -0.235555f, 0.225255f, -0.228316f, 0.251338f, -0.0592531f, 0.194377f, -0.297403f, -0.282513f, 0.811137f, -0.231202f, 0.369398f, 0.390241f, -0.370919f, 0.279303f, 0.319676f, -0.331098f, 0.314811f, 0.338441f, 0.285473f, 0.431892f, 0.411534f, -0.429208f, -0.423523f, 0.339615f, -0.425702f, -0.406956f, -0.403893f, 0.274328f, 0.292235f, 0.440492f, 0.350801f, 0.333076f, 0.291294f, -0.395539f, 0.376256f, 0.41386f, -0.328553f, 0.295611f, 0.317178f, 0.251311f, 0.28074f, -0.367861f, -0.367438f, -0.0140023f, 0.386603f, 0.351703f, 0.113581f, 0.504158f, -0.390251f, -0.395837f, 0.315225f, 0.314305f, -0.349378f, -0.403343f, 0.481539f, 0.430096f, 0.510183f, -0.338992f, 0.327904f, -0.358538f, 0.626967f, 0.420674f, 0.422722f, 0.341068f, -0.412864f, 0.351495f, 0.53941f, 0.579613f, -0.191784f, 0.378605f, -0.358006f, -0.479399f, 0.996936f, -0.387605f, 0.108954f, 0.221572f, -0.122325f, 0.284289f, 0.0357301f, -0.134014f, 0.1424f, 0.337351f, 0.214974f, 0.27791f, 0.231705f, -0.121512f, -0.0962304f, 0.177933f, -0.38048f, -0.305226f, -0.240732f, 0.196756f, 0.162702f, 0.269918f, 0.104865f, 0.185338f, 0.229805f, -0.404439f, 0.23318f, 0.186489f, -0.264237f, -0.0833724f, 0.282605f, 0.358322f, 0.363139f, -0.252312f, -0.272217f, 0.391736f, 0.206354f, 0.185641f, -0.0126735f, 0.327625f, -0.270849f, -0.221633f, 0.238607f, 0.125709f, -0.266263f, -0.142663f, 0.28435f, 0.256496f, 0.342364f, -0.29405f, 0.211074f, -0.168167f, 0.403277f, 0.227939f, 0.181339f, 0.222679f, -0.209901f, 0.221502f, -0.237734f, 0.481628f, -0.220895f, 0.22485f, -0.0592914f, -0.274332f, 0.666573f, -0.192692f, -0.410142f, 0.115178f, -0.0784163f, -0.0486908f, 0.214595f, -0.168477f, 0.101383f, 0.0139011f, 0.103151f, 0.137509f, 0.147913f, -0.231564f, -0.145663f, 0.0984321f, -0.00238841f, -0.147452f, -0.250834f, 0.0683556f, 0.170684f, 0.445099f, 0.168504f, 0.282291f, 0.18098f, 0.0124517f, 0.162016f, 0.179294f, -0.135994f, 0.180023f, 0.13271f, -0.108859f, -0.0147587f, -0.0215866f, -0.200777f, -0.673948f, 0.127962f, 0.163465f, -0.258157f, 0.0837923f, -0.0756665f, -0.305415f, 0.0867349f, 0.158782f, -0.0837804f, -0.180023f, 0.109462f, 0.127051f, 0.142069f, 0.143861f, 0.128167f, -0.118189f, 0.153459f, -0.0121548f, 0.231083f, 0.168402f, -0.222204f, 0.128066f, -0.190775f, -0.0652145f, -0.0485893f, 0.156782f, -0.122296f, -0.30449f, 0.64902f, -0.210956f, 0.0574174f, 0.234389f, -0.208773f, -0.145177f, 0.227716f, -0.266999f, 0.125867f, 0.184588f, 0.133689f, 0.285285f, 0.319349f, -0.396718f, -0.179921f, 0.0997811f, -0.142402f, -0.247277f, -0.319458f, 0.18121f, 0.223002f, -0.0734019f, 0.180704f, 0.497347f, 0.385627f, -0.208561f, 0.275091f, 0.19304f, -0.253147f, -0.110287f, 0.290269f, 0.241253f, -0.0563772f, -0.0296443f, -0.276056f, -0.248907f, 0.207276f, 0.2393f, -0.350156f, 0.214404f, -0.0767594f, -0.250012f, 0.20857f, 0.221042f, -0.225087f, -0.190393f, 0.251928f, 0.237866f, 0.330792f, 0.117291f, 0.149372f, -0.262906f, 0.405511f, 0.0345566f, 0.20271f, 0.197317f, -0.273887f, 0.22686f, 0.531326f, 0.153736f, 0.00288367f, 0.218847f, -0.308928f, -0.292032f, 0.659711f, -0.275348f, -0.222431f, 0.165082f, -0.149484f, 0.0457284f, 0.0355357f, -0.0642681f, 0.0418851f, 0.223436f, 0.0792975f, 0.212354f, 0.186616f, -0.222066f, -0.0531161f, -0.00979124f, -0.113867f, -0.123542f, -0.193267f, 0.16783f, 0.0824869f, -0.0106096f, 0.0758056f, 0.329999f, 0.257932f, -0.214898f, 0.168143f, 0.0229075f, -0.209178f, -0.342465f, 0.202302f, 0.299346f, 0.25869f, -0.03226f, -0.167826f, 0.204429f, 0.125327f, 0.0956765f, -0.306913f, 0.165133f, -0.0404995f, 0.00542095f, 0.160975f, 0.097063f, -0.27302f, -0.0479996f, 0.172678f, 0.133329f, 0.240162f, -0.0707717f, 0.0715785f, -0.149838f, 0.302035f, -0.0312277f, 0.0150638f, 0.0953851f, -0.0633347f, 0.141286f, -0.323627f, 0.0918484f, 0.132915f, 0.0733498f, -0.124731f, -0.16095f, 0.403907f, -0.0907782f, -0.305642f}; - model->setOperandValue(op59, op59_init, sizeof(float) * 576); - static float op61_init[] = {-0.177357f, -0.172575f, 0.420523f, 0.408536f, 0.0945462f, 0.823321f, 0.190204f, -0.372994f, 1.65173f, -0.167175f, 0.917709f, 0.954723f, 1.42432f, 0.403654f, 0.262092f, 1.04362f, 0.410467f, 0.524981f, 0.298542f, 0.199161f, 0.46461f, -0.0522062f, -0.0135337f, 1.47301f, -0.217844f, 0.813746f, 1.6661f, 0.120697f, 0.533689f, 0.0370758f, 0.292968f, 0.582308f, -0.170719f, 0.2191f, 0.20729f, -0.00339969f, -0.0205846f, 1.16228f, 1.13561f, 0.0840446f, 0.641917f, -0.698399f, 0.926493f, 0.994014f, 0.307775f, -1.16438f, 0.0498876f, 0.0659915f, 0.851326f, -0.27261f, 0.0224912f, 0.188224f, 0.514921f, 1.66756f, 0.222884f, 0.49327f, 0.657591f, 0.859542f, 1.33821f, -0.109509f, 0.630131f, 0.407995f, 0.0164108f, 1.03597f, 0.405228f, 0.285473f, 1.25656f, 0.442834f, 1.16541f, -0.147579f, 0.234518f, 0.564133f, -0.672565f, -0.272256f, -0.843866f, 0.548911f, -0.145986f, -0.401038f, 0.016665f, 0.260142f, 0.58887f, 1.14477f, 0.883501f, 0.161531f, 0.997724f, 0.0126923f, 0.0445706f, -0.680047f, 1.00268f, 0.48881f, 0.556223f, 0.504591f, 0.917035f, 0.0473247f, 0.191936f, 0.804381f, 0.49664f, -0.0397248f, 0.92709f, -0.139789f, 0.415914f, -0.0327383f, -1.44135f, 0.67827f, 0.814107f, 0.656546f, -1.07413f, -0.0251088f, 0.414193f, 0.0932675f, 0.0132784f, 0.292683f, 0.329189f, 1.05858f, -0.998873f, 0.42996f, -0.197071f, 0.698117f, 0.622712f, 0.231817f, 0.0806911f, 0.0574902f, 0.227412f, 0.182395f, 0.478429f, 1.13242f, 0.602f, 0.848106f}; - model->setOperandValue(op61, op61_init, sizeof(float) * 128); - static float op62_init[] = {-0.0953387f, -0.0241844f, -0.245502f, 0.223093f, -0.00234177f, 0.0567266f, 0.141229f, 0.0298321f, 0.0805704f, -0.332235f, -0.0699764f, 0.140453f, 0.191067f, 0.0697032f, -0.0457525f, -0.173236f, -0.143561f, -0.0576257f, 0.145713f, -0.159888f, 0.110944f, -0.0165625f, 0.149536f, 0.0979629f, 0.193839f, -0.101296f, 0.220719f, 0.152941f, 0.12349f, -0.178112f, -0.137191f, 0.110603f, 0.104661f, -0.11074f, 0.0687107f, -0.00464365f, 0.191844f, -0.305179f, 0.106459f, -0.283352f, -0.160429f, 0.143435f, 0.264719f, 0.401874f, 0.0653386f, -0.144139f, -0.0904675f, 0.215576f, -0.026088f, -0.0908799f, 0.00507214f, -0.0940384f, -0.240861f, 0.171879f, 0.0145143f, 0.0972819f, 0.170262f, 0.0256994f, -0.199153f, 0.120436f, -0.124941f, -0.0445037f, 0.150898f, -0.0594313f, 0.0442631f, -0.0491294f, -0.0957917f, -0.110671f, 0.0988712f, -0.048392f, -0.0406623f, -0.0533968f, 0.156252f, -0.0346959f, 0.207246f, -0.100856f, 0.0313422f, 0.00839215f, 0.193575f, 0.119389f, 0.130725f, -0.0987341f, -0.0147687f, -0.0253771f, 0.0580884f, -0.0174773f, 0.0859878f, 0.00799177f, 0.0880935f, 0.0876891f, -0.011503f, 0.00780917f, -0.174531f, -0.135223f, 0.148092f, -0.127871f, -0.041436f, 0.30698f, -0.127153f, -0.0786735f, -0.113721f, -0.109395f, 0.218485f, 0.0407013f, 0.00677337f, 0.0606415f, -0.0847764f, -0.0283162f, 0.0375975f, 0.235108f, 0.0247004f, -0.0103627f, 0.0494522f, -0.15386f, 0.0450112f, -0.00381239f, 0.057318f, -0.0486842f, 0.0650243f, -0.298533f, -0.216992f, 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-0.00795014f, -0.0579334f, 0.0152749f, 0.0201549f, -0.166492f, -0.105059f, -0.20479f, -0.134313f, 0.0255906f, 0.0594173f, -0.0898327f, -0.0590535f, -0.0108681f, 0.0314731f, -0.168973f, -0.118293f, 0.255022f, -0.0750191f, 0.0726015f, -0.147087f, -0.00603523f, 0.267165f, -0.255247f, -0.0412965f, 0.0288737f, 0.132844f, -0.0326177f, -0.0856135f, -0.2972f, 0.155763f, 0.28698f, 0.0412092f, -0.224144f, -0.210393f, -0.342643f, 0.0719187f, 0.158874f, 0.226267f, -0.133495f, -0.0857504f, 0.0611778f, -0.0589801f, 0.110317f, -0.0190942f, 0.1773f, 0.00961829f, 0.0677457f, -0.0899815f, -0.138899f, -0.05035f, 0.193441f, -0.139859f, -0.270932f, -0.115539f, -0.316846f, 0.075208f, 0.0431652f, 0.146713f, 0.0285186f, 0.10137f, -0.187721f, -0.0891028f, -0.25393f, 0.106163f, -0.165763f, 0.0463147f, 0.194327f, -0.0429842f, -0.112328f, -0.281324f, -0.00156711f, -0.159594f, 0.00456123f, 0.0655478f, 0.136689f, -0.0740155f, -0.0616994f, 0.254053f}; - model->setOperandValue(op62, op62_init, sizeof(float) * 8192); - static float op64_init[] = {1.27791f, -0.896893f, -0.0904288f, -0.367291f, -0.255839f, -0.584457f, 0.359154f, -0.651333f, -0.599677f, 0.35768f, 1.10981f, -0.620459f, 0.21688f, 0.173693f, -0.860731f, 1.62973f, -0.0480904f, 0.931926f, -0.0432689f, -0.649775f, -0.886409f, -0.419683f, 0.974362f, -0.860229f, -0.222268f, -0.290182f, 0.1913f, -0.356828f, 0.557647f, 1.52403f, -0.593978f, -0.517028f, -0.445634f, 0.28466f, -1.16102f, 1.12105f, -0.285626f, -0.286889f, 0.171823f, -0.836316f, -0.472779f, 0.00341682f, -0.00346886f, -0.262715f, -0.808707f, -0.710406f, 0.10792f, 1.15153f, 0.324643f, -0.0285354f, 0.0859959f, -0.209573f, -0.0140537f, -0.0960617f, -0.0413799f, -0.887105f, 1.3194f, -0.265272f, 1.38463f, -0.279711f, 0.0327209f, -0.0295412f, -0.415826f, 0.972281f, -0.574921f, 1.09703f, -0.302285f, -0.317815f, -0.114017f, -0.437542f, 0.0160477f, 1.4915f, -0.568098f, 0.820527f, -0.687973f, -0.1323f, -0.0332663f, 0.576985f, 0.06609f, 0.745705f, 0.191202f, -0.345881f, -0.472346f, -0.329724f, -0.892812f, -0.616852f, -0.00505598f, -0.270389f, -0.216163f, -0.172727f, -0.44142f, 1.19072f, -0.209099f, -0.0503139f, 0.288438f, 1.03214f, -0.344693f, -0.338285f, 1.43376f, 0.0838178f, -0.137882f, 0.0360903f, -0.588352f, 0.0101241f, 1.33968f, -0.325155f, -0.411048f, 1.6552f, -0.629519f, 0.647605f, -0.208642f, -0.518543f, 1.01164f, -0.36279f, 0.24322f, -0.616136f, 0.0506595f, -0.76616f, -0.405525f, -0.381892f, -0.582617f, -0.506614f, -0.0410523f, 0.0965909f, -0.659473f, -1.02532f, -0.325496f, -0.486067f}; - model->setOperandValue(op64, op64_init, sizeof(float) * 128); - static float op65_init[] = {-0.0709381f, -0.128049f, 0.34379f, 0.147408f, -0.1438f, 0.0135833f, -0.759943f, -0.384337f, -0.239562f, -0.0991685f, 0.642744f, 0.277097f, -1.09486f, -0.560633f, 0.216758f, -0.888341f, -0.518082f, -0.279118f, 0.680989f, -0.492512f, 1.2603f, 0.0466598f, -0.213212f, 0.384692f, -0.651653f, -0.169377f, -0.398121f, -0.0391595f, 0.490388f, -0.672391f, 0.0497536f, -0.245464f, -0.625069f, -0.230214f, 0.288063f, 0.0303378f, -0.0401801f, 0.541154f, 0.329236f, 0.0499154f, 0.159806f, 0.941858f, -0.187031f, 0.214894f, -0.468224f, 0.999631f, -0.354627f, -0.161143f, -0.518824f, 0.271098f, -0.106679f, -0.469782f, 0.336682f, 0.260217f, -0.382269f, -0.238424f, -0.15755f, 0.0634591f, 0.0461773f, -0.0368848f, -0.160486f, -0.261701f, -0.0579005f, -0.561818f, 0.943888f, -0.53118f, -0.0525675f, 0.460865f, -0.572441f, -0.170843f, -0.0466553f, -0.474068f, 0.0738518f, 0.185746f, -0.454275f, -0.495732f, 0.141482f, 0.765303f, 0.318613f, -0.232587f, -0.260385f, -0.654987f, 0.504436f, -0.14194f, 0.567181f, 1.0334f, 0.426854f, -0.117148f, -0.415611f, -0.306669f, 0.330773f, -0.446631f, 1.64556f, 0.683654f, 0.378299f, -0.0379948f, 0.180195f, 0.348585f, 0.542005f, -0.91213f, -0.382145f, -0.430299f, 0.180373f, -0.605352f, -0.130889f, 0.00551913f, 0.648318f, -0.570121f, 0.227888f, 0.843207f, -0.187785f, 0.925533f, -0.576444f, 0.103704f, 1.05763f, 0.561724f, -0.31587f, -0.580066f, -0.130743f, 0.293338f, 0.447277f, -0.127014f, -0.395864f, -0.806486f, -0.0919789f, 0.088212f, -0.109243f, 0.410984f, -0.0109117f, 0.960286f, 1.04658f, -0.204002f, 1.37218f, -1.15064f, -0.895014f, -0.170768f, -0.649843f, -0.396972f, 0.0512205f, -0.0992547f, -0.158354f, -0.484801f, 0.289718f, -1.02207f, 1.7189f, -0.359976f, 1.56887f, 0.417944f, 0.597117f, 1.43457f, -0.299231f, 0.623607f, -0.205163f, -0.249012f, -0.339769f, -0.108222f, -0.30356f, -0.575407f, 0.909879f, -0.060081f, 0.586879f, 1.55296f, 0.636148f, -0.746165f, 0.43247f, -0.448612f, 0.747422f, 0.259496f, -1.09887f, -0.0358834f, -0.382366f, -0.792576f, 1.43005f, 0.196588f, 0.652059f, -0.311974f, 0.746497f, -0.582559f, -0.621747f, 0.0114799f, 1.41466f, 0.967424f, -0.608617f, -0.334149f, -0.366489f, 0.381975f, -0.266688f, 0.365394f, -0.747447f, -0.455778f, -1.06527f, -0.739692f, -0.453246f, -0.618083f, -0.452114f, -0.909178f, -0.0974019f, 0.302433f, -0.719995f, -0.414704f, 1.09482f, 0.0623939f, 0.629705f, -0.0790356f, 0.00773394f, -0.081795f, 0.549912f, -0.808324f, -0.682389f, 1.38853f, 0.198479f, 0.276656f, -0.681216f, 1.06903f, 0.893365f, -0.441687f, 1.24876f, 1.8621f, 1.17992f, -0.975346f, 0.920257f, -0.203736f, -0.67214f, -0.254283f, 0.412605f, 0.404941f, -0.255573f, 0.500838f, -0.19859f, -0.767112f, 0.0717921f, 0.433305f, -0.56418f, -0.107743f, 0.159745f, -0.852961f, 0.144353f, -0.362589f, 0.49508f, -0.560953f, -1.14736f, 0.137257f, 0.601483f, -0.614651f, -0.87189f, 1.44979f, -0.833047f, 0.688882f, 0.321005f, 1.0737f, -0.236968f, 0.823725f, 2.28361f, 0.0812771f, -0.0605274f, 0.10736f, -0.194868f, 0.718866f, 0.236371f, -0.347496f, -0.571536f, 0.103581f, -0.129792f, 0.419577f, -0.0259013f, -0.505849f, 0.166899f, 0.347665f, -0.66851f, -0.436971f, 0.345747f, -0.675295f, 0.87038f, -0.331299f, -0.403529f, 0.519759f, 0.262051f, -0.25085f, -0.0725889f, 0.359883f, 0.492467f, -0.0943171f, 0.214194f, -0.0776945f, -0.225263f, 0.610915f, 0.621991f, 0.127142f, -0.120977f, -1.18563f, 0.0533153f, 0.00669916f, -0.278448f, -0.370341f, 0.203951f, -0.0748655f, 0.149499f, 0.608627f, -0.170591f, 0.57339f, -0.300863f, -0.133246f, -0.190121f, -0.07103f, -0.921217f, -0.477982f, 0.291881f, -0.0674137f, -0.152687f, 0.424704f, -0.067885f, 0.579372f, -0.194176f, -0.175602f, -0.0288839f, -0.136824f, -0.858922f, 0.726294f, -0.421512f, -0.0974054f, 0.550395f, -0.162152f, -0.467728f, 0.366064f, -0.167801f, -0.0753653f, -0.198316f, -0.137023f, -0.0627235f, 0.0106006f, 0.592916f, -0.158033f, -0.548551f, -0.235923f, 0.369672f, 1.30827f, -0.750535f, 0.570025f, 0.321352f, 0.202508f, -0.203108f, -0.26941f, -0.212697f, 0.0714036f, 0.551129f, 0.351327f, -0.392821f, 1.19204f, -0.213496f, -0.579066f, -0.0250779f, 0.142676f, 0.49299f, -0.515014f, -0.284034f, -0.0178803f, 0.0809394f, -0.37963f, 0.331529f, 0.046923f, -0.269256f, 0.269847f, 0.00773318f, -0.151664f, -0.076353f, -1.02137f, -0.534778f, -0.22219f, -0.275138f, -0.625355f, -0.633931f, 0.721776f, 0.261691f, -0.362185f, -0.0779834f, -0.0601082f, -0.440214f, 0.445368f, 0.693759f, 0.0917294f, 1.36943f, 0.323218f, -0.107106f, -0.183899f, -0.365459f, -0.275454f, -0.483642f, 0.797942f, -0.909086f, -0.185676f, -0.0226698f, 0.466425f, -0.004201f, -0.4242f, -0.396341f, -0.0352481f, 1.06241f, 1.01883f, 0.561441f, -0.265551f, -0.288297f, -0.46146f, 0.423533f, 0.472996f, 0.77336f, 0.6662f, -0.213503f, 1.01491f, 1.62445f, -0.898291f, 1.11469f, 0.377022f, 0.274644f, -0.714398f, 0.163676f, 0.868197f, 0.342184f, 0.289812f, 0.381599f, 0.059939f, 0.304634f, 0.938513f, 0.436808f, -0.389308f, -0.193604f, 0.394266f, 1.05865f, 1.48062f, 0.719217f, 0.295424f, 0.0187049f, -0.440856f, -0.942464f, -0.923774f, 2.5526f, 0.0839399f, 0.454711f, 0.286152f, -0.0304864f, 0.354859f, -0.253294f, 0.191791f, -0.729741f, -0.44744f, -0.0782481f, -0.39976f, 0.519996f, -0.199241f, 0.0073006f, -0.445146f, -0.531936f, 0.284462f, -0.452162f, 0.0482401f, -0.0619033f, -1.31827f, -0.210492f, -0.121312f, 0.37903f, -0.153958f, 1.04133f, -0.763192f, -0.113485f, -0.558982f, 0.54512f, -0.526908f, 0.867164f, -0.206167f, 0.927759f, -0.558727f, 0.411553f, 0.0124118f, 0.100906f, -0.521091f, 0.698503f, 0.931193f, 0.990837f, 1.33259f, 0.663775f, -0.32597f, 1.0028f, 0.544204f, -0.379747f, -1.08854f, -0.965737f, 0.389885f, 0.92887f, 0.0718976f, -0.184999f, -0.461237f, -0.706826f, 0.243244f, -0.397723f, 0.747435f, 0.161502f, 0.453201f, -0.376094f, -0.0359757f, 0.0353741f, -0.287866f, -0.744642f, 1.22875f, 0.182842f, 0.3801f, 0.773255f, -0.358024f, -0.770755f, -1.34188f, -0.0852203f, 0.139821f, 0.14041f, 1.08967f, -0.905575f, 0.116723f, -0.29376f, 0.163174f, 0.610681f, 2.0524f, 0.195715f, 1.48414f, 1.21102f, -0.442612f, -0.808774f, 0.0675436f, -0.331371f, 0.00445637f, 0.980394f, -0.172441f, -0.905661f, -0.725557f, -1.54261f, 0.711161f, -0.215032f, -0.0153047f, -0.536626f, 0.314412f, -0.106242f, 2.33498f, 0.188198f, -0.344982f, 0.433289f, -1.04402f, 0.299941f, 1.99992f, 0.275313f, 1.44375f, 1.37658f, -0.646558f, 0.732569f, 0.054636f, -0.319402f, 0.584826f, -0.604223f, -0.399064f, 0.0517761f, -1.09654f, 1.61721f, 0.348668f, 1.73956f, -0.327831f, 0.860558f, 0.70006f, -0.346379f, 1.02589f, -0.40399f, 0.0614666f, -0.539451f, 1.44714f, -0.465946f, 1.3293f, -0.556735f, 1.71808f, 0.165918f, -0.579726f, 0.152724f, -0.980241f, 0.621521f, -0.424599f, 0.731271f, -1.13193f, -0.314276f, 0.290947f, 0.129665f, -0.513516f, 0.387313f, -0.135605f, 1.04872f, -0.150452f, 0.45206f, -0.528278f, -0.310732f, -0.135223f, 0.49935f, -0.396638f, 0.683956f, 0.902601f, -0.0316321f, 0.843894f, -0.502775f, 0.91836f, 0.0786264f, 0.341963f, 0.406261f, -1.25424f, -1.48405f, -0.390925f, -1.14482f, -0.674129f, -0.354457f, -0.30644f, -0.56675f, 2.38773f, 0.187012f, 0.906624f, 0.368549f, -0.672948f, -0.628065f, -0.0138419f, 0.532509f, -1.29148f, 0.513439f, 0.807188f, -0.561104f, 1.42839f, -0.0811011f, -0.367103f, -0.70638f, 0.047558f, 0.0499724f, 1.03425f, 1.2915f, 1.10444f, -0.155478f, 0.697564f, 0.00560132f, 1.15162f, -0.771271f, 1.88617f, -1.6177f, 0.603189f, -0.192907f, 0.343646f, 0.259441f, 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0.95778f, -0.165669f, 1.5066f, 0.302125f, -0.273887f, 1.69497f, 0.338063f, 0.180147f, 0.159032f, 0.100377f, -0.0110788f, -0.126068f, -0.0102687f, 0.4543f, -0.00557386f, -0.916492f, -0.550707f, 0.288461f, 0.103516f, 0.0799136f, 1.15876f, 2.40522f, -0.836259f, 0.160002f, 0.0725624f, -0.527644f, 1.76999f, -0.540915f, 0.374064f, -0.427791f, -0.357631f, -0.197245f, -0.566252f, 0.976098f, 2.39025f, -0.266651f, -0.445977f, 0.993438f, 0.0851502f, 0.252378f, -0.44096f, -0.672909f, 0.337162f, 1.64552f, -0.474397f, 0.00330378f, -0.0926167f, -0.563775f, 2.11952f, 0.548761f, -0.407726f, -0.383753f, -0.448106f, -0.322102f, 1.00177f, 0.579059f, 1.57776f, 0.340912f, 0.108392f, -0.420985f, 0.940979f, 0.536696f, 1.49649f, 0.63357f, 1.61203f, 0.0379834f, 0.0595333f, -0.568936f, 0.341707f, -0.198204f, 0.332623f, -0.530509f, -0.0811962f, -0.67568f, 0.813611f, -0.868695f, -0.455278f, -0.22f, 1.01564f, -0.890534f, -0.0985721f, 0.914227f, -0.819383f, -0.600254f, -0.886954f, 0.072988f, -0.260885f, -0.0702166f, 0.625261f, -0.0558828f, -0.0617894f, 0.292336f, -0.523875f, 0.0229542f, 1.58632f, -0.231722f, -0.139989f, 0.823556f, -0.43111f, 1.19132f, 1.29603f, -0.630984f, 1.36702f, -0.130364f, 1.80865f, 0.669308f, -0.461492f, 0.927909f, -0.114352f, 0.079242f, 1.59835f, -0.632411f, 1.06819f, -0.45636f, 0.716334f, 0.00625565f, 0.214278f, 0.207884f, -1.21746f, -0.149759f, 3.24622f, -0.399609f, 0.0437373f, 0.689106f, -0.0589484f, -0.334215f, 0.836515f, -0.508468f, 0.157755f, -0.194959f, -0.397927f, 0.583678f, -0.512436f, -0.125795f, 0.0111994f, 0.0851677f, 0.328478f, 0.573699f, -0.31069f, -0.236373f, -0.2399f, 0.0487571f, 0.0334681f, -0.107831f, -0.0429967f, -0.158088f, 0.455646f, 0.463307f, -0.35324f, 0.245849f, -0.193381f, -0.0638097f, 0.28329f, -0.0610263f, -0.209242f, -0.0422409f, -0.175848f, -0.540178f, 0.0882148f, -0.444746f, -0.121683f, 0.331229f, 0.937666f, -0.338491f, -0.121416f, -0.435662f, -0.00479998f, -0.0466618f, -0.557701f, 0.13406f, 0.030059f, -0.253511f, -0.111068f, 0.187428f, 0.0376202f, 0.407873f, -0.0685584f, -0.194871f, -0.235567f, 0.0541845f, -0.5765f, -0.490645f, 0.538366f, -0.106681f, -0.0181633f, 0.239972f, -0.1993f, 0.619089f, 0.492641f, 0.0914387f, 0.767015f, 0.470372f, 0.334601f, -0.380493f, 0.0277119f, -0.371967f, -0.486814f, 1.78414f, -0.712404f, 0.549763f, -0.0734719f, -0.502121f, 0.501455f, 0.624837f, -0.400229f, 0.471742f, -0.420348f, 0.234315f, -0.249998f, 0.10115f, -0.504935f, -0.0442974f, 0.221133f, -0.773235f, 0.127533f, -0.677374f, -0.0106931f, 0.163513f, -0.0414988f, -0.70847f, -0.106571f, -0.402491f, 0.256679f, 0.185025f, 0.0496665f, 1.18988f, -0.527038f, -0.0258471f, 0.777272f, 0.191414f, -0.0571052f, -0.107474f, 0.799273f, 0.351308f, 0.583249f, 1.368f, 0.452056f, 0.142411f, 0.450808f, -0.0474152f, 0.232903f, -0.00997257f, -0.278714f, -0.0901303f, 0.300765f, 0.282296f, 0.503486f, 0.566803f, -0.187164f, 0.22737f}; - model->setOperandValue(op65, op65_init, sizeof(float) * 1152); - static float op67_init[] = {-0.305398f, -0.685788f, -0.525249f, 0.301772f, 0.289891f, 0.630685f, -0.209829f, -0.79012f, 0.24649f, -0.608516f, -0.563271f, 0.0645603f, 1.55794f, 1.00103f, 0.880371f, -0.118016f, -0.0790257f, 0.956679f, 0.278375f, -0.625173f, -0.0493856f, 0.87827f, 0.253075f, 0.0546886f, 0.355621f, 0.178734f, 0.478416f, 0.0492583f, 0.293355f, 0.543042f, -0.724245f, 0.990581f, 1.46284f, 0.446866f, -0.79063f, 0.652788f, 0.597898f, 0.917416f, 0.0871194f, -0.284282f, 0.324214f, -0.1572f, 0.0399736f, 0.769288f, -0.960233f, -0.00885646f, -0.643283f, 0.721671f, 1.19157f, -0.721341f, -0.236599f, -0.149836f, 0.213326f, -0.0532691f, 1.27426f, 0.226731f, 0.77594f, 1.29236f, 0.792703f, 0.532026f, 0.153868f, -1.30736f, -0.16838f, 0.0649575f, -0.211678f, -0.0951798f, 0.215633f, -0.501761f, 0.512893f, 0.134215f, -0.124359f, 1.09245f, 0.668202f, -0.251388f, 0.558093f, -0.0925646f, 0.167238f, 0.572327f, -0.754583f, 0.831167f, -0.152979f, 0.445553f, -0.23448f, -0.120027f, 1.47701f, 0.761829f, 0.721299f, -0.123061f, 1.02031f, 0.206746f, 0.605342f, 0.272333f, -0.313723f, -0.415643f, 0.867801f, 0.0943753f, 0.267981f, 0.182397f, -0.311686f, -0.0201961f, 0.135552f, -0.89852f, -0.38088f, 0.504533f, 0.409304f, 0.0599384f, 0.271603f, -0.153014f, 0.395172f, 0.659796f, 0.518529f, 0.550892f, -0.396646f, -1.35361f, 0.502179f, 0.0585412f, 0.658f, -0.330476f, 0.361273f, 0.474181f, 0.626912f, 0.657814f, 0.380077f, 0.90833f, -0.168524f, -0.049237f, 0.177989f, -0.447172f}; - model->setOperandValue(op67, op67_init, sizeof(float) * 128); - static float op68_init[] = {-0.0241622f, 0.0617369f, 0.00313696f, -0.0361745f, 0.0089011f, -0.0771454f, -0.0184935f, 0.0252865f, -0.0486987f, -0.0406999f, -0.0799738f, -0.0141762f, 0.00437186f, -0.0573011f, -0.00957927f, 0.046172f, 0.0570609f, 0.00471992f, 0.0971188f, 0.0576618f, 0.103793f, 0.0197602f, 0.122559f, -0.00102334f, -0.0784559f, 0.155006f, -0.111748f, 0.101551f, 0.0827965f, -0.1318f, 0.0495612f, -0.115198f, 0.15614f, 0.101033f, 0.0604104f, -0.209218f, 0.0122279f, 0.105561f, -0.0860308f, -0.0634802f, -0.0399273f, -0.015346f, -0.0853545f, -0.0609623f, 0.0339712f, 0.0696919f, 0.0744721f, -0.00465823f, -0.146284f, 0.104007f, 0.0193562f, 0.00289296f, 0.0756617f, -0.0586377f, 0.00066429f, -0.0160939f, -0.157602f, 0.105187f, 0.0278241f, 0.156465f, 0.0953226f, -0.0423266f, 0.00946264f, 0.0967963f, -0.0094248f, 0.1983f, -0.0606185f, 0.0422851f, -0.0475956f, -0.209028f, -0.11849f, 0.0630176f, -0.0602975f, 0.0253162f, -0.152982f, -0.186771f, 0.122462f, -0.0890817f, 0.0114347f, 0.0382302f, -0.0130611f, 0.0985855f, 0.106371f, -0.0704771f, 0.13717f, -0.125649f, 0.0763975f, -0.0448528f, -0.116665f, -0.0059271f, 0.0596776f, -0.175521f, 0.320232f, 0.262752f, -0.0392024f, -0.145799f, 0.024413f, -0.104194f, 0.0707348f, 0.108472f, 0.139796f, 0.0529258f, 0.0417071f, -0.0210183f, 0.0914108f, -0.0426374f, 0.00167011f, 0.00624434f, 0.0219409f, -0.029013f, 0.0858393f, -0.0919931f, 0.0904368f, 0.0253718f, 0.00171692f, -0.111899f, 0.0305154f, 0.00299299f, -0.039191f, -0.0160044f, 0.0847733f, 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-0.158577f, 0.0884011f, -0.119132f, -0.109142f, 0.218508f, -0.0235336f, -0.115687f, -0.0622109f, -0.0530095f, -0.00247333f, -0.0040968f, 0.467918f, 0.0968093f, -0.0822504f, -0.283234f, 0.0335439f, 0.190095f, 0.0845923f, 0.203457f, -0.179509f, -0.0774762f, -0.174176f, -0.0256955f, -0.0675088f, 0.0378122f, 0.01191f, -0.00550909f, -0.0197953f, 0.0619587f, -0.010253f, 0.126631f, 0.250306f, 0.0288037f, 0.0906605f, 0.0615545f, 0.079101f, -0.180439f, 0.192944f, 0.0781765f, -0.0304704f, -0.0804992f, -0.214004f, -0.100559f, -0.0197503f, -0.0635601f, 0.0344422f, 0.206626f, -0.113922f, 0.167333f, 0.042957f, 0.072754f, 0.0165848f, 0.161062f, 0.131324f, -0.0806729f, -0.241418f, 0.0882478f, 0.0560616f, 0.11652f, 0.0387292f, -0.210602f, -0.0811962f, 0.00595869f, 0.0906331f, -0.0257964f, 0.0262241f, -0.0583798f, -0.168545f, -0.0824084f, -0.0540559f, 0.055865f, -0.14463f, 0.0536092f, 0.00479177f, -0.0521163f, 0.114327f, -0.087216f, 0.12057f, 0.0343528f, -0.159405f, -0.0428211f, -0.00926862f, 0.204067f, -0.0982124f, -0.00597636f, 0.0857122f, -0.0434118f, 0.0350282f, 0.0122245f, 0.0143983f, -0.065527f, -0.00821878f, -0.0869701f, -0.142122f, 0.0224319f, -0.115208f, -0.00811133f, -0.0997508f, -0.0214029f, -0.0734632f, -0.0765375f, 0.124415f, -0.0181815f, 0.121771f, 0.0431451f, -0.114346f, 0.0580838f, -0.166955f, -0.108603f, -0.0180719f, 0.0187707f, 0.142396f, 0.125052f, 0.0445839f, -0.0869693f, 0.100758f, 0.0827412f, -0.0503467f, 0.0750536f, -0.107036f, -0.0859819f, 0.0949795f, -0.00923993f, 0.137961f, -0.0385899f, -0.0261251f, 0.0364985f, 0.147038f, 0.096688f, -0.036745f, 0.209835f, 0.217248f, -0.149919f, 0.00545082f}; - model->setOperandValue(op68, op68_init, sizeof(float) * 16384); - static float op70_init[] = {-0.66364f, 0.0184158f, 0.52283f, -0.355377f, 1.1477f, -0.584167f, -0.148238f, -0.370158f, -0.863358f, -0.920222f, -0.232067f, -0.392408f, -0.370706f, -0.582042f, 1.07807f, -0.739755f, -0.868499f, 0.849338f, 1.00954f, -0.559628f, -1.00847f, -0.363234f, -0.777592f, -0.361464f, -0.524833f, -0.51576f, 1.09786f, -0.602967f, -0.460839f, -0.0461734f, -0.640552f, -0.550366f, 0.0366958f, 1.41672f, -0.0482828f, 1.01145f, -0.661914f, -0.476727f, -0.420703f, -0.333822f, -0.474296f, -0.325794f, -0.37077f, -0.183918f, 0.7784f, -0.905561f, 1.04277f, 1.13302f, -0.611757f, -0.454366f, 1.04722f, 0.213438f, -0.812226f, 0.415241f, 0.134023f, 0.0181792f, -0.725888f, -0.667944f, 0.14743f, 1.38813f, -0.758548f, -0.0950601f, -0.110067f, 1.08892f, 1.25227f, -0.65893f, -0.258646f, 1.1129f, 0.785756f, -0.573376f, -0.562164f, 0.648412f, -0.650372f, -0.198207f, 1.32062f, -0.446544f, -0.375616f, 0.832923f, 0.515747f, 1.4061f, 0.967074f, -0.545499f, -0.0706069f, 0.594321f, -0.645749f, -0.528063f, 1.13031f, -0.572182f, -0.127073f, -0.797284f, -0.702862f, -0.0707006f, -0.615686f, -0.52278f, 0.253001f, 1.1845f, -0.387047f, -0.0304347f, 0.380325f, -0.40854f, -0.549549f, -0.513802f, -0.813182f, -0.697618f, 0.235504f, -0.408502f, 1.44695f, -0.782359f, 0.764666f, -0.376841f, -0.307161f, -0.436902f, -0.439395f, -0.00343463f, -0.705699f, -0.557165f, -0.443319f, 1.1431f, -0.503291f, -0.611081f, -0.120243f, 1.15355f, -0.372295f, -0.474172f, 1.23777f, -0.441865f, -0.00485877f, -0.540569f}; - model->setOperandValue(op70, op70_init, sizeof(float) * 128); - static float op71_init[] = {0.190358f, 0.76666f, 0.00328711f, 0.564753f, -0.350922f, -0.129309f, -0.3002f, 0.0597919f, 0.426195f, 0.396956f, -0.0503945f, -0.0959898f, 0.7998f, 0.20007f, -0.470447f, -0.0882118f, 0.387503f, -0.154394f, -0.527967f, -0.382423f, 0.439986f, -0.26216f, 0.675393f, 0.600835f, 0.710669f, -0.144166f, -0.623441f, -0.25641f, 0.146244f, 0.0956447f, 0.530404f, -0.0558362f, -0.228722f, -0.615698f, -0.232535f, -0.188218f, 0.463525f, 0.208408f, 0.202256f, -0.459553f, 0.290275f, -0.0482571f, 0.0479941f, -0.480289f, -0.359739f, 0.29264f, -0.189077f, -0.101516f, -0.327849f, 0.0132966f, -0.478556f, -0.667986f, -0.00429771f, -0.395361f, 0.591137f, 0.438846f, 0.270845f, 0.22185f, -0.114037f, -0.242702f, 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0.0106714f, -0.591842f, 0.596678f, 0.11119f, -0.274552f, -0.201105f, 0.444937f, 0.952961f, -0.507004f, 1.01494f, 0.77933f, 0.279006f, 0.0737236f, 1.11775f, -0.398997f, 0.227235f, -0.399368f, -0.51299f, -0.615961f, 0.245222f, 0.489889f, 0.159064f, 0.326207f, 0.364678f, 0.381169f, -0.593003f, -0.0868912f, 0.131847f, 0.0749275f, -0.197579f, -0.111212f, 0.0223139f, 0.243874f, 0.156878f, 0.190403f, 0.450317f, -0.150603f, -0.246352f, -0.128152f, 0.115508f, 0.493985f, -0.222931f, 0.452042f, -0.242511f, -0.215531f, -0.15448f, 0.105351f, 0.219143f, -0.0308257f, 0.486673f, -0.283853f, -0.163485f, -0.274629f, 0.247168f, 0.465471f, -0.047011f, 0.130996f, 0.5783f, -0.236574f, -1.03715f, 0.12173f, -0.257977f, -0.102828f, -0.165848f, -0.0104392f, 0.378396f, 0.273506f, 0.113921f, 0.078316f, -0.405008f, -0.0091235f, -0.0771289f, -0.43577f, 0.373244f, -0.396689f, -0.118428f, -0.124698f, -0.163262f, -0.539655f, -0.487564f, 0.0727633f, -0.102938f, -0.415128f, -0.222664f, 0.920234f, -0.177296f, -0.515815f, 0.0770235f, -0.37286f, -0.401904f, 0.0224006f, 0.350639f, 0.262495f, 0.375316f, 0.00518776f, -0.373577f, 0.0238316f, -0.171652f, -0.54584f, 0.200506f, -0.166763f, -0.17752f, 0.22408f, -0.335509f, 0.249634f, 0.101166f, -0.149211f, 0.298221f, -0.316278f, -0.167293f, -0.34354f, -0.166304f, 0.156108f, -0.197453f, -0.0979077f, -0.00206543f, 0.0643555f, 0.253949f, 0.857866f, 0.426089f, -0.101682f, -0.214549f, 0.202225f, -0.379808f, 0.291689f, -0.0552537f, 0.638515f, 0.0758169f, -0.254243f, -0.176746f, 0.909293f, -0.06176f, -0.185409f, -0.000147814f, -0.122751f, 0.411986f, -0.229916f, -0.231603f, 0.0380824f, -0.0611861f, 0.171022f, 0.391192f, 0.192886f}; - model->setOperandValue(op71, op71_init, sizeof(float) * 1152); - static float op73_init[] = {-0.459464f, -0.0353652f, 0.65298f, 0.492282f, -0.434366f, -0.0646238f, 0.168169f, 0.841487f, 0.821593f, 0.0824923f, 0.324534f, 0.244733f, 0.687792f, 0.679983f, 1.20542f, -0.0494685f, 0.580938f, -0.125653f, 0.979836f, 0.518563f, -0.230283f, 0.0469813f, 0.293084f, -0.718912f, -1.0622f, 0.113178f, -0.0678228f, 0.916626f, 0.0892698f, 0.474777f, 0.352559f, -0.518444f, 0.298361f, 0.152684f, 1.02351f, 0.858021f, -0.138064f, 0.432028f, 0.262955f, 0.410308f, -0.175447f, 0.437843f, 0.290894f, -0.251734f, -0.156011f, 0.395701f, 0.683667f, 0.13276f, 0.649738f, -0.0994671f, 0.862117f, 0.679516f, 0.626916f, 0.591512f, 0.349128f, 0.647349f, 0.321387f, -0.39079f, 0.130351f, 0.373571f, 0.0105721f, 0.139855f, 0.288337f, 0.00182344f, 0.492347f, 0.395679f, 0.063744f, 0.591242f, -0.105116f, 0.568728f, 0.893553f, 0.830799f, -0.786496f, 0.308447f, -0.304133f, 0.713135f, -0.664118f, 0.0611865f, 0.317401f, -0.209737f, -0.0484893f, 0.404629f, 0.56469f, -0.461555f, 0.163955f, 0.376575f, 0.965699f, -0.0480532f, 0.186419f, 1.13773f, 0.266542f, 0.402838f, -0.423273f, -0.647047f, 1.55817f, -0.330907f, 0.103328f, 0.505363f, 0.156278f, 0.3413f, 0.792374f, -0.115844f, -0.365503f, -0.418147f, -0.402983f, 0.228505f, 0.70761f, 0.260362f, 0.703803f, 0.584213f, -0.312215f, 0.22878f, 0.916701f, -0.740095f, 0.555479f, 0.60881f, 0.638773f, 0.308882f, 0.515022f, 0.0501348f, -0.0876003f, 0.527447f, -0.310391f, 0.354625f, 0.119797f, 0.541051f, -0.176547f, -0.32494f}; - model->setOperandValue(op73, op73_init, sizeof(float) * 128); - static float op74_init[] = {0.181329f, -0.0840758f, -0.0933245f, 0.0235837f, 0.0135975f, 0.326603f, -0.0139708f, -0.112846f, 0.181431f, 0.300608f, 0.0366979f, -0.00359797f, 0.0227877f, 0.00927314f, -0.18318f, 0.0122945f, 0.124818f, 0.00987374f, 0.156316f, -0.289415f, 0.10349f, 0.0364732f, 0.0231318f, 0.0581194f, -0.100904f, 0.13513f, -0.0616679f, 0.08791f, -0.0899955f, -0.0814202f, 0.0770271f, -0.0352028f, 0.0505355f, 0.0157475f, -0.174024f, 0.0321904f, 0.0789286f, 0.0553437f, -0.0721803f, -0.124891f, -0.0141911f, -0.0998818f, 0.0907457f, -0.0333116f, -0.0323912f, 0.0260731f, 0.0414896f, -0.0498239f, 0.0463238f, -0.0758002f, -0.0464296f, 0.036594f, 0.218559f, -0.021053f, -0.10772f, 0.148092f, 0.00719467f, 0.166783f, 0.202187f, 0.00202994f, -0.0944071f, 0.091252f, -0.0095911f, 0.280841f, -0.134233f, 0.0669713f, 0.0791813f, 0.167962f, 0.0732689f, 0.0647677f, 0.021687f, -0.0849598f, -0.0170565f, -0.0668448f, 0.0414721f, 0.0164082f, 0.128536f, 0.0603419f, 0.111924f, 0.0195282f, -0.0824856f, -0.00289862f, -0.0404369f, 0.00626191f, -0.0131989f, -0.0766574f, 0.0125038f, -0.107742f, -0.171477f, -0.0210237f, 0.0715486f, -0.160486f, 0.0299287f, -0.00204968f, -0.0410816f, 0.130856f, 0.0253766f, 0.0401149f, 0.205151f, -0.319885f, -0.10841f, -0.0544789f, 0.161881f, 0.00546593f, 0.125587f, 0.0206626f, 0.0800018f, -0.135352f, -0.00646288f, -0.0362635f, -0.0392136f, -0.0451879f, -0.00607048f, -0.0221049f, -0.153833f, -0.180066f, 0.0460595f, 0.0781421f, 0.155008f, 0.175934f, -0.0677201f, 0.0495859f, -0.124263f, -0.0609894f, 0.0250959f, 0.0826162f, -0.0430149f, -0.0212291f, -0.0417825f, 0.0418202f, -0.0399981f, 0.0225818f, 0.038297f, -0.0687589f, 0.0434539f, 0.0139929f, -0.144466f, 0.0586925f, 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-0.158048f, 0.108506f, 0.0246635f, 0.0169756f, 0.10831f, 0.026912f, -0.00203766f, 0.0312686f, 0.0894737f, 0.0162477f, -0.0618999f, -0.0265796f, 0.114227f, -0.15638f, -0.0510761f, -0.0377173f, 0.0226881f, 0.0418164f, -0.0366621f, -0.035809f, -0.0266003f, 0.0467199f, 0.171896f, -0.0271323f, -0.0163066f, 0.028144f, 0.0436655f, 0.128942f, -0.0194696f, -0.105502f, 0.0681745f, -0.0502478f, 0.0555929f, -0.05233f, -0.0198393f, 0.132248f, 0.126557f, 0.00828652f, -0.090131f, -0.0212319f, -0.0250166f, -0.0118503f, -0.0875313f, 0.0768847f, 0.0485072f, -0.019315f, 0.284054f, 0.100281f, 0.108431f, -0.0830946f, -0.023745f, 0.0625827f, -0.0561233f, 0.0932393f, 0.0275348f, -0.1307f, -0.0600125f, -0.0451818f, -0.102182f, 0.165792f, -0.130795f, -0.0328623f, -0.241256f, -0.1522f, 0.13973f, -0.0225733f, -0.0121984f, -0.0512724f, 0.0964481f, -0.112301f, -0.147525f, 0.258343f, 0.0169823f, 0.038229f, 0.0942557f, -0.0375277f, 0.0529383f, 0.0917328f, -0.106428f, -0.00183135f, -0.0535075f, 0.011453f, 0.0899797f, -0.00914698f, 0.0447383f, 0.0399944f}; - model->setOperandValue(op74, op74_init, sizeof(float) * 16384); - static float op76_init[] = {1.2227f, 0.664689f, -0.697727f, -0.607951f, 1.29794f, -0.073003f, -0.811291f, 1.23139f, 1.26409f, -0.384498f, 1.08346f, -0.668803f, -0.435764f, 1.3942f, -0.371175f, 1.2419f, 1.24256f, -0.621671f, 0.0760495f, -0.765654f, 0.196979f, 0.214335f, -0.58529f, 1.2814f, -0.120833f, -0.844234f, -0.455902f, -0.965084f, -0.586658f, 1.11645f, 1.24002f, -0.565542f, -0.540129f, 1.22517f, -0.544301f, -0.591493f, 0.403981f, -0.113213f, -0.0999279f, -0.508504f, -0.361916f, -0.666335f, 1.19214f, 0.180251f, -0.793999f, -0.0519839f, 0.243307f, 0.488556f, -0.5879f, -0.68376f, 1.20553f, 0.855387f, 1.32818f, -0.668094f, -0.432518f, -0.730011f, -0.825747f, 1.08466f, -0.689586f, -0.46422f, -0.608787f, -0.436489f, -0.52064f, -0.699725f, 0.262406f, -0.617448f, -0.73687f, -0.799998f, -0.0571513f, 0.0468143f, 0.826328f, -0.361061f, -0.636886f, 1.059f, -0.588544f, 1.1137f, 0.737585f, -0.896787f, -0.209556f, 0.201041f, -0.0373591f, -0.49788f, 1.02235f, -0.42106f, -0.61662f, -0.0805732f, -0.500747f, 0.549504f, -0.0802444f, -0.517048f, -0.975228f, -0.374565f, -0.310484f, 1.02079f, -0.465238f, 0.877757f, -0.00396147f, -0.467624f, -0.467418f, -0.672751f, -0.255117f, -0.619892f, -0.743825f, 1.06921f, -0.667405f, 0.997503f, -0.877923f, -0.697879f, 0.385421f, -0.496918f, 0.223972f, 1.11902f, -1.05091f, 0.614654f, 0.849332f, -0.454338f, -0.412169f, -0.688757f, -0.551979f, -0.0854241f, -0.195676f, -0.542179f, -0.701308f, -0.446594f, -0.435718f, -0.867095f, 1.29558f, -0.85324f}; - model->setOperandValue(op76, op76_init, sizeof(float) * 128); - static float op77_init[] = {-0.232485f, 0.750123f, 0.158505f, 0.452319f, -0.340535f, 0.304917f, 0.601414f, -0.213319f, -0.527085f, 0.51019f, -0.266959f, 0.367842f, -0.206907f, -0.323959f, -0.159575f, -0.177844f, -0.178491f, -0.143209f, -0.744888f, 0.171007f, -0.056294f, 0.0066854f, -0.0159956f, -0.338152f, 0.595454f, 0.0494067f, -0.242471f, 0.395647f, -0.0994157f, -0.1077f, -0.690943f, 0.298448f, -0.0125066f, 0.189733f, -0.257466f, -0.0827938f, -0.480399f, -0.300462f, -0.335141f, 0.0223836f, 0.550395f, 0.0419619f, 0.226111f, -0.176909f, 0.127377f, -0.0545993f, -0.387069f, -0.768176f, -0.0824907f, 0.0591839f, -0.0952336f, -0.355136f, -0.498724f, 0.0494056f, 0.0379881f, 0.225825f, 0.173812f, -0.544722f, -0.133118f, -0.0791841f, 0.0940735f, -0.135813f, 0.381779f, 0.150541f, -0.218683f, 0.0192335f, 0.0824092f, 0.0248153f, 0.767578f, 0.0404393f, -0.870468f, 0.278391f, 0.431434f, 0.153796f, -0.129333f, -0.532381f, -0.253057f, 0.631776f, -0.252957f, -1.17293f, 0.684977f, 0.0593484f, -0.090116f, -0.0440624f, 0.625979f, 0.408575f, -0.305075f, 0.37687f, -0.216258f, 0.425133f, 0.202026f, 0.114214f, 0.476932f, -0.630176f, 0.873207f, 0.075089f, -0.0424989f, 0.788774f, 0.667274f, -0.0367483f, -0.0493914f, 0.00828958f, 0.226763f, -0.303594f, 0.32115f, -0.65989f, -0.061986f, 0.0610993f, -0.73698f, -0.268586f, -0.0016077f, 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0.278372f, -0.508936f, 0.510272f, -0.358697f, 0.450372f, 0.326299f, -0.564039f, 0.649753f, 0.255396f, 0.226893f, 0.193944f, -0.0455373f, -0.637073f, -0.406078f, 0.247549f, 0.2822f, 0.461711f, 0.0955357f, -0.261276f, 0.548638f, -0.0557388f, 0.182164f, 0.551088f, -0.131262f, -0.192677f, 0.743369f, 0.496859f, -0.110602f, 0.665807f, -0.341119f, -0.574006f, 0.253317f, -0.0412391f, -0.187952f, -0.549984f, -0.380044f, -0.30571f, 0.0611864f, 0.245165f, 0.221235f, 0.508958f, -0.151121f, 0.397989f, 0.00208155f, -0.154384f, -0.084145f, -0.459448f, 0.254966f, -0.01605f, 0.613941f, -0.0170916f, 0.208544f, -0.182594f, -0.0684111f, 0.189469f, -0.00860068f, -0.331824f, -0.523698f, -0.0657233f, -0.252375f, 0.612957f, 0.0379591f, 0.209828f, 0.297172f, -0.402651f, -0.174693f, -0.138906f, 0.395919f, -0.200965f, -0.550715f, -0.529917f, 0.181403f, -0.457216f, 0.688771f, 0.73705f, 0.317982f, -0.128821f, -0.48089f, 0.612106f, -0.172449f, -0.450366f, 0.796766f, 0.665507f, 0.813123f, 0.555636f, -0.362653f, -0.316043f, -0.17264f, -0.229654f}; - model->setOperandValue(op77, op77_init, sizeof(float) * 1152); - static float op79_init[] = {-0.108669f, -0.24239f, 0.892824f, 0.100496f, -0.175517f, -0.0614264f, 0.896133f, -0.142274f, -0.0590312f, 0.125f, 0.864931f, -0.283458f, -0.13665f, -0.0803576f, -0.146452f, -0.195702f, -0.582552f, -0.339264f, -1.01993f, 0.447734f, -0.917057f, 0.225414f, -0.306775f, 0.594087f, 0.556081f, -0.327338f, -0.0957925f, -0.309899f, 0.650205f, 0.348621f, 0.255781f, 0.371079f, 0.915888f, 0.978555f, 0.799558f, -0.32679f, -0.427624f, -0.0823039f, 0.369625f, -0.57637f, -0.0263745f, -0.282986f, 0.635218f, 0.378849f, -0.155068f, 0.707911f, 0.0484146f, -0.726708f, -0.212411f, 0.0321696f, 0.0862673f, -0.0752373f, -0.778846f, -0.0985055f, 0.452711f, 0.608385f, 0.616601f, -0.33512f, 0.409252f, 0.778583f, 0.181552f, -0.955649f, -0.456805f, 0.0249269f, 0.30359f, 0.823123f, 0.711778f, 0.600027f, -0.352327f, 0.512934f, 0.34219f, -0.29652f, 0.505236f, 0.175734f, 0.874702f, -0.275469f, 0.580716f, 0.635559f, -0.546206f, 0.457047f, 0.461714f, 0.346941f, 0.637959f, -0.0761546f, 0.555644f, 0.542373f, 0.292184f, 0.784725f, -0.502565f, 0.582605f, -0.178634f, -0.627065f, -0.08808f, -0.801258f, 0.225673f, 0.672628f, 0.596107f, -0.159289f, -0.652513f, 0.538379f, -0.145736f, 0.317523f, 0.695517f, 1.03021f, -0.495469f, -0.164966f, 0.740315f, -0.159693f, 0.504333f, 0.386047f, -0.121875f, -0.222074f, 0.59883f, -0.149101f, -0.71422f, 0.54467f, 0.694151f, -0.447638f, 0.090233f, -0.220637f, 0.724413f, 0.366921f, 0.136273f, 0.615985f, 0.185512f, 0.0515735f, -0.342775f, 0.584152f}; - model->setOperandValue(op79, op79_init, sizeof(float) * 128); - static float op80_init[] = {0.143542f, -0.139317f, -0.0557127f, 0.00193462f, -0.0166539f, 0.216436f, 0.0694379f, 0.0774276f, -0.061387f, 0.193228f, -0.0234282f, 0.0303868f, -0.10475f, 0.225842f, -0.0953071f, 0.0518284f, -0.177148f, 0.0706816f, -0.116163f, 0.23116f, 0.0276252f, 0.361075f, 0.114999f, -0.179934f, 0.0362276f, 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0.0289976f, 0.125323f, -0.053071f, 0.0550674f, -0.0443678f, -0.0726763f, -0.0886086f, -0.116334f, -0.0209192f, 0.063885f, 0.077968f, -0.104398f, -0.179266f, -0.194832f, -0.0639122f, -0.0518158f, 0.256032f, -0.108148f, -0.137127f, -0.104448f, 0.033398f, -0.0646876f, 0.0763196f, -0.0495609f, -0.100906f, -0.134571f, -0.161793f, 0.153519f, 0.149706f, -0.222311f, 0.0359835f, -0.00287598f, -0.160517f, 0.00736621f, 0.039841f, -0.000592701f, -0.056578f, 0.131453f, 0.0138361f, -0.134636f, -0.06908f, -0.0854272f, 0.0779619f, -0.0139243f, -0.0182492f, -0.0930189f, -0.00353997f, 0.0685898f, -0.0102112f, 0.0606511f, 0.063219f, -0.0267228f, 0.00053972f, -0.181391f, 0.0220468f, 0.202779f, 0.228042f, -0.191374f, -0.0386773f, -0.0833115f, 0.132745f, -0.0764369f, -0.0667909f, 0.0471235f, -0.0951605f, 0.0251998f, 0.109606f, -0.0551212f, 0.0423476f, -0.0914923f, 0.104139f, -0.166891f, -0.144794f, 0.00263478f, 0.0549706f, -0.0192015f, -0.094959f, -0.0470626f}; - model->setOperandValue(op80, op80_init, sizeof(float) * 16384); - static float op83_init[] = {-0.0293549f, -0.0835053f, -0.228576f, -0.0980885f, -0.0370638f, -0.195879f, -0.0452258f, 0.0371641f, -0.0585841f, -0.0984154f, -0.141293f}; - model->setOperandValue(op83, op83_init, sizeof(float) * 11); - static float op84_init[] = {0.0984852f, 0.672424f, 0.392549f, -0.262182f, 0.303914f, -0.0118188f, 0.027943f, 0.0164078f, 0.200583f, -0.808626f, 0.234772f, -0.0253635f, -0.198519f, -0.176824f, -0.580674f, 0.0681573f, -0.0134279f, 0.172173f, -0.284882f, -0.0895141f, 0.0142356f, -0.0479431f, 0.0736678f, 0.00298977f, 0.152355f, -0.0370715f, 0.463201f, 0.0146613f, 0.0971624f, -0.0791196f, 0.556621f, -0.00950762f, 0.0160531f, 0.091037f, 0.376353f, -0.0996081f, -0.0418334f, -0.427482f, -0.202679f, -0.197079f, 0.021873f, -0.105617f, 0.36447f, 0.389277f, 0.0429815f, 0.0480496f, -0.170086f, -0.191548f, -0.237921f, 0.155838f, -0.100796f, 0.0539355f, 0.103154f, 0.0441985f, -0.20672f, 0.358565f, -0.105794f, -0.635905f, 0.193301f, 0.112419f, 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-0.133913f, 0.165726f, -0.176839f, -0.0940447f, -0.145421f, -0.0471074f, 0.00950449f, 0.0308656f, -0.00761046f, -0.19397f, -0.161623f, 0.10975f, -0.0398157f, 0.00168868f, 0.0626417f, -0.118388f, -0.134741f, -0.243707f, 0.146451f, -0.165854f, 0.0585878f, 0.0269307f, 0.163195f, -0.197056f, 0.0438799f, -0.152668f, -0.178631f, -0.167278f, 0.0258257f, -0.22958f, -0.101918f, 0.0360034f, -0.165612f, -0.112482f, -0.419959f, -0.369384f, 0.0468117f, 0.202511f, 0.161559f, 0.0360435f, -0.211843f, 0.0480519f, -0.252478f, -0.0951382f, 0.100791f, -0.379245f, -0.129869f, -0.036501f, 0.0685223f, 0.0247177f, -0.0751386f, -0.12451f, 0.244585f, -0.0103249f, -0.346383f, -0.300614f, 0.230366f, -0.187795f, -0.0326416f, 0.0735751f, -0.0136039f, -0.0219528f, 0.0629145f, -0.0308739f, -0.101514f, -0.169444f, 0.058706f, -0.133274f, -0.200294f, -0.372511f, -0.214898f, -0.184366f, 0.253648f, -0.0362453f, 0.0618937f, 0.0838244f, -0.0386255f, 0.129191f, -0.147435f, -0.180809f, -0.0797491f, -0.286544f, -0.273005f, 0.116222f, -0.255255f, -0.0504643f, -0.0567216f, -0.0204081f, 0.206331f, -0.225266f, -0.211665f, -0.259216f, -0.0676753f, -0.176153f, 0.285802f, -0.00560349f, -0.0253936f, -0.182537f, -0.344487f, -0.341246f, -0.171879f, 0.24462f, 0.015354f, -0.0255803f, -0.0855239f, -0.151488f, -0.0329621f, 0.311794f, 0.0889872f, -0.142655f, -0.00124048f, 0.0175189f, 0.0459686f, 0.279491f, -0.237445f, 0.0570048f, -0.00665275f, -0.0558817f, 0.0731352f, 0.0291331f, 0.0918153f, 0.0276626f, -0.135103f, -0.303909f, 0.0283329f, -0.203482f, -0.0849922f, -0.284485f, -0.214908f, 0.0836636f, -0.219738f, 0.136157f, 0.0332432f, -0.143305f, 0.0283252f, -0.178703f, -0.0742534f, -0.153174f, 0.02235f, -0.0753622f, -0.210102f, -0.0915751f, -0.0189732f, -0.239039f, -0.135349f, -0.104589f, -0.0658414f, -0.183206f, -0.123006f, 0.0835748f, -0.0703047f, -0.207461f, -0.274129f, -0.225327f, -0.113485f, 0.13316f, 0.0295303f, -0.0958281f}; - model->setOperandValue(op84, op84_init, sizeof(float) * 2816); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op86, op2, op1, b87, b88, b89, b90}, {op0}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op0, op29, op28, b91, b92, b93, b94, b95}, {op27}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op27, op32, op31, b96, b97, b98, b99}, {op30}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op30, op35, op34, b100, b101, b102, b103, b104}, {op33}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op33, op38, op37, b105, b106, b107, b108}, {op36}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op36, op41, op40, b109, b110, b111, b112, b113}, {op39}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op39, op44, op43, b114, b115, b116, b117}, {op42}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op42, op47, op46, b118, b119, b120, b121, b122}, {op45}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op45, op50, op49, b123, b124, b125, b126}, {op48}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op48, op53, op52, b127, b128, b129, b130, b131}, {op51}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op51, op56, op55, b132, b133, b134, b135}, {op54}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op54, op59, op58, b136, b137, b138, b139, b140}, {op57}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op57, op62, op61, b141, b142, b143, b144}, {op60}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op60, op65, op64, b145, b146, b147, b148, b149}, {op63}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op63, op68, op67, b150, b151, b152, b153}, {op66}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op66, op71, op70, b154, b155, b156, b157, b158}, {op69}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op69, op74, op73, b159, b160, b161, b162}, {op72}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op72, op77, op76, b163, b164, b165, b166, b167}, {op75}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op75, op80, op79, b168, b169, b170, b171}, {op78}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op78, op5, op4, b172, b173, b174, b175, b176}, {op3}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op3, op8, op7, b177, b178, b179, b180}, {op6}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op6, op11, op10, b181, b182, b183, b184, b185}, {op9}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op9, op14, op13, b186, b187, b188, b189}, {op12}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op12, op17, op16, b190, b191, b192, b193, b194}, {op15}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op15, op20, op19, b195, b196, b197, b198}, {op18}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op18, op23, op22, b199, b200, b201, b202, b203}, {op21}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op21, op26, op25, b204, b205, b206, b207}, {op24}); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op24, b208, b209, b210, b211, b212, b213}, {op81}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op81, op84, op83, b214, b215, b216, b217}, {op82}); - model->addOperation(ANEURALNETWORKS_LOGISTIC, {op82}, {op85}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op86}, - {op85}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mobilenet_quantized.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mobilenet_quantized.model.cpp deleted file mode 100644 index 0f7692336..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mobilenet_quantized.model.cpp +++ /dev/null @@ -1,719 +0,0 @@ -// Generated file (from: mobilenet_quantized.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::FLOAT32, {}); - OperandType type0(Type::INT32, {}); - OperandType type70(Type::TENSOR_INT32, {1001}, 0.000139550233143, 0); - OperandType type20(Type::TENSOR_INT32, {1024}, 0.000338657468092, 0); - OperandType type24(Type::TENSOR_INT32, {1024}, 0.000470138824312, 0); - OperandType type22(Type::TENSOR_INT32, {1024}, 0.00115174765233, 0); - OperandType type39(Type::TENSOR_INT32, {128}, 0.000212795624975, 0); - OperandType type42(Type::TENSOR_INT32, {128}, 0.000274334714049, 0); - OperandType type35(Type::TENSOR_INT32, {128}, 0.00031740454142, 0); - OperandType type37(Type::TENSOR_INT32, {128}, 0.00105069368146, 0); - OperandType type49(Type::TENSOR_INT32, {256}, 0.000161647229106, 0); - OperandType type52(Type::TENSOR_INT32, {256}, 0.000237506290432, 0); - OperandType type45(Type::TENSOR_INT32, {256}, 0.000254691782175, 0); - OperandType type47(Type::TENSOR_INT32, {256}, 0.000832252670079, 0); - OperandType type73(Type::TENSOR_INT32, {2}, 0, 0); - OperandType type5(Type::TENSOR_INT32, {32}, 0.000220113914111, 0); - OperandType type26(Type::TENSOR_INT32, {32}, 0.0132417660207, 0); - OperandType type66(Type::TENSOR_INT32, {512}, 0.000138872928801, 0); - OperandType type58(Type::TENSOR_INT32, {512}, 0.000161869538715, 0); - OperandType type10(Type::TENSOR_INT32, {512}, 0.000162604788784, 0); - OperandType type17(Type::TENSOR_INT32, {512}, 0.0001660654234, 0); - OperandType type54(Type::TENSOR_INT32, {512}, 0.000187459911103, 0); - OperandType type14(Type::TENSOR_INT32, {512}, 0.000200755792321, 0); - OperandType type62(Type::TENSOR_INT32, {512}, 0.000229732482694, 0); - OperandType type12(Type::TENSOR_INT32, {512}, 0.000354939606041, 0); - OperandType type64(Type::TENSOR_INT32, {512}, 0.000396122573875, 0); - OperandType type8(Type::TENSOR_INT32, {512}, 0.000443405413534, 0); - OperandType type56(Type::TENSOR_INT32, {512}, 0.000600085710175, 0); - OperandType type60(Type::TENSOR_INT32, {512}, 0.000628261826932, 0); - OperandType type29(Type::TENSOR_INT32, {64}, 0.000249237316893, 0); - OperandType type32(Type::TENSOR_INT32, {64}, 0.00194196344819, 0); - OperandType type69(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1001}, 0.14927123487, 69); - OperandType type68(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1024}, 0.023528477177, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 1001}, 0.00390625, 0); - OperandType type72(Type::TENSOR_QUANT8_ASYMM, {1, 1001}, 0.14927123487, 69); - OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 112, 112, 32}, 0.023528477177, 0); - OperandType type28(Type::TENSOR_QUANT8_ASYMM, {1, 112, 112, 64}, 0.023528477177, 0); - OperandType type51(Type::TENSOR_QUANT8_ASYMM, {1, 14, 14, 256}, 0.023528477177, 0); - OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 14, 14, 512}, 0.023528477177, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 224, 224, 3}, 0.0078125, 128); - OperandType type41(Type::TENSOR_QUANT8_ASYMM, {1, 28, 28, 128}, 0.023528477177, 0); - OperandType type44(Type::TENSOR_QUANT8_ASYMM, {1, 28, 28, 256}, 0.023528477177, 0); - OperandType type23(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1024}, 0.0489512197673, 174); - OperandType type43(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 128}, 0.0116596883163, 139); - OperandType type38(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 128}, 0.0446562543511, 115); - OperandType type53(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 256}, 0.0100944191217, 116); - OperandType type48(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 256}, 0.0353721417487, 71); - OperandType type27(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 32}, 0.562797427177, 49); - OperandType type18(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.00705806072801, 111); - OperandType type13(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.0150855332613, 132); - OperandType type65(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.0168358776718, 132); - OperandType type9(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.0188454780728, 140); - OperandType type57(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.025504656136, 118); - OperandType type61(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 512}, 0.026702189818, 106); - OperandType type33(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 64}, 0.08253672719, 46); - OperandType type34(Type::TENSOR_QUANT8_ASYMM, {1, 56, 56, 128}, 0.023528477177, 0); - OperandType type31(Type::TENSOR_QUANT8_ASYMM, {1, 56, 56, 64}, 0.023528477177, 0); - OperandType type19(Type::TENSOR_QUANT8_ASYMM, {1, 7, 7, 1024}, 0.023528477177, 0); - OperandType type16(Type::TENSOR_QUANT8_ASYMM, {1, 7, 7, 512}, 0.023528477177, 0); - OperandType type71(Type::TENSOR_QUANT8_ASYMM, {1001, 1, 1, 1024}, 0.00593112036586, 66); - OperandType type25(Type::TENSOR_QUANT8_ASYMM, {1024, 1, 1, 1024}, 0.0199816934764, 126); - OperandType type21(Type::TENSOR_QUANT8_ASYMM, {1024, 1, 1, 512}, 0.0143935140222, 139); - OperandType type40(Type::TENSOR_QUANT8_ASYMM, {128, 1, 1, 128}, 0.00904417410493, 122); - OperandType type36(Type::TENSOR_QUANT8_ASYMM, {128, 1, 1, 64}, 0.0134902289137, 81); - OperandType type46(Type::TENSOR_QUANT8_ASYMM, {256, 1, 1, 128}, 0.0108248302713, 123); - OperandType type50(Type::TENSOR_QUANT8_ASYMM, {256, 1, 1, 256}, 0.0068702804856, 124); - OperandType type6(Type::TENSOR_QUANT8_ASYMM, {32, 3, 3, 3}, 0.0281745810062, 112); - OperandType type55(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 256}, 0.00796736311167, 146); - OperandType type67(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 512}, 0.0059023341164, 116); - OperandType type59(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 512}, 0.00687972875312, 110); - OperandType type11(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 512}, 0.00691097788513, 136); - OperandType type15(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 512}, 0.00853246077895, 115); - OperandType type63(Type::TENSOR_QUANT8_ASYMM, {512, 1, 1, 512}, 0.00976401846856, 160); - OperandType type30(Type::TENSOR_QUANT8_ASYMM, {64, 1, 1, 32}, 0.0105930073187, 138); - // Phase 1, operands - auto b104 = model->addOperand(&type0); - auto b105 = model->addOperand(&type0); - auto b106 = model->addOperand(&type0); - auto b107 = model->addOperand(&type0); - auto b108 = model->addOperand(&type0); - auto b109 = model->addOperand(&type0); - auto b110 = model->addOperand(&type0); - auto b111 = model->addOperand(&type0); - auto b112 = model->addOperand(&type0); - auto b113 = model->addOperand(&type0); - auto b114 = model->addOperand(&type0); - auto b115 = model->addOperand(&type0); - auto b116 = model->addOperand(&type0); - auto b117 = model->addOperand(&type0); - auto b118 = model->addOperand(&type0); - auto b119 = model->addOperand(&type0); - auto b120 = model->addOperand(&type0); - auto b121 = model->addOperand(&type0); - auto b122 = model->addOperand(&type0); - auto b123 = model->addOperand(&type0); - auto b124 = model->addOperand(&type0); - auto b125 = model->addOperand(&type0); - auto b126 = model->addOperand(&type0); - auto b127 = model->addOperand(&type0); - auto b128 = model->addOperand(&type0); - auto b129 = model->addOperand(&type0); - auto b130 = model->addOperand(&type0); - auto b131 = model->addOperand(&type0); - auto b132 = model->addOperand(&type0); - auto b133 = model->addOperand(&type0); - auto b134 = model->addOperand(&type0); - auto b135 = model->addOperand(&type0); - auto b136 = model->addOperand(&type0); - auto b137 = model->addOperand(&type0); - auto b138 = model->addOperand(&type0); - auto b139 = model->addOperand(&type0); - auto b140 = model->addOperand(&type0); - auto b141 = model->addOperand(&type0); - auto b142 = model->addOperand(&type0); - auto b143 = model->addOperand(&type0); - auto b144 = model->addOperand(&type0); - auto b145 = model->addOperand(&type0); - auto b146 = model->addOperand(&type0); - auto b147 = model->addOperand(&type0); - auto b148 = model->addOperand(&type0); - auto b149 = model->addOperand(&type0); - auto b150 = model->addOperand(&type0); - auto b151 = model->addOperand(&type0); - auto b152 = model->addOperand(&type0); - auto b153 = model->addOperand(&type0); - auto b154 = model->addOperand(&type0); - auto b155 = model->addOperand(&type0); - auto b156 = model->addOperand(&type0); - auto b157 = model->addOperand(&type0); - auto b158 = model->addOperand(&type0); - auto b159 = model->addOperand(&type0); - auto b160 = model->addOperand(&type0); - auto b161 = model->addOperand(&type0); - auto b162 = model->addOperand(&type0); - auto b163 = model->addOperand(&type0); - auto b164 = model->addOperand(&type0); - auto b165 = model->addOperand(&type0); - auto b166 = model->addOperand(&type0); - auto b167 = model->addOperand(&type0); - auto b168 = model->addOperand(&type0); - auto b169 = model->addOperand(&type0); - auto b170 = model->addOperand(&type0); - auto b171 = model->addOperand(&type0); - auto b172 = model->addOperand(&type0); - auto b173 = model->addOperand(&type0); - auto b174 = model->addOperand(&type0); - auto b175 = model->addOperand(&type0); - auto b176 = model->addOperand(&type0); - auto b177 = model->addOperand(&type0); - auto b178 = model->addOperand(&type0); - auto b179 = model->addOperand(&type0); - auto b180 = model->addOperand(&type0); - auto b181 = model->addOperand(&type0); - auto b182 = model->addOperand(&type0); - auto b183 = model->addOperand(&type0); - auto b184 = model->addOperand(&type0); - auto b185 = model->addOperand(&type0); - auto b186 = model->addOperand(&type0); - auto b187 = model->addOperand(&type0); - auto b188 = model->addOperand(&type0); - auto b189 = model->addOperand(&type0); - auto b190 = model->addOperand(&type0); - auto b191 = model->addOperand(&type0); - auto b192 = model->addOperand(&type0); - auto b193 = model->addOperand(&type0); - auto b194 = model->addOperand(&type0); - auto b195 = model->addOperand(&type0); - auto b196 = model->addOperand(&type0); - auto b197 = model->addOperand(&type0); - auto b198 = model->addOperand(&type0); - auto b199 = model->addOperand(&type0); - auto b200 = model->addOperand(&type0); - auto b201 = model->addOperand(&type0); - auto b202 = model->addOperand(&type0); - auto b203 = model->addOperand(&type0); - auto b204 = model->addOperand(&type0); - auto b205 = model->addOperand(&type0); - auto b206 = model->addOperand(&type0); - auto b207 = model->addOperand(&type0); - auto b208 = model->addOperand(&type0); - auto b209 = model->addOperand(&type0); - auto b210 = model->addOperand(&type0); - auto b211 = model->addOperand(&type0); - auto b212 = model->addOperand(&type0); - auto b213 = model->addOperand(&type0); - auto b214 = model->addOperand(&type0); - auto b215 = model->addOperand(&type0); - auto b216 = model->addOperand(&type0); - auto b217 = model->addOperand(&type0); - auto b218 = model->addOperand(&type0); - auto b219 = model->addOperand(&type0); - auto b220 = model->addOperand(&type0); - auto b221 = model->addOperand(&type0); - auto b222 = model->addOperand(&type0); - auto b223 = model->addOperand(&type0); - auto b224 = model->addOperand(&type0); - auto b225 = model->addOperand(&type0); - auto b226 = model->addOperand(&type0); - auto b227 = model->addOperand(&type0); - auto b228 = model->addOperand(&type0); - auto b229 = model->addOperand(&type0); - auto b230 = model->addOperand(&type0); - auto b231 = model->addOperand(&type0); - auto b232 = model->addOperand(&type0); - auto b233 = model->addOperand(&type0); - auto b234 = model->addOperand(&type0); - auto b235 = model->addOperand(&type1); - auto op88 = model->addOperand(&type2); - auto op85 = model->addOperand(&type3); - auto op0 = model->addOperand(&type4); - auto op1 = model->addOperand(&type5); - auto op2 = model->addOperand(&type6); - auto op3 = model->addOperand(&type7); - auto op4 = model->addOperand(&type8); - auto op5 = model->addOperand(&type9); - auto op6 = model->addOperand(&type7); - auto op7 = model->addOperand(&type10); - auto op8 = model->addOperand(&type11); - auto op9 = model->addOperand(&type7); - auto op10 = model->addOperand(&type12); - auto op11 = model->addOperand(&type13); - auto op12 = model->addOperand(&type7); - auto op13 = model->addOperand(&type14); - auto op14 = model->addOperand(&type15); - auto op15 = model->addOperand(&type16); - auto op16 = model->addOperand(&type17); - auto op17 = model->addOperand(&type18); - auto op18 = model->addOperand(&type19); - auto op19 = model->addOperand(&type20); - auto op20 = model->addOperand(&type21); - auto op21 = model->addOperand(&type19); - auto op22 = model->addOperand(&type22); - auto op23 = model->addOperand(&type23); - auto op24 = model->addOperand(&type19); - auto op25 = model->addOperand(&type24); - auto op26 = model->addOperand(&type25); - auto op27 = model->addOperand(&type4); - auto op28 = model->addOperand(&type26); - auto op29 = model->addOperand(&type27); - auto op30 = model->addOperand(&type28); - auto op31 = model->addOperand(&type29); - auto op32 = model->addOperand(&type30); - auto op33 = model->addOperand(&type31); - auto op34 = model->addOperand(&type32); - auto op35 = model->addOperand(&type33); - auto op36 = model->addOperand(&type34); - auto op37 = model->addOperand(&type35); - auto op38 = model->addOperand(&type36); - auto op39 = model->addOperand(&type34); - auto op40 = model->addOperand(&type37); - auto op41 = model->addOperand(&type38); - auto op42 = model->addOperand(&type34); - auto op43 = model->addOperand(&type39); - auto op44 = model->addOperand(&type40); - auto op45 = model->addOperand(&type41); - auto op46 = model->addOperand(&type42); - auto op47 = model->addOperand(&type43); - auto op48 = model->addOperand(&type44); - auto op49 = model->addOperand(&type45); - auto op50 = model->addOperand(&type46); - auto op51 = model->addOperand(&type44); - auto op52 = model->addOperand(&type47); - auto op53 = model->addOperand(&type48); - auto op54 = model->addOperand(&type44); - auto op55 = model->addOperand(&type49); - auto op56 = model->addOperand(&type50); - auto op57 = model->addOperand(&type51); - auto op58 = model->addOperand(&type52); - auto op59 = model->addOperand(&type53); - auto op60 = model->addOperand(&type7); - auto op61 = model->addOperand(&type54); - auto op62 = model->addOperand(&type55); - auto op63 = model->addOperand(&type7); - auto op64 = model->addOperand(&type56); - auto op65 = model->addOperand(&type57); - auto op66 = model->addOperand(&type7); - auto op67 = model->addOperand(&type58); - auto op68 = model->addOperand(&type59); - auto op69 = model->addOperand(&type7); - auto op70 = model->addOperand(&type60); - auto op71 = model->addOperand(&type61); - auto op72 = model->addOperand(&type7); - auto op73 = model->addOperand(&type62); - auto op74 = model->addOperand(&type63); - auto op75 = model->addOperand(&type7); - auto op76 = model->addOperand(&type64); - auto op77 = model->addOperand(&type65); - auto op78 = model->addOperand(&type7); - auto op79 = model->addOperand(&type66); - auto op80 = model->addOperand(&type67); - auto op81 = model->addOperand(&type68); - auto op82 = model->addOperand(&type69); - auto op83 = model->addOperand(&type70); - auto op84 = model->addOperand(&type71); - auto op86 = model->addOperand(&type72); - auto op87 = model->addOperand(&type73); - // Phase 2, operations - static int32_t b104_init[] = {1}; - model->setOperandValue(b104, b104_init, sizeof(int32_t) * 1); - static int32_t b105_init[] = {2}; - model->setOperandValue(b105, b105_init, sizeof(int32_t) * 1); - static int32_t b106_init[] = {2}; - model->setOperandValue(b106, b106_init, sizeof(int32_t) * 1); - static int32_t b107_init[] = {0}; - model->setOperandValue(b107, b107_init, sizeof(int32_t) * 1); - static int32_t b108_init[] = {1}; - model->setOperandValue(b108, b108_init, sizeof(int32_t) * 1); - static int32_t b109_init[] = {1}; - model->setOperandValue(b109, b109_init, sizeof(int32_t) * 1); - static int32_t b110_init[] = {1}; - model->setOperandValue(b110, b110_init, sizeof(int32_t) * 1); - static int32_t b111_init[] = {1}; - model->setOperandValue(b111, b111_init, sizeof(int32_t) * 1); - static int32_t b112_init[] = {0}; - model->setOperandValue(b112, b112_init, sizeof(int32_t) * 1); - static int32_t b113_init[] = {1}; - model->setOperandValue(b113, b113_init, sizeof(int32_t) * 1); - static int32_t b114_init[] = {1}; - model->setOperandValue(b114, b114_init, sizeof(int32_t) * 1); - static int32_t b115_init[] = {1}; - model->setOperandValue(b115, b115_init, sizeof(int32_t) * 1); - static int32_t b116_init[] = {0}; - model->setOperandValue(b116, b116_init, sizeof(int32_t) * 1); - static int32_t b117_init[] = {1}; - model->setOperandValue(b117, b117_init, sizeof(int32_t) * 1); - static int32_t b118_init[] = {2}; - model->setOperandValue(b118, b118_init, sizeof(int32_t) * 1); - static int32_t b119_init[] = {2}; - model->setOperandValue(b119, b119_init, sizeof(int32_t) * 1); - static int32_t b120_init[] = {1}; - model->setOperandValue(b120, b120_init, sizeof(int32_t) * 1); - static int32_t b121_init[] = {0}; - model->setOperandValue(b121, b121_init, sizeof(int32_t) * 1); - static int32_t b122_init[] = {1}; - model->setOperandValue(b122, b122_init, sizeof(int32_t) * 1); - static int32_t b123_init[] = {1}; - model->setOperandValue(b123, b123_init, sizeof(int32_t) * 1); - static int32_t b124_init[] = {1}; - model->setOperandValue(b124, b124_init, sizeof(int32_t) * 1); - static int32_t b125_init[] = {0}; - model->setOperandValue(b125, b125_init, sizeof(int32_t) * 1); - static int32_t b126_init[] = {1}; - model->setOperandValue(b126, b126_init, sizeof(int32_t) * 1); - static int32_t b127_init[] = {1}; - model->setOperandValue(b127, b127_init, sizeof(int32_t) * 1); - static int32_t b128_init[] = {1}; - model->setOperandValue(b128, b128_init, sizeof(int32_t) * 1); - static int32_t b129_init[] = {1}; - model->setOperandValue(b129, b129_init, sizeof(int32_t) * 1); - static int32_t b130_init[] = {0}; - model->setOperandValue(b130, b130_init, sizeof(int32_t) * 1); - static int32_t b131_init[] = {1}; - model->setOperandValue(b131, b131_init, sizeof(int32_t) * 1); - static int32_t b132_init[] = {1}; - model->setOperandValue(b132, b132_init, sizeof(int32_t) * 1); - static int32_t b133_init[] = {1}; - model->setOperandValue(b133, b133_init, sizeof(int32_t) * 1); - static int32_t b134_init[] = {0}; - model->setOperandValue(b134, b134_init, sizeof(int32_t) * 1); - static int32_t b135_init[] = {1}; - model->setOperandValue(b135, b135_init, sizeof(int32_t) * 1); - static int32_t b136_init[] = {2}; - model->setOperandValue(b136, b136_init, sizeof(int32_t) * 1); - static int32_t b137_init[] = {2}; - model->setOperandValue(b137, b137_init, sizeof(int32_t) * 1); - static int32_t b138_init[] = {1}; - model->setOperandValue(b138, b138_init, sizeof(int32_t) * 1); - static int32_t b139_init[] = {0}; - model->setOperandValue(b139, b139_init, sizeof(int32_t) * 1); - static int32_t b140_init[] = {1}; - model->setOperandValue(b140, b140_init, sizeof(int32_t) * 1); - static int32_t b141_init[] = {1}; - model->setOperandValue(b141, b141_init, sizeof(int32_t) * 1); - static int32_t b142_init[] = {1}; - model->setOperandValue(b142, b142_init, sizeof(int32_t) * 1); - static int32_t b143_init[] = {0}; - model->setOperandValue(b143, b143_init, sizeof(int32_t) * 1); - static int32_t b144_init[] = {1}; - model->setOperandValue(b144, b144_init, sizeof(int32_t) * 1); - static int32_t b145_init[] = {1}; - model->setOperandValue(b145, b145_init, sizeof(int32_t) * 1); - static int32_t b146_init[] = {1}; - model->setOperandValue(b146, b146_init, sizeof(int32_t) * 1); - static int32_t b147_init[] = {1}; - model->setOperandValue(b147, b147_init, sizeof(int32_t) * 1); - static int32_t b148_init[] = {0}; - model->setOperandValue(b148, b148_init, sizeof(int32_t) * 1); - static int32_t b149_init[] = {1}; - model->setOperandValue(b149, b149_init, sizeof(int32_t) * 1); - static int32_t b150_init[] = {1}; - model->setOperandValue(b150, b150_init, sizeof(int32_t) * 1); - static int32_t b151_init[] = {1}; - model->setOperandValue(b151, b151_init, sizeof(int32_t) * 1); - static int32_t b152_init[] = {0}; - model->setOperandValue(b152, b152_init, sizeof(int32_t) * 1); - static int32_t b153_init[] = {1}; - model->setOperandValue(b153, b153_init, sizeof(int32_t) * 1); - static int32_t b154_init[] = {2}; - model->setOperandValue(b154, b154_init, sizeof(int32_t) * 1); - static int32_t b155_init[] = {2}; - model->setOperandValue(b155, b155_init, sizeof(int32_t) * 1); - static int32_t b156_init[] = {1}; - model->setOperandValue(b156, b156_init, sizeof(int32_t) * 1); - static int32_t b157_init[] = {0}; - model->setOperandValue(b157, b157_init, sizeof(int32_t) * 1); - static int32_t b158_init[] = {1}; - model->setOperandValue(b158, b158_init, sizeof(int32_t) * 1); - static int32_t b159_init[] = {1}; - model->setOperandValue(b159, b159_init, sizeof(int32_t) * 1); - static int32_t b160_init[] = {1}; - model->setOperandValue(b160, b160_init, sizeof(int32_t) * 1); - static int32_t b161_init[] = {0}; - model->setOperandValue(b161, b161_init, sizeof(int32_t) * 1); - static int32_t b162_init[] = {1}; - model->setOperandValue(b162, b162_init, sizeof(int32_t) * 1); - static int32_t b163_init[] = {1}; - model->setOperandValue(b163, b163_init, sizeof(int32_t) * 1); - static int32_t b164_init[] = {1}; - model->setOperandValue(b164, b164_init, sizeof(int32_t) * 1); - static int32_t b165_init[] = {1}; - model->setOperandValue(b165, b165_init, sizeof(int32_t) * 1); - static int32_t b166_init[] = {0}; - model->setOperandValue(b166, b166_init, sizeof(int32_t) * 1); - static int32_t b167_init[] = {1}; - model->setOperandValue(b167, b167_init, sizeof(int32_t) * 1); - static int32_t b168_init[] = {1}; - model->setOperandValue(b168, b168_init, sizeof(int32_t) * 1); - static int32_t b169_init[] = {1}; - model->setOperandValue(b169, b169_init, sizeof(int32_t) * 1); - static int32_t b170_init[] = {0}; - model->setOperandValue(b170, b170_init, sizeof(int32_t) * 1); - static int32_t b171_init[] = {1}; - model->setOperandValue(b171, b171_init, sizeof(int32_t) * 1); - static int32_t b172_init[] = {1}; - model->setOperandValue(b172, b172_init, sizeof(int32_t) * 1); - static int32_t b173_init[] = {1}; - model->setOperandValue(b173, b173_init, sizeof(int32_t) * 1); - static int32_t b174_init[] = {1}; - model->setOperandValue(b174, b174_init, sizeof(int32_t) * 1); - static int32_t b175_init[] = {0}; - model->setOperandValue(b175, b175_init, sizeof(int32_t) * 1); - static int32_t b176_init[] = {1}; - model->setOperandValue(b176, b176_init, sizeof(int32_t) * 1); - static int32_t b177_init[] = {1}; - model->setOperandValue(b177, b177_init, sizeof(int32_t) * 1); - static int32_t b178_init[] = {1}; - model->setOperandValue(b178, b178_init, sizeof(int32_t) * 1); - static int32_t b179_init[] = {0}; - model->setOperandValue(b179, b179_init, sizeof(int32_t) * 1); - static int32_t b180_init[] = {1}; - model->setOperandValue(b180, b180_init, sizeof(int32_t) * 1); - static int32_t b181_init[] = {1}; - model->setOperandValue(b181, b181_init, sizeof(int32_t) * 1); - static int32_t b182_init[] = {1}; - model->setOperandValue(b182, b182_init, sizeof(int32_t) * 1); - static int32_t b183_init[] = {1}; - model->setOperandValue(b183, b183_init, sizeof(int32_t) * 1); - static int32_t b184_init[] = {0}; - model->setOperandValue(b184, b184_init, sizeof(int32_t) * 1); - static int32_t b185_init[] = {1}; - model->setOperandValue(b185, b185_init, sizeof(int32_t) * 1); - static int32_t b186_init[] = {1}; - model->setOperandValue(b186, b186_init, sizeof(int32_t) * 1); - static int32_t b187_init[] = {1}; - model->setOperandValue(b187, b187_init, sizeof(int32_t) * 1); - static int32_t b188_init[] = {0}; - model->setOperandValue(b188, b188_init, sizeof(int32_t) * 1); - static int32_t b189_init[] = {1}; - model->setOperandValue(b189, b189_init, sizeof(int32_t) * 1); - static int32_t b190_init[] = {1}; - model->setOperandValue(b190, b190_init, sizeof(int32_t) * 1); - static int32_t b191_init[] = {1}; - model->setOperandValue(b191, b191_init, sizeof(int32_t) * 1); - static int32_t b192_init[] = {1}; - model->setOperandValue(b192, b192_init, sizeof(int32_t) * 1); - static int32_t b193_init[] = {0}; - model->setOperandValue(b193, b193_init, sizeof(int32_t) * 1); - static int32_t b194_init[] = {1}; - model->setOperandValue(b194, b194_init, sizeof(int32_t) * 1); - static int32_t b195_init[] = {1}; - model->setOperandValue(b195, b195_init, sizeof(int32_t) * 1); - static int32_t b196_init[] = {1}; - model->setOperandValue(b196, b196_init, sizeof(int32_t) * 1); - static int32_t b197_init[] = {0}; - model->setOperandValue(b197, b197_init, sizeof(int32_t) * 1); - static int32_t b198_init[] = {1}; - model->setOperandValue(b198, b198_init, sizeof(int32_t) * 1); - static int32_t b199_init[] = {1}; - model->setOperandValue(b199, b199_init, sizeof(int32_t) * 1); - static int32_t b200_init[] = {1}; - model->setOperandValue(b200, b200_init, sizeof(int32_t) * 1); - static int32_t b201_init[] = {1}; - model->setOperandValue(b201, b201_init, sizeof(int32_t) * 1); - static int32_t b202_init[] = {0}; - model->setOperandValue(b202, b202_init, sizeof(int32_t) * 1); - static int32_t b203_init[] = {1}; - model->setOperandValue(b203, b203_init, sizeof(int32_t) * 1); - static int32_t b204_init[] = {1}; - model->setOperandValue(b204, b204_init, sizeof(int32_t) * 1); - static int32_t b205_init[] = {1}; - model->setOperandValue(b205, b205_init, sizeof(int32_t) * 1); - static int32_t b206_init[] = {0}; - model->setOperandValue(b206, b206_init, sizeof(int32_t) * 1); - static int32_t b207_init[] = {1}; - model->setOperandValue(b207, b207_init, sizeof(int32_t) * 1); - static int32_t b208_init[] = {2}; - model->setOperandValue(b208, b208_init, sizeof(int32_t) * 1); - static int32_t b209_init[] = {2}; - model->setOperandValue(b209, b209_init, sizeof(int32_t) * 1); - static int32_t b210_init[] = {1}; - model->setOperandValue(b210, b210_init, sizeof(int32_t) * 1); - static int32_t b211_init[] = {0}; - model->setOperandValue(b211, b211_init, sizeof(int32_t) * 1); - static int32_t b212_init[] = {1}; - model->setOperandValue(b212, b212_init, sizeof(int32_t) * 1); - static int32_t b213_init[] = {1}; - model->setOperandValue(b213, b213_init, sizeof(int32_t) * 1); - static int32_t b214_init[] = {1}; - model->setOperandValue(b214, b214_init, sizeof(int32_t) * 1); - static int32_t b215_init[] = {0}; - model->setOperandValue(b215, b215_init, sizeof(int32_t) * 1); - static int32_t b216_init[] = {1}; - model->setOperandValue(b216, b216_init, sizeof(int32_t) * 1); - static int32_t b217_init[] = {1}; - model->setOperandValue(b217, b217_init, sizeof(int32_t) * 1); - static int32_t b218_init[] = {1}; - model->setOperandValue(b218, b218_init, sizeof(int32_t) * 1); - static int32_t b219_init[] = {1}; - model->setOperandValue(b219, b219_init, sizeof(int32_t) * 1); - static int32_t b220_init[] = {0}; - model->setOperandValue(b220, b220_init, sizeof(int32_t) * 1); - static int32_t b221_init[] = {1}; - model->setOperandValue(b221, b221_init, sizeof(int32_t) * 1); - static int32_t b222_init[] = {1}; - model->setOperandValue(b222, b222_init, sizeof(int32_t) * 1); - static int32_t b223_init[] = {1}; - model->setOperandValue(b223, b223_init, sizeof(int32_t) * 1); - static int32_t b224_init[] = {0}; - model->setOperandValue(b224, b224_init, sizeof(int32_t) * 1); - static int32_t b225_init[] = {2}; - model->setOperandValue(b225, b225_init, sizeof(int32_t) * 1); - static int32_t b226_init[] = {2}; - model->setOperandValue(b226, b226_init, sizeof(int32_t) * 1); - static int32_t b227_init[] = {2}; - model->setOperandValue(b227, b227_init, sizeof(int32_t) * 1); - static int32_t b228_init[] = {7}; - model->setOperandValue(b228, b228_init, sizeof(int32_t) * 1); - static int32_t b229_init[] = {7}; - model->setOperandValue(b229, b229_init, sizeof(int32_t) * 1); - static int32_t b230_init[] = {0}; - model->setOperandValue(b230, b230_init, sizeof(int32_t) * 1); - static int32_t b231_init[] = {1}; - model->setOperandValue(b231, b231_init, sizeof(int32_t) * 1); - static int32_t b232_init[] = {1}; - model->setOperandValue(b232, b232_init, sizeof(int32_t) * 1); - static int32_t b233_init[] = {1}; - model->setOperandValue(b233, b233_init, sizeof(int32_t) * 1); - static int32_t b234_init[] = {0}; - model->setOperandValue(b234, b234_init, sizeof(int32_t) * 1); - static float b235_init[] = {1.0f}; - model->setOperandValue(b235, b235_init, sizeof(float) * 1); - static int32_t op1_init[] = {18729, 13775, 11414, -1774, 12820, 13621, -5657, 12176, 7969, 11393, -2189, 8677, 13409, 13221, 15620, -12188, 1593, 10103, 84, 12516, 13811, 14050, 4619, -494, 12001, 12980, 1348, 1523, 13151, 12435, 12940, 10961}; - model->setOperandValue(op1, op1_init, sizeof(int32_t) * 32); - static uint8_t op2_init[] = {95, 32, 209, 125, 53, 193, 154, 58, 160, 16, 0, 255, 58, 35, 247, 100, 45, 198, 86, 37, 216, 79, 47, 238, 85, 37, 212, 120, 117, 112, 106, 105, 110, 56, 57, 88, 118, 110, 111, 135, 138, 124, 112, 116, 118, 102, 101, 106, 112, 110, 113, 123, 127, 118, 109, 102, 106, 164, 173, 138, 111, 110, 110, 104, 102, 106, 111, 111, 105, 93, 96, 106, 113, 113, 113, 110, 108, 109, 120, 121, 122, 129, 130, 126, 117, 116, 112, 114, 115, 107, 98, 83, 110, 109, 97, 115, 118, 119, 111, 54, 37, 77, 114, 109, 118, 132, 140, 119, 114, 108, 114, 109, 106, 109, 111, 114, 112, 115, 114, 117, 125, 126, 119, 147, 154, 134, 108, 108, 108, 100, 101, 106, 112, 108, 112, 107, 108, 110, 103, 102, 106, 108, 106, 110, 106, 108, 110, 103, 103, 108, 107, 106, 110, 113, 114, 110, 113, 112, 112, 108, 111, 114, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 118, 124, 121, 100, 103, 106, 111, 112, 114, 165, 177, 140, 98, 92, 100, 121, 116, 118, 99, 92, 100, 101, 100, 110, 106, 109, 108, 130, 125, 84, 153, 139, 76, 123, 118, 91, 126, 123, 83, 140, 127, 78, 123, 117, 96, 111, 119, 99, 117, 117, 98, 111, 121, 103, 105, 119, 106, 104, 122, 109, 117, 109, 113, 110, 132, 103, 77, 134, 91, 94, 121, 103, 109, 123, 95, 94, 129, 94, 118, 120, 108, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 128, 91, 130, 126, 92, 121, 123, 99, 114, 136, 88, 136, 125, 92, 124, 121, 98, 117, 119, 95, 117, 122, 98, 119, 121, 100, 117, 113, 113, 111, 97, 94, 102, 111, 112, 109, 110, 113, 113, 94, 91, 99, 113, 113, 115, 112, 115, 111, 108, 109, 111, 114, 111, 113, 138, 138, 125, 103, 121, 111, 97, 121, 102, 87, 106, 97, 58, 87, 92, 113, 128, 112, 133, 115, 109, 85, 97, 99, 117, 122, 106, 107, 103, 125, 109, 107, 123, 107, 103, 122, 103, 99, 125, 105, 105, 125, 110, 108, 122, 112, 106, 122, 106, 101, 125, 110, 101, 125, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 95, 95, 106, 112, 107, 118, 119, 96, 120, 112, 150, 113, 113, 131, 115, 113, 96, 112, 138, 192, 122, 108, 126, 106, 103, 84, 102, 101, 100, 109, 104, 103, 112, 107, 108, 111, 115, 114, 107, 156, 168, 119, 116, 115, 108, 111, 107, 102, 124, 115, 101, 119, 111, 111, 110, 111, 111, 110, 111, 111, 110, 111, 111, 110, 111, 111, 110, 111, 111, 110, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 112, 106, 107, 106, 130, 128, 120, 110, 111, 108, 65, 57, 84, 107, 108, 111, 114, 118, 115, 105, 103, 111, 129, 126, 116, 113, 112, 111, 98, 113, 139, 119, 126, 166, 109, 110, 115, 101, 111, 122, 105, 116, 141, 108, 109, 118, 114, 117, 105, 110, 111, 113, 116, 113, 102, 103, 119, 116, 93, 118, 121, 98, 116, 122, 102, 122, 116, 94, 121, 121, 96, 117, 124, 100, 117, 117, 100, 120, 120, 101, 116, 120, 176, 76, 64, 176, 91, 68, 159, 105, 84, 187, 72, 61, 183, 91, 65, 160, 104, 77, 160, 102, 82, 164, 97, 76, 155, 98, 78, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 115, 114, 111, 107, 103, 112, 123, 117, 115, 116, 117, 117, 62, 49, 77, 119, 120, 112, 120, 120, 113, 131, 135, 120, 123, 122, 114, 117, 117, 117, 155, 162, 137, 115, 112, 113, 100, 98, 105, 108, 103, 107, 112, 111, 112, 112, 114, 114, 114, 114, 110, 106, 108, 112, 114, 114, 112, 114, 114, 112, 114, 114, 112, 114, 114, 111, 114, 115, 112, 114, 114, 111, 114, 114, 111, 114, 114, 111, 114, 114, 111, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 118, 116, 115, 109, 107, 108, 144, 143, 130, 111, 108, 113, 98, 94, 103, 110, 113, 110, 112, 113, 112, 111, 112, 115, 116, 117, 114, 124, 108, 105, 148, 81, 77, 130, 100, 100, 140, 96, 92, 148, 95, 83, 135, 107, 98, 126, 106, 104, 128, 113, 100, 121, 112, 104, 57, 51, 83, 120, 124, 121, 111, 116, 108, 122, 126, 120, 121, 120, 114, 111, 111, 111, 111, 112, 112, 114, 113, 110, 112, 112, 112, 91, 139, 104, 93, 121, 96, 92, 132, 108, 73, 142, 121, 46, 179, 159, 61, 150, 132, 79, 134, 116, 51, 161, 149, 78, 133, 119}; - model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 864); - static int32_t op4_init[] = {1496, 2937, -3899, 3591, 7857, -1279, -700, -934, 1078, 4298, -52, -891, 4098, 2694, 538, -1972, 4381, -1253, -1999, 3212, -2316, 4227, -902, 3205, 2506, 3398, 3644, 4153, -1574, 5634, 3844, -5110, -811, 3091, 362, -2900, -4870, 2950, -7100, 4009, -3667, 2969, -4569, 3370, -538, 6224, -2623, 3948, 2510, -1861, -2007, -3055, -1918, 6472, 5133, 6484, -1663, 4903, 4314, -1330, -1257, -388, 5184, 4744, -2389, 6815, -1705, -4801, 2574, -3169, -215, -1089, 4730, -55, -2494, 10058, 5010, -2874, 3156, 6491, -1006, 8008, 2848, -2658, 2871, -3074, -208, 3603, 3941, -2306, 3910, 4945, -354, -5856, -2661, -2819, 3103, -2970, 2758, -134, -1682, -1125, -1278, -3058, 3144, -1147, -3510, 5614, -1629, 5932, -2648, 3015, 4606, 3711, 2322, 4496, -579, 3013, 5137, 3674, -746, 4248, -802, -968, -2708, 2681, 1587, 6617, -817, -591, 3980, -3066, -6111, -1418, 5161, -1213, 18, -3805, -3492, -1638, -1775, 3156, 4578, 3714, 2653, -81, 7227, 4737, 1017, -2077, 4207, 1428, -846, 3170, -3291, 3483, 2726, 3918, -1339, 2852, -2447, -1499, 3178, 4064, 2707, -3851, 6569, 4382, -483, 3364, 2386, 4094, -2544, -1254, 4263, -451, 3634, 3596, -3908, -1412, -1682, 3310, -1968, 3009, 5027, 3614, 11153, 2156, -1096, -1851, 6211, -1438, 3135, 2960, 2133, 2490, 3468, -1853, -1613, -1137, -3226, 799, -1296, -1774, 396, 3723, 6826, -5274, -2812, 1566, 6352, 4376, 1503, 2568, -154, -1541, -1276, -133, 2857, 4363, -525, -1843, -6894, -1824, 4036, -3132, -677, -3463, 2755, -623, -1297, 2648, -215, -4589, -8386, -1467, -4637, -544, -2313, -1869, 13, 2808, -5675, -2618, -7763, 2831, 2452, 2808, 407, -3335, 2460, 6615, 2235, 2711, 3820, 3864, 5032, -3061, -2200, -1101, -647, -4344, -2739, 5440, 4575, -2263, 2592, -760, 2679, -1118, 4294, 4196, 5774, -304, -895, 4782, -825, -3518, 4118, 2627, -7164, 1041, 3909, -867, -272, 3551, -2142, -2468, 1929, 7167, -1999, -2558, -1497, 3186, 6304, 8550, -12, 4233, 5275, -817, 9390, -3530, -105, -3086, -2303, -621, 4707, -3283, -2267, 5022, 7045, 8487, 3891, -2019, 3492, 5739, -330, -1120, -2640, 100, 157, 6455, -5099, 6983, -1682, -1793, -1397, -972, 3450, 4024, -616, 4889, 6351, -439, 8374, -327, 4120, -2423, -1260, -2093, 1925, 2788, 4301, 197, 5843, -1382, 96, 9314, -1413, -1137, -3328, 1309, -4557, 4729, 2069, -2440, 4349, -4837, 513, -1572, 3314, 5302, 6908, 3285, -2325, 2941, 5633, 967, -3903, 3712, 3563, -6405, 940, -942, 3857, -1143, -5092, 4075, 4387, 5023, -4256, 3609, -304, 2447, 5628, -2799, 2801, 4036, 5155, -2228, 1505, 4082, 8400, -5199, -1032, -1332, -1288, -350, -3430, 2799, -1634, -3927, -4000, 5382, 2361, -4985, 3663, 345, -1331, -5878, -5651, 968, 7389, -3011, -3674, -731, 3296, 2652, 6334, -1160, 6521, -4080, 5341, 4734, -2369, -3391, 7538, -3635, -2702, 6352, 6995, 1329, -1004, -4818, -2044, -1270, 5398, -2579, 2344, 4546, -7556, 2884, 2812, 9821, 195, -1856, -3410, 5536, 3489, 2949, -2517, 3036, 6062, 3344, -1726, 3318, -1682, -368, -2381, -284, 5928, 967, 4869, 4184, 3524, 3788, 3102, -595, 3848, -525, -2317, -1333, -1649, 5768, -254, 3423, -1208, -2506, -2448, -2281, 3006, 4499, 2742, 582, -322, -471, 5666, 4052, 1969, -1556, 3014, -433, 5421, -1211, 3119, -2772, 3774, 3963, -6388, 4297, -582, 3062, -1959, 3366, 5033, -1908, 6499, -4880, -1495, 1925, -1112, 2566}; - model->setOperandValue(op4, op4_init, sizeof(int32_t) * 512); - static uint8_t op5_init[] = {118, 132, 157, 140, 147, 143, 141, 154, 121, 137, 163, 136, 163, 132, 191, 161, 153, 142, 139, 132, 152, 116, 141, 135, 129, 130, 145, 182, 125, 121, 125, 151, 143, 135, 147, 181, 153, 157, 155, 139, 157, 130, 163, 119, 125, 131, 114, 142, 127, 153, 152, 176, 160, 148, 141, 154, 129, 124, 135, 138, 135, 171, 141, 133, 156, 126, 161, 141, 118, 165, 117, 139, 140, 119, 155, 147, 144, 141, 135, 142, 144, 124, 123, 152, 122, 143, 146, 118, 134, 149, 131, 129, 164, 166, 150, 101, 127, 148, 129, 157, 170, 182, 154, 159, 151, 139, 165, 138, 155, 146, 138, 128, 121, 111, 121, 135, 149, 136, 139, 162, 139, 127, 135, 141, 148, 120, 157, 133, 136, 146, 136, 149, 165, 162, 135, 115, 166, 173, 145, 113, 140, 146, 156, 112, 130, 138, 146, 131, 136, 128, 137, 137, 148, 135, 142, 121, 149, 137, 137, 109, 146, 147, 132, 133, 140, 137, 125, 128, 134, 111, 135, 134, 141, 145, 138, 129, 125, 134, 132, 150, 154, 133, 163, 131, 124, 139, 142, 134, 148, 132, 130, 144, 119, 140, 150, 175, 136, 136, 155, 160, 138, 132, 131, 179, 158, 142, 134, 154, 153, 153, 148, 148, 135, 127, 169, 131, 127, 155, 128, 134, 133, 141, 160, 141, 133, 153, 140, 144, 131, 149, 156, 135, 137, 141, 151, 173, 159, 157, 147, 147, 126, 129, 174, 145, 152, 135, 131, 135, 132, 143, 132, 147, 139, 130, 134, 139, 139, 149, 172, 147, 160, 166, 136, 150, 130, 146, 170, 149, 135, 136, 116, 127, 117, 138, 143, 144, 136, 156, 126, 125, 148, 90, 125, 154, 123, 149, 178, 157, 195, 135, 145, 139, 150, 135, 131, 145, 171, 136, 136, 167, 128, 166, 154, 137, 146, 142, 137, 153, 136, 123, 150, 139, 138, 143, 121, 108, 143, 137, 137, 140, 146, 155, 169, 134, 140, 156, 175, 153, 123, 131, 132, 152, 128, 136, 145, 156, 135, 163, 140, 168, 142, 126, 133, 185, 134, 140, 156, 136, 153, 186, 132, 145, 154, 142, 158, 176, 123, 155, 142, 147, 141, 129, 130, 135, 135, 155, 160, 137, 152, 132, 139, 139, 151, 143, 135, 138, 171, 129, 121, 143, 141, 135, 127, 122, 147, 121, 129, 140, 124, 150, 135, 128, 140, 156, 169, 142, 111, 207, 144, 133, 128, 155, 162, 141, 113, 179, 120, 144, 154, 166, 146, 131, 152, 146, 143, 143, 133, 131, 169, 137, 154, 135, 120, 156, 143, 139, 144, 143, 145, 147, 132, 141, 147, 142, 147, 158, 139, 125, 133, 136, 154, 125, 132, 148, 133, 170, 159, 124, 131, 138, 142, 125, 127, 131, 146, 118, 188, 137, 136, 147, 137, 123, 151, 138, 143, 143, 134, 148, 137, 164, 149, 164, 142, 132, 163, 121, 140, 163, 134, 151, 126, 170, 133, 139, 160, 108, 146, 131, 166, 137, 133, 178, 131, 147, 146, 169, 133, 131, 156, 119, 165, 141, 154, 135, 132, 138, 153, 150, 143, 153, 152, 124, 119, 124, 103, 126, 107, 133, 130, 184, 114, 120, 169, 126, 142, 130, 142, 152, 177, 142, 148, 140, 164, 130, 124, 136, 125, 136, 139, 139, 140, 116, 114, 155, 137, 126, 177, 155, 155, 178, 155, 132, 219, 126, 161, 125, 135, 112, 110, 127, 114, 147, 163, 122, 165, 132, 122, 154, 162, 134, 132, 156, 145, 182, 145, 127, 182, 138, 177, 149, 120, 185, 123, 134, 123, 89, 190, 150, 157, 148, 129, 139, 145, 142, 135, 148, 122, 148, 161, 141, 116, 161, 137, 111, 126, 170, 129, 99, 135, 150, 129, 163, 175, 201, 136, 145, 133, 145, 149, 148, 166, 167, 166, 126, 140, 126, 131, 134, 169, 140, 140, 138, 147, 120, 143, 133, 149, 135, 142, 145, 138, 159, 150, 131, 134, 156, 142, 115, 189, 145, 138, 139, 145, 138, 142, 109, 135, 136, 133, 141, 142, 157, 132, 183, 173, 128, 139, 138, 154, 137, 94, 129, 142, 157, 131, 119, 193, 137, 106, 136, 168, 146, 133, 135, 143, 197, 110, 185, 124, 127, 147, 141, 194, 126, 150, 121, 122, 134, 138, 130, 163, 128, 121, 148, 124, 109, 137, 196, 135, 144, 159, 147, 140, 127, 150, 190, 192, 134, 131, 136, 156, 175, 150, 145, 147, 128, 182, 133, 128, 178, 140, 126, 155, 148, 136, 138, 112, 148, 150, 137, 141, 148, 124, 124, 151, 140, 131, 189, 151, 151, 131, 184, 109, 110, 148, 128, 130, 129, 134, 153, 96, 132, 123, 149, 113, 128, 148, 206, 112, 146, 179, 118, 166, 158, 163, 120, 133, 131, 190, 163, 124, 134, 142, 120, 143, 152, 139, 136, 137, 201, 142, 133, 129, 107, 123, 165, 124, 129, 73, 135, 156, 128, 118, 132, 145, 132, 119, 138, 151, 140, 138, 181, 123, 112, 157, 142, 147, 146, 146, 91, 99, 125, 151, 131, 158, 153, 128, 128, 152, 162, 131, 135, 130, 156, 153, 130, 139, 116, 191, 113, 126, 127, 107, 151, 152, 138, 131, 130, 137, 162, 152, 147, 143, 120, 121, 150, 122, 140, 201, 125, 100, 153, 103, 165, 121, 135, 147, 220, 132, 129, 128, 173, 200, 128, 131, 132, 150, 145, 175, 134, 172, 128, 134, 126, 145, 143, 130, 131, 152, 137, 122, 130, 112, 115, 112, 129, 156, 92, 125, 117, 128, 160, 122, 142, 142, 172, 78, 144, 114, 174, 187, 134, 153, 164, 172, 149, 135, 144, 108, 174, 160, 167, 134, 129, 162, 133, 180, 140, 132, 101, 168, 135, 123, 154, 129, 175, 144, 143, 164, 152, 138, 145, 125, 144, 157, 143, 158, 144, 122, 97, 128, 127, 161, 138, 126, 144, 124, 144, 201, 107, 132, 131, 145, 123, 124, 111, 130, 143, 95, 147, 136, 159, 124, 125, 163, 136, 131, 153, 125, 150, 122, 177, 177, 195, 140, 123, 196, 117, 133, 177, 147, 164, 132, 147, 135, 129, 168, 107, 130, 122, 151, 157, 128, 192, 127, 141, 201, 170, 135, 133, 178, 124, 131, 148, 170, 132, 144, 148, 139, 141, 155, 164, 195, 134, 139, 131, 160, 138, 151, 141, 141, 146, 125, 141, 159, 142, 111, 112, 141, 158, 150, 141, 139, 138, 151, 118, 143, 136, 132, 129, 127, 114, 129, 125, 147, 143, 189, 136, 144, 172, 153, 152, 155, 140, 149, 132, 162, 122, 128, 134, 113, 144, 132, 153, 155, 175, 154, 147, 139, 152, 132, 124, 136, 135, 137, 159, 144, 134, 157, 127, 163, 166, 118, 163, 156, 139, 142, 116, 146, 145, 143, 141, 136, 142, 140, 127, 124, 149, 126, 144, 143, 138, 137, 150, 156, 130, 128, 158, 149, 101, 129, 148, 128, 156, 165, 179, 153, 142, 153, 137, 152, 140, 151, 144, 127, 130, 140, 117, 123, 136, 145, 137, 121, 130, 138, 122, 136, 141, 145, 120, 154, 130, 135, 145, 137, 147, 153, 157, 142, 114, 162, 146, 145, 132, 139, 146, 128, 111, 131, 139, 146, 153, 136, 125, 138, 136, 142, 135, 149, 124, 148, 116, 141, 113, 144, 148, 131, 138, 135, 141, 126, 160, 182, 177, 136, 152, 140, 180, 138, 115, 162, 136, 128, 153, 141, 135, 169, 134, 112, 137, 140, 137, 146, 130, 131, 141, 122, 145, 125, 172, 138, 136, 157, 161, 140, 133, 139, 176, 157, 140, 136, 145, 153, 153, 148, 148, 185, 130, 169, 136, 157, 150, 129, 135, 133, 138, 155, 141, 137, 160, 139, 144, 184, 149, 154, 136, 132, 138, 145, 165, 161, 154, 145, 141, 128, 133, 173, 143, 151, 135, 132, 134, 135, 145, 134, 145, 140, 134, 131, 129, 141, 147, 169, 147, 155, 161, 184, 149, 130, 146, 167, 148, 135, 134, 144, 129, 159, 137, 145, 144, 138, 152, 125, 125, 147, 180, 126, 154, 126, 138, 125, 154, 118, 137, 145, 137, 150, 134, 134, 145, 168, 135, 138, 160, 132, 167, 151, 137, 144, 144, 170, 159, 138, 128, 147, 134, 120, 143, 124, 114, 143, 134, 139, 152, 141, 151, 158, 133, 140, 157, 167, 151, 124, 134, 136, 151, 149, 136, 144, 120, 136, 149, 137, 159, 141, 130, 134, 147, 136, 138, 146, 138, 151, 135, 133, 143, 155, 146, 150, 131, 128, 156, 140, 124, 137, 122, 131, 135, 135, 120, 156, 136, 150, 132, 139, 174, 99, 142, 132, 141, 152, 131, 123, 143, 141, 135, 127, 125, 146, 120, 129, 140, 125, 148, 136, 133, 139, 158, 167, 141, 121, 205, 138, 132, 130, 150, 158, 140, 116, 123, 122, 142, 151, 174, 148, 133, 148, 149, 144, 144, 132, 134, 166, 138, 152, 135, 121, 152, 141, 156, 144, 148, 147, 146, 136, 141, 142, 141, 97, 152, 141, 170, 132, 136, 169, 128, 134, 148, 135, 161, 159, 127, 133, 138, 144, 129, 126, 133, 104, 144, 128, 139, 139, 142, 140, 169, 150, 138, 142, 143, 135, 149, 161, 158, 142, 154, 142, 137, 159, 127, 142, 159, 132, 150, 129, 139, 144, 138, 154, 103, 146, 133, 161, 135, 134, 171, 135, 146, 140, 156, 152, 131, 155, 123, 164, 139, 150, 138, 146, 139, 152, 146, 143, 154, 131, 124, 148, 122, 174, 171, 123, 146, 175, 143, 132, 136, 138, 144, 158, 138, 144, 164, 144, 149, 152, 119, 151, 127, 158, 130, 130, 133, 164, 195, 154, 133, 111, 161, 162, 122, 135, 151, 149, 133, 148, 136, 208, 126, 152, 123, 151, 145, 156, 128, 121, 153, 115, 156, 136, 137, 143, 150, 167, 122, 126, 148, 145, 176, 140, 134, 201, 134, 154, 141, 126, 136, 88, 144, 122, 146, 131, 147, 136, 157, 129, 135, 156, 129, 134, 147, 121, 153, 144, 112, 146, 136, 126, 133, 163, 146, 160, 183, 132, 145, 127, 147, 153, 156, 162, 172, 160, 147, 177, 136, 155, 161, 196, 131, 132, 128, 128, 134, 160, 131, 143, 191, 145, 114, 160, 134, 154, 124, 137, 140, 140, 143, 133, 177, 158, 151, 119, 123, 146, 169, 131, 185, 148, 133, 166, 120, 133, 147, 161, 133, 140, 146, 117, 127, 150, 134, 157, 133, 140, 144, 149, 127, 149, 152, 133, 140, 128, 148, 104, 109, 131, 112, 136, 116, 155, 48, 131, 172, 126, 132, 129, 151, 200, 135, 126, 133, 126, 128, 132, 136, 154, 149, 126, 150, 123, 135, 163, 134, 124, 150, 146, 143, 123, 165, 160, 167, 189, 171, 125, 155, 160, 147, 140, 140, 134, 125, 154, 143, 92, 134, 124, 125, 139, 146, 165, 142, 145, 156, 145, 155, 136, 149, 178, 148, 159, 143, 181, 164, 145, 149, 138, 161, 138, 110, 162, 163, 162, 130, 136, 129, 116, 145, 126, 143, 138, 127, 130, 119, 140, 149, 174, 150, 147, 122, 136, 149, 134, 154, 156, 150, 132, 160, 125, 129, 106, 152, 150, 139, 144, 144, 107, 134, 165, 130, 132, 148, 135, 169, 191, 171, 229, 140, 147, 139, 152, 130, 127, 143, 160, 135, 137, 143, 138, 182, 147, 151, 149, 148, 119, 175, 149, 137, 134, 128, 133, 144, 125, 129, 148, 153, 148, 109, 135, 143, 159, 126, 147, 161, 150, 159, 122, 132, 132, 144, 132, 149, 144, 201, 132, 138, 147, 139, 131, 120, 133, 148, 135, 146, 124, 125, 168, 153, 141, 141, 153, 136, 153, 172, 126, 186, 138, 164, 132, 122, 125, 131, 138, 137, 134, 140, 150, 127, 124, 163, 157, 144, 126, 139, 146, 130, 126, 127, 151, 121, 122, 130, 147, 166, 130, 185, 129, 153, 134, 120, 148, 130, 210, 146, 140, 148, 143, 123, 159, 145, 138, 141, 140, 162, 128, 135, 151, 104, 144, 137, 141, 146, 170, 139, 135, 118, 165, 145, 98, 165, 136, 148, 154, 140, 149, 150, 148, 145, 120, 142, 149, 146, 137, 143, 132, 97, 136, 129, 148, 122, 136, 149, 138, 163, 171, 143, 113, 139, 140, 125, 121, 153, 110, 124, 140, 139, 136, 144, 133, 130, 141, 134, 133, 136, 129, 160, 108, 155, 165, 131, 155, 129, 169, 127, 136, 151, 132, 129, 133, 173, 124, 147, 171, 136, 140, 126, 139, 158, 129, 176, 141, 145, 134, 58, 124, 131, 157, 142, 162, 135, 150, 131, 114, 151, 145, 166, 160, 157, 167, 125, 29, 125, 147, 143, 94, 191, 150, 186, 140, 124, 132, 181, 137, 140, 126, 171, 104, 191, 197, 148, 198, 130, 137, 215, 146, 132, 132, 142, 193, 186, 167, 195, 179, 124, 150, 182, 202, 133, 174, 124, 160, 129, 191, 133, 199, 120, 199, 123, 126, 185, 176, 178, 132, 92, 100, 76, 190, 132, 120, 193, 140, 194, 123, 196, 137, 161, 195, 153, 144, 158, 151, 180, 119, 133, 132, 67, 122, 182, 125, 107, 188, 116, 138, 171, 138, 186, 186, 151, 112, 183, 129, 117, 89, 193, 171, 223, 137, 176, 138, 179, 170, 179, 149, 160, 56, 193, 124, 149, 170, 69, 170, 127, 147, 138, 140, 116, 153, 216, 146, 125, 182, 154, 180, 121, 211, 121, 171, 76, 201, 208, 170, 162, 148, 194, 129, 134, 128, 176, 171, 181, 195, 124, 133, 143, 126, 182, 68, 125, 194, 201, 132, 150, 185, 197, 153, 130, 141, 148, 208, 140, 199, 164, 132, 116, 150, 246, 214, 141, 113, 149, 194, 139, 188, 216, 157, 184, 108, 125, 255, 200, 255, 199, 153, 138, 130, 120, 0, 194, 168, 200, 126, 192, 139, 118, 110, 87, 124, 195, 192, 172, 245, 112, 198, 219, 96, 136, 94, 146, 155, 118, 67, 111, 129, 123, 184, 194, 121, 174, 126, 119, 208, 194, 185, 218, 128, 198, 182, 180, 135, 194, 149, 97, 174, 174, 166, 218, 190, 179, 133, 194, 188, 153, 138, 113, 171, 125, 131, 205, 137, 208, 134, 103, 154, 123, 154, 117, 76, 214, 135, 204, 168, 187, 120, 89, 129, 183, 70, 170, 185, 186, 146, 114, 138, 183, 179, 99, 162, 149, 125, 138, 169, 110, 208, 189, 136, 130, 85, 157, 127, 104, 199, 211, 168, 121, 123, 58, 194, 122, 114, 172, 71, 138, 167, 201, 183, 187, 104, 178, 190, 118, 116, 70, 160, 185, 121, 104, 203, 181, 202, 114, 128, 82, 159, 125, 192, 180, 201, 190, 130, 217, 215, 109, 101, 190, 76, 118, 112, 182, 203, 129, 196, 124, 120, 128, 125, 185, 145, 72, 189, 116, 135, 179, 203, 109, 158, 201, 133, 151, 130, 222, 104, 125, 117, 129, 227, 156, 141, 135, 195, 128, 117, 164, 182, 190, 124, 193, 154, 122, 122, 124, 138, 135, 245, 130, 61, 199, 148, 154, 133, 176, 194, 140, 67, 155, 108, 177, 131, 65, 188, 129, 188, 193, 166, 95, 141, 154, 168, 183, 172, 140, 202, 130, 70, 200, 210, 187, 132, 142, 58, 199, 128, 214, 145, 96, 204, 165, 91, 198, 194, 91, 110, 198, 185, 198, 155, 184, 131, 137, 137, 214, 177, 123, 198, 61, 200, 204, 109, 156, 125, 121, 173, 124, 140, 105, 133, 130, 131, 200, 227, 186, 115, 136, 101, 121, 117, 127, 126, 185, 159, 186, 185, 132, 168, 115, 145, 126, 141, 220, 227, 179, 133, 100, 143, 209, 183, 222, 84, 196, 119, 185, 128, 202, 132, 184, 130, 146, 134, 129, 193, 152, 165, 191, 169, 122, 142, 197, 100, 138, 178, 84, 191, 135, 188, 126, 169, 126, 126, 142, 175, 137, 134, 135, 137, 138, 129, 137, 137, 163, 147, 144, 146, 169, 146, 126, 159, 127, 131, 135, 128, 110, 146, 131, 194, 151, 158, 125, 136, 148, 142, 134, 142, 138, 201, 126, 140, 124, 148, 145, 144, 130, 124, 145, 140, 149, 137, 142, 148, 152, 153, 127, 128, 146, 143, 168, 140, 131, 190, 133, 128, 153, 133, 138, 173, 140, 126, 140, 134, 150, 140, 149, 131, 141, 151, 131, 134, 144, 122, 147, 140, 148, 145, 132, 158, 136, 121, 140, 154, 170, 132, 140, 128, 145, 143, 118, 158, 138, 162, 142, 151, 137, 146, 165, 189, 132, 148, 126, 129, 139, 157, 125, 121, 115, 141, 113, 155, 137, 150, 130, 132, 142, 136, 135, 131, 174, 169, 141, 171, 139, 137, 147, 125, 119, 139, 139, 119, 120, 134, 145, 164, 161, 136, 141, 120, 126, 149, 131, 139, 136, 142, 127, 146, 127, 143, 146, 133, 143, 128, 140, 95, 184, 170, 198, 133, 176, 150, 182, 128, 165, 164, 134, 125, 141, 180, 130, 223, 133, 134, 134, 150, 132, 151, 140, 130, 145, 120, 136, 126, 135, 126, 144, 118, 136, 119, 162, 136, 157, 190, 122, 130, 147, 145, 145, 145, 140, 148, 127, 143, 135, 198, 128, 126, 128, 133, 146, 163, 130, 143, 147, 141, 152, 154, 141, 179, 149, 157, 139, 171, 157, 139, 145, 138, 151, 136, 110, 160, 164, 159, 131, 137, 128, 117, 137, 126, 146, 136, 130, 131, 125, 144, 146, 172, 141, 147, 117, 190, 149, 137, 146, 165, 147, 133, 154, 149, 130, 178, 144, 146, 142, 141, 143, 113, 134, 179, 229, 128, 142, 135, 132, 72, 166, 122, 140, 144, 132, 149, 131, 131, 148, 154, 135, 140, 145, 136, 176, 144, 140, 146, 141, 168, 165, 142, 138, 138, 131, 110, 140, 126, 132, 137, 148, 139, 156, 137, 149, 148, 127, 140, 154, 147, 153, 119, 128, 126, 147, 169, 140, 149, 94, 136, 122, 135, 142, 131, 120, 138, 129, 137, 140, 126, 126, 160, 135, 140, 139, 147, 138, 117, 178, 127, 185, 136, 128, 137, 118, 127, 131, 129, 114, 134, 141, 144, 129, 127, 142, 92, 142, 128, 137, 155, 130, 130, 128, 152, 123, 116, 127, 151, 163, 129, 122, 132, 151, 130, 136, 150, 126, 208, 143, 126, 159, 146, 128, 149, 138, 137, 146, 139, 135, 128, 132, 145, 170, 139, 136, 149, 140, 169, 131, 136, 120, 172, 139, 105, 157, 136, 151, 145, 166, 152, 146, 139, 147, 121, 136, 147, 145, 163, 137, 131, 170, 137, 127, 166, 125, 133, 153, 131, 161, 162, 141, 114, 141, 134, 128, 118, 158, 184, 149, 156, 133, 127, 142, 133, 169, 141, 135, 133, 136, 133, 153, 189, 146, 155, 134, 150, 128, 171, 128, 136, 140, 126, 122, 130, 122, 161, 137, 161, 125, 142, 122, 138, 150, 133, 159, 139, 139, 137, 157, 162, 135, 154, 140, 159, 132, 148, 133, 166, 144, 147, 164, 156, 144, 162, 123, 169, 129, 173, 159, 136, 138, 163, 133, 150, 138, 142, 152, 138, 135, 148, 143, 138, 138, 140, 121, 146, 124, 172, 134, 121, 130, 161, 174, 147, 132, 108, 162, 201, 119, 139, 157, 159, 142, 152, 134, 170, 133, 161, 117, 138, 151, 202, 129, 127, 144, 177, 143, 143, 148, 159, 144, 186, 135, 137, 155, 168, 160, 132, 133, 161, 128, 158, 144, 123, 125, 109, 146, 132, 168, 143, 142, 133, 141, 134, 135, 139, 141, 131, 149, 126, 135, 144, 121, 157, 145, 130, 121, 171, 165, 141, 146, 132, 144, 112, 142, 154, 92, 152, 138, 153, 142, 149, 116, 118, 145, 172, 136, 118, 128, 125, 138, 149, 135, 144, 157, 143, 124, 140, 153, 150, 123, 123, 144, 138, 128, 123, 156, 165, 145, 131, 178, 129, 143, 153, 179, 144, 115, 150, 130, 127, 149, 146, 128, 137, 149, 128, 139, 140, 138, 156, 127, 122, 145, 134, 130, 143, 149, 131, 152, 140, 125, 122, 124, 133, 122, 140, 127, 142, 149, 136, 156, 145, 139, 134, 149, 148, 136, 142, 126, 121, 129, 142, 138, 144, 145, 134, 140, 128, 133, 160, 127, 134, 135, 156, 155, 134, 176, 139, 58, 158, 154, 141, 146, 149, 143, 147, 131, 138, 128, 128, 148, 137, 135, 129, 133, 132, 144, 176, 128, 147, 144, 151, 147, 136, 136, 141, 154, 152, 154, 151, 115, 140, 140, 168, 139, 141, 127, 168, 158, 149, 135, 129, 134, 157, 144, 126, 135, 140, 132, 122, 143, 167, 133, 159, 144, 133, 155, 142, 163, 126, 152, 130, 141, 135, 153, 124, 137, 131, 144, 151, 143, 146, 133, 119, 125, 155, 140, 134, 124, 166, 161, 164, 148, 155, 148, 149, 140, 138, 132, 131, 142, 162, 138, 137, 158, 131, 145, 131, 141, 143, 143, 139, 169, 147, 138, 129, 126, 142, 147, 145, 160, 135, 135, 142, 131, 146, 145, 153, 128, 142, 137, 119, 150, 130, 135, 140, 134, 132, 138, 153, 159, 137, 151, 139, 142, 137, 114, 162, 137, 144, 146, 126, 142, 163, 156, 159, 133, 143, 125, 161, 116, 139, 146, 175, 146, 143, 117, 126, 135, 127, 153, 126, 157, 140, 136, 137, 165, 141, 141, 136, 144, 138, 123, 120, 136, 174, 136, 130, 125, 148, 168, 154, 149, 129, 149, 139, 120, 146, 162, 177, 143, 178, 143, 135, 126, 142, 149, 164, 140, 159, 172, 145, 127, 141, 95, 145, 165, 146, 141, 188, 143, 132, 162, 165, 140, 109, 152, 139, 138, 144, 159, 134, 145, 145, 137, 137, 135, 141, 144, 133, 158, 133, 177, 150, 137, 165, 131, 138, 145, 139, 136, 158, 141, 136, 158, 147, 134, 122, 154, 117, 122, 175, 140, 130, 137, 139, 140, 140, 138, 138, 126, 133, 148, 130, 152, 139, 133, 144, 130, 154, 140, 148, 109, 147, 146, 123, 173, 122, 141, 132, 137, 150, 138, 140, 140, 127, 93, 122, 146, 135, 134, 134, 130, 147, 144, 150, 131, 146, 137, 132, 143, 135, 162, 146, 144, 151, 121, 194, 124, 176, 140, 118, 140, 170, 135, 182, 141, 137, 181, 137, 126, 138, 172, 116, 144, 142, 139, 143, 143, 172, 132, 125, 130, 140, 142, 150, 152, 137, 160, 141, 131, 129, 178, 139, 127, 153, 131, 135, 124, 151, 116, 150, 153, 169, 130, 128, 134, 153, 138, 152, 149, 174, 129, 148, 125, 117, 139, 197, 176, 120, 136, 169, 131, 119, 150, 147, 116, 123, 156, 122, 217, 155, 142, 121, 162, 130, 135, 151, 112, 135, 144, 146, 149, 116, 144, 173, 143, 141, 127, 150, 159, 141, 219, 132, 155, 125, 124, 161, 113, 172, 157, 137, 143, 144, 130, 120, 147, 197, 133, 141, 139, 129, 133, 148, 128, 138, 133, 144, 129, 153, 193, 170, 125, 97, 152, 142, 123, 123, 129, 135, 99, 146, 166, 121, 119, 177, 148, 147, 133, 145, 135, 134, 158, 152, 143, 135, 159, 178, 128, 116, 141, 148, 140, 122, 146, 151, 128, 142, 148, 131, 166, 128, 100, 82, 128, 139, 144, 137, 133, 143, 193, 183, 161, 161, 126, 129, 171, 205, 137, 176, 136, 122, 124, 145, 140, 145, 159, 140, 132, 128, 140, 151, 129, 123, 150, 157, 165, 133, 193, 158, 75, 77, 133, 113, 147, 138, 130, 137, 120, 139, 128, 125, 154, 135, 125, 129, 125, 110, 143, 147, 103, 170, 149, 140, 131, 134, 137, 177, 158, 166, 200, 134, 95, 172, 126, 172, 129, 132, 124, 131, 174, 136, 132, 135, 126, 200, 148, 134, 129, 182, 132, 120, 133, 165, 139, 165, 153, 121, 156, 147, 168, 120, 147, 113, 123, 136, 161, 145, 136, 144, 151, 160, 136, 173, 137, 142, 131, 134, 136, 151, 127, 161, 133, 190, 111, 152, 151, 169, 138, 115, 132, 124, 150, 168, 136, 137, 161, 130, 130, 123, 140, 142, 146, 151, 155, 153, 138, 123, 141, 160, 136, 119, 147, 125, 143, 153, 149, 145, 125, 134, 114, 151, 193, 122, 155, 131, 132, 149, 125, 145, 148, 152, 127, 145, 153, 131, 135, 125, 122, 176, 122, 148, 146, 74, 135, 158, 139, 203, 122, 126, 126, 152, 176, 136, 123, 149, 125, 142, 128, 122, 128, 122, 145, 121, 155, 130, 131, 137, 145, 129, 141, 125, 146, 140, 119, 134, 133, 160, 125, 132, 132, 147, 214, 166, 122, 137, 136, 153, 136, 162, 176, 206, 141, 168, 95, 134, 131, 150, 145, 138, 139, 153, 144, 161, 110, 131, 134, 150, 164, 131, 155, 78, 145, 134, 197, 141, 145, 152, 157, 155, 116, 153, 160, 131, 156, 147, 127, 115, 137, 132, 147, 142, 148, 140, 173, 169, 129, 144, 129, 147, 148, 148, 167, 82, 155, 145, 160, 170, 123, 124, 148, 152, 134, 106, 129, 125, 127, 129, 122, 131, 135, 131, 127, 132, 159, 138, 83, 129, 130, 157, 124, 73, 124, 177, 155, 159, 149, 125, 154, 134, 147, 125, 145, 146, 158, 128, 146, 134, 115, 140, 139, 126, 155, 137, 131, 149, 152, 144, 134, 137, 135, 136, 147, 128, 126, 155, 117, 115, 127, 164, 129, 165, 121, 137, 138, 159, 131, 147, 138, 143, 149, 143, 117, 138, 136, 138, 138, 139, 153, 146, 123, 167, 137, 126, 129, 121, 128, 147, 134, 173, 159, 133, 125, 139, 154, 159, 142, 151, 134, 164, 136, 162, 118, 138, 148, 198, 133, 127, 143, 142, 144, 140, 146, 154, 144, 184, 132, 139, 154, 160, 155, 134, 134, 159, 132, 98, 168, 123, 127, 159, 144, 134, 168, 140, 141, 136, 143, 135, 134, 138, 143, 133, 149, 130, 136, 144, 139, 154, 145, 162, 123, 143, 166, 143, 130, 132, 141, 118, 143, 155, 110, 150, 152, 153, 141, 145, 118, 121, 144, 171, 139, 141, 134, 129, 140, 149, 137, 127, 127, 144, 125, 139, 151, 148, 121, 129, 141, 138, 133, 123, 156, 150, 152, 142, 174, 130, 148, 150, 125, 143, 116, 123, 133, 128, 144, 147, 150, 136, 147, 130, 140, 141, 139, 155, 130, 125, 123, 134, 134, 142, 149, 130, 150, 142, 130, 120, 161, 150, 178, 139, 163, 143, 164, 134, 154, 154, 140, 135, 145, 136, 138, 177, 127, 121, 128, 134, 139, 143, 142, 133, 143, 124, 135, 141, 126, 136, 136, 154, 151, 136, 174, 143, 63, 156, 127, 140, 153, 135, 144, 147, 133, 148, 133, 136, 149, 156, 136, 132, 134, 133, 143, 170, 132, 146, 149, 151, 149, 137, 140, 137, 154, 145, 145, 152, 119, 137, 143, 161, 141, 142, 129, 170, 156, 149, 136, 129, 138, 150, 145, 127, 135, 138, 133, 122, 142, 160, 139, 156, 145, 134, 154, 145, 159, 128, 151, 136, 140, 136, 152, 146, 137, 139, 145, 151, 143, 142, 137, 114, 128, 147, 151, 136, 124, 165, 137, 182, 150, 130, 146, 147, 139, 140, 132, 133, 140, 156, 138, 136, 155, 130, 149, 134, 143, 142, 144, 177, 165, 146, 139, 131, 129, 130, 148, 146, 156, 140, 136, 141, 174, 187, 145, 160, 126, 142, 129, 117, 150, 133, 138, 141, 133, 153, 136, 153, 127, 138, 173, 139, 141, 137, 118, 156, 131, 143, 146, 130, 140, 156, 134, 153, 135, 144, 128, 154, 96, 138, 149, 147, 135, 143, 125, 127, 136, 129, 125, 128, 155, 141, 136, 137, 143, 119, 142, 133, 146, 146, 125, 116, 137, 170, 136, 130, 125, 146, 163, 152, 139, 131, 151, 139, 133, 144, 154, 184, 144, 170, 147, 137, 129, 144, 152, 163, 139, 160, 124, 144, 131, 143, 184, 145, 161, 145, 142, 180, 141, 133, 157, 165, 138, 101, 151, 138, 141, 143, 144, 137, 145, 146, 139, 140, 135, 142, 146, 152, 157, 134, 147, 148, 138, 140, 132, 139, 143, 140, 129, 137, 141, 136, 156, 147, 136, 122, 150, 196, 142, 134, 141, 130, 137, 137, 158, 141, 138, 139, 128, 133, 146, 123, 157, 136, 138, 143, 133, 154, 140, 146, 108, 139, 145, 126, 138, 162, 143, 132, 132, 149, 135, 140, 139, 129, 110, 125, 146, 137, 156, 158, 132, 146, 143, 150, 132, 145, 137, 150, 143, 138, 158, 145, 139, 149, 121}; - model->setOperandValue(op5, op5_init, sizeof(uint8_t) * 4608); - static int32_t op7_init[] = {-16901, 4449, 3965, -23286, -10059, -1785, -2234, -21891, 9417, -2026, 9101, 12667, 10299, 11108, 3619, 4683, 5799, -16001, 5942, -11312, -1171, 4334, 8675, 16691, 21512, -6780, 3812, -10836, -20051, 12559, 31177, 4525, -10155, 15375, -1332, 3547, 2048, 9557, 7992, 1621, -3693, 1824, 9352, 1859, 2062, -4582, -25, -20228, -5152, -13316, 15779, 10439, -4107, -4946, -12821, 6077, -16842, 4528, -12243, 1023, -4170, -17171, 2325, 16396, 12713, 7235, 16232, 14657, 8080, -6237, -8406, -5553, -12955, 2926, 7067, 4498, -4133, 15279, -5016, 6875, -21536, -7709, 14359, 12586, 123, -4673, -6055, -16397, 15179, 1279, -7635, -12538, -3795, -1332, 4332, 6731, -3023, 4746, -20239, 3065, 12195, 10044, 4433, -8726, -970, -1935, -11520, 6733, -6513, 24782, -6492, 11639, -3920, 13047, 8108, 16503, 48, -11619, -3842, -15885, -1757, 11147, 15750, -14682, -2592, -6612, -11423, -7749, 13756, -6593, -6915, -1805, -11682, 9916, -12425, -5655, 14255, -17784, 1185, 20663, 9882, -27549, 21974, -84, 1919, 1548, -494, 15093, 6235, 13820, -18676, -16112, 5643, 146, 7939, 1603, 1037, -5267, -3660, -15951, 20711, -1049, 20945, -18690, -6009, -9156, 9504, -15344, -4052, -11799, -2073, -12886, -16852, -11185, 1192, -25083, -12645, -640, -3940, 5531, -7229, -12719, -3698, -2881, 9241, -21153, 2735, 12272, -21537, 15757, 10138, 12391, 3447, -13813, 19177, 23879, 70, 10091, -20352, 11425, -3253, 3787, 18077, -3557, 13823, 693, -1297, 11054, -8435, 12973, -923, -9163, -8248, 9414, 16947, -2134, -20935, -28123, 10053, 4490, -2214, -23097, -9177, -30591, 9190, 7921, -761, 18767, -1328, 22459, 17329, 15465, 7843, 8051, -6954, -22734, 3038, -8171, -10148, 5029, 21180, -1052, 14303, 7171, -11095, 18595, 2503, -6009, 5944, -3300, 5731, -13998, 7337, 11717, -11183, 13014, 11609, 177, -488, 526, -8657, -4750, 8371, -10453, -8307, 5185, 14345, -11431, 11040, 1160, 15900, -1401, -12290, -1433, 15265, 4175, 12776, -20427, 16135, -15025, -10462, -37082, 4406, 8702, -17378, 11579, -8827, -10279, -4222, 16632, -1762, -31314, 323, 5283, 10865, -11773, 3823, -19999, 6989, -18313, -3277, -7689, -14748, -8464, -30811, -2777, 666, -4384, -19020, -30122, -7262, 6990, -2015, 6365, -13495, -12688, 26821, -8526, 19070, 21416, 9448, 288, 11415, 1273, -9658, -3833, -14345, 10084, -5824, -3904, 9669, 4332, -675, -2236, -3854, 5123, -7581, 1066, -2452, -9572, 8330, 15054, 15034, 11075, 176, 2706, 3172, -294, 1245, -13372, -14225, -11066, -8162, -11312, 8846, -3256, -1683, -6746, -23836, -13750, -8273, -9716, 11006, 26046, -14739, 14409, -13172, 6745, 19747, 8155, 10942, -22692, 10969, -18064, 10597, -1499, 5057, -6984, 15501, -749, -17462, 20375, -10586, -6092, 26871, 878, 7549, -33189, -10011, 19277, -12227, -18829, 8400, -1775, 10221, -9755, -7572, 12170, -4053, 6579, 24473, -17152, 112, -13584, -3371, 20573, -6723, 21620, -2802, -16869, -9138, 15488, 4183, -10783, -14081, 12424, 1468, -12846, 4267, 7994, 18709, -9331, 2630, 14006, -16854, -3820, 2525, 16678, -3602, 9535, 5280, 4403, 8962, 8832, -3734, 11573, 1223, -596, 3990, -20316, 1488, 15797, 19137, 13164, 16770, 10930, 16829, -1483, -23118, -24575, 7059, 588, -13253, -21165, -8986, -4418, 8744, 2545, -6338, -25916, -2508, -3207, -17457, 9626, 2462, -6383, 11525, -2759, 994, -2043, 15717, 19734, -582, 7986, 9676, 20477, 10243, 9840, -4244, -17841, 9844, -9827, -6838, 7419, 2182, -2991, 5775, -22777, 1785, 5472, -10683, -11764, 5235, -6382, -15058, 22259, -12756, 7974, 10406, -20242, -11524, -21360, 19737, 3245, -12707, -8404, 16428, -4580, 11957, -16344, -8386, 9355}; - model->setOperandValue(op7, op7_init, sizeof(int32_t) * 512); - static uint8_t op8_init[] = {126, 134, 134, 139, 135, 128, 139, 137, 140, 137, 135, 135, 140, 133, 129, 137, 143, 137, 136, 143, 137, 135, 128, 150, 145, 124, 137, 133, 133, 161, 132, 135, 147, 130, 145, 139, 141, 132, 145, 136, 141, 137, 136, 139, 138, 129, 160, 135, 136, 139, 140, 133, 143, 133, 138, 148, 141, 134, 153, 137, 137, 150, 128, 134, 133, 156, 127, 134, 138, 127, 139, 142, 141, 140, 134, 131, 144, 146, 133, 132, 119, 146, 136, 136, 133, 143, 128, 135, 142, 139, 133, 134, 132, 155, 150, 143, 139, 136, 130, 142, 135, 133, 138, 133, 136, 142, 140, 147, 131, 141, 133, 134, 137, 139, 140, 131, 141, 158, 147, 136, 139, 134, 134, 143, 142, 143, 131, 106, 143, 134, 134, 136, 134, 143, 140, 138, 145, 136, 143, 132, 160, 132, 142, 137, 142, 118, 145, 153, 151, 143, 135, 130, 140, 143, 135, 136, 137, 140, 140, 141, 134, 141, 130, 141, 133, 132, 141, 133, 140, 135, 104, 136, 130, 140, 143, 141, 140, 134, 133, 132, 128, 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142, 129, 139, 137, 135, 129, 144, 141, 154, 131, 128, 144, 141, 142, 128, 130, 123, 134, 147, 135, 127, 137, 132, 146, 129, 124, 137, 127, 135, 151, 141, 138, 137, 142, 148, 132, 129, 160, 130, 111, 141, 138, 139, 152, 151, 135, 125, 139, 121, 155, 144, 143, 137, 133, 137, 124, 128, 124, 134, 133, 155, 138, 137, 136, 130, 135, 138, 127, 143, 129, 134, 141, 127, 145, 143, 138, 134, 149, 148, 131, 139, 127, 118, 137, 135, 161, 128, 131, 113, 127, 147, 155, 125, 137, 137, 121, 128, 137, 120, 157, 124, 144, 126, 127, 151, 149, 124, 132, 138, 132, 148, 135, 134, 134, 143, 148, 135, 124, 145, 136, 123, 124, 128, 132, 137, 136, 151, 144, 133, 140, 136, 126, 132, 140, 134, 133, 123, 142, 139, 139, 140, 129, 130, 143, 144, 119, 139, 142, 140, 131, 139, 135, 135, 131, 131, 147, 148, 138, 131, 152, 126, 132, 133, 148, 141, 141, 139, 141, 135, 115, 138, 137, 132, 126, 141, 144, 136, 136, 139, 139, 138, 137, 123, 136, 138, 124, 139, 139, 127, 134, 140, 132, 140, 134, 121, 145, 140, 142, 135, 117, 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135, 138, 140, 121, 141, 136, 126, 135, 133, 127, 138, 131, 131, 129, 144, 129, 135, 132, 143, 147, 127, 140, 139, 128, 141, 133, 124, 137, 147, 132, 126, 125, 141, 126, 135, 147, 123, 135, 136, 131, 134, 127, 130, 138, 122, 155, 143, 139, 138, 136, 142, 119, 140, 127, 126, 137, 133, 135, 133, 138, 137, 136, 142, 144, 130, 133, 140, 143, 131, 130, 145, 136, 132, 150, 142, 125, 125, 158, 138, 136, 141, 142, 142, 133, 136, 127, 134, 147, 135, 139, 131, 141, 135, 151, 150, 132, 139, 156, 117, 145, 139, 143, 138, 123, 136, 128, 142, 135, 143, 127, 143, 146, 147, 142, 133, 144, 137, 139, 141, 123, 137, 122, 147, 146, 134, 133, 135, 129, 138, 145, 140, 133, 138, 141, 145, 131, 141, 132, 128, 120, 138, 137, 127, 135, 114, 140, 133, 135, 134, 135, 136, 140, 138, 140, 138, 145, 128, 133, 159, 130, 140, 135, 127, 146, 131, 145, 140, 139, 168, 133, 126, 129, 141, 136, 138, 123, 132, 127, 133, 136, 145, 134, 134, 132, 143, 140, 135, 128, 137, 141, 141, 136, 138, 136, 141, 133, 122, 123, 146, 137, 124, 140, 140, 149, 154, 143, 132, 119, 135, 126, 144, 141, 149, 142, 146, 136, 142, 136, 154, 128, 130, 134, 128, 138, 123, 125, 126, 115, 123, 145, 126, 127, 141, 134, 126, 138, 132, 137, 150, 134, 144, 127, 128, 142, 125, 140, 137, 125, 141, 132, 143, 138, 127, 143, 142, 124, 135, 135, 123, 132, 121, 137, 131, 139, 148, 138, 123, 140, 137, 154, 141, 131, 154, 147, 123, 141, 137, 134, 142, 137, 128, 128, 120, 139, 130, 152, 137, 140, 135, 142, 125, 134, 134, 144, 132, 149, 130, 136, 140, 131, 135, 139, 133, 140, 133, 146, 140, 163, 129, 148, 142, 130, 115, 130, 137, 142, 122, 131, 120, 129, 114, 128, 149, 131, 143, 148, 140, 152, 134, 142, 142, 135, 130, 130, 135, 128, 128, 125, 125, 131, 140, 135, 136, 134, 135, 138, 125, 134, 143, 143, 139, 114, 137, 153, 138, 136, 134, 151, 135, 131, 124, 120, 131, 127, 132, 150, 134, 143, 138, 140, 151, 142, 140, 137, 132, 127, 147, 144, 145, 145, 130, 138, 135, 145, 128, 143, 143, 134, 140, 137, 133, 148, 124, 145, 133, 142, 131, 130, 124, 138, 140, 133, 139, 148, 163, 144, 131, 129, 134, 135, 132, 139, 154, 143, 158, 149, 135, 142, 128, 150, 149, 143, 140, 133, 140, 131, 136, 137, 127, 123, 138, 132, 130, 143, 152, 131, 128, 152, 129, 141, 176, 129, 139, 145, 137, 165, 140, 130, 123, 137, 133, 128, 130, 137, 131, 142, 128, 136, 125, 128, 144, 164, 145, 141, 142, 101, 126, 145, 138, 136, 131, 130, 108, 137, 135, 159, 146, 144, 141, 132, 130, 128, 145, 138, 157, 147, 129, 118, 146, 128, 138, 110, 135, 151, 140, 133, 134, 129, 128, 138, 131, 135, 129, 134, 137, 132, 132, 135, 103, 148, 129, 146, 132, 115, 137, 131, 141, 132, 154, 126, 140, 139, 128, 142, 139, 146, 125, 139, 143, 125, 143, 139, 135, 148, 145, 144, 139, 140, 143, 138, 130, 136, 143, 129, 141, 133, 139, 133, 143, 135, 139, 135, 158, 130, 137, 134, 137, 139, 130, 130, 131, 142, 136, 128, 141, 136, 126, 137, 145, 132, 139, 139, 138, 145, 137, 136, 144, 137, 136, 135, 144, 144, 136, 139, 139, 134, 140, 129, 133, 140, 138, 136, 133, 136, 141, 145, 126, 159, 131, 142, 132, 133, 142, 141, 129, 130, 146, 136, 138, 125, 144, 133, 138, 144, 159, 139, 131, 145, 135, 137, 133, 135, 131, 139, 130, 141, 136, 130, 133, 143, 129, 129, 145, 143, 140, 128, 132, 141, 142, 134, 139, 132, 136, 128, 144, 138, 128, 128, 128, 139, 138, 133, 132, 130, 127, 137, 143, 124, 136, 127, 138, 142, 141, 140, 140, 132, 139, 139, 139, 134, 127, 132, 134, 136, 128, 141, 135, 135, 126, 141, 131, 139, 137, 130, 138, 145, 137, 136, 129, 137, 141, 138, 133, 135, 128, 138, 131, 141, 141, 132, 139, 134, 129, 140, 136, 136, 130, 129, 145, 126, 127, 140, 135, 126, 129, 122, 131, 140, 141, 133, 125, 141, 137, 158, 147, 130, 133, 133, 130, 127, 138, 120, 131, 131, 139, 141, 138, 129, 143, 138, 130, 140, 135, 134, 125, 135, 143, 116, 138, 133, 132, 133, 136, 139, 139, 134, 133, 138, 121, 131, 132, 129, 130, 128, 144, 131, 143, 133, 134, 130, 133, 139, 135, 130, 141, 136, 149, 135, 151, 125, 142, 143, 141, 134, 141, 126, 130, 134, 137, 134, 124, 126, 141, 127, 145, 140, 133, 131, 137, 125, 140, 138, 144, 125, 139, 131, 138, 135, 141, 130, 122, 137, 146, 155, 129, 131, 120, 130, 136, 132, 135, 137, 126, 130, 137, 147, 134, 142, 137, 141, 129, 134, 141, 127, 140, 134, 138, 133, 129, 128, 140, 135, 136, 143, 141, 154, 138, 130, 130, 136, 123, 143, 132, 133, 140, 134, 137, 139, 128, 136, 131, 137, 132, 134, 128, 142, 138, 134, 149, 135, 136, 139, 133, 134, 135, 131, 135, 132, 142, 134, 135, 135, 130, 134, 134, 134, 134, 126, 132, 139, 129, 137, 144, 143, 142, 136, 135, 147, 137, 134, 128, 139, 145, 152, 138, 141, 130, 120, 136, 138, 131, 133, 142, 137, 134, 140, 138, 129, 138, 129, 147, 142, 130, 134, 143, 138, 132, 133, 131, 135, 130, 128, 130, 133, 143, 143, 139, 137, 134, 126, 142, 142, 127, 133, 141, 143, 150, 139, 159, 144, 136, 135, 129, 139, 132, 136, 135, 129, 140, 140, 124, 134, 130, 132, 135, 151, 127, 129, 134, 139, 137, 135, 142, 124, 140, 126, 114, 153, 120, 138, 130, 129, 140, 139, 140, 135, 142, 136, 134, 134, 136, 145, 140, 131, 151, 133, 143, 134, 139, 134, 136, 145, 136, 134, 127, 136, 125, 141, 143, 143, 137, 129, 135, 128, 141, 141, 136, 138, 143, 134, 137, 136, 143, 134, 136, 137, 139, 113, 143, 136, 133, 149, 148, 147, 131, 142, 130, 137, 140, 119, 147, 135, 117, 145, 142, 135, 134, 132, 135, 132, 136, 136, 139, 139, 139, 133, 141, 130, 136, 136, 136, 133, 134, 139, 139, 137, 138, 140, 140, 138, 134, 140, 135, 139, 138, 137, 130, 135, 135, 140, 138, 136, 142, 118, 133, 130, 139, 140, 136, 137, 132, 127, 138, 140, 139, 140, 132, 140, 129, 134, 137, 139, 139, 138, 137, 147, 137, 140, 144, 136, 137, 136, 138, 137, 126, 138, 141, 136, 134, 129, 137, 137, 141, 133, 137, 134, 132, 144, 137, 134, 145, 134, 126, 138, 133, 136, 141, 136, 132, 132, 135, 142, 137, 142, 146, 144, 132, 136, 135, 138, 137, 131, 124, 137, 132, 137, 141, 139, 138, 140, 139, 142, 139, 132, 131, 136, 134, 129, 141, 133, 136, 136, 141, 135, 134, 135, 141, 134, 147, 123, 139, 129, 136, 131, 130, 136, 137, 148, 133, 149, 138, 132, 139, 133, 134, 143, 147, 139, 152, 142, 145, 131, 128, 145, 134, 128, 136, 139, 137, 136, 147, 131, 137, 137, 136, 137, 138, 124, 136, 133, 136, 134, 136, 137, 140, 138, 132, 135, 137, 128, 136, 134, 138, 132, 138, 133, 138, 132, 140, 143, 138, 131, 134, 140, 135, 136, 140, 140, 135, 137, 132, 120, 139, 135, 141, 139, 133, 135, 135, 138, 137, 133, 136, 137, 155, 132, 136, 138, 133, 128, 140, 134, 136, 129, 126, 137, 138, 140, 137, 135, 134, 135, 127, 134, 122, 136, 144, 133, 135, 133, 138, 137, 140, 132, 136, 134, 139, 141, 139, 133, 138, 143, 131, 134, 140, 139, 159, 138, 137, 136, 132, 136, 140, 139, 141, 133, 140, 136, 135, 139, 136, 134, 135, 136, 134, 135, 141, 132, 138, 141, 133, 144, 139, 119, 139, 138, 139, 135, 126, 137, 134, 133, 139, 136, 141, 133, 133, 151, 137, 118, 140, 140, 139, 137, 137, 128, 141, 134, 134, 137, 140, 131, 133, 133, 134, 138, 135, 143, 144, 138, 135, 136, 134, 159, 137, 131, 135, 152, 137, 138, 139, 133, 117, 136, 138, 147, 136, 133, 130, 126, 129, 135, 136, 136, 136, 138, 135, 135, 122, 138, 131, 134, 156, 140, 134, 132, 132, 130, 137, 133, 133, 142, 131, 146, 141, 139, 128, 129, 134, 141, 134, 141, 137, 136, 132, 134, 134, 134, 138, 132, 134, 129, 140, 135, 138, 130, 139, 134, 131, 137, 138, 134, 139, 133, 125, 136, 138, 135, 135, 129, 138, 136, 142, 137, 141, 136, 137, 127, 138, 132, 140, 139, 140, 145, 138, 131, 143, 139, 139, 136, 133, 133, 136, 134, 138, 136, 136, 137, 137, 135, 137, 138, 137, 133, 135, 136, 139, 129, 135, 133, 143, 134, 138, 149, 132, 144, 136, 124, 135, 139, 136, 139, 135, 135, 134, 139, 140, 135, 136, 137, 139, 135, 137, 133, 135, 130, 139, 148, 134, 137, 140, 136, 120, 140, 131, 131, 144, 134, 144, 130, 132, 140, 132, 133, 134, 142, 128, 137, 139, 134, 124, 135, 139, 136, 135, 139, 143, 137, 141, 137, 146, 134, 139, 122, 132, 137, 133, 132}; - model->setOperandValue(op8, op8_init, sizeof(uint8_t) * 262144); - static int32_t op10_init[] = {11394, -1999, 4880, -2676, 6208, -1105, 13567, -3025, 7011, -1269, -1839, 10977, 2367, -1538, 6346, 7114, -1589, 4172, -1565, 7236, 2240, -921, 5150, 7512, 4914, -2337, -4988, -1603, -1501, -60, 4461, 4212, -1223, -8203, -21123, 5439, 4482, -3120, -507, 5387, -1463, 5896, 2931, 4335, 8023, 5484, -387, -1285, 3424, -136, 6466, 9839, -1431, 6363, 870, 7697, -1310, -2239, -1293, 3602, 768, 1057, 4505, -5877, -819, 6231, 3928, -7132, -6937, -645, -2878, 3467, 9156, -6209, 8901, 6379, -937, -2695, -451, -3679, -234, -726, 3643, -4771, 11820, -4237, 5937, 4491, 4777, 5911, -2484, 11144, -2659, 5340, 5249, 6817, 8248, 4418, 5158, 7165, 7354, 6955, -5204, -1949, -1639, 3884, -1104, 6225, -2427, 3711, 4914, 7990, 7537, 4974, -1025, 4957, 6372, -1436, -11, 4887, -2766, 264, 3382, 3865, 2613, 8539, 619, -3938, 6047, 4838, 5081, 2058, -126, 7885, -2315, 6576, 7233, -1056, 9418, 3103, -967, -2402, 5792, 7169, 10065, -2573, -6037, 4164, 7358, -180, -4190, -1877, 4940, -1126, -2485, 6054, -2241, 5144, 5471, -1185, 4573, 11596, -2306, -271, 11568, -4868, 6297, 4310, 8493, 7158, -4687, 1092, -880, 8145, 4638, 5246, -3870, 6122, 4818, 5611, -6418, 3942, -726, 3765, 9262, -974, 4955, 6057, 2953, -1974, 8506, -1170, -182, -160, 5044, 7437, -941, 3471, 4592, -1498, -2839, -630, 7125, 12836, -2712, 6760, 4981, 5536, -3242, 8943, -12592, -1263, -363, 3183, 11373, -976, -4168, -3316, 7544, -2969, 7960, 190, -4384, -1401, -2441, 8935, -3100, 9739, 8520, 7164, 5582, 259, 4969, 1731, -2431, 4411, -8167, 3930, -2725, 4913, 9081, -1463, 8334, -4279, 1631, 2244, 3563, 5701, 6420, 8175, 5574, 5615, 1863, 5644, 4750, 11416, 2735, -1347, 8907, 7140, -2160, -2441, -5500, -1027, -1330, -2412, 8310, 2451, 3676, -24, 5032, 6362, 8087, 7991, 2292, -5364, 6109, -2061, 12315, -3751, 4143, 4095, 1474, 7327, -6058, 5209, -1022, -571, -1694, 7873, 4929, 9169, -556, 4284, -991, 1403, -1486, -723, 4641, -522, 5800, -685, 5460, -1055, 5394, 4026, -1925, -741, -317, 4803, 6134, 2507, -1658, 7487, 729, -645, 8845, 9774, 3303, 3966, -4893, -657, 3860, -4120, -6229, 2897, 6727, -4947, 5823, -1722, 5974, 5215, 9125, 4255, 3514, -7826, -333, 4326, -1431, -1605, -10157, 10010, 6902, 530, 8868, 4708, -4545, 3823, 4508, 8464, -1728, 2169, -1069, 8100, 187, -36, -1112, -2222, -1821, 1090, -1200, -882, 813, 6197, 6063, 4471, 7066, 4782, 5108, -1383, 8965, -1319, -3987, -736, 3377, 5801, -533, 6329, -878, 5219, -146, 3487, 4417, -1205, 4950, 347, -1160, 834, -859, -5883, 8376, -657, 5913, -1517, 6189, 8613, -644, 4226, -1382, -795, 7929, -5572, -669, -392, -312, 5799, -269, -1577, -2377, 10646, 4558, 1608, 11593, 4036, -1487, 1163, -102, -380, -198, 13137, 7407, -5967, 4983, 3186, -3160, 412, -802, -949, 4604, -761, -599, -1345, 7225, -6091, -2165, -1707, 9026, 6189, -6400, -2132, -870, -7421, -1639, 12630, 2969, -9679, 4710, -2587, 7510, -1211, -1062, 9572, 4754, -4692, 9473, -3837, 9613, 4919, -721, 438, 3676, -497, 5660, -730, -892, -1250, 4344, 3307, -737, 4226, 7502, 3824, 4976, 3555, 3395, 4751, 5951, 5744, 4975, -893, 3099, -1599, 10522, -636, 7461, 5596, -2487, -1367, -4188, 4852, -9787, 7933, 5158, -484, -1047, 7642, -1660, -1338, -4094, -172, 4555, 4808, 6629, 9908, 5737, 4096, 3805, -1661, -2275, 9717, -1954, -2624}; - model->setOperandValue(op10, op10_init, sizeof(int32_t) * 512); - static uint8_t op11_init[] = {126, 152, 123, 196, 129, 149, 123, 151, 103, 137, 92, 131, 115, 153, 126, 116, 156, 108, 148, 125, 111, 132, 122, 194, 109, 25, 119, 161, 170, 179, 118, 114, 171, 154, 154, 112, 106, 157, 145, 110, 137, 110, 112, 118, 142, 124, 165, 154, 104, 189, 119, 106, 149, 103, 154, 105, 164, 99, 146, 129, 179, 46, 114, 168, 148, 121, 115, 161, 148, 157, 112, 123, 118, 158, 110, 109, 141, 188, 78, 144, 161, 168, 112, 152, 127, 144, 119, 100, 124, 114, 134, 128, 166, 112, 131, 99, 115, 127, 117, 124, 89, 162, 126, 149, 164, 119, 147, 164, 82, 111, 191, 123, 115, 100, 104, 121, 186, 172, 128, 116, 141, 166, 115, 109, 125, 105, 158, 144, 133, 188, 119, 152, 184, 127, 161, 119, 168, 148, 129, 149, 147, 157, 126, 112, 141, 165, 128, 103, 104, 142, 137, 166, 134, 143, 98, 189, 176, 125, 119, 135, 124, 103, 143, 157, 131, 146, 102, 115, 118, 101, 137, 179, 152, 115, 130, 153, 142, 122, 121, 133, 203, 114, 160, 114, 151, 165, 119, 114, 132, 149, 130, 154, 180, 154, 126, 112, 157, 109, 121, 145, 151, 138, 104, 75, 61, 113, 144, 100, 82, 137, 165, 127, 153, 116, 135, 180, 93, 151, 90, 149, 119, 224, 156, 136, 105, 141, 151, 87, 122, 113, 107, 159, 107, 124, 159, 124, 177, 115, 174, 102, 109, 69, 93, 103, 192, 150, 110, 115, 109, 106, 95, 114, 117, 122, 126, 139, 93, 123, 117, 152, 155, 150, 151, 152, 177, 165, 145, 147, 108, 196, 119, 133, 130, 119, 108, 152, 157, 177, 138, 147, 115, 113, 164, 120, 172, 113, 125, 125, 142, 109, 114, 116, 155, 123, 144, 190, 127, 204, 115, 166, 145, 137, 120, 148, 114, 116, 140, 145, 152, 119, 116, 158, 163, 116, 121, 154, 104, 128, 141, 120, 106, 126, 112, 126, 149, 173, 84, 151, 124, 129, 122, 120, 155, 120, 101, 153, 174, 109, 154, 171, 154, 125, 112, 128, 103, 121, 162, 119, 109, 110, 135, 145, 132, 115, 144, 185, 168, 154, 175, 121, 146, 153, 116, 141, 114, 121, 118, 171, 114, 147, 121, 175, 133, 146, 121, 123, 124, 112, 125, 99, 107, 110, 117, 112, 114, 99, 139, 183, 149, 67, 124, 162, 119, 164, 161, 126, 144, 120, 153, 134, 111, 163, 157, 178, 115, 155, 168, 156, 150, 140, 121, 160, 133, 116, 163, 99, 123, 145, 93, 136, 127, 147, 113, 111, 164, 111, 136, 132, 120, 111, 130, 158, 121, 178, 160, 88, 111, 118, 152, 164, 128, 153, 152, 128, 147, 155, 117, 165, 123, 163, 95, 95, 122, 173, 122, 149, 137, 113, 179, 152, 117, 145, 112, 164, 151, 97, 114, 112, 158, 115, 108, 109, 118, 111, 113, 141, 121, 181, 127, 145, 113, 152, 132, 143, 118, 103, 119, 121, 144, 107, 152, 143, 168, 146, 165, 114, 171, 100, 159, 161, 120, 88, 155, 126, 112, 210, 107, 150, 100, 121, 168, 94, 107, 172, 113, 121, 119, 182, 115, 171, 113, 119, 123, 91, 98, 154, 122, 111, 186, 116, 158, 115, 87, 149, 118, 157, 132, 148, 117, 172, 192, 164, 113, 114, 92, 153, 141, 111, 112, 171, 80, 107, 63, 110, 123, 120, 95, 142, 187, 181, 116, 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124, 110, 107, 146, 115, 166, 124, 157, 158, 142, 117, 127, 164, 129, 136, 124, 175, 169, 124, 114, 123, 118, 212, 124, 88, 174, 131, 122, 149, 118, 157, 144, 177, 128, 185, 189, 181, 118, 131, 126, 146, 134, 143, 158, 109, 174, 126, 123, 137, 165, 108, 154, 112, 116, 128, 162, 157, 186, 118, 145, 131, 123, 127, 148, 161, 170, 195, 154, 115, 138, 112, 123, 147, 144, 183, 129, 149, 163, 124, 135, 114, 165, 127, 124, 121, 127, 181, 145, 154, 182, 163, 131, 119, 106, 116, 142, 124, 129, 181, 135, 138, 122, 145, 114, 115, 118, 131, 127, 126, 110, 136, 121, 108, 115, 199, 124, 143, 93, 121, 130, 176, 138, 122, 142, 106, 132, 112, 104, 34, 151, 145, 123, 127, 127, 148, 115, 149, 133, 127, 131, 159, 123, 125, 165, 139, 90, 168, 105, 108, 133, 168, 130, 194, 122, 113, 162, 128, 105, 132, 113, 154, 147, 132, 122, 137, 131, 188, 102, 125, 144, 151, 99, 159, 135, 121, 138, 122, 119, 113, 119, 127, 148, 130, 128, 116, 141, 121, 132, 121, 131, 126, 130, 147, 114, 115, 167, 145, 163, 118, 119, 124, 115, 146, 148, 122, 157, 124, 171, 147, 156, 148, 119, 181, 175, 126, 135, 122, 109, 173, 137, 143, 121, 142, 131, 163, 116, 125, 142, 125, 136, 108, 125, 148, 120, 59, 123, 125, 113, 160, 124, 104, 113, 118, 124, 115, 114, 133, 117, 156, 116, 164, 125, 165, 163, 144, 125, 131, 126, 148, 152, 158, 133, 152, 183, 110, 77, 124, 122, 143, 114, 164, 126, 94, 127, 152, 139, 113, 117, 105, 116, 127, 135, 147, 166, 150, 154, 141, 116, 133, 122, 49, 98, 150, 102, 118, 139, 152, 169, 123, 122, 119, 116, 206, 117, 161, 115, 111, 82, 122, 121, 173, 151, 118, 165, 100, 119, 109, 143, 120, 106, 167, 118, 171, 138, 124, 166, 129, 124, 137, 123, 115, 134, 157, 110, 152, 181, 133, 102, 152, 164, 141, 122, 139, 121, 133, 123, 147, 115, 136, 122, 114, 113, 145, 138, 187, 195, 143, 123, 170, 117, 110, 124, 121, 95, 117, 149, 123, 139, 204, 130, 114, 112, 92, 121, 144, 139, 149, 120, 122, 156, 130, 181, 170, 99, 142, 125, 153, 102, 119, 132, 134, 169, 143, 156, 157, 190, 120, 182, 104, 97, 160, 110, 122, 123, 155, 155, 121, 120, 145, 127, 113, 173, 133, 136, 170, 133, 161, 106, 124, 172, 173, 159, 143, 164, 121, 183, 148, 98, 123, 178, 134, 151, 133, 141, 144, 159, 179, 226, 135, 115, 109, 157, 116, 102, 124, 148, 125, 111, 135, 143, 114, 124, 139, 121, 125, 126, 120, 153, 117, 148, 179, 179, 122, 116, 154, 130, 122, 121, 119, 111, 114, 125, 117, 134, 160, 124, 114, 144, 122, 151, 119, 120, 185, 173, 155, 116, 153, 149, 109, 119, 156, 111, 119, 168, 116, 151, 123, 127, 119, 146, 125, 73, 121, 140, 161, 122, 180, 105, 132, 149, 143, 126, 132, 140, 134, 156, 116, 139, 155, 148, 142, 151, 104, 131, 117, 133, 138, 130, 154, 147, 127, 102, 124, 186, 142, 134, 152, 147, 128, 119, 146, 141, 153, 136, 164, 131, 146, 144, 148, 118, 132, 119, 159, 174, 140, 113, 119, 44, 124, 130, 146, 137, 120, 126, 130, 155, 144, 145, 134, 177, 119, 147, 137, 129, 130, 143, 149, 144, 146, 159, 120, 138, 112, 121, 137, 77, 162, 141, 150, 149, 121, 148, 137, 143, 124, 130, 123, 122, 152, 136, 84, 141, 148, 174, 119, 119, 124, 142, 110, 114, 155, 134, 150, 118, 135, 131, 139, 124, 115, 132, 125, 102, 107, 120, 123, 94, 168, 120, 152, 96, 121, 121, 155, 135, 125, 142, 123, 121, 124, 114, 176, 115, 140, 131, 105, 132, 147, 92, 140, 147, 116, 123, 142, 125, 124, 139, 157, 107, 139, 156, 112, 151, 156, 106, 164, 123, 126, 155, 130, 110, 135, 154, 142, 142, 109, 122, 131, 125, 145, 111, 133, 135, 164, 120, 147, 129, 125, 105, 104, 126, 135, 127, 135, 136, 173, 134, 136, 145, 120, 136, 109, 139, 127, 127, 132, 122, 124, 152, 153, 144, 113, 148, 156, 121, 153, 129, 155, 146, 157, 174, 142, 131, 144, 131, 167, 157, 148, 136, 127, 130, 139, 136, 153, 129, 131, 170, 148, 127, 124, 128, 112, 170, 136, 125, 166, 120, 152, 112, 110, 127, 124, 179, 112, 123, 112, 125, 116, 124, 132, 124, 135, 132, 172, 135, 135, 157, 138, 103, 151, 131, 153, 131, 148, 119, 142, 163, 117, 113, 123, 125, 152, 130, 141, 156, 131, 115, 138, 142, 114, 119, 118, 124, 145, 110, 144, 138, 136, 135, 140, 118, 143, 126, 117, 113, 158, 119, 127, 145, 174, 145, 126, 138, 121, 124, 149, 145, 144, 125, 115, 115, 134, 123, 143, 151, 117, 138, 116, 128, 176, 138, 122, 128, 142, 125, 158, 154, 132, 138, 124, 122, 117, 125, 125, 153, 156, 115, 138, 129, 143, 127, 142, 144, 141, 127, 135, 123, 111, 123, 135, 129, 139, 127, 113, 105, 129, 133, 168, 176, 139, 132, 140, 129, 119, 129, 123, 130, 127, 140, 125, 129, 151, 150, 126, 114, 120, 121, 156, 169, 171, 122, 124, 158, 132, 148, 154, 130, 147, 117, 140, 135, 122, 145, 155, 157, 134, 170, 140, 152, 132, 150, 140, 138, 140, 135, 142, 131, 140, 143, 125, 130, 136, 121, 136, 147, 138, 133, 152, 121, 143, 145, 124, 139, 135, 145, 133, 156, 122, 155, 144, 112, 129, 143, 135, 136, 128, 128, 141, 115, 156, 133, 134, 136, 123, 128, 122, 152, 130, 158, 145, 133, 167, 126, 126, 149, 113, 122, 129, 135, 121, 132, 124, 113, 149, 149, 105, 120, 151, 120, 107, 117, 122, 121, 113, 102, 123, 122, 155, 145, 114, 144, 133, 149, 127, 114, 156, 164, 175, 121, 157, 134, 129, 135, 115, 112, 91, 185, 93, 143, 128, 96, 121, 139, 118, 158, 114, 144, 139, 123, 98, 165}; - model->setOperandValue(op11, op11_init, sizeof(uint8_t) * 4608); - static int32_t op13_init[] = {-4877, 721, 678, 4292, 10071, 1898, -2621, -19306, -21498, -13936, 8924, -10957, -15039, -8760, 10726, -1940, -2609, -13946, -15129, -10331, -15062, -7899, -16830, 2096, 1485, -16444, -7274, 2650, -8279, -3054, 11104, -27779, 5796, -4926, 24092, -17812, 2693, -6712, -13479, 9877, -14408, 3498, -17612, 9954, 753, 1425, -21266, 5028, -3375, -1855, -23583, -24441, -11628, -3866, 1589, -40614, -23824, -1652, -1879, 5411, 5459, -25641, -5617, 19247, -956, 26940, 5175, 8275, 8123, -27174, -20716, 8311, -11679, -10654, -22645, 13393, 6458, -2921, 182, 6807, -5019, -21492, -17806, -30854, 15622, 4366, 9048, -7021, 2901, 14294, 2528, -121, 3775, 6381, -3824, -4337, -9583, -11327, 20171, 2507, -18367, 22500, -406, -28676, 10885, -26140, -3397, -16997, -21130, -14684, 29333, 21191, 2520, 13654, -28552, 12486, 1026, 4616, 5706, 5356, -18821, 5628, -7792, -12895, 8208, 10447, -389, 4966, -7798, -16495, -7945, 7699, -9645, 26447, -18442, 15937, -5922, -1229, 6794, 13569, -2549, -5813, -6050, -26988, -20065, -24300, -22053, -27818, 2256, 10564, -13191, 5268, 7864, -9014, -14307, -22404, 11324, -16337, -2087, -19364, -17940, 6916, -17699, -8865, -13785, -10609, -6527, -12828, -10222, -33670, 3599, 2772, 24751, -13469, -555, -8745, -20538, -11486, -4666, -19643, 2269, -24734, -5421, -7096, 14939, -19271, -16481, 3595, 12307, -11509, -19461, -8256, -15293, -10568, -26738, -5637, -21581, -24955, -18708, -8090, -12189, 17218, -11559, 11904, 2785, -4204, -32033, 15474, -15519, -13122, -6947, -9276, -12824, 187, 21634, -26, -9278, -26205, -1350, -28679, 11271, 10390, -20143, 7467, 10104, 7998, 5846, -19189, -18263, 8629, 1139, -4108, -5539, -15491, -16658, 375, -6915, -13589, -6239, -5451, 20095, -15352, 899, 541, 5650, -639, -2976, 11613, 11304, -8192, -31960, 4233, -17844, -31553, -22933, -1883, -18179, -1943, -8066, -17810, -16706, -9513, -10900, -1222, -17231, -4421, -10161, -35927, -2653, -22824, -2462, -25862, 1057, -7506, 13392, -27146, -13084, 3736, -6578, -31150, 776, -22934, 20460, -46451, 9939, 3712, 4418, -4606, -289, -21906, -482, 6887, 11068, 9434, -1696, -13174, -558, -4058, 4646, 12758, -9873, -16593, -33326, 1282, 10015, -12681, -17861, 4800, 6978, -12072, -28769, 4991, -23681, -5425, 12025, -20827, 5364, -9880, -7133, 1398, -3699, -6872, 4494, -13878, -39027, -23532, -26402, -11359, -8668, 1792, 2562, 86, -5300, -3241, 11484, 5093, -23266, 11526, -5602, 10598, -18329, -8082, -23669, -10421, 17809, -17662, -23783, 5655, -13770, 9115, 8536, -4997, 11552, -7150, -25158, -2703, 14584, -10293, 8408, -9911, 2729, 28004, -23819, -16981, -21319, -14831, -26859, -9853, 196, -26785, 16281, -18332, 14625, -14248, 1007, -22995, 6964, 7100, -12551, 19360, -10236, -7287, -1589, -10854, 3629, 207, -11070, -9435, -13274, -20483, -19296, 2756, -3152, -892, -8157, 18293, 5096, -151, -10006, -20583, -69, -6878, -1009, 13213, -14484, -1917, -29455, -15284, 13163, 1377, -9193, -901, -13882, -9328, 6492, 5410, 9050, -26715, -6989, -5216, -20873, -30210, -1157, 18495, 22359, -12892, 884, -8823, -2142, 5722, 6952, -33411, 420, -36310, -3378, 211, -10879, -23100, 17165, -35053, -14920, 2745, 5247, -6643, -2567, 1052, 5052, 14906, 14310, 1363, -7743, -21934, 5841, 9654, -1299, -13084, -17796, 12736, -10214, -25004, -4992, -26200, -10513, 29618, 12890, -8492, -17917, -7479, -36350, 10551, -9685, -19198, -5370, -13439, 9733, -22280, -13104, 8214, -1993, 10421, -28592, -6668, -34387, 8927, -14614, 5269, 17166, 10229, -9886, -28003, -14966, 4052, 3941, 4526, 832, -9720, 11659, -12904, 4272, -1152, -1744, 17584, -25673, -11159, 722, -19940, -19347, -19639, 12573, -5779, -6954, 11062}; - model->setOperandValue(op13, op13_init, sizeof(int32_t) * 512); - static uint8_t op14_init[] = {116, 122, 116, 103, 124, 118, 132, 111, 101, 108, 110, 123, 101, 116, 121, 124, 114, 104, 105, 97, 103, 127, 113, 110, 109, 116, 126, 122, 117, 109, 118, 100, 112, 116, 110, 116, 94, 127, 111, 120, 116, 118, 123, 104, 121, 122, 96, 117, 107, 117, 96, 122, 99, 105, 115, 121, 105, 121, 114, 109, 119, 113, 126, 117, 125, 100, 115, 109, 103, 119, 117, 113, 126, 119, 112, 121, 111, 117, 112, 120, 128, 111, 123, 115, 107, 115, 109, 105, 108, 131, 105, 110, 106, 119, 117, 113, 119, 101, 110, 118, 122, 118, 110, 105, 118, 122, 111, 94, 112, 122, 107, 104, 98, 101, 104, 111, 100, 116, 108, 117, 111, 120, 123, 121, 108, 124, 115, 101, 115, 104, 118, 119, 113, 109, 128, 115, 106, 121, 118, 116, 114, 110, 110, 101, 124, 111, 109, 106, 110, 121, 101, 109, 113, 115, 106, 122, 116, 101, 122, 134, 107, 116, 115, 116, 127, 121, 113, 114, 130, 120, 113, 114, 110, 122, 105, 112, 112, 113, 117, 111, 114, 111, 109, 115, 122, 101, 119, 121, 108, 99, 120, 120, 114, 115, 113, 139, 116, 115, 125, 101, 116, 105, 125, 115, 107, 120, 113, 125, 112, 109, 118, 110, 100, 112, 96, 121, 116, 109, 113, 123, 125, 108, 108, 111, 110, 120, 118, 124, 119, 114, 101, 114, 116, 118, 119, 119, 118, 120, 116, 117, 123, 118, 106, 112, 109, 107, 112, 124, 118, 112, 119, 124, 116, 116, 110, 95, 113, 111, 98, 118, 123, 136, 111, 106, 114, 112, 111, 125, 110, 126, 97, 115, 115, 117, 109, 132, 102, 112, 110, 113, 110, 126, 124, 118, 108, 107, 119, 107, 118, 111, 120, 114, 105, 117, 105, 111, 132, 119, 115, 115, 114, 109, 105, 104, 107, 123, 107, 128, 107, 110, 103, 118, 103, 112, 111, 108, 126, 117, 115, 114, 114, 108, 103, 100, 110, 103, 97, 106, 119, 117, 121, 121, 114, 122, 111, 119, 110, 108, 120, 119, 128, 115, 120, 124, 116, 109, 117, 111, 119, 119, 120, 124, 124, 117, 116, 114, 128, 100, 115, 135, 117, 123, 117, 100, 123, 105, 120, 120, 120, 104, 118, 122, 110, 119, 104, 109, 123, 113, 104, 101, 117, 124, 120, 116, 105, 115, 126, 96, 109, 123, 112, 126, 121, 110, 107, 127, 121, 119, 111, 122, 112, 120, 100, 115, 97, 131, 112, 117, 112, 129, 112, 120, 113, 121, 122, 114, 114, 110, 110, 127, 115, 109, 125, 126, 111, 119, 112, 123, 124, 122, 121, 123, 125, 107, 109, 105, 114, 93, 103, 125, 110, 111, 117, 106, 130, 116, 119, 124, 125, 117, 110, 125, 117, 104, 113, 125, 121, 112, 109, 114, 101, 101, 119, 107, 114, 106, 125, 114, 112, 105, 117, 117, 131, 119, 125, 124, 110, 120, 110, 89, 110, 108, 111, 117, 117, 113, 117, 106, 119, 110, 104, 131, 122, 126, 112, 112, 110, 114, 104, 110, 101, 114, 116, 115, 122, 123, 103, 122, 109, 122, 103, 117, 94, 107, 130, 111, 114, 107, 113, 117, 112, 117, 121, 124, 121, 107, 109, 124, 106, 123, 114, 105, 115, 118, 113, 110, 114, 115, 117, 106, 125, 110, 115, 109, 117, 115, 104, 110, 105, 112, 113, 116, 100, 115, 114, 112, 106, 124, 113, 121, 115, 110, 129, 105, 112, 113, 107, 108, 105, 116, 122, 109, 123, 114, 115, 103, 109, 124, 113, 108, 105, 101, 115, 120, 127, 126, 115, 108, 102, 117, 122, 105, 112, 107, 112, 113, 120, 119, 114, 106, 119, 115, 109, 117, 115, 110, 122, 114, 113, 116, 120, 128, 117, 106, 109, 115, 122, 105, 113, 111, 108, 118, 105, 130, 120, 112, 123, 119, 113, 115, 122, 99, 112, 118, 103, 116, 112, 121, 115, 121, 108, 109, 120, 98, 111, 108, 128, 108, 103, 123, 124, 128, 108, 112, 111, 118, 146, 121, 117, 121, 123, 120, 111, 115, 106, 109, 118, 114, 114, 110, 109, 116, 115, 110, 120, 102, 121, 114, 129, 120, 116, 120, 118, 106, 110, 110, 111, 119, 123, 106, 122, 109, 110, 109, 111, 113, 114, 109, 116, 116, 109, 102, 105, 121, 113, 122, 107, 109, 109, 109, 114, 117, 111, 104, 110, 110, 111, 117, 116, 117, 118, 96, 106, 115, 108, 129, 116, 112, 102, 105, 98, 95, 117, 112, 109, 132, 112, 111, 114, 106, 139, 109, 114, 109, 103, 112, 105, 113, 112, 106, 116, 109, 120, 115, 123, 120, 108, 113, 118, 120, 118, 113, 117, 118, 127, 114, 119, 110, 126, 115, 109, 115, 109, 113, 105, 105, 99, 118, 96, 126, 109, 106, 104, 124, 129, 117, 105, 124, 114, 115, 107, 125, 120, 115, 120, 112, 112, 107, 108, 117, 105, 109, 117, 127, 112, 114, 127, 104, 115, 114, 116, 112, 111, 120, 121, 115, 106, 115, 118, 117, 110, 119, 121, 116, 110, 122, 94, 109, 118, 103, 106, 114, 115, 94, 105, 129, 116, 107, 101, 109, 120, 104, 117, 107, 108, 104, 91, 118, 126, 116, 112, 116, 118, 103, 111, 114, 108, 115, 116, 108, 109, 117, 115, 126, 109, 122, 113, 109, 129, 120, 124, 123, 111, 112, 122, 114, 103, 118, 106, 108, 113, 118, 127, 124, 118, 114, 106, 112, 109, 106, 114, 106, 113, 117, 122, 129, 122, 116, 114, 113, 104, 124, 121, 141, 115, 103, 98, 101, 108, 114, 105, 118, 103, 106, 105, 125, 104, 129, 115, 113, 111, 142, 115, 114, 110, 112, 116, 105, 102, 129, 117, 118, 105, 120, 117, 115, 116, 136, 108, 118, 116, 113, 108, 123, 112, 111, 102, 118, 113, 120, 114, 115, 122, 100, 119, 115, 100, 135, 124, 104, 118, 112, 121, 120, 107, 107, 117, 128, 121, 117, 103, 119, 118, 124, 117, 119, 98, 118, 117, 108, 116, 123, 127, 114, 124, 106, 109, 124, 114, 129, 115, 122, 106, 111, 120, 105, 109, 110, 117, 106, 118, 101, 107, 128, 111, 113, 118, 135, 108, 117, 114, 119, 109, 101, 127, 122, 116, 124, 107, 110, 112, 117, 121, 124, 111, 119, 103, 123, 112, 119, 121, 107, 111, 102, 126, 110, 116, 123, 109, 107, 124, 109, 120, 106, 96, 116, 102, 130, 123, 111, 137, 124, 100, 111, 119, 107, 112, 124, 131, 121, 110, 111, 101, 126, 111, 132, 117, 112, 110, 115, 115, 102, 111, 122, 105, 118, 117, 111, 117, 125, 102, 114, 111, 124, 120, 95, 102, 107, 120, 110, 131, 105, 118, 107, 109, 110, 104, 119, 123, 118, 110, 129, 118, 113, 109, 111, 117, 96, 98, 125, 118, 132, 115, 101, 111, 108, 123, 114, 123, 115, 110, 104, 106, 123, 114, 114, 115, 119, 107, 112, 128, 122, 116, 111, 106, 111, 95, 107, 122, 115, 122, 113, 112, 114, 110, 127, 122, 107, 122, 134, 124, 114, 110, 119, 119, 118, 118, 109, 109, 113, 116, 124, 113, 112, 112, 103, 113, 124, 109, 115, 112, 118, 111, 115, 106, 115, 131, 119, 97, 111, 111, 131, 113, 116, 117, 113, 121, 115, 105, 117, 125, 120, 113, 125, 116, 115, 125, 111, 116, 112, 133, 116, 119, 120, 111, 122, 130, 113, 116, 107, 115, 113, 118, 101, 129, 109, 102, 118, 123, 128, 102, 116, 121, 105, 114, 110, 102, 118, 129, 114, 124, 115, 123, 115, 137, 121, 110, 119, 114, 131, 111, 116, 118, 114, 110, 108, 102, 121, 113, 124, 114, 115, 115, 114, 125, 109, 99, 114, 111, 110, 111, 129, 115, 115, 121, 117, 108, 112, 109, 130, 104, 112, 105, 108, 121, 119, 116, 111, 109, 112, 103, 111, 126, 123, 106, 95, 108, 114, 119, 131, 119, 121, 115, 114, 131, 104, 109, 118, 108, 100, 114, 126, 111, 96, 99, 114, 100, 106, 108, 133, 111, 103, 116, 114, 116, 113, 120, 114, 121, 113, 110, 98, 126, 117, 113, 101, 106, 115, 105, 108, 110, 108, 103, 120, 123, 108, 106, 117, 120, 120, 113, 114, 113, 116, 115, 125, 112, 122, 114, 121, 116, 117, 115, 127, 119, 108, 125, 104, 120, 117, 118, 112, 117, 108, 120, 108, 127, 106, 121, 109, 120, 116, 122, 110, 110, 118, 112, 107, 105, 125, 119, 110, 129, 114, 124, 126, 118, 113, 128, 116, 106, 107, 115, 118, 117, 117, 109, 106, 117, 110, 124, 105, 116, 119, 110, 132, 117, 124, 100, 130, 131, 104, 121, 114, 111, 116, 120, 113, 118, 115, 113, 108, 120, 101, 113, 126, 106, 116, 111, 118, 116, 111, 110, 126, 114, 115, 112, 125, 119, 117, 109, 112, 94, 123, 103, 110, 127, 121, 110, 111, 103, 100, 110, 108, 117, 117, 112, 138, 112, 111, 99, 114, 105, 110, 129, 110, 106, 96, 112, 117, 126, 109, 118, 120, 123, 100, 110, 123, 115, 111, 116, 108, 104, 102, 112, 111, 114, 105, 142, 125, 112, 117, 126, 129, 103, 115, 124, 117, 116, 128, 117, 116, 117, 112, 106, 113, 106, 114, 116, 119, 115, 111, 113, 113, 111, 121, 102, 104, 114, 112, 120, 115, 112, 116, 114, 106, 113, 111, 102, 113, 133, 128, 108, 127, 117, 114, 111, 124, 116, 123, 117, 121, 122, 113, 116, 108, 113, 118, 116, 118, 114, 126, 106, 115, 111, 114, 103, 114, 125, 114, 123, 112, 116, 118, 115, 111, 114, 117, 108, 110, 122, 116, 112, 114, 115, 111, 124, 114, 109, 111, 103, 104, 104, 122, 116, 114, 107, 124, 118, 116, 113, 118, 118, 115, 123, 118, 119, 122, 127, 109, 115, 118, 112, 114, 115, 113, 109, 118, 113, 118, 127, 114, 122, 108, 113, 112, 109, 121, 119, 123, 122, 110, 110, 110, 116, 112, 115, 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102, 114, 104, 112, 120, 124, 109, 124, 104, 100, 138, 113, 96, 106, 127, 96, 131, 112, 115, 111, 116, 117, 119, 102, 115, 118, 123, 94, 109, 113, 113, 124, 112, 100, 99, 114, 95, 155, 121, 112, 119, 125, 99, 107, 116, 109, 112, 100, 104, 132, 106, 123, 133, 87, 103, 125, 104, 101, 116, 84, 113, 123, 113, 113, 120, 119, 117, 102, 108, 105, 108, 126, 120, 106, 117, 129, 115, 98, 102, 109, 114, 112, 113, 116, 136, 98, 122, 119, 116, 111, 141, 128, 111, 121, 110, 91, 90, 101, 120, 109, 117, 118, 115, 107, 101, 107, 110, 93, 117, 119, 139, 116, 124, 123, 112, 106, 112, 115, 119, 112, 103, 128, 99, 115, 123, 128, 97, 117, 113, 121, 110, 118, 124, 103, 121, 123, 117, 118, 103, 116, 118, 104, 92, 92, 162, 117, 132, 127, 125, 101, 121, 126, 120, 109, 116, 116, 107, 116, 120, 130, 114, 126, 123, 105, 111, 103, 92, 129, 105, 101, 110, 114, 119, 132, 116, 102, 111, 113, 107, 142, 111, 84, 114, 117, 113, 104, 117, 112, 115, 117, 122, 115, 100, 133, 109, 126, 106, 163, 114, 93, 123, 104, 109, 115, 124, 108, 121, 119, 118, 119, 113, 104, 111, 108, 118, 120, 111, 131, 113, 126, 112, 109, 105, 117, 122, 103, 101, 116, 136, 101, 101, 110, 109, 113, 110, 102, 110, 92, 130, 106, 108, 117, 148, 105, 136, 117, 110, 117, 118, 117, 103, 106, 113, 113, 108, 120, 145, 111, 131, 119, 115, 120, 99, 118, 108, 115, 99, 116, 100, 127, 123, 107, 95, 116, 97, 108, 118, 104, 104, 130, 105, 133}; - model->setOperandValue(op14, op14_init, sizeof(uint8_t) * 262144); - static int32_t op16_init[] = {-373, 18951, -2733, 15414, 20558, 9709, 16138, -3792, 16558, -650, 7012, 20511, -274, -1847, -296, -372, 15068, 17543, 1854, 74, -4582, 9756, -132, 19846, -137, 1657, -3355, 14715, 5999, -109, 1181, -896, 15621, 22768, 18127, -50, 8284, 9109, -4146, 16821, -252, -167, -265, 17612, 17446, -1860, -174, 32085, 10056, 20509, -3583, -567, -1516, -177, 8726, -658, 21203, 7016, 16924, 7913, -327, -213, -147, 5525, -7896, 16433, 5983, 11020, 249, -51, -208, 16425, -472, -540, 19888, 19421, 19490, -251, 16580, 9068, -9075, -1014, -453, -308, 10261, -11969, 12104, -303, -334, -220, 8595, 12251, 11179, 8047, 8653, -665, 21897, 101, 18662, -20520, 14391, 7059, 11269, -226, 11338, -989, -276, -262, -423, -70, 15504, 17173, 12849, -242, 11750, -11968, 17192, 16985, -305, 20467, -92, 13467, 12979, 17623, 20170, 18187, -5626, 19962, -4048, -190, 23006, -320, 7595, 10846, 20, 18355, 15397, 7568, 15015, 14491, 10174, -177, 8623, -914, -778, -693, 22450, 23497, 18379, 9846, -514, 12853, 20087, -308, -87, -255, 23893, -2604, -1064, 17113, -206, 23683, -89, 18467, -803, -7233, 1299, -290, -773, -117, 25412, -172, 9850, 15154, -172, -198, -7326, -265, -214, 9724, 18236, -362, -452, -7554, 9933, -4, -127, -223, 27793, -194, 9869, -428, 11611, 14325, -268, 18914, -118, -1019, 13637, 19062, 13774, 18618, -5808, 16918, 7092, -218, -868, 16691, 14164, 11990, 22363, 22618, 7830, -8205, 11259, -3831, -1406, -236, 102, -348, 2647, -284, 17992, -349, 30338, 18878, 9241, 96, -1265, 22850, 23634, -910, -350, 18553, -566, 12124, 22674, -299, -2035, -86, 25130, -178, -610, -1100, 22637, -153, 12396, 13115, 27149, -57, -304, 15693, 22380, 22082, -662, -6306, -518, 18296, -281, -464, -1263, 3, -140, 11747, 54, 12219, 3128, -155, 20521, -356, -10700, -539, 16197, -151, 17991, -102, -12174, -2030, 23213, -278, -203, -188, 17503, -304, 345, -142, 12505, -83, 19624, -135, -313, 19070, 19043, 10744, 9237, -1029, 17365, -2722, 8900, 9481, -344, -97, -12518, 17213, -3963, -254, 4028, 20124, 18711, -5954, -199, 2229, -258, -1966, -176, -262, 18063, 18465, -403, 18370, 11978, 18498, 19527, -347, -188, -271, 20380, -154, -2272, 20353, 11041, -144, 17950, -205, -375, 23924, -394, 18961, -253, 22110, -163, 21443, -1523, 13, 20507, -174, -256, 14703, 14486, 9154, 16687, 9980, 9810, -5803, 18126, -219, 18072, 19486, 17649, 9061, 15158, 10930, 16784, 26204, -92, -172, -1126, -271, 6833, 1, 8767, 8469, -1538, -2265, -1815, 717, 8828, 14576, 15026, 12379, 18820, 9101, -704, 7009, 19635, 9413, -507, -2955, -16, -1749, 18378, 17066, 8416, 15201, -2713, 13941, 19216, 13617, 9155, 20763, 9344, 1533, 8326, 16614, -120, -188, 1820, -1160, 11339, 13681, 18114, 9511, 9853, 9411, 13324, 16781, -2262, -1990, -350, 8337, 16602, -410, 11323, 18129, -131, -1119, -336, -350, 8603, 15452, 8164, -325, -1780, -363, -1131, 8952, -9907, -1509, 18187, -1339, -439, 21121, 15708, 21219, -65, 16491, 20942, 13862, -8041, -945, -397, -63, -3379, 16563, 67, 14948, -724, 9500, 124, -615, 19791, -1002, -336, 10514, 8578, -1919, 3883, -115, -316, 18782, 23092, -191, 13692, -332, 16282, -105, -475, 22437, -63, 13576, -421, -358, -519, 20581, 16634, 18871, 12517, -146, -189, -156, -3347, 6190, -404, -586, 16365, -86, 37494, 19932, 19, -7693, -1984, 16309, -1494, -312, 20347, -312, -221, -2841, 7908, -354, -687, -9984}; - model->setOperandValue(op16, op16_init, sizeof(int32_t) * 512); - static uint8_t op17_init[] = {144, 73, 143, 88, 88, 99, 98, 156, 84, 157, 95, 87, 162, 47, 152, 149, 88, 90, 153, 161, 161, 83, 152, 92, 141, 166, 162, 79, 164, 150, 39, 165, 98, 87, 97, 169, 92, 73, 163, 125, 149, 153, 186, 72, 88, 143, 147, 50, 99, 83, 150, 144, 149, 155, 85, 158, 84, 64, 87, 88, 147, 166, 147, 155, 168, 95, 159, 77, 151, 172, 152, 54, 146, 148, 90, 68, 88, 155, 66, 90, 79, 147, 145, 162, 60, 117, 72, 157, 160, 145, 80, 90, 60, 79, 84, 164, 79, 142, 83, 180, 97, 91, 85, 186, 88, 150, 153, 156, 163, 165, 83, 83, 90, 152, 79, 61, 67, 94, 157, 68, 151, 55, 66, 87, 77, 88, 146, 91, 167, 148, 90, 151, 86, 161, 172, 78, 80, 84, 93, 88, 71, 148, 96, 166, 143, 151, 79, 84, 87, 91, 181, 93, 91, 147, 152, 148, 75, 150, 137, 90, 156, 56, 150, 90, 156, 178, 147, 169, 188, 179, 60, 143, 77, 87, 157, 164, 171, 151, 149, 72, 84, 156, 148, 146, 101, 149, 154, 147, 64, 187, 144, 142, 86, 53, 156, 63, 155, 153, 87, 91, 83, 88, 178, 94, 63, 164, 154, 70, 86, 77, 87, 82, 86, 55, 64, 142, 143, 144, 147, 151, 71, 142, 91, 137, 53, 66, 93, 150, 153, 80, 56, 167, 134, 87, 161, 95, 84, 149, 158, 142, 66, 146, 141, 152, 84, 152, 85, 99, 48, 147, 168, 92, 79, 84, 148, 157, 150, 85, 152, 153, 140, 144, 146, 91, 169, 168, 111, 155, 89, 145, 139, 154, 84, 151, 119, 161, 170, 131, 85, 157, 149, 153, 107, 165, 160, 151, 86, 150, 94, 163, 145, 82, 93, 101, 75, 154, 83, 157, 85, 90, 152, 147, 151, 139, 151, 158, 96, 85, 93, 70, 149, 146, 146, 146, 159, 150, 75, 86, 146, 83, 85, 82, 88, 156, 149, 149, 81, 152, 137, 92, 97, 151, 85, 138, 46, 73, 155, 68, 152, 65, 148, 83, 152, 144, 75, 155, 150, 94, 93, 83, 77, 87, 84, 153, 73, 57, 87, 90, 90, 82, 95, 63, 99, 86, 147, 164, 169, 158, 78, 168, 79, 90, 40, 142, 136, 148, 86, 93, 86, 82, 85, 87, 148, 85, 74, 74, 141, 153, 155, 167, 91, 71, 78, 88, 146, 84, 102, 33, 78, 79, 70, 121, 81, 94, 143, 142, 158, 162, 118, 75, 95, 92, 94, 76, 82, 90, 161, 171, 165, 78, 96, 172, 97, 92, 149, 151, 155, 147, 88, 92, 74, 159, 151, 157, 139, 90, 172, 159, 89, 144, 166, 75, 95, 80, 155, 86, 66, 76, 143, 150, 206, 147, 147, 83, 150, 88, 145, 82, 151, 163, 87, 143, 147, 99, 86, 158, 144, 146, 153, 91, 77, 156, 93, 147, 89, 181, 176, 65, 152, 61, 145, 148, 147, 82, 85, 55, 89, 158, 179, 145, 152, 45, 147, 146, 94, 153, 41, 85, 151, 44, 146, 113, 160, 139, 86, 142, 151, 141, 81, 148, 136, 153, 153, 62, 147, 89, 83, 99, 92, 156, 77, 164, 89, 72, 183, 177, 162, 155, 86, 87, 165, 181, 164, 74, 164, 86, 156, 178, 164, 78, 180, 155, 110, 164, 97, 81, 92, 195, 90, 69, 168, 126, 157, 170, 197, 63, 81, 141, 162, 38, 98, 77, 156, 157, 153, 172, 88, 172, 79, 68, 80, 89, 166, 184, 164, 161, 177, 89, 112, 75, 159, 189, 161, 53, 153, 152, 83, 67, 85, 174, 70, 87, 79, 154, 160, 174, 79, 121, 74, 162, 174, 162, 89, 88, 59, 82, 81, 173, 68, 150, 77, 179, 93, 90, 85, 197, 83, 156, 162, 169, 177, 185, 85, 81, 83, 165, 73, 54, 74, 91, 157, 63, 162, 51, 56, 79, 67, 77, 147, 82, 177, 163, 83, 157, 86, 166, 183, 76, 72, 87, 87, 79, 67, 156, 94, 185, 157, 162, 71, 68, 82, 89, 187, 95, 81, 162, 161, 165, 63, 154, 145, 84, 175, 49, 170, 84, 164, 170, 160, 185, 211, 194, 40, 154, 81, 78, 174, 177, 182, 157, 160, 64, 78, 163, 161, 129, 97, 166, 181, 163, 44, 203, 148, 153, 81, 47, 160, 58, 171, 166, 83, 85, 77, 84, 183, 90, 61, 176, 163, 73, 82, 70, 84, 74, 84, 73, 65, 149, 149, 154, 158, 161, 57, 149, 85, 147, 58, 55, 83, 166, 160, 71, 46, 182, 143, 75, 182, 86, 79, 158, 152, 157, 52, 153, 150, 163, 75, 160, 83, 99, 46, 155, 188, 90, 76, 74, 152, 173, 160, 80, 154, 166, 139, 148, 155, 91, 190, 114, 181, 177, 79, 161, 132, 169, 79, 163, 117, 176, 170, 118, 75, 167, 163, 163, 110, 179, 112, 165, 84, 167, 91, 171, 156, 77, 83, 99, 84, 168, 72, 169, 83, 91, 163, 165, 155, 111, 151, 159, 93, 80, 89, 200, 161, 159, 162, 156, 168, 157, 66, 77, 159, 73, 83, 78, 82, 171, 164, 161, 73, 167, 142, 85, 92, 160, 73, 147, 33, 66, 171, 62, 168, 66, 158, 74, 165, 154, 71, 156, 160, 92, 87, 81, 78, 88, 88, 153, 67, 140, 88, 84, 85, 91, 90, 65, 102, 82, 150, 178, 185, 173, 75, 178, 80, 85, 157, 152, 139, 169, 80, 93, 79, 78, 72, 88, 154, 78, 64, 81, 143, 157, 162, 181, 90, 68, 88, 81, 167, 87, 101, 81, 88, 73, 88, 128, 88, 93, 148, 150, 174, 176, 95, 75, 89, 93, 95, 87, 82, 86, 162, 185, 184, 75, 91, 184, 93, 86, 163, 155, 166, 150, 89, 88, 74, 177, 151, 172, 148, 88, 21, 165, 83, 153, 168, 66, 91, 72, 174, 83, 62, 79, 151, 150, 229, 171, 144, 76, 164, 82, 153, 88, 160, 180, 80, 150, 161, 95, 88, 160, 153, 157, 168, 86, 70, 161, 94, 155, 84, 199, 177, 64, 164, 61, 163, 147, 159, 75, 71, 57, 85, 169, 204, 157, 164, 53, 147, 152, 86, 158, 25, 80, 160, 15, 157, 114, 168, 150, 79, 146, 160, 141, 89, 158, 144, 146, 138, 83, 127, 99, 91, 105, 101, 149, 88, 139, 98, 94, 146, 54, 140, 137, 92, 98, 144, 146, 150, 90, 143, 96, 132, 154, 149, 96, 157, 138, 159, 146, 103, 96, 101, 154, 103, 80, 137, 123, 140, 145, 168, 88, 97, 137, 140, 77, 102, 92, 137, 134, 137, 140, 96, 144, 92, 73, 91, 98, 140, 150, 143, 142, 156, 98, 87, 89, 141, 149, 135, 70, 140, 139, 97, 94, 94, 154, 82, 98, 90, 139, 139, 142, 76, 162, 78, 142, 153, 141, 85, 98, 74, 85, 94, 152, 94, 133, 96, 104, 100, 94, 88, 152, 89, 143, 140, 139, 147, 145, 99, 92, 95, 146, 88, 81, 90, 99, 143, 82, 140, 74, 81, 97, 91, 94, 132, 96, 150, 140, 93, 138, 93, 145, 151, 84, 85, 87, 100, 94, 85, 140, 98, 156, 133, 138, 92, 89, 96, 100, 162, 98, 93, 140, 140, 142, 97, 140, 130, 95, 138, 84, 149, 98, 144, 127, 141, 153, 162, 155, 80, 136, 87, 94, 144, 151, 168, 142, 143, 90, 92, 144, 142, 135, 103, 136, 153, 134, 90, 159, 135, 133, 97, 76, 144, 78, 140, 131, 96, 96, 88, 91, 138, 99, 76, 146, 136, 91, 94, 88, 90, 93, 94, 80, 74, 135, 134, 134, 144, 144, 80, 133, 96, 139, 78, 93, 100, 138, 145, 88, 67, 148, 125, 89, 149, 99, 93, 144, 151, 131, 94, 140, 132, 144, 92, 142, 95, 103, 88, 140, 159, 98, 93, 95, 133, 146, 140, 93, 147, 140, 133, 132, 138, 96, 154, 91, 187, 145, 91, 134, 138, 144, 92, 139, 119, 145, 139, 132, 90, 145, 140, 140, 107, 149, 37, 142, 95, 140, 96, 145, 138, 90, 96, 102, 83, 141, 86, 150, 99, 96, 138, 139, 140, 81, 137, 144, 100, 94, 98, 34, 140, 144, 142, 135, 153, 140, 94, 94, 135, 93, 97, 92, 94, 139, 137, 141, 90, 144, 127, 96, 100, 135, 91, 133, 57, 90, 145, 92, 132, 81, 141, 92, 144, 137, 93, 140, 138, 98, 94, 93, 86, 93, 93, 144, 91, 57, 93, 98, 95, 97, 97, 78, 106, 95, 137, 151, 146, 145, 89, 145, 85, 97, 31, 133, 127, 138, 95, 97, 93, 91, 91, 91, 136, 88, 78, 85, 130, 146, 142, 148, 99, 92, 89, 94, 142, 88, 106, 39, 88, 89, 79, 117, 88, 101, 133, 134, 146, 154, 65, 85, 99, 99, 102, 86, 87, 98, 147, 165, 148, 90, 101, 133, 105, 98, 145, 138, 145, 138, 97, 100, 83, 142, 137, 146, 133, 98, 163, 144, 98, 138, 143, 86, 102, 88, 145, 94, 85, 83, 135, 137, 152, 139, 132, 94, 137, 96, 134, 87, 138, 152, 95, 137, 141, 105, 95, 140, 138, 136, 143, 98, 88, 140, 100, 138, 98, 159, 156, 81, 146, 77, 143, 142, 138, 90, 91, 82, 96, 146, 157, 135, 140, 63, 144, 141, 99, 142, 83, 94, 140, 35, 129, 115, 144, 132, 98, 134, 145, 128, 87, 136, 131, 139, 157, 64, 150, 85, 80, 95, 93, 163, 79, 165, 121, 70, 189, 71, 164, 157, 87, 83, 167, 164, 160, 73, 166, 87, 153, 158, 166, 83, 181, 165, 55, 169, 89, 83, 89, 192, 90, 128, 147, 101, 159, 166, 213, 91, 87, 145, 166, 49, 97, 83, 146, 156, 153, 160, 89, 160, 71, 109, 81, 89, 159, 179, 160, 108, 162, 93, 172, 110, 160, 193, 162, 111, 157, 151, 79, 83, 82, 168, 104, 91, 200, 144, 151, 167, 86, 126, 70, 168, 175, 158, 76, 90, 104, 80, 84, 168, 67, 155, 80, 180, 91, 116, 85, 170, 121, 158, 161, 172, 178, 186, 85, 79, 82, 164, 67, 175, 74, 94, 162, 102, 160, 106, 118, 79, 74, 77, 142, 85, 164, 165, 76, 157, 82, 119, 184, 80, 72, 84, 87, 90, 111, 158, 89, 186, 153, 157, 71, 61, 82, 87, 183, 92, 84, 169, 160, 149, 66, 158, 150, 88, 168, 58, 168, 81, 158, 131, 163, 160, 194, 210, 65, 151, 89, 82, 167, 173, 181, 161, 154, 59, 87, 160, 160, 233, 97, 173, 162, 153, 55, 205, 111, 156, 84, 100, 167, 106, 173, 157, 87, 84, 77, 84, 134, 96, 106, 170, 165, 78, 78, 76, 80, 75, 87, 220, 113, 151, 149, 163, 149, 160, 113, 158, 84, 154, 54, 71, 84, 182, 163, 74, 65, 182, 151, 74, 163, 91, 85, 157, 158, 153, 69, 152, 150, 154, 72, 155, 85, 92, 53, 157, 175, 86, 74, 74, 158, 162, 162, 80, 158, 168, 136, 143, 148, 95, 189, 160, 78, 171, 80, 158, 220, 162, 79, 165, 96, 177, 142, 113, 76, 165, 162, 162, 86, 186, 169, 163, 91, 161, 87, 183, 153, 83, 86, 94, 77, 160, 78, 161, 88, 95, 166, 159, 147, 148, 150, 160, 126, 79, 89, 55, 161, 166, 164, 163, 175, 166, 81, 81, 167, 82, 83, 80, 84, 165, 161, 150, 75, 163, 137, 83, 94, 161, 78, 149, 122, 60, 164, 79, 180, 69, 157, 76, 168, 152, 75, 155, 158, 87, 89, 87, 80, 87, 90, 153, 70, 161, 91, 87, 84, 91, 89, 110, 87, 79, 164, 170, 178, 175, 118, 188, 115, 81, 94, 146, 148, 167, 83, 90, 93, 86, 80, 84, 147, 120, 65, 78, 154, 158, 163, 181, 88, 79, 79, 86, 159, 85, 92, 91, 83, 72, 88, 144, 86, 89, 150, 150, 149, 183, 115, 84, 86, 89, 96, 79, 73, 86, 171, 173, 178, 72, 92, 176, 91, 87, 156, 151, 164, 160, 90, 87, 110, 176, 155, 166, 151, 89, 151, 164, 86, 151, 169, 68, 93, 76, 172, 82, 71, 72, 137, 148, 164, 161, 147, 92, 164, 93, 157, 84, 157, 174, 79, 155, 161, 97, 83, 160, 151, 150, 161, 86, 69, 167, 92, 161, 85, 194, 187, 59, 164, 111, 160, 156, 159, 73, 73, 77, 95, 106, 213, 157, 149, 104, 160, 156, 88, 165, 20, 81, 160, 255, 149, 97, 164, 147, 79, 150, 157, 140, 87, 151, 145, 156, 165, 46, 153, 82, 83, 97, 89, 168, 75, 184, 118, 70, 210, 190, 184, 166, 84, 81, 175, 183, 156, 72, 185, 83, 158, 166, 174, 69, 193, 174, 113, 181, 88, 72, 83, 220, 91, 129, 155, 97, 168, 180, 214, 83, 79, 134, 176, 43, 95, 74, 151, 162, 154, 173, 106, 173, 61, 110, 74, 93, 171, 199, 171, 105, 168, 88, 111, 110, 169, 216, 178, 111, 159, 151, 72, 77, 78, 186, 108, 97, 221, 152, 164, 191, 120, 126, 91, 172, 182, 170, 87, 91, 109, 100, 97, 201, 56, 170, 74, 186, 87, 114, 105, 185, 117, 169, 170, 190, 203, 188, 84, 78, 84, 177, 70, 188, 74, 93, 166, 98, 185, 106, 111, 76, 71, 74, 139, 86, 153, 178, 64, 167, 91, 113, 192, 80, 75, 94, 87, 85, 108, 164, 89, 203, 164, 173, 54, 49, 79, 86, 213, 95, 79, 189, 173, 162, 54, 156, 150, 79, 179, 49, 185, 73, 168, 150, 167, 169, 203, 229, 62, 165, 95, 75, 190, 196, 200, 171, 168, 56, 82, 174, 169, 224, 91, 184, 186, 170, 47, 227, 107, 167, 87, 92, 179, 104, 189, 164, 80, 77, 72, 80, 168, 91, 108, 195, 191, 77, 81, 67, 76, 64, 94, 203, 116, 152, 152, 178, 162, 171, 115, 158, 80, 160, 36, 62, 75, 196, 176, 63, 50, 191, 158, 73, 181, 87, 77, 164, 150, 163, 50, 160, 160, 164, 68, 167, 84, 91, 41, 156, 199, 83, 62, 62, 164, 171, 177, 73, 154, 181, 133, 155, 157, 99, 204, 112, 140, 185, 77, 171, 217, 180, 72, 167, 95, 197, 148, 127, 63, 180, 178, 178, 86, 213, 116, 175, 88, 179, 86, 198, 165, 75, 80, 95, 97, 173, 75, 175, 78, 100, 177, 176, 155, 101, 145, 170, 125, 74, 88, 194, 178, 172, 183, 179, 191, 179, 78, 71, 183, 81, 83, 66, 81, 173, 176, 160, 60, 179, 137, 79, 91, 175, 78, 157, 120, 54, 172, 70, 194, 62, 170, 69, 177, 163, 67, 159, 169, 82, 88, 95, 84, 81, 99, 163, 64, 188, 93, 81, 76, 96, 87, 114, 81, 71, 170, 175, 202, 204, 118, 212, 112, 72, 209, 146, 156, 178, 85, 87, 89, 88, 69, 93, 152, 118, 55, 88, 160, 164, 174, 193, 82, 71, 86, 80, 172, 84, 92, 133, 100, 66, 103, 151, 95, 88, 160, 154, 163, 183, 96, 89, 90, 89, 98, 99, 75, 83, 173, 197, 203, 88, 88, 181, 87, 77, 165, 156, 174, 172, 101, 84, 110, 198, 151, 180, 155, 93, 55, 165, 82, 154, 172, 60, 89, 62, 183, 82, 57, 79, 141, 157, 153, 180, 154, 84, 177, 88, 165, 88, 166, 188, 67, 161, 172, 95, 87, 167, 165, 173, 181, 84, 62, 181, 87, 168, 79, 212, 202, 59, 172, 110, 177, 156, 168, 64, 62, 75, 95, 103, 245, 178, 155, 105, 167, 165, 81, 174, 0, 76, 170, 188, 154, 96, 176, 157, 74, 168, 165, 143, 102, 154, 151, 149, 152, 78, 146, 93, 89, 99, 98, 155, 88, 150, 120, 81, 169, 68, 158, 143, 90, 91, 156, 145, 146, 79, 155, 93, 149, 143, 155, 91, 173, 156, 159, 147, 94, 83, 95, 175, 95, 124, 141, 102, 149, 157, 187, 94, 95, 141, 154, 72, 100, 88, 143, 139, 137, 151, 90, 152, 85, 118, 88, 93, 148, 146, 150, 111, 147, 96, 78, 114, 144, 170, 154, 113, 154, 147, 89, 91, 89, 160, 106, 93, 195, 137, 146, 161, 84, 152, 75, 154, 160, 149, 78, 94, 110, 81, 89, 160, 81, 145, 89, 118, 93, 116, 87, 156, 118, 147, 156, 162, 164, 171, 89, 88, 90, 151, 83, 158, 75, 101, 157, 104, 154, 107, 118, 94, 86, 84, 137, 92, 154, 153, 86, 147, 90, 114, 165, 87, 79, 88, 97, 98, 115, 144, 92, 167, 144, 159, 80, 78, 92, 91, 175, 97, 90, 152, 146, 141, 76, 144, 141, 95, 155, 77, 159, 89, 141, 138, 150, 151, 176, 190, 82, 144, 89, 88, 160, 165, 179, 148, 148, 71, 89, 154, 147, 184, 102, 148, 153, 148, 78, 187, 109, 144, 88, 99, 160, 107, 160, 143, 95, 89, 81, 87, 101, 98, 109, 155, 154, 86, 89, 81, 88, 80, 92, 148, 115, 143, 136, 147, 144, 151, 112, 146, 85, 148, 73, 87, 91, 168, 148, 84, 79, 163, 136, 80, 152, 97, 87, 149, 147, 145, 74, 145, 143, 148, 87, 146, 90, 97, 78, 143, 161, 91, 85, 84, 151, 154, 146, 89, 158, 149, 133, 138, 144, 96, 178, 75, 154, 153, 87, 146, 171, 150, 85, 152, 103, 163, 134, 113, 82, 161, 153, 149, 90, 160, 46, 151, 97, 149, 93, 167, 143, 86, 93, 96, 86, 154, 84, 154, 89, 97, 154, 152, 136, 57, 141, 142, 125, 88, 97, 67, 156, 153, 152, 151, 168, 150, 93, 89, 151, 88, 87, 85, 88, 153, 147, 141, 84, 160, 134, 93, 95, 154, 86, 139, 115, 77, 155, 88, 169, 83, 148, 84, 152, 143, 91, 145, 151, 96, 94, 89, 90, 91, 89, 134, 78, 145, 95, 95, 93, 91, 95, 113, 95, 84, 148, 160, 172, 162, 118, 168, 115, 90, 94, 138, 141, 154, 89, 95, 99, 94, 81, 87, 140, 112, 75, 88, 144, 149, 154, 172, 93, 92, 86, 95, 157, 87, 98, 99, 86, 82, 85, 139, 89, 95, 142, 143, 142, 160, 69, 87, 94, 97, 99, 77, 82, 93, 163, 171, 161, 81, 99, 156, 95, 98, 148, 145, 152, 148, 91, 95, 112, 159, 142, 152, 144, 93, 142, 147, 97, 145, 158, 75, 99, 83, 158, 89, 86, 76, 135, 146, 126, 146, 144, 97, 151, 100, 144, 89, 147, 157, 90, 143, 151, 105, 90, 151, 144, 144, 152, 91, 77, 158, 98, 147, 91, 177, 176, 77, 147, 111, 149, 146, 147, 79, 83, 93, 102, 106, 189, 145, 143, 109, 148, 141, 95, 155, 50, 87, 150, 243, 138, 102, 154, 136, 86, 143, 146, 136, 90, 142, 136, 148, 147, 83, 141, 85, 83, 90, 100, 153, 97, 142, 155, 82, 164, 66, 153, 142, 96, 92, 141, 145, 148, 89, 152, 91, 143, 120, 152, 91, 156, 152, 35, 138, 91, 90, 92, 160, 95, 167, 145, 70, 141, 140, 160, 99, 95, 131, 150, 77, 89, 95, 143, 140, 137, 131, 88, 138, 83, 158, 92, 88, 141, 153, 138, 83, 147, 99, 159, 163, 141, 154, 143, 155, 144, 143, 87, 87, 96, 142, 117, 91, 15, 131, 134, 145, 67, 116, 81, 147, 149, 143, 82, 94, 145, 82, 85, 148, 83, 135, 89, 111, 93, 157, 85, 144, 178, 142, 141, 152, 151, 153, 92, 94, 94, 146, 82, 143, 83, 101, 156, 125, 140, 135, 154, 90, 91, 89, 140, 92, 145, 147, 93, 137, 89, 79, 153, 100, 88, 89, 90, 105, 158, 141, 91, 156, 144, 149, 84, 80, 94, 88, 146, 94, 94, 147, 139, 127, 80, 150, 140, 99, 156, 83, 147, 92, 129, 151, 142, 126, 149, 164, 80, 142, 89, 91, 144, 159, 172, 141, 142, 83, 96, 142, 140, 100, 102, 144, 147, 137, 87, 162, 87, 138, 93, 126, 154, 134, 161, 139, 95, 89, 84, 93, 115, 104, 163, 145, 152, 90, 89, 94, 93, 82, 90, 41, 143, 146, 136, 148, 134, 142, 161, 150, 91, 150, 77, 83, 93, 166, 142, 88, 76, 161, 150, 88, 145, 95, 95, 144, 137, 137, 82, 145, 145, 140, 88, 141, 83, 93, 69, 138, 140, 97, 84, 86, 145, 143, 144, 92, 142, 147, 122, 130, 132, 93, 157, 158, 61, 148, 90, 143, 114, 130, 92, 144, 74, 153, 145, 139, 93, 146, 144, 137, 75, 158, 158, 143, 98, 136, 97, 165, 131, 97, 92, 93, 80, 144, 91, 145, 89, 99, 154, 143, 150, 127, 139, 144, 147, 92, 97, 89, 154, 141, 145, 148, 150, 147, 96, 94, 143, 83, 88, 87, 91, 144, 137, 134, 90, 156, 141, 97, 95, 143, 86, 139, 161, 86, 153, 95, 174, 75, 141, 90, 155, 140, 82, 138, 145, 83, 97, 90, 89, 86, 88, 142, 77, 7, 101, 98, 94, 84, 97, 149, 77, 89, 146, 138, 159, 154, 160, 155, 162, 93, 44, 144, 139, 145, 94, 94, 100, 103, 91, 84, 132, 155, 82, 79, 142, 146, 149, 156, 95, 99, 83, 94, 149, 93, 87, 41, 85, 87, 94, 153, 85, 90, 143, 138, 126, 151, 116, 86, 92, 92, 91, 80, 73, 94, 155, 151, 152, 84, 99, 122, 94, 96, 143, 135, 138, 153, 91, 98, 164, 150, 136, 143, 149, 91, 164, 133, 96, 134, 146, 83, 100, 94, 154, 93, 80, 78, 139, 139, 94, 140, 138, 108, 144, 109, 140, 93, 136, 139, 86, 143, 142, 104, 89, 145, 134, 140, 136, 91, 81, 144, 97, 134, 95, 150, 140, 86, 138, 150, 141, 139, 136, 84, 86, 87, 98, 56, 173, 139, 142, 180, 143, 137, 94, 148, 67, 91, 138, 25, 137, 83, 156, 131, 89, 136, 138, 131, 86, 135, 131, 153, 157, 75, 151, 82, 81, 88, 95, 165, 90, 156, 158, 76, 181, 169, 164, 150, 89, 88, 165, 149, 149, 74, 166, 87, 150, 129, 154, 82, 176, 166, 116, 151, 89, 91, 89, 180, 88, 174, 150, 67, 154, 153, 190, 103, 89, 129, 160, 59, 83, 81, 143, 148, 146, 145, 89, 149, 76, 151, 87, 90, 152, 161, 151, 76, 160, 95, 108, 168, 146, 175, 154, 152, 155, 149, 77, 76, 87, 157, 126, 89, 28, 138, 146, 157, 74, 122, 79, 152, 162, 153, 83, 87, 145, 80, 83, 165, 69, 144, 88, 126, 91, 157, 86, 148, 186, 153, 151, 168, 171, 170, 87, 87, 91, 160, 76, 141, 71, 99, 165, 126, 153, 137, 158, 88, 82, 82, 145, 92, 153, 159, 77, 153, 83, 77, 170, 93, 78, 85, 86, 104, 157, 148, 89, 170, 144, 152, 71, 75, 86, 87, 173, 92, 86, 156, 148, 143, 62, 152, 141, 90, 163, 76, 162, 82, 146, 140, 159, 140, 161, 202, 72, 152, 85, 89, 156, 176, 194, 150, 151, 69, 90, 150, 153, 104, 101, 158, 154, 146, 78, 192, 86, 153, 80, 122, 159, 132, 167, 146, 91, 81, 76, 85, 135, 98, 165, 160, 162, 79, 79, 86, 85, 77, 90, 26, 150, 156, 143, 158, 145, 153, 163, 162, 86, 164, 58, 77, 85, 183, 149, 78, 75, 170, 158, 84, 154, 91, 86, 152, 155, 142, 72, 151, 145, 151, 78, 148, 84, 89, 63, 143, 162, 89, 75, 73, 151, 147, 151, 86, 150, 156, 125, 141, 141, 94, 182, 116, 106, 160, 86, 150, 91, 142, 84, 158, 71, 163, 148, 126, 79, 159, 153, 148, 68, 172, 111, 145, 93, 148, 90, 178, 147, 88, 90, 92, 79, 146, 84, 166, 78, 94, 157, 150, 156, 95, 142, 152, 150, 81, 93, 162, 165, 155, 152, 157, 169, 156, 87, 89, 159, 84, 92, 82, 87, 150, 148, 147, 79, 166, 134, 88, 88, 156, 86, 145, 172, 79, 164, 83, 183, 65, 153, 82, 168, 149, 79, 153, 156, 83, 94, 92, 88, 86, 87, 146, 64, 94, 98, 92, 83, 86, 94, 148, 76, 82, 153, 148, 174, 174, 161, 173, 159, 87, 136, 148, 145, 150, 85, 91, 98, 101, 83, 82, 142, 164, 78, 76, 155, 160, 164, 170, 90, 100, 90, 89, 159, 84, 83, 74, 89, 80, 82, 158, 84, 88, 154, 148, 140, 156, 103, 82, 88, 90, 92, 75, 70, 92, 164, 181, 166, 82, 96, 130, 90, 89, 147, 137, 149, 162, 89, 91, 159, 167, 144, 156, 151, 89, 64, 145, 91, 137, 161, 75, 93, 84, 158, 92, 79, 73, 149, 150, 87, 152, 149, 104, 155, 107, 151, 91, 143, 161, 85, 150, 157, 102, 85, 158, 139, 150, 148, 85, 71, 164, 95, 146, 86, 170, 175, 69, 145, 149, 159, 145, 148, 75, 74, 83, 97, 59, 200, 154, 149, 178, 161, 155, 92, 153, 43, 85, 153, 67, 141, 78, 163, 138, 85, 144, 150, 138, 86, 137, 138, 169, 138, 94, 131, 97, 89, 98, 101, 133, 104, 131, 144, 89, 147, 79, 144, 127, 100, 97, 135, 116, 131, 91, 141, 99, 131, 115, 133, 105, 142, 141, 150, 126, 96, 98, 99, 141, 100, 154, 132, 81, 134, 131, 151, 108, 103, 129, 138, 102, 97, 94, 131, 129, 132, 128, 95, 128, 92, 143, 101, 89, 134, 125, 131, 89, 137, 104, 96, 150, 129, 144, 131, 145, 134, 137, 93, 97, 98, 136, 116, 97, 14, 126, 128, 126, 86, 164, 97, 134, 142, 141, 95, 103, 136, 86, 95, 129, 92, 127, 103, 101, 99, 142, 93, 130, 162, 134, 133, 140, 137, 147, 103, 103, 101, 136, 89, 134, 98, 107, 142, 125, 130, 129, 150, 95, 98, 101, 134, 97, 139, 131, 96, 137, 98, 92, 137, 105, 101, 97, 103, 111, 145, 131, 97, 137, 134, 131, 90, 92, 99, 94, 130, 98, 94, 138, 126, 127, 106, 139, 133, 102, 146, 109, 137, 96, 117, 129, 131, 128, 141, 142, 104, 128, 101, 98, 137, 137, 143, 130, 130, 99, 99, 132, 137, 61, 107, 129, 133, 130, 109, 129, 92, 133, 92, 128, 139, 130, 139, 135, 104, 97, 96, 95, 98, 106, 152, 133, 136, 100, 92, 100, 93, 94, 102, 40, 134, 132, 130, 138, 130, 136, 149, 142, 97, 140, 98, 99, 100, 155, 130, 97, 99, 160, 139, 99, 133, 102, 96, 129, 131, 129, 102, 138, 133, 136, 93, 133, 96, 97, 100, 126, 134, 98, 96, 91, 136, 134, 135, 98, 135, 136, 119, 122, 123, 99, 145, 79, 111, 137, 98, 131, 78, 120, 98, 131, 84, 135, 141, 139, 96, 131, 130, 130, 90, 144, 43, 132, 105, 122, 100, 148, 123, 101, 99, 99, 94, 137, 100, 131, 95, 102, 135, 132, 139, 60, 131, 134, 137, 97, 101, 78, 137, 131, 131, 139, 146, 138, 105, 101, 133, 93, 97, 92, 97, 147, 128, 128, 99, 137, 135, 96, 103, 140, 94, 133, 144, 104, 133, 111, 158, 98, 131, 96, 143, 129, 102, 132, 135, 100, 101, 98, 100, 90, 98, 126, 92, 17, 105, 104, 99, 94, 104, 142, 95, 97, 130, 133, 143, 144, 146, 139, 149, 104, 41, 129, 133, 136, 100, 99, 107, 110, 95, 91, 124, 140, 95, 83, 135, 133, 131, 142, 101, 109, 90, 100, 138, 98, 99, 61, 94, 89, 99, 131, 94, 98, 129, 128, 125, 142, 91, 98, 99, 102, 99, 87, 86, 100, 147, 144, 128, 95, 102, 127, 102, 105, 129, 125, 131, 135, 96, 103, 150, 138, 128, 134, 138, 98, 149, 126, 102, 127, 133, 90, 102, 97, 142, 98, 99, 90, 131, 132, 72, 128, 132, 108, 133, 110, 129, 99, 127, 134, 95, 131, 130, 106, 98, 134, 130, 126, 131, 97, 88, 136, 105, 130, 100, 141, 123, 91, 125, 143, 130, 133, 129, 91, 92, 102, 104, 65, 152, 135, 133, 159, 132, 131, 99, 130, 102, 98, 126, 25, 132, 93, 147, 124, 94, 126, 126, 127, 97, 124, 124, 141}; - model->setOperandValue(op17, op17_init, sizeof(uint8_t) * 4608); - static int32_t op19_init[] = {2015, 2252, -7158, -8776, 4559, -7675, -5317, 384, 15209, -2835, 2142, 6095, -7565, -12538, -5225, -3422, -8100, -3695, 7238, 2660, 3196, -4916, -156, 9310, -1848, -868, 8351, -13226, -1114, -3035, 10306, 7851, -2385, 6276, -9794, 1480, -7412, 1316, -3618, -9899, -9214, -372, -7151, -14155, 3060, 2623, -2391, -11752, 4557, -10514, -6892, -4725, -11796, -12758, 5265, -2845, -5437, -22236, -328, 7032, -999, -10355, -1750, -1393, -10252, 2409, 9349, -1490, 2425, 12795, -69, -8090, 2001, -4637, -8355, 2454, -3380, -3652, -3898, 4040, 4847, 2189, -14425, -7767, -8404, 498, 8005, -12186, -5052, 11295, 579, 2205, 8087, 17380, 10088, 5745, 4150, -1580, 13249, -1956, -19547, -6181, 2637, 18875, -11469, 2667, -3275, -5412, 8553, 7128, -11776, -1209, -3777, -2727, -10413, -20, -10772, -1840, -920, 7333, -8364, 3736, 7388, -3717, -4159, -7569, 9450, 1077, -1644, -7028, -8182, -2970, -1975, -8735, -2393, -8422, 576, -6755, -6628, -12845, -5175, 325, -6736, -11983, 6939, 7344, 3666, -1544, 6155, 1180, 18175, -11815, -6120, -852, -7652, 126, 7004, 4698, 637, -7, -787, -13464, -2172, -10907, 5264, -20194, -15871, 16337, -3785, 293, 4017, -1324, -2413, -9001, -12499, 303, 2535, 7808, 640, -6284, 4020, -8467, 5640, -8751, -4361, 9996, 4330, -4476, 3733, -9577, 5687, 1471, -6841, 1425, 4722, 5794, -12545, 28, 2502, 3420, -3327, 2330, -2030, -16410, 2811, 3040, -3917, -9085, -2482, -855, -14314, -3228, 8759, 3666, 2128, -5816, -4385, -6046, 4358, 14522, -5141, 4298, -10085, 1313, 5042, -9837, -5176, 4211, -10376, 10274, -1156, 6403, -5828, 530, -3070, 10123, -4485, -10962, -8548, 4519, -3071, -15821, -10075, -11862, -10904, 1195, -3726, -18396, -11586, -1307, -3809, -14013, 3268, -9379, -3719, -10583, 14427, 6777, 4493, -8848, 1861, 10176, 4933, 3939, -7013, -15711, -8088, 1929, 978, -4554, -3565, -4952, 4256, 1025, -14799, 8236, 13091, -8749, 3005, -4785, 1966, 3858, -4514, -1219, -7539, 10947, -9854, -8797, -8780, -559, 5350, -3313, 1490, -19392, -2878, 5595, -563, 3580, 138, -11224, -339, -2436, -4844, 3247, -2452, -155, 2602, -4253, -10425, 673, 973, -1356, 8257, -7715, -9053, 6263, 5693, -2888, -10434, -14476, 8996, 3974, 1697, -950, 10315, -1245, -1053, 13221, 4465, -7740, -8947, 14151, -1464, 7660, -3936, -5436, 6284, -10609, 402, 6692, -10092, -7954, -8152, 3760, 1362, 5695, 2578, 1484, -5728, -9467, -9278, -4715, 3064, -2701, 10140, -7187, -1516, -1093, 6538, -8702, 5540, 288, -2118, 5235, 2736, -7879, 4586, -1225, 324, 12652, -1873, -9351, -3049, 21486, 5615, -12413, -7555, -9166, 5662, -4058, 1662, 123, -4434, 13476, -9030, -3852, 2811, 291, -3347, 8229, -4678, 11540, 2186, 7657, 6897, -12147, -145, -5743, -3953, -4553, -8932, -1971, -9640, -7467, 5326, -6436, -3404, -6487, -1922, 9165, 2379, -434, -10381, -10608, 2033, -727, 4433, -567, -288, -9723, 585, -2541, -9758, -10890, -9931, -8111, 9953, 6454, 4790, 3272, -3882, -2203, -2482, 5380, -6697, 3194, -2475, -2556, -5794, -3945, -4251, 6926, 550, -18434, 4855, -8811, 12996, -9496, -13817, -9020, 8114, -2000, -11235, -1728, -11181, -7940, 6603, 7480, -2211, -269, 1192, -2016, 1352, -4957, -10558, 11818, -6506, -1329, -6263, 19626, -7816, -11878, -3475, 1090, 3447, -12815, -5922, -10138, 20317, -5177, -15506, -6306, 1683, -7149, -8539, -399, 19405, -5711, -5934, 2947, -9027, 6151, 1881, 1362, 2439, 8809, -9878, -14512, -8341, -7307, 13319, -1820, -7324, -5905, -2198, -7313, -10034, 7759, -1103, -7044, 1680, 6374, 413, -5637, -6159, -11429, -13, -2184, -851, -3434, 2923, -2983, 7193, -17795, 2659, -5515, -7116, 18826, 2776, 2131, -6273, -565, 4544, -11131, -9164, -9862, -6126, 1472, 2932, -683, -5630, -7113, 1107, -22390, 5581, -1861, -3289, -570, 3190, -8019, -15193, 3182, -8516, 1248, 4627, 847, 1997, -4343, -17602, 12533, 9275, -3592, 13751, -4753, -1401, 1997, -13854, -11211, -13422, -4810, 5100, -5142, 3930, -2696, 621, -22303, 2815, -12049, -8506, -6054, 3305, -14908, -7766, -9479, 11964, 10788, -7024, -4979, -2886, -15398, -1291, 3512, -7558, -9065, 3090, 4743, 6947, 1278, 2418, 3427, -16355, -1151, 3638, -8226, -510, -11155, -8883, -1178, 4843, -3068, -1674, 3312, -9140, -10707, -10252, 2334, -9146, 14804, -8260, 6643, 4350, 9588, -7330, -3797, -11959, -10415, -858, -6068, -4569, -7252, 5160, 11017, -3204, 2545, -4122, -12560, -9669, -11172, -5157, 8535, 591, -1464, -999, 13185, 2922, -9218, 5085, -4869, 11588, 5864, 13142, 1196, 134, 4274, -8770, 6691, -2992, -7509, 11057, -8046, 7639, 14175, -11756, -17655, -11116, -2081, 119, 4176, -452, -9193, -4134, 3892, -6229, -12621, 4992, -4063, -215, 4338, -2531, -5691, -7096, -7520, -2340, -3829, -3173, -6084, -8242, -1287, -15385, 4300, 2811, 2884, 6979, 327, 14929, -7135, -2547, -1782, 2092, -690, -7156, 12555, -2311, 7337, -5703, -3140, 8919, 17756, -2205, -17317, 3411, 2955, 2683, 2198, 2134, -885, 10004, -6617, 1073, -5433, 6128, 2013, -12654, 2876, -9768, 9206, -2512, -548, 5771, -115, 9395, 347, 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918, 4404, -7680, -6912, -694, 4704, 6685, 1458, -7188, -3415, 5149, 7700, -5539, -3851, 11233, 4931, 1676, 1044, 3808, 20480, -2081, -6706, 2301, -3442, -4577, 506, -3648, 4999, -3536, 5024, -10637, -5345, -654, -5961, -6026, 3945, -5708, -4514, 11307, -4489, 19877, -21637, 9481, -418, 725, 2338, -4296, -555, -439, -2610, 3675, -4372, -3780, -8943, 5844, 15973, 6270, 2650, 3198, -7859, -3533, 10754, -7711, -1566, -12184, -13692, -10986, 2511, -6056, 3907, -6377, -611, -2425, -2450, -1582, 2754, -620, 10811, -21355, -4777, -2347, -5633, -1801, 5910, 15548, -19099, 16361, -3143, -576, 2778, -5598, -745, 4320, -3632, 5622, -9353, -7684, 457, -1269, -7526, 19807, -9393, 1842, -4179, 8680, 6087, -7901, -844, 6684, 10195, -2014, 16421, 667, -5906, -10169, -9646, 1487, -3172, -12235, 2234, 2498, 3641, -8389, -502, -8895, -15624, -1820, 2568, -6705, 1715, -6761, -3740, -14603, 4914, -5038, 6961, -17590, 222, 2968}; - model->setOperandValue(op19, op19_init, sizeof(int32_t) * 1024); - static uint8_t op20_init[] = {131, 133, 141, 143, 139, 138, 134, 138, 148, 128, 129, 146, 142, 136, 137, 134, 137, 147, 147, 135, 139, 130, 142, 131, 133, 138, 137, 138, 151, 136, 129, 135, 136, 140, 128, 128, 131, 140, 142, 145, 130, 138, 130, 134, 143, 131, 148, 144, 144, 133, 140, 141, 137, 136, 137, 140, 140, 138, 142, 139, 135, 135, 146, 142, 133, 142, 147, 132, 138, 134, 145, 144, 132, 135, 132, 144, 133, 137, 136, 139, 136, 136, 149, 140, 138, 137, 131, 140, 141, 142, 136, 124, 141, 126, 129, 144, 137, 135, 140, 141, 152, 142, 141, 137, 142, 140, 136, 130, 137, 141, 136, 145, 138, 132, 147, 139, 146, 146, 144, 142, 147, 142, 138, 139, 146, 138, 130, 137, 135, 135, 136, 144, 135, 140, 132, 140, 143, 141, 145, 136, 135, 142, 145, 138, 146, 141, 131, 143, 131, 145, 131, 140, 138, 148, 140, 140, 139, 138, 132, 135, 140, 137, 138, 144, 134, 139, 144, 136, 143, 136, 134, 143, 139, 139, 124, 145, 137, 135, 137, 135, 138, 153, 138, 133, 134, 133, 143, 133, 133, 153, 145, 142, 135, 147, 156, 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164, 191, 167, 169, 163, 184, 160, 188, 153, 185, 167, 182, 181, 167, 183, 167, 158, 167, 182, 168, 183, 119, 167, 166, 167, 166, 166, 165, 185, 166, 184, 167, 188, 183, 187, 182, 167, 184, 193, 183, 167, 155, 188, 186, 192, 164, 197, 187, 152, 190, 183, 166, 168, 186, 166, 167, 189, 189, 191, 187, 169, 158, 166, 187, 169, 163, 189, 182, 167, 184, 185, 187, 187, 183, 168, 189, 186, 166, 183, 162, 146, 212, 183, 199, 185, 167, 194, 185, 167, 182, 185, 166, 169, 182, 184, 180, 185, 166, 185, 166, 168, 168, 166, 167, 188, 166, 166}; - model->setOperandValue(op23, op23_init, sizeof(uint8_t) * 9216); - static int32_t op25_init[] = {-26162, -6613, -35505, -13651, 4205, -19287, -15670, 8894, 1826, -20286, 635, -23150, -16263, -3742, 493, 942, -25564, -2058, -1912, -28030, -11305, 12381, 3314, 738, 7125, 4763, 1879, -12973, 7870, -17670, -23782, -5104, -2387, -20615, -4407, 828, -2961, -9653, -8080, -30691, -25058, 19254, 3774, -6460, -393, -13196, -20676, -18123, -4261, 1189, -9397, 5694, 20380, -13173, -18620, -2947, -1953, 352, 4544, -1677, 9000, 5035, -17370, -13016, -1935, 3669, -5581, 4263, -12720, 4940, -7854, -2269, 14923, 9484, -13580, -20631, 4369, -13059, 21561, -9196, -3355, -3902, -20436, -10399, 18447, -8978, -17818, -2435, 6770, -12412, 6257, -3901, 3171, 9348, -15047, -9, 3959, -11360, -9952, -7715, 8133, -7501, -7636, 8210, -22938, -15602, 2910, -28592, 14006, -37239, -22862, -9260, -18794, -2159, -13955, 2878, -6744, -5564, -1159, 12463, -175, -16231, 1528, -4875, -13822, -13564, -6928, -1312, -18731, 3920, 13706, 12257, -8810, -12139, -16525, -8218, -13866, -1432, -4876, -10133, -21051, -10786, -640, 10879, 749, -10442, -4192, -4545, -7910, -22521, -13701, -6391, -1128, 6703, 8342, -3225, -9932, -12819, -13828, 6806, -1111, 11234, 1834, 2515, 8383, 23131, -11768, -3710, -6171, 6122, -16934, 20966, 9313, -1695, -18109, -3377, -8631, 6854, 13926, 7761, 13234, 3554, -14130, -2726, -9053, -16697, -17426, -1673, 1815, -20214, 6983, 5676, -13490, -1714, 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120, 131, 142, 143, 126, 142, 138, 126, 125, 124, 111, 133, 150, 133, 118, 162, 117, 107, 127, 140, 136, 123, 138, 129, 141, 116, 155, 136, 118, 102, 124, 138, 132, 128, 123, 131, 133, 130, 129, 126, 114, 126, 131, 122, 123, 127, 118, 141, 112, 146, 108, 128, 140, 128, 124, 108, 127, 138, 129, 144, 131, 119, 127, 126, 122, 115, 126, 124, 121, 126, 124, 122, 114, 141, 116, 110, 137, 111, 118, 123, 135, 125, 126, 124, 122, 148, 124, 130, 136, 112, 123, 111, 145, 110, 146, 107, 121, 129, 129, 111, 111, 115, 111, 119, 140, 120, 125, 147, 94, 141, 117, 128, 134, 131, 134, 147, 133, 128, 123, 136, 142, 127, 128, 122, 149, 129, 136, 127, 142, 129, 133, 128, 133, 110, 111, 129, 130, 136, 106, 124, 114, 141, 120, 125, 101, 112, 131, 127, 128, 135, 89, 110, 130, 131, 133, 128, 132, 130, 127, 133, 118, 111, 118, 111, 114, 116, 118, 106, 150, 121, 116, 109, 125, 129, 129, 144, 126, 140, 124, 149, 117, 115, 118, 124, 142, 121, 125, 123, 139, 107, 118, 126, 122, 113, 128, 122, 116, 111, 110, 118, 110, 126, 111, 120, 125, 110, 133, 134, 112, 140, 141, 110, 129, 117, 116, 127, 137, 105, 125}; - model->setOperandValue(op26, op26_init, sizeof(uint8_t) * 1048576); - static int32_t op28_init[] = {-214, 37, 177, -2, 224, 317, -103, 216, 476, 474, -28, 469, 217, 378, 588, -201, -3, 142, -11, 224, 453, -40, -58, -34, 194, 206, -2, -1, 341, 555, 282, 490}; - model->setOperandValue(op28, op28_init, sizeof(int32_t) * 32); - static uint8_t op29_init[] = {49, 48, 48, 49, 38, 37, 60, 49, 49, 49, 55, 50, 54, 50, 54, 72, 49, 47, 49, 41, 49, 48, 49, 49, 46, 46, 49, 49, 48, 50, 50, 49, 48, 50, 46, 51, 57, 0, 49, 40, 50, 50, 55, 50, 46, 54, 49, 42, 51, 57, 49, 80, 49, 47, 48, 47, 21, 54, 49, 49, 50, 48, 44, 50, 49, 49, 53, 49, 47, 49, 40, 44, 49, 49, 54, 49, 48, 52, 53, 39, 49, 48, 49, 60, 49, 48, 49, 46, 54, 47, 49, 49, 50, 49, 54, 50, 49, 64, 72, 49, 22, 60, 88, 49, 50, 51, 54, 49, 97, 51, 46, 7, 49, 49, 49, 55, 50, 49, 48, 50, 56, 56, 49, 49, 84, 50, 53, 51, 59, 46, 27, 52, 79, 53, 101, 71, 41, 41, 50, 37, 47, 30, 12, 127, 52, 45, 49, 34, 42, 67, 58, 50, 71, 66, 49, 49, 14, 36, 55, 39, 46, 47, 42, 51, 51, 47, 101, 41, 51, 49, 51, 52, 4, 57, 58, 1, 51, 49, 49, 24, 50, 44, 46, 50, 45, 49, 49, 49, 48, 52, 16, 50, 48, 47, 51, 49, 51, 51, 53, 49, 49, 49, 53, 50, 53, 49, 56, 35, 49, 49, 49, 51, 49, 48, 49, 45, 46, 53, 49, 49, 49, 50, 47, 50, 48, 42, 55, 49, 47, 93, 56, 49, 51, 50, 54, 51, 41, 48, 55, 255, 49, 48, 49, 46, 49, 45, 46, 47, 54, 20, 49, 49, 47, 53, 63, 50, 48, 49, 50, 49, 48, 51, 85, 50, 48, 49, 58, 49, 53, 50, 54, 83, 49, 50, 49, 51, 49, 49, 49, 48, 49, 51, 49, 49, 50, 49, 60, 50}; - model->setOperandValue(op29, op29_init, sizeof(uint8_t) * 288); - static int32_t op31_init[] = {-6964, 10465, 6932, 769, 9820, -27892, 16191, 12325, 3922, 576, 7832, -31349, 8476, 1446, 7604, 14775, 23991, -5413, 14563, 13075, 3752, 8458, -17350, -4570, 7793, 11717, -18004, 9922, 8681, 11955, 9022, 16446, -1849, 8243, -3433, 8905, 7633, 9763, 11686, 10638, 7641, -12636, 11025, 10665, 14012, 5747, -10068, 318, 10564, 21012, -13789, -11988, 12228, -15600, 11258, 13908, 11669, 10026, -7522, 15192, 14940, 13538, 1850, -1946}; - model->setOperandValue(op31, op31_init, sizeof(int32_t) * 64); - static uint8_t op32_init[] = {139, 168, 128, 140, 112, 141, 138, 138, 151, 133, 138, 134, 137, 147, 144, 138, 140, 165, 138, 119, 144, 146, 138, 138, 154, 108, 138, 138, 112, 135, 140, 139, 141, 146, 141, 145, 146, 134, 138, 138, 146, 135, 138, 149, 142, 133, 135, 138, 130, 129, 138, 143, 142, 134, 141, 138, 139, 137, 138, 138, 126, 134, 138, 140, 137, 137, 138, 137, 139, 136, 138, 140, 134, 140, 138, 127, 137, 143, 139, 138, 133, 144, 138, 137, 145, 72, 144, 138, 136, 137, 138, 138, 139, 120, 138, 155, 138, 139, 140, 199, 136, 138, 138, 138, 142, 130, 138, 152, 139, 134, 136, 138, 131, 135, 138, 137, 158, 138, 140, 138, 138, 140, 138, 138, 142, 134, 136, 143, 138, 141, 141, 139, 144, 138, 138, 126, 147, 149, 138, 151, 140, 143, 136, 138, 138, 127, 138, 142, 142, 134, 144, 138, 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139, 138, 138, 151, 137, 138, 138, 136, 148, 129, 138, 151, 139, 138, 134, 138, 123, 133, 138, 139, 156, 137, 141, 138, 139, 137, 138, 138, 142, 128, 138, 145, 141, 135, 146, 138, 133, 137, 138, 141, 152, 130, 138, 142, 138, 129, 135, 138, 134, 133, 138, 133, 146, 141, 140, 138, 144, 142, 138, 138, 132, 126, 133, 145, 138, 139, 129, 141, 140, 139, 138, 129, 122, 150, 138, 135, 138, 150, 140, 138, 137, 151, 138, 143, 127, 136, 137, 138, 141, 138, 138, 138, 143, 146, 141, 131, 138, 141, 145, 138, 140, 138, 138, 129, 149, 146, 138, 150, 142, 138, 134, 138, 139, 129, 138, 138, 145, 136, 143, 138, 142, 134, 138, 138, 136, 129, 134, 132, 144, 140, 131, 142, 143, 140, 138, 131, 138, 135, 138, 145, 145, 136, 137, 138, 133, 145, 138, 132, 136, 134, 143, 138, 145, 143, 138, 138, 135, 138, 144, 143, 139, 132, 167, 134, 161, 139, 138, 122, 145, 145, 138, 142, 143, 129, 130, 138, 142, 174, 138, 133, 141, 140, 139, 138, 156, 124, 138, 138, 157, 129, 115, 133, 136, 147, 137, 132, 145, 134, 138, 135, 143, 146, 138, 144, 144, 143, 136, 138, 143, 126, 138, 145, 142, 137, 140, 138, 137, 130, 138, 138, 137, 137, 137, 131, 139, 139, 146, 138, 135, 140, 138, 145, 153, 126, 138, 140, 134, 127, 135, 138, 139, 126, 138, 134, 147, 141, 139, 138, 143, 146, 138, 138, 133, 129, 134, 147, 130, 139, 139, 128, 138, 138, 138, 137, 124, 128, 138, 162, 139, 134, 158, 138, 147, 143, 138, 138, 131, 141, 129, 138, 136, 138, 138, 138, 140, 139, 139, 118, 136, 148, 146, 136, 134, 126, 138, 143, 149, 135, 138, 134, 136, 146, 139, 138, 147, 110, 138, 150, 152, 139, 139, 138, 136, 122, 138, 138, 139, 127, 127, 136, 134, 141, 133, 133, 137, 136, 138, 140, 140, 112, 138, 217, 136, 125, 153, 138, 138, 136, 138, 140, 152, 154, 138, 138, 136, 137, 138, 138, 146, 145, 140, 123, 140, 137, 141, 138, 120, 147, 138, 113, 145, 137, 138, 141, 138, 126, 131, 138, 146, 177, 138, 115, 140, 142, 138, 138, 160, 138, 138, 138, 138, 129, 132, 141, 141, 130, 133, 143, 137, 139, 138, 136, 135, 130, 138, 147, 141, 134, 135, 138, 131, 143, 138, 136, 134, 137, 141, 138, 140, 146, 138, 138, 135, 136, 147, 149, 141, 142, 137, 141, 135, 141, 138, 138, 141, 134, 138, 140, 137, 135, 133, 138, 74, 130, 138, 140, 146, 136, 137, 138, 138, 140, 138, 138, 142, 131, 138, 143, 135, 134, 139, 145, 135, 136, 138, 139, 92, 161, 138, 120, 142, 148, 160, 138, 165, 169, 138, 142, 214, 161, 127, 138, 128, 135, 138, 138, 135, 148, 141, 108, 134, 139, 108, 134, 132, 139, 138, 170, 140, 132, 138, 131, 150, 127, 136, 138, 143, 160, 138, 153, 140, 139, 134, 138, 163, 146, 138, 138, 156, 139, 112, 147, 144, 138, 139, 140, 140, 138, 138, 140, 138, 152, 138, 102, 137, 142, 131, 138, 132, 139, 138, 140, 134, 116, 149, 138, 140, 137, 138, 138, 138, 129, 138, 151, 148, 137, 139, 142, 141, 139, 138, 137, 255, 139, 138, 136, 140, 138, 144, 138, 149, 137, 138, 136, 132, 143, 134, 138, 137, 142, 138, 138, 137, 138, 135, 139, 138, 140, 142, 135, 137, 104, 138, 146, 138, 139, 138, 138, 132, 146, 138, 138, 142, 128, 138, 151, 141, 139, 136, 138, 130, 126, 138, 138, 143, 135, 128, 135, 140, 139, 137, 143, 137, 136, 138, 138, 159, 139, 138, 136, 139, 138, 127, 138, 135, 136, 138, 139, 134, 146, 133, 138, 138, 138, 138, 138, 138, 143, 138, 140, 134, 142, 141, 133, 143, 136, 138, 142, 140, 144, 138, 134, 138, 147, 140, 138, 144, 125, 138, 147, 145, 137, 138, 138, 134, 133, 138, 138, 140, 140, 133, 131, 134, 137, 137, 137, 138, 136, 138, 138, 132, 122, 138, 163, 138, 134, 150, 138, 145, 136, 138, 137, 142, 147, 134, 138, 138, 138, 138, 138, 137, 144, 140, 128, 140, 138, 122, 146, 161, 153, 138, 128, 133, 139, 138, 140, 161, 131, 137, 138, 134, 153, 138, 130, 131, 139, 137, 138, 142, 147, 138, 138, 128, 142, 152, 137, 138, 144, 137, 135, 132, 146, 138, 139, 139, 140, 138, 129, 138, 135, 142, 138, 151, 0, 138, 136, 134, 137, 143, 138, 150, 143, 138, 138, 139, 136, 143, 136, 142, 153, 114, 138, 136, 135, 138, 102, 125, 141, 138, 145, 134, 126, 128, 138, 142, 124, 138, 131, 132, 130, 138, 138, 108, 161, 138, 138, 142, 144, 106, 141, 141, 138, 137, 145, 140, 137, 138, 139, 137, 141, 138, 131, 140, 137, 136, 138, 126, 135, 138, 138, 151, 121, 151, 138, 136, 137, 138, 138, 138, 120, 138, 151, 135, 142, 140, 135, 143, 139, 138, 144, 128, 77, 138, 218, 139, 137, 157, 138, 150, 140, 138, 134, 149, 158, 138, 138, 136, 136, 138, 138, 144, 143, 138, 124, 137, 138, 136, 143, 138, 137, 138, 139, 157, 139, 138, 142, 138, 136, 126, 138, 137, 129, 138, 139, 130, 168, 89, 138, 138, 141, 138, 138, 139, 178, 138, 118}; - model->setOperandValue(op32, op32_init, sizeof(uint8_t) * 2048); - static int32_t op34_init[] = {3049, 1380, 52, -29, -50, 3034, 9, 2144, 4103, 1083, -123, 16, 3044, 3052, 53, 99, 3415, 3056, 245, 3490, 6, -26, 156, -280, 368, 44, 79, 3, -44, 41, 44, 673, 77, 1252, 2850, -161, -83, 1487, 79, 19, -236, 51, 74, 244, 3141, 814, 29, 87, 194, 3437, 3071, 3068, -51, 24, 24, 965, 148, 82, 2992, 2322, 36, 217, 2456, -398}; - model->setOperandValue(op34, op34_init, sizeof(int32_t) * 64); - static uint8_t op35_init[] = {40, 46, 48, 48, 49, 41, 45, 53, 44, 45, 55, 48, 40, 43, 46, 56, 45, 41, 56, 44, 49, 42, 48, 0, 38, 43, 51, 35, 48, 46, 51, 60, 47, 51, 47, 57, 37, 34, 38, 33, 66, 51, 48, 43, 45, 52, 48, 46, 46, 44, 44, 43, 36, 49, 46, 62, 53, 42, 44, 45, 51, 37, 43, 49, 40, 53, 49, 49, 36, 40, 48, 45, 43, 45, 36, 48, 41, 39, 46, 28, 43, 41, 41, 42, 53, 37, 49, 157, 48, 56, 53, 40, 49, 52, 56, 67, 48, 39, 49, 58, 31, 58, 54, 65, 42, 52, 43, 42, 42, 51, 49, 44, 50, 43, 44, 41, 33, 48, 46, 73, 57, 63, 43, 45, 52, 55, 44, 49, 44, 39, 47, 47, 55, 44, 46, 43, 46, 45, 46, 46, 44, 41, 45, 53, 43, 44, 43, 45, 51, 49, 47, 189, 50, 40, 48, 50, 47, 39, 52, 50, 48, 49, 47, 47, 39, 45, 45, 40, 41, 50, 42, 51, 44, 46, 47, 45, 48, 44, 45, 43, 45, 46, 45, 50, 51, 50, 45, 45, 49, 52, 46, 47, 40, 54, 49, 48, 40, 39, 55, 51, 43, 43, 61, 50, 41, 38, 52, 39, 44, 40, 63, 44, 52, 64, 48, 107, 41, 40, 51, 31, 49, 45, 39, 44, 48, 53, 43, 37, 46, 52, 34, 63, 29, 52, 29, 44, 44, 50, 48, 46, 41, 44, 43, 40, 39, 49, 49, 39, 40, 30, 43, 43, 52, 29, 43, 49, 38, 41, 51, 49, 67, 37, 26, 33, 43, 42, 34, 51, 39, 38, 58, 66, 43, 38, 39, 42, 56, 49, 49, 142, 57, 63, 54, 51, 50, 59, 35, 38, 50, 40, 38, 37, 53, 46, 58, 26, 51, 55, 41, 39, 41, 50, 49, 46, 29, 43, 42, 39, 50, 50, 50, 40, 33, 47, 41, 41, 55, 50, 44, 50, 41, 43, 47, 47, 31, 40, 54, 53, 45, 43, 45, 48, 42, 43, 53, 33, 44, 42, 36, 44, 51, 37, 47, 36, 43, 35, 51, 59, 48, 34, 39, 40, 49, 44, 41, 44, 51, 43, 45, 49, 53, 50, 50, 58, 43, 48, 47, 47, 37, 45, 44, 43, 55, 47, 46, 43, 38, 58, 43, 43, 50, 58, 46, 47, 44, 50, 47, 46, 51, 42, 39, 36, 45, 43, 53, 48, 44, 42, 40, 44, 45, 45, 50, 46, 51, 33, 46, 98, 52, 42, 47, 44, 47, 44, 47, 40, 48, 51, 42, 46, 54, 51, 41, 40, 46, 50, 60, 44, 45, 44, 47, 52, 51, 46, 45, 41, 50, 47, 49, 38, 45, 43, 45, 44, 49, 42, 46, 47, 42, 38, 47, 46, 33, 39, 64, 55, 45, 42, 40, 50, 43, 43, 34, 43, 45, 43, 43, 46, 51, 53, 46, 146, 43, 54, 51, 55, 48, 54, 47, 31, 49, 48, 43, 45, 56, 38, 52, 51, 47, 51, 55, 43, 44, 43, 47, 52, 58, 46, 44, 42, 57, 47, 51, 26, 49, 35, 43, 42, 51, 44, 46, 47, 43, 50, 47, 46, 53, 43, 36, 46, 46, 45, 45, 47, 43, 45, 39, 51, 46, 43, 44, 45, 47, 51, 46, 255, 43, 41, 49, 49, 47, 39, 47, 42, 47, 40, 46, 45, 48, 48, 46, 47, 43, 48, 45, 52, 45, 44, 46, 49, 54, 46, 45, 44, 50, 47, 49, 38, 47, 47, 44, 44, 49, 48, 46, 47}; - model->setOperandValue(op35, op35_init, sizeof(uint8_t) * 576); - static int32_t op37_init[] = {10397, 3213, -2474, 1866, 4100, 9003, 11272, -8980, 14390, 5932, 11170, 1932, 6737, 13536, 12361, -7455, 2899, 6621, 9845, 13715, -21328, 7959, 12321, 8043, 3516, 4947, 1869, 9866, 6310, 7097, 13905, -17880, 2152, 5290, -15, 14086, 7135, 16648, 3167, 12634, 13741, 12230, -1292, 7005, -13784, 12578, 3562, 13880, 11489, 11887, 5735, -3976, 12120, 5583, 10549, 11043, 3871, 6745, 13992, 6784, 16743, 11465, 14683, -20253, 11955, -15759, 11190, 9863, 14881, 10631, 292, -2224, -4152, 10468, 3854, -3690, 11788, 7017, -15797, 10840, 6459, -8864, 5128, 1248, 4696, 11864, 7653, 3309, 10205, 7388, 7069, 30024, 10215, 9415, 9863, 6629, 6958, 4688, 3928, 9759, -2777, 6053, -6279, 15762, 18437, -21163, 1963, -7203, 4405, 6417, 5878, 6739, 741, -13719, -5667, -23613, 9525, 15965, -5816, 7987, 6860, 20016, 9765, 1172, 15322, 10155, 4962, 24292}; - model->setOperandValue(op37, op37_init, sizeof(int32_t) * 128); - static uint8_t op38_init[] = {78, 81, 81, 103, 83, 75, 81, 79, 82, 83, 80, 92, 79, 78, 81, 82, 82, 80, 81, 78, 85, 85, 78, 81, 81, 83, 83, 84, 84, 86, 83, 82, 92, 83, 82, 82, 86, 79, 84, 81, 83, 84, 81, 84, 81, 86, 87, 82, 82, 62, 81, 79, 81, 89, 85, 84, 81, 83, 77, 77, 82, 85, 77, 77, 81, 72, 65, 82, 82, 80, 81, 78, 61, 83, 80, 81, 83, 83, 79, 80, 76, 77, 54, 89, 76, 81, 73, 81, 83, 81, 79, 134, 79, 85, 77, 56, 81, 77, 79, 79, 82, 81, 76, 76, 82, 77, 84, 88, 75, 80, 71, 84, 78, 81, 85, 81, 38, 85, 82, 119, 82, 64, 82, 81, 83, 87, 85, 82, 81, 80, 106, 97, 77, 81, 81, 81, 98, 83, 81, 89, 84, 85, 78, 77, 85, 83, 85, 105, 78, 77, 79, 81, 81, 80, 75, 89, 80, 79, 79, 85, 79, 84, 84, 79, 85, 81, 79, 80, 80, 78, 76, 82, 86, 82, 100, 79, 81, 74, 66, 86, 87, 96, 77, 85, 80, 83, 84, 79, 76, 85, 82, 100, 79, 80, 96, 92, 80, 81, 81, 83, 81, 79, 80, 95, 82, 79, 80, 81, 82, 81, 80, 91, 79, 81, 97, 81, 81, 77, 81, 84, 73, 79, 81, 82, 77, 81, 83, 81, 82, 81, 79, 81, 81, 80, 82, 80, 96, 80, 109, 77, 79, 100, 58, 83, 78, 105, 80, 81, 83, 85, 88, 76, 81, 83, 87, 97, 82, 100, 81, 79, 75, 83, 75, 78, 85, 83, 96, 84, 71, 86, 80, 72, 81, 82, 82, 76, 69, 85, 79, 81, 61, 134, 65, 80, 70, 141, 81, 87, 70, 83, 76, 77, 86, 87, 74, 77, 85, 74, 79, 40, 82, 86, 80, 83, 78, 80, 82, 77, 82, 81, 82, 84, 81, 77, 95, 78, 73, 75, 79, 79, 87, 81, 81, 82, 84, 86, 82, 78, 79, 75, 77, 75, 81, 92, 81, 76, 67, 93, 79, 83, 80, 76, 77, 81, 78, 79, 82, 79, 75, 85, 67, 71, 85, 81, 70, 64, 66, 80, 78, 79, 74, 73, 73, 81, 80, 80, 83, 82, 89, 77, 85, 82, 76, 82, 89, 73, 55, 77, 88, 87, 81, 76, 85, 83, 70, 83, 84, 90, 84, 78, 86, 76, 85, 80, 77, 93, 81, 85, 99, 78, 83, 77, 81, 76, 82, 87, 80, 81, 81, 80, 82, 81, 68, 79, 67, 81, 79, 80, 48, 89, 81, 76, 79, 84, 79, 83, 110, 81, 76, 55, 83, 112, 96, 75, 79, 89, 80, 91, 107, 84, 78, 82, 81, 84, 76, 79, 80, 75, 96, 80, 78, 83, 37, 97, 49, 105, 76, 86, 99, 87, 64, 115, 56, 49, 82, 102, 84, 83, 88, 57, 83, 81, 75, 97, 92, 82, 98, 94, 47, 94, 86, 83, 82, 49, 93, 94, 102, 41, 57, 52, 37, 83, 83, 74, 84, 71, 40, 89, 78, 71, 81, 78, 73, 96, 48, 92, 76, 72, 63, 89, 80, 75, 85, 78, 81, 85, 88, 65, 92, 76, 80, 77, 93, 81, 64, 79, 72, 92, 82, 84, 96, 83, 94, 86, 80, 81, 83, 96, 93, 87, 95, 90, 73, 70, 95, 84, 88, 71, 73, 80, 101, 89, 84, 81, 78, 89, 80, 80, 77, 73, 77, 83, 82, 70, 75, 81, 73, 67, 70, 80, 72, 83, 95, 87, 80, 81, 86, 75, 83, 88, 65, 53, 114, 81, 78, 76, 60, 86, 79, 96, 61, 108, 73, 88, 84, 79, 55, 110, 89, 81, 68, 62, 86, 69, 83, 66, 59, 72, 62, 101, 78, 57, 83, 86, 59, 71, 98, 66, 59, 70, 79, 90, 80, 72, 69, 91, 80, 103, 64, 83, 86, 86, 56, 89, 52, 79, 104, 92, 80, 82, 75, 79, 85, 79, 84, 78, 82, 80, 81, 79, 83, 91, 78, 77, 85, 84, 83, 78, 82, 80, 86, 86, 94, 81, 82, 84, 86, 82, 85, 80, 84, 85, 85, 84, 80, 86, 87, 79, 85, 84, 83, 87, 85, 80, 79, 85, 76, 84, 84, 105, 70, 76, 84, 84, 85, 81, 84, 83, 78, 70, 84, 83, 87, 85, 84, 78, 86, 100, 78, 79, 83, 81, 84, 83, 81, 83, 80, 82, 80, 81, 78, 81, 82, 52, 83, 79, 90, 81, 80, 75, 77, 75, 77, 77, 81, 89, 85, 79, 84, 82, 82, 77, 79, 78, 82, 82, 80, 79, 85, 80, 63, 77, 78, 79, 90, 80, 66, 99, 98, 89, 79, 70, 78, 81, 85, 85, 101, 60, 83, 82, 85, 80, 80, 81, 81, 83, 80, 82, 81, 77, 78, 80, 80, 82, 81, 82, 108, 73, 80, 79, 79, 81, 81, 83, 82, 73, 83, 84, 80, 59, 82, 81, 81, 79, 84, 81, 81, 80, 81, 80, 80, 81, 75, 83, 83, 80, 81, 80, 81, 83, 82, 83, 83, 99, 80, 80, 82, 78, 84, 69, 78, 82, 83, 83, 82, 81, 80, 81, 84, 80, 80, 86, 83, 79, 80, 83, 77, 76, 76, 79, 77, 84, 82, 98, 82, 81, 80, 84, 85, 86, 79, 77, 81, 77, 83, 69, 80, 80, 84, 82, 84, 83, 85, 81, 84, 75, 82, 74, 80, 81, 80, 78, 83, 79, 80, 78, 63, 77, 86, 74, 70, 83, 81, 74, 79, 85, 77, 85, 87, 79, 84, 75, 85, 76, 79, 74, 82, 83, 76, 77, 88, 82, 79, 76, 85, 80, 84, 83, 89, 81, 84, 84, 85, 85, 87, 86, 77, 81, 80, 84, 62, 81, 85, 80, 77, 82, 78, 87, 89, 89, 77, 80, 77, 98, 90, 95, 75, 77, 85, 82, 75, 80, 83, 79, 89, 78, 86, 80, 90, 87, 83, 88, 75, 80, 79, 83, 78, 79, 73, 80, 74, 77, 83, 74, 79, 79, 85, 82, 84, 84, 83, 82, 81, 81, 82, 87, 84, 75, 84, 84, 86, 88, 88, 87, 81, 77, 90, 78, 80, 78, 83, 76, 81, 88, 93, 83, 82, 79, 80, 73, 81, 83, 74, 115, 78, 87, 77, 76, 81, 56, 85, 81, 84, 97, 79, 82, 85, 93, 79, 78, 84, 81, 83, 74, 77, 95, 80, 79, 83, 83, 63, 81, 83, 81, 81, 84, 82, 81, 82, 84, 81, 79, 82, 81, 86, 88, 89, 81, 84, 79, 77, 82, 82, 82, 84, 84, 86, 79, 83, 97, 87, 79, 85, 84, 75, 82, 83, 83, 110, 91, 82, 81, 91, 76, 82, 88, 77, 79, 75, 73, 80, 80, 77, 85, 83, 83, 81, 79, 78, 80, 86, 86, 59, 90, 78, 76, 78, 81, 80, 109, 81, 81, 78, 77, 78, 75, 82, 77, 96, 82, 77, 86, 85, 63, 76, 81, 77, 79, 67, 76, 89, 78, 83, 101, 81, 90, 94, 79, 80, 80, 76, 81, 71, 85, 83, 87, 73, 83, 77, 83, 81, 80, 93, 82, 82, 82, 72, 56, 81, 91, 83, 89, 79, 83, 61, 86, 91, 77, 76, 93, 80, 79, 122, 75, 84, 81, 79, 58, 103, 81, 65, 68, 93, 80, 68, 78, 82, 86, 94, 79, 69, 78, 79, 82, 82, 75, 77, 81, 80, 85, 87, 83, 81, 40, 83, 80, 80, 80, 94, 73, 90, 69, 103, 80, 82, 81, 81, 78, 77, 82, 82, 80, 83, 81, 85, 88, 68, 85, 84, 80, 77, 83, 82, 80, 76, 75, 84, 79, 81, 81, 70, 98, 83, 79, 60, 78, 76, 77, 109, 60, 83, 78, 79, 85, 84, 81, 80, 81, 81, 48, 82, 83, 81, 81, 78, 78, 82, 83, 86, 92, 79, 84, 80, 75, 64, 81, 83, 73, 77, 77, 79, 132, 82, 80, 86, 88, 81, 108, 82, 81, 65, 81, 93, 89, 80, 79, 90, 83, 81, 80, 97, 81, 79, 81, 67, 94, 66, 88, 81, 76, 79, 85, 88, 71, 80, 70, 82, 90, 90, 82, 85, 69, 77, 121, 78, 88, 81, 85, 75, 81, 83, 68, 85, 80, 92, 82, 78, 77, 87, 82, 77, 82, 79, 81, 81, 80, 85, 74, 81, 80, 81, 82, 71, 81, 82, 76, 82, 79, 79, 81, 81, 80, 80, 59, 82, 80, 90, 81, 81, 80, 81, 81, 79, 81, 84, 82, 81, 83, 83, 81, 84, 81, 80, 80, 82, 81, 81, 81, 94, 81, 78, 81, 81, 82, 81, 81, 82, 97, 80, 75, 84, 83, 82, 80, 81, 79, 92, 93, 76, 82, 72, 106, 82, 76, 81, 76, 78, 83, 79, 99, 80, 82, 81, 81, 85, 78, 79, 68, 82, 82, 75, 81, 80, 84, 82, 82, 76, 84, 80, 82, 75, 76, 79, 81, 77, 80, 80, 80, 83, 81, 80, 84, 71, 78, 71, 83, 83, 74, 87, 81, 82, 85, 93, 86, 81, 83, 81, 78, 83, 81, 73, 70, 80, 80, 99, 80, 81, 80, 81, 81, 94, 80, 83, 82, 80, 80, 80, 82, 82, 81, 78, 91, 82, 80, 81, 81, 80, 81, 83, 81, 81, 80, 81, 80, 81, 81, 82, 81, 81, 80, 81, 81, 80, 81, 81, 81, 95, 82, 86, 81, 81, 84, 76, 80, 81, 68, 81, 81, 82, 81, 80, 81, 81, 81, 83, 66, 86, 76, 88, 82, 81, 78, 76, 82, 78, 79, 76, 74, 85, 84, 83, 81, 77, 81, 68, 76, 83, 82, 74, 81, 84, 77, 76, 109, 79, 79, 85, 74, 79, 82, 81, 80, 81, 80, 76, 80, 81, 74, 81, 80, 77, 83, 85, 80, 81, 78, 76, 83, 73, 81, 80, 65, 82, 134, 81, 85, 82, 52, 83, 77, 83, 82, 92, 87, 80, 82, 81, 82, 93, 83, 80, 84, 81, 82, 81, 81, 80, 80, 81, 82, 80, 80, 86, 81, 80, 80, 81, 81, 81, 80, 81, 81, 80, 79, 82, 80, 81, 80, 81, 81, 83, 81, 81, 81, 95, 81, 69, 81, 81, 72, 81, 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81, 80, 85, 91, 82, 83, 79, 83, 83, 84, 73, 78, 80, 64, 80, 80, 73, 82, 82, 79, 80, 82, 78, 82, 84, 78, 83, 62, 80, 81, 95, 84, 82, 80, 81, 76, 83, 80, 83, 83, 82, 58, 86, 64, 83, 83, 88, 85, 101, 81, 84, 81, 83, 99, 70, 81, 83, 82, 83, 83, 80, 82, 102, 80, 80, 77, 83, 82, 83, 82, 82, 66, 83, 83, 80, 80, 85, 75, 82, 84, 80, 85, 82, 80, 82, 83, 80, 81, 87, 83, 84, 79, 79, 81, 81, 82, 80, 80, 84, 81, 79, 79, 84, 85, 90, 88, 85, 89, 82, 82, 77, 84, 96, 71, 81, 75, 85, 76, 81, 74, 81, 82, 81, 80, 81, 76, 79, 79, 80, 77, 81, 85, 84, 81, 81, 81, 84, 81, 81, 81, 81, 81, 84, 82, 81, 80, 81, 81, 82, 80, 81, 81, 79, 69, 79, 81, 89, 81, 81, 80, 80, 79, 82, 81, 80, 80, 79, 81, 81, 81, 81, 82, 81, 81, 81, 80, 81, 81, 97, 82, 63, 81, 81, 76, 86, 81, 85, 94, 82, 77, 81, 81, 81, 81, 81, 80, 91, 79, 35, 98, 86, 77, 75, 96, 58, 86, 68, 68, 86, 73, 81, 13, 65, 72, 67, 0, 82, 82, 63, 53, 67, 81, 82, 86, 78, 77, 84, 90, 152, 56, 67, 77, 108, 177, 68, 79, 87, 77, 103, 134, 52, 83, 92, 62, 78, 129, 97, 82, 80, 98, 76, 72, 66, 74, 56, 81, 84, 96, 64, 77, 86, 95, 81, 82, 89, 77, 82, 79, 82, 79, 85, 77, 87, 93, 77, 78, 82, 83, 84, 79, 83, 82, 83, 82, 93, 81, 81, 85, 83, 82, 85, 82, 83, 81, 82, 83, 84, 83, 84, 82, 86, 82, 83, 82, 82, 83, 82, 85, 77, 82, 80, 108, 68, 78, 83, 85, 78, 82, 83, 84, 78, 77, 81, 84, 86, 82, 84, 80, 75, 72, 80, 82, 81, 82, 81, 82, 80, 72, 82, 82, 80, 82, 81, 81, 80, 86, 80, 78, 72, 81, 78, 79, 78, 82, 81, 79, 84, 77, 79, 80, 88, 77, 79, 82, 80, 79, 77, 78, 76, 78, 88, 80, 82, 78, 77, 70, 92, 84, 82, 73, 79, 76, 80, 78, 84, 88, 78, 77, 85, 83, 80, 81, 81, 89, 81, 81, 80, 84, 65, 81, 83, 83, 80, 80, 77, 79, 78, 79, 83, 58, 79, 81, 85, 81, 82, 79, 79, 90, 79, 79, 78, 84, 78, 80, 81, 79, 79, 83, 80, 82, 81, 81, 81, 80, 70, 82, 87, 79, 83, 72, 83, 82, 87, 117, 83, 73, 84, 85, 84, 75, 79, 81, 77, 117, 86, 79, 80, 79, 77, 86, 79, 83, 79, 83, 75, 67, 87, 89, 76, 77, 79, 84, 75, 83, 73, 79, 76, 81, 78, 75, 73, 75, 76, 78, 79, 75, 69, 79, 81, 79, 73, 81, 74, 76, 77, 72, 78, 81, 87, 70, 79, 77, 78, 81, 87, 89, 78, 77, 75, 76, 78, 79, 87, 90, 73, 76, 84, 83, 88, 108, 83, 84, 62, 91, 77, 52, 83, 85, 89, 81, 98, 83, 86, 84, 79, 85, 81, 81, 89, 85, 80, 81, 93, 64, 74, 87, 80, 89, 82, 84, 77, 82, 88, 78, 81, 0, 81, 86, 74, 79, 80, 88, 83, 82, 85, 80, 84, 80, 78, 78, 84, 81, 80, 78, 78, 77, 79, 85, 71, 78, 79, 83, 80, 77, 81, 79, 79, 82, 80, 83, 81, 74, 84, 82, 80, 81, 81, 83, 79, 78, 82, 81, 78, 79, 80, 81, 80, 76, 79, 79, 92, 90, 80, 83, 74, 81, 83, 81, 77, 84, 74, 83, 79, 82, 81, 89, 80, 84, 80, 79, 80, 83, 81, 81, 80, 83, 97, 81, 82, 84, 88, 78, 81, 86, 83, 82, 80, 82, 80, 86, 83, 80, 87, 79, 74, 77, 80, 80, 78, 87, 72, 82, 71, 80, 70, 67, 85, 82, 74, 81, 78, 79, 79, 45, 76, 79, 74, 18, 71, 75, 82, 79, 80, 80, 77, 86, 80, 79, 85, 80, 85, 75, 69, 80, 80, 79, 82, 83, 175, 63, 76, 69, 78, 114, 83, 92, 80, 92, 68, 83, 81, 65, 80, 84, 81, 79, 80, 79, 78, 76, 93, 85, 77, 84, 83, 86, 79, 84, 78, 82, 87, 86, 86, 81, 87, 72, 83, 81, 93, 75, 80, 84, 63, 58, 75, 80, 84, 79, 78, 82, 87, 78, 83, 68, 82, 96, 81, 85, 80, 86, 81, 81, 80, 82, 95, 85, 77, 86, 94, 80, 74, 92, 81, 81, 67, 81, 81, 81, 80, 81, 80, 81, 80, 86, 80, 83, 81, 97, 82, 81, 88, 93, 84, 83, 79, 79, 84, 81, 77, 81, 82, 84, 78, 85, 73, 78, 80, 79, 98, 82, 78, 81, 84, 79, 84, 99, 75, 78, 79, 92, 80, 100, 86, 85, 78, 82, 77, 84, 81, 79, 95, 78, 82, 92, 78, 81, 79, 83, 79, 70, 95, 71, 73, 88, 88, 81, 86, 80, 81, 44, 75, 88, 83, 86, 76, 81, 105, 68, 82, 79, 70, 81, 83, 73, 87, 109, 81, 67, 65, 129, 64, 81, 79, 84, 59, 87, 81, 76, 76, 89, 85, 77, 65, 68, 96, 83, 83, 72, 79, 79, 157, 73, 75, 50, 70, 68, 76, 70, 90, 59, 79, 66, 90, 80, 79, 73, 80, 80, 80, 85, 81, 78, 81, 73, 79, 81, 81, 81, 27, 83, 83, 84, 86, 78, 80, 81, 80, 83, 83, 73, 83, 85, 85, 88, 96, 87, 84, 76, 61, 79, 84, 83, 75, 81, 80, 83, 85, 103, 81, 72, 78, 84, 82, 73, 77, 78, 124, 91, 78, 83, 89, 80, 94, 85, 81, 82, 79, 78, 82, 81, 81, 73, 85, 82, 82, 77, 97, 88, 65, 77, 82, 85, 73, 80, 86, 76, 98, 81, 87, 81, 85, 88, 95, 82, 86, 84, 83, 79, 86, 81, 84, 77, 78, 80, 97, 84, 80, 83, 80, 83, 75, 79, 84, 77, 83, 82, 77, 65, 82, 84, 75, 78, 78, 84, 78, 75, 89, 86, 77, 77, 72, 79, 82, 81, 83, 63, 93, 80, 79, 77, 82, 85, 67, 77, 82, 91, 82, 71, 82, 81, 88, 87, 84, 81, 80, 81, 91, 78, 76, 80, 81, 81, 75, 84, 86, 80, 80, 80, 86, 85, 86, 86, 79, 81, 79, 82, 80, 78, 84, 82, 79, 72, 79, 82, 80, 85, 84, 89, 78, 73, 102, 83, 83, 78, 73, 94, 81, 81, 63, 126, 63, 84, 79, 80, 78, 85, 112, 78, 79, 68, 88, 72, 96, 80, 12, 87, 82, 81, 81, 89, 52, 96, 108, 85, 83, 76, 104, 85, 74, 86, 81, 86, 73, 71, 92, 90, 83, 85, 77, 93, 78, 86, 88, 84, 79, 148, 87, 83, 93, 73, 90, 84, 62, 73, 36, 95, 80, 76, 87, 83, 80, 82, 76, 85, 78, 83, 82, 82, 83, 79, 78, 76, 88, 76, 81, 76, 78, 81, 80, 73, 81, 81, 78, 84, 83, 81, 74, 83, 136, 84, 80, 79, 77, 124, 79, 80, 85, 78, 80, 74, 76, 82, 80, 80, 79, 90, 97, 80, 82, 84, 87, 80, 82, 78, 77, 81, 85, 82, 73, 75, 81, 79, 90, 70, 82, 84, 81, 79, 87, 77, 84, 76, 76, 82, 70, 87, 78, 73, 81, 92, 83, 83, 72, 100, 80, 81, 77, 58, 102, 84, 84, 81, 82, 75, 84, 132, 79, 83, 78, 95, 85, 81, 95, 77, 80, 97, 86, 81, 83, 82, 76, 80, 84, 93, 80, 83, 78, 80, 75, 84, 77, 84, 74, 84, 81, 81, 78, 88, 85, 84, 87, 78, 82, 95, 82, 83, 90, 79, 81, 82, 86, 79, 78, 81, 84, 80, 86, 83, 83, 81, 68, 84, 85, 83, 75, 83, 82, 88, 96, 81, 79, 86, 73, 76, 84, 87, 78, 83, 85, 85, 82, 86, 82, 79, 85, 83, 79, 81, 84, 82, 89, 78, 78, 78, 81, 80, 83, 76, 82, 78, 74, 99, 77, 80, 83, 71, 79, 78, 83, 91, 113, 86, 80, 78, 80, 80, 88, 78, 91, 83, 83, 87, 82, 81, 76, 55, 82, 78, 61, 49, 87, 87, 89, 100, 82, 80, 76, 76, 110, 79, 90, 86, 86, 64, 84, 76, 83, 75, 84, 81, 78, 88, 87, 81, 79, 79, 82, 75, 69, 86, 77, 60, 75, 80, 79, 82, 72, 87, 79, 83, 82, 81, 50, 83, 84, 85, 80, 80, 88, 84, 82, 80, 83, 77, 76, 84, 67, 81, 76, 86, 86, 82, 81, 78, 84, 87, 83, 80, 82, 80, 97, 78, 85, 76, 85, 80, 81, 81, 49, 85, 142, 79, 82, 78, 68, 81, 79, 37, 79, 90, 80, 87, 75, 75, 82, 83, 81, 93, 82, 89, 83, 80, 86, 79, 81, 76, 81, 80, 83, 82, 82, 77, 82, 80, 97, 80, 87, 81, 80, 82, 79, 81, 87, 79, 83, 82, 97, 84, 82, 84, 69, 78, 78, 82, 87, 73, 79, 86, 83, 83, 84, 91, 80, 79, 82, 86, 79, 83, 81, 79, 81, 81, 75, 76, 87, 79, 88, 81, 81, 83, 84, 84, 86, 86, 81, 102, 75, 95, 74, 89, 67, 74, 83, 98, 85, 83, 78, 74, 65, 82, 81, 66, 74, 76, 110, 81, 90, 82, 89, 76, 70, 83, 82, 84, 78, 87, 75, 88, 99, 80, 86, 77, 78, 73, 81, 83, 70, 83, 65, 79, 85, 124, 62, 82, 74, 67, 68, 88, 80, 85, 90, 49, 80, 84, 82, 83, 94, 127, 81, 79, 81, 89, 113, 90, 84, 76, 81, 79, 68, 90, 81, 128, 73, 92, 84, 79, 68, 68, 82, 81, 97, 84, 96, 83, 86, 93, 86, 78, 82, 42, 77, 86, 80, 96, 41, 74, 57, 74, 87, 55, 79, 85, 79, 82, 72, 83, 80, 95, 78, 79, 78, 83, 76, 87, 79, 84, 80, 90, 82, 79, 78, 86, 79, 71, 85, 78, 85, 79, 70, 80, 79, 77, 74, 81, 78, 82, 76, 81, 92, 82, 84, 85, 111, 81, 83, 81, 77, 87, 76, 78, 80, 86, 76, 90, 79, 80, 70, 77, 81, 84, 80, 81, 85, 84, 67, 79, 77, 83, 84, 108, 72, 78, 86, 69, 70, 90, 81, 84, 86, 87, 80, 80, 80, 92, 88, 73, 88, 85, 87, 84, 83, 144, 83, 92, 71, 83, 90, 98, 79, 87, 69, 109, 82, 84, 74, 93, 83, 81, 26, 80, 73, 84, 90, 84, 85, 93, 84, 87, 68, 124, 60, 111, 91, 140, 69, 75, 70, 66, 87, 70, 85, 67, 80, 85, 80, 88, 86, 92, 75, 76, 65, 77, 81, 75, 79, 73, 77, 73, 81, 81, 78, 83, 82, 81, 81, 81, 80, 81, 79, 82, 83, 81, 81, 81, 81, 81, 83, 95, 82, 81, 74, 81, 82, 82, 81, 82, 79, 82, 80, 81, 81, 81, 81, 82, 79, 80, 82, 81, 80, 81, 81, 82, 70, 81, 101, 81, 81, 85, 78, 81, 76, 73, 81, 85, 81, 79, 80, 81, 80, 81, 68, 83, 86, 79, 88, 71, 82, 79, 81, 79, 81, 88, 78, 75, 83, 76, 78, 80, 83, 77, 80, 88, 78, 84, 84, 81, 82, 81, 80, 80, 84, 81, 85, 81, 73, 82, 65, 76, 83, 79, 79, 84, 80, 76, 81, 81, 86, 79, 80, 101, 89, 86, 79, 79, 86, 79, 49, 86, 86, 82, 74, 80, 83, 82, 90, 90, 77, 77, 86, 79, 80, 83, 82, 79, 82, 89, 74, 127, 80, 79, 80, 81, 80, 82, 72, 79, 81, 81, 80, 81, 79, 73, 82, 75, 76, 81, 76, 73, 75, 77, 82, 72, 81, 80, 72, 78, 82, 85, 79, 80, 80, 84, 95, 77, 79, 68, 84, 80, 77, 95, 86, 75, 78, 75, 89, 77, 81, 73, 75, 71, 72, 76, 81, 82, 82, 81, 84, 81, 81, 84, 81, 79, 86, 78, 92, 84, 82, 72, 74, 80, 75, 89, 79, 81, 82, 71, 77, 75, 66, 73, 96, 87, 72, 79, 75, 94, 93, 82, 71, 83, 86, 91, 87, 76, 80, 89, 79, 88, 94, 79, 82, 91, 83, 84, 93, 93, 97, 84, 84, 76, 81, 74, 77, 81, 81, 82, 80, 105, 82, 80, 82, 80, 84, 83, 81, 99, 81, 81, 81, 81, 83, 81, 81, 77, 81, 83, 74, 81, 81, 82, 82, 83, 83, 82, 80, 79, 82, 82, 81, 79, 84, 81, 82, 82, 81, 82, 80, 82, 80, 83, 86, 82, 82, 63, 85, 82, 81, 86, 82, 82, 81, 79, 80, 80, 80, 82, 75, 77, 70, 83, 79, 84, 85, 70, 87, 80, 82, 78, 90, 88, 72, 68, 85, 93, 81, 68, 83, 80, 91, 89, 87, 81, 83, 86, 93, 81, 85, 81, 90, 85, 83, 81, 89, 86, 88, 79, 92, 90, 90, 91, 85, 81, 77, 95, 78, 79, 86, 84, 77, 72, 83, 82, 88, 85, 88, 83, 64, 65, 94, 87, 83, 78, 81, 82, 85, 58, 80, 82, 82, 81, 87, 78, 80, 72, 82, 79, 80, 81, 81, 80, 80, 79, 79, 82, 86, 81, 80, 79, 77, 81, 80, 80, 81, 80, 84, 81, 81, 82, 77, 80, 79, 81, 81, 81, 83, 78, 79, 83, 66, 80, 80, 96, 86, 84, 84, 69, 82, 82, 83, 80, 79, 84, 81, 81, 89, 73, 75, 77, 90, 78, 86, 80, 81, 83, 96, 69, 81, 84, 85, 74, 94, 80, 82, 77, 82, 70, 86, 78, 82, 81, 82, 80, 79, 88, 72, 83, 97, 39, 88, 82, 88, 95, 82, 87, 84, 83, 85, 87, 78, 80, 89, 100, 78, 70, 89, 78, 80, 81, 103, 80, 98, 112, 79, 78, 90, 75, 83, 76, 74, 79, 59, 80, 82, 80, 76, 64, 98, 87, 79, 81, 73, 82, 80, 104, 79, 102, 89, 60, 75, 83, 74, 104, 80, 81, 79, 63, 80, 73, 78, 67, 78, 87, 83, 84, 84, 74, 84, 83, 67, 78, 99, 56, 68, 73, 82, 78, 79, 81, 88, 79, 81, 85, 83, 81, 81, 85, 83, 76, 83, 83, 99, 76, 81, 81, 83, 81, 84, 58, 79, 82, 81, 81, 78, 79, 80, 66, 81, 82, 81, 81, 82, 80, 81, 85, 80, 80, 87, 81, 80, 81, 81, 80, 80, 81, 79, 81, 82, 81, 80, 80, 78, 82, 80, 80, 80, 80, 80, 80, 82, 80, 77, 79, 80, 100, 77, 82, 80, 78, 80, 81, 80, 80, 81, 83, 81, 82, 88, 87, 75, 81, 80, 93, 82, 80, 81, 83, 81, 83, 83, 82, 84, 66, 78, 90, 104, 70, 77, 80, 84, 85, 85, 81, 85, 83, 81, 86, 86, 86, 103, 82, 80, 82, 105, 102, 95, 82, 80, 85, 94, 93, 82, 83, 70, 86, 78, 70, 73, 77, 80, 76, 84, 81, 71, 83, 117, 79, 73, 75, 84, 79, 80, 79, 54, 79, 82, 82, 82, 73, 87, 80, 82, 82, 81, 81, 85, 53, 73, 85, 75, 52, 80, 81, 79, 84, 83, 81, 79, 84, 78, 78, 73, 81, 73, 78, 71, 82, 84, 80, 77, 80, 83, 83, 85, 108, 78, 81, 80, 81, 79, 74, 88, 86, 81, 83, 76, 79, 79, 78, 77, 84, 88, 73, 90, 82, 81, 80}; - model->setOperandValue(op38, op38_init, sizeof(uint8_t) * 8192); - static int32_t op40_init[] = {518, 2789, 647, -259, -1601, 712, 289, 1468, 3580, 4537, 98, 1244, -59, 68, 2265, -231, 593, -1620, -6, 42, 3718, -133, 311, 2639, -60, 150, -1999, 22, -1973, -189, 28, 2487, 2570, 157, -1716, 2638, 16, 1363, -464, 290, 222, 1091, 4184, 82, 2165, 56, -628, 152, 1094, 920, 1590, -1231, 4661, 4487, 1757, 1657, 138, 3384, 734, -52, -19, 3295, 5841, 1952, 5486, 1686, 9854, -1466, 221, 1366, 710, -1652, -14, -382, -316, 2411, 4122, 2079, 1222, 1689, 1569, -35, 191, -45, -734, 1771, 1981, 1967, -40, 67, -58, 1093, 176, 8471, 2074, 1607, -1123, 416, 1634, 1678, -740, 4056, -907, 731, 52, 3104, -792, -1329, 452, 252, -877, -23, -50, -57, -2578, 5709, 32, 500, -2343, 797, 1081, 2721, 150, -15, 5426, 1879, 141, 736}; - model->setOperandValue(op40, op40_init, sizeof(int32_t) * 128); - static uint8_t op41_init[] = {130, 106, 119, 96, 123, 111, 103, 111, 123, 112, 130, 112, 136, 143, 130, 132, 125, 107, 116, 123, 123, 115, 111, 139, 135, 143, 115, 95, 134, 119, 108, 111, 108, 128, 128, 95, 107, 182, 115, 96, 125, 113, 106, 96, 128, 81, 121, 113, 101, 118, 134, 119, 111, 111, 139, 99, 129, 112, 117, 92, 123, 128, 120, 109, 115, 108, 108, 111, 118, 91, 120, 136, 121, 91, 111, 138, 113, 146, 136, 87, 117, 115, 111, 94, 117, 112, 70, 118, 109, 92, 82, 107, 133, 93, 113, 109, 113, 158, 104, 115, 111, 120, 114, 128, 121, 115, 114, 108, 104, 103, 123, 86, 112, 117, 103, 117, 143, 123, 104, 105, 80, 118, 96, 110, 115, 152, 106, 114, 101, 109, 126, 86, 113, 99, 99, 118, 110, 111, 116, 105, 74, 83, 110, 143, 136, 124, 124, 106, 95, 61, 109, 104, 76, 116, 118, 116, 80, 111, 125, 112, 113, 142, 105, 110, 128, 58, 112, 129, 163, 108, 86, 65, 129, 47, 135, 80, 123, 101, 158, 133, 114, 115, 83, 103, 101, 130, 110, 141, 74, 108, 106, 113, 114, 112, 95, 122, 154, 15, 100, 128, 123, 154, 116, 106, 108, 113, 98, 104, 106, 127, 99, 117, 130, 134, 132, 134, 125, 140, 54, 106, 158, 93, 109, 107, 104, 64, 109, 101, 80, 104, 118, 79, 176, 110, 116, 116, 158, 125, 136, 149, 127, 117, 114, 119, 113, 143, 109, 138, 50, 127, 72, 103, 110, 118, 94, 120, 111, 108, 103, 110, 113, 111, 109, 110, 120, 120, 98, 117, 131, 117, 113, 99, 118, 117, 113, 115, 120, 109, 107, 107, 125, 88, 110, 134, 131, 107, 111, 111, 114, 119, 114, 126, 110, 106, 112, 113, 117, 117, 137, 106, 103, 104, 101, 114, 115, 124, 115, 106, 123, 113, 118, 127, 112, 113, 108, 114, 123, 115, 114, 113, 116, 113, 106, 113, 114, 103, 113, 115, 116, 116, 115, 99, 122, 107, 139, 150, 120, 115, 112, 131, 102, 102, 142, 123, 114, 114, 102, 103, 123, 118, 120, 113, 114, 120, 106, 124, 124, 114, 112, 126, 73, 119, 113, 114, 111, 113, 104, 114, 103, 114, 107, 116, 88, 115, 108, 112, 84, 114, 108, 112, 119, 76, 109, 112, 171, 111, 123, 109, 139, 119, 126, 121, 108, 111, 172, 112, 81, 76, 103, 143, 111, 116, 121, 137, 104, 126, 106, 252, 103, 172, 118, 70, 155, 113, 104, 109, 109, 98, 161, 129, 93, 54, 117, 110, 98, 113, 108, 120, 116, 124, 87, 117, 122, 158, 113, 123, 113, 103, 122, 101, 149, 140, 124, 139, 111, 177, 103, 110, 109, 113, 106, 116, 111, 118, 150, 116, 123, 138, 120, 107, 110, 117, 117, 153, 106, 126, 122, 78, 129, 90, 149, 99, 83, 149, 119, 110, 113, 87, 110, 112, 115, 87, 111, 121, 122, 109, 125, 142, 86, 109, 123, 120, 127, 99, 123, 149, 119, 118, 142, 121, 174, 106, 106, 90, 104, 123, 118, 122, 111, 208, 125, 121, 78, 120, 137, 108, 113, 163, 147, 140, 87, 95, 100, 108, 159, 155, 77, 144, 106, 149, 134, 91, 105, 183, 172, 72, 146, 111, 146, 118, 52, 145, 143, 103, 155, 85, 95, 108, 146, 161, 140, 105, 84, 110, 87, 121, 127, 120, 138, 151, 140, 70, 112, 160, 94, 98, 95, 94, 77, 101, 142, 75, 149, 65, 79, 112, 90, 105, 106, 147, 80, 120, 102, 111, 129, 82, 139, 117, 97, 64, 0, 100, 105, 123, 147, 118, 137, 121, 92, 89, 143, 83, 120, 119, 109, 115, 107, 179, 148, 147, 112, 85, 155, 97, 138, 74, 108, 106, 138, 150, 77, 137, 134, 84, 144, 139, 229, 88, 111, 96, 255, 129, 98, 72, 120, 133, 91, 109, 152, 141, 100, 111, 111, 119, 114, 110, 116, 114, 112, 119, 76, 120, 108, 117, 134, 101, 110, 97, 111, 120, 118, 112, 103, 18, 104, 59, 112, 156, 149, 111, 104, 115, 102, 109, 114, 130, 106, 121, 114, 116, 107, 116, 121, 119, 110, 117, 125, 117, 106, 108, 115, 110, 118, 114, 135, 115, 122, 96, 110, 127, 111, 101, 119, 114, 114, 114, 107, 113, 114, 124, 116, 114, 111, 115, 111, 129, 117, 127, 148, 95, 116, 115, 116, 150, 107, 144, 101, 110, 119, 113, 120, 112, 113, 117, 116, 91, 114, 115, 111, 133, 105, 116, 112, 146, 130, 115, 114, 113, 116, 110, 107, 112, 107, 113, 119, 125, 61, 109, 106, 97, 116, 132, 114, 117, 118, 24, 118, 112, 121, 106, 104, 137, 115, 111, 113, 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117, 115, 105, 116, 108, 99, 122, 129, 112, 114, 171, 113, 79, 118, 117, 93, 115, 135, 112, 112, 117, 115, 117, 103, 81, 115, 116, 104, 108, 117, 99, 112, 110, 104, 127, 118, 116, 116, 104, 140, 211, 133, 153, 110, 115, 113, 105, 108, 112, 107, 111, 121, 113, 114, 114, 111, 117, 116, 112, 119, 107, 113, 116, 103, 110, 114, 113, 114, 113, 119, 114, 103, 114, 104, 109, 123, 121, 114, 113, 114, 116, 113, 115, 93, 113, 110, 113, 115, 111, 117, 117, 121, 112, 124, 116, 112, 111, 115, 114, 108, 117, 114, 119, 123, 116, 129, 112, 107, 110, 116, 115, 114, 120, 114, 113, 108, 117, 125, 111, 114, 116, 114, 113, 118, 120, 122, 122, 115, 117, 113, 113, 120, 113, 115, 111, 115, 112, 115, 125, 108, 109, 114, 116, 119, 113, 104, 110, 117, 114, 119, 114, 121, 130, 117, 113, 111, 115, 114, 116, 117, 109, 111, 122, 116, 106, 112, 111, 105, 138, 113, 122, 115, 117, 91, 111, 112}; - model->setOperandValue(op41, op41_init, sizeof(uint8_t) * 1152); - static int32_t op43_init[] = {-9119, 406, 8026, -2442, 22035, 7830, 5239, -16418, 3066, 244, 17626, -11691, -4541, 14457, 8384, 3859, -8220, -7972, -3881, -3717, 2242, -11349, -13582, 486, 3455, -2435, -1912, 3185, -10701, 3372, -3150, 6759, 10970, 6665, 16016, 2817, 14058, -556, 7218, -8752, 11568, -9512, -11502, -4894, 4890, -1483, 7517, 9618, 3061, 12316, 19276, 10143, -23717, -5637, 13399, -2423, 4260, 799, 2376, 3188, 4820, 7535, -3916, 13854, 14245, -704, -770, 1514, 3930, 745, 1798, -5644, -3217, 19698, -265, -26449, 11816, -20746, -2139, 1080, -13117, -2232, 4019, -4479, -6283, 1058, 16636, 16248, 3525, -2806, -2207, 3284, 952, 6117, 15768, -10433, 14577, -8859, -11789, 7781, 16966, 18525, -3438, 7560, 211, 3327, -807, 17839, -2552, 9905, 2938, -3158, 5764, 7175, 4135, -10571, 16909, 17722, 16820, 1561, 9099, 7644, 21318, 1901, 22449, 60, -7215, -9300}; - model->setOperandValue(op43, op43_init, sizeof(int32_t) * 128); - static uint8_t op44_init[] = {117, 122, 124, 128, 124, 104, 122, 132, 111, 133, 115, 130, 122, 119, 110, 126, 129, 110, 114, 115, 128, 111, 113, 124, 128, 126, 126, 125, 121, 115, 115, 132, 118, 105, 130, 136, 125, 113, 120, 122, 117, 118, 125, 123, 103, 117, 107, 122, 104, 116, 118, 125, 119, 132, 118, 125, 121, 125, 155, 120, 125, 127, 117, 120, 112, 121, 126, 121, 121, 120, 120, 121, 112, 108, 173, 136, 126, 117, 134, 114, 131, 120, 104, 124, 124, 128, 122, 116, 116, 114, 123, 125, 132, 129, 131, 156, 116, 119, 121, 133, 120, 125, 118, 126, 125, 123, 113, 121, 116, 119, 122, 119, 113, 117, 118, 124, 123, 143, 121, 137, 142, 133, 136, 105, 127, 121, 88, 100, 127, 125, 122, 116, 119, 131, 112, 127, 112, 114, 126, 124, 123, 135, 125, 122, 106, 117, 123, 136, 138, 119, 107, 123, 126, 125, 121, 129, 128, 133, 108, 137, 85, 106, 105, 97, 125, 132, 98, 122, 115, 122, 125, 122, 124, 126, 145, 143, 107, 123, 116, 123, 114, 123, 137, 118, 116, 138, 141, 119, 128, 122, 131, 133, 120, 117, 122, 135, 119, 123, 108, 122, 126, 139, 108, 115, 123, 118, 125, 133, 125, 131, 116, 126, 125, 103, 124, 102, 120, 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133, 120, 131, 118, 127, 126, 123, 114, 120, 124, 120, 116, 127, 126, 128, 125, 116, 158, 129, 96, 126, 120, 128, 123, 129, 125, 105, 123, 133, 119, 118, 124, 123, 116, 121, 135, 121, 117, 125, 119, 126, 121, 125, 123, 149, 95, 123, 128, 131, 115, 131, 126, 88, 100, 126, 109, 188, 122, 122, 126, 116, 121, 127, 120, 122, 114, 116, 127, 119, 120, 137, 127, 113, 124, 112, 121, 127, 121, 123, 128, 128, 118, 118, 124, 123, 114, 113, 114, 125, 121, 118, 128, 116, 125, 124, 124, 122, 113, 121, 119, 125, 130, 124, 126, 122, 122, 120, 117, 120, 129, 83, 114, 114, 114, 112, 122, 120, 115, 122, 120, 122, 132, 122, 127, 100, 184, 121, 105, 124, 120, 129, 120, 125, 122, 126, 123, 139, 118, 128, 113, 131, 130, 118, 117, 127, 136, 112, 116, 130, 116, 127, 125, 119, 122, 117, 117, 143, 117, 127, 128, 121, 115, 135, 107, 117, 98, 126, 105, 118, 117, 122, 128, 128, 111, 118, 127, 132, 141, 126, 116, 113, 143, 116, 109, 117, 120, 98, 129, 120, 129, 117, 122, 122, 113, 124, 115, 106, 115, 129, 134, 106, 134, 109, 114, 120, 112, 126, 118, 124, 126, 123, 131, 122, 117, 117, 113, 118, 105, 117, 138, 129, 123, 115, 137, 150, 115, 123, 121, 139, 118, 117, 112, 133, 122, 120, 117, 119, 118, 117, 113, 123, 130, 116, 112, 127, 117, 110, 123, 122, 101, 123, 126, 113, 118, 123, 121, 149, 121, 123, 135, 138, 125, 144, 114, 123, 121, 114, 113, 115, 106, 117, 123, 125, 113, 117, 128, 120, 117, 150, 123, 121, 120, 123, 122, 124, 105, 121, 120, 138, 123, 116, 108, 111, 124, 123}; - model->setOperandValue(op44, op44_init, sizeof(uint8_t) * 16384); - static int32_t op46_init[] = {-4, 19041, 11198, 12939, 89, 21739, 4637, -436, -1022, 1301, 19261, -30, 1277, 861, 299, -3221, 472, 8599, 8650, 500, 8521, -366, -3871, 7440, 146, -11, 17873, -202, 653, 21242, 14690, 1682, 38, 2498, -1230, 102, 7678, 9604, 8617, 364, 12383, 3741, 9240, 7630, 11816, -1966, 9708, -6149, 7842, 18989, -13873, -36, 17319, 21848, 7300, 2687, 8614, 8988, 5411, 888, 192, 9617, 252, 13840, -290, 242, 10315, 5935, 4901, 9698, 7611, 3060, 3275, 22662, 8285, -2336, 1784, -21, 9092, 11491, -211, 42, 12242, 21922, 8100, 17614, 9945, 21637, 19098, 9326, 2513, 135, 18572, 11188, -1950, 7730, 11608, 9488, -335, 197, -1356, 16575, 183, 620, 17908, 6314, 2500, -5468, 513, 5895, -123, 176, 2709, 10640, 12294, 7853, -18153, -8770, 19015, 8094, -21784, 1519, -5720, 176, 18060, 313, 14927, 1630}; - model->setOperandValue(op46, op46_init, sizeof(int32_t) * 128); - static uint8_t op47_init[] = {161, 114, 136, 103, 179, 98, 177, 159, 154, 113, 117, 163, 156, 151, 169, 155, 160, 106, 117, 149, 119, 185, 188, 123, 163, 152, 140, 161, 158, 131, 135, 159, 172, 175, 148, 163, 139, 118, 123, 161, 119, 160, 121, 116, 115, 150, 130, 155, 135, 123, 146, 151, 121, 121, 124, 88, 107, 88, 162, 153, 153, 125, 154, 121, 154, 163, 124, 143, 148, 128, 150, 163, 104, 126, 116, 211, 175, 165, 128, 153, 160, 159, 102, 86, 126, 122, 120, 124, 124, 129, 109, 168, 121, 128, 255, 125, 125, 126, 143, 85, 105, 119, 160, 128, 118, 154, 174, 153, 191, 154, 149, 159, 162, 127, 118, 124, 161, 189, 117, 102, 170, 165, 152, 165, 156, 154, 122, 154, 167, 106, 119, 103, 188, 86, 32, 171, 159, 89, 109, 173, 157, 154, 173, 160, 160, 98, 114, 154, 115, 181, 195, 111, 175, 162, 115, 180, 170, 125, 129, 163, 178, 180, 156, 169, 122, 100, 120, 170, 114, 171, 114, 109, 114, 158, 113, 158, 125, 117, 161, 155, 111, 113, 117, 65, 97, 133, 175, 170, 159, 122, 163, 113, 159, 169, 115, 126, 159, 125, 163, 173, 168, 122, 115, 223, 125, 174, 124, 162, 167, 181, 106, 75, 128, 111, 118, 119, 121, 125, 65, 176, 112, 100, 105, 112, 121, 122, 148, 146, 119, 116, 164, 85, 109, 155, 180, 157, 189, 175, 153, 174, 165, 119, 114, 113, 165, 75, 110, 118, 173, 178, 159, 183, 159, 172, 117, 168, 150, 126, 117, 127, 157, 116, 165, 153, 148, 86, 127, 156, 143, 147, 152, 147, 148, 120, 131, 145, 127, 154, 152, 125, 159, 152, 115, 160, 156, 131, 131, 153, 148, 151, 149, 155, 118, 120, 128, 154, 131, 154, 129, 126, 128, 148, 116, 147, 121, 129, 148, 146, 125, 130, 123, 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92, 114, 137, 192, 66, 44, 186, 167, 135, 108, 180, 171, 162, 191, 170, 177, 78, 100, 165, 103, 166, 225, 101, 190, 172, 94, 191, 182, 119, 118, 176, 193, 189, 161, 182, 145, 94, 112, 183, 109, 184, 115, 100, 105, 168, 93, 169, 113, 107, 175, 169, 95, 101, 108, 147, 86, 163, 189, 184, 165, 114, 169, 110, 171, 181, 106, 150, 171, 118, 171, 183, 142, 113, 100, 237, 143, 188, 108, 171, 180, 199, 134, 57, 103, 97, 110, 104, 109, 116, 137, 192, 96, 135, 0, 105, 109, 114, 168, 146, 60, 106, 178, 123, 96, 165, 191, 168, 125, 152, 163, 188, 177, 107, 101, 101, 170, 43, 104, 156, 181, 196, 171, 201, 114, 190, 105, 192, 160, 113, 113, 151, 162, 97, 118, 160, 154, 184, 126, 157, 144, 149, 163, 153, 159, 102, 116, 150, 116, 153, 163, 121, 161, 156, 113, 162, 161, 127, 122, 158, 164, 163, 152, 163, 126, 119, 122, 165, 128, 162, 131, 114, 119, 153, 106, 150, 123, 123, 155, 153, 118, 121, 115, 133, 110, 168, 154, 164, 152, 122, 157, 122, 153, 156, 119, 124, 159, 132, 166, 162, 194, 128, 121, 181, 88, 159, 125, 155, 161, 171, 159, 104, 122, 121, 123, 122, 123, 128, 129, 165, 116, 119, 217, 112, 125, 126, 154, 201, 191, 121, 157, 195, 121, 152, 162, 151, 110, 158, 150, 162, 158, 124, 125, 113, 154, 198, 121, 173, 178, 168, 154, 163, 126, 175, 121, 172, 154, 123, 137, 144, 162, 113, 204, 158, 151, 185, 133, 151, 147, 149, 151, 147, 145, 126, 123, 145, 127, 157, 170, 122, 157, 150, 123, 162, 154, 136, 126, 150, 149, 143, 145, 152, 161, 113, 130, 152, 134, 151, 131, 126, 131, 148, 119, 150, 118, 129, 154, 150, 124, 129, 133, 174, 118, 142, 160, 158, 149, 131, 149, 125, 151, 152, 126, 170, 150, 132, 143, 152, 118, 131, 126, 183, 150, 157, 123, 149, 153, 157, 143, 106, 125, 125, 131, 127, 130, 126, 148, 158, 118, 176, 74, 128, 126, 128, 146, 74, 180, 122, 152, 98, 125, 152, 152, 149, 124, 103, 146, 158, 153, 126, 130, 119, 159, 174, 118, 135, 161, 164, 150, 162, 120, 149, 130, 159, 163, 114, 131, 166, 167, 103, 135, 162, 153, 193, 127, 152, 150, 150, 165, 151, 160, 106, 106, 149, 118, 157, 176, 119, 162, 152, 121, 158, 160, 133, 120, 157, 171, 162, 148, 159, 161, 108, 121, 162, 131, 161, 130, 118, 125, 153, 113, 154, 120, 124, 162, 154, 114, 120, 123, 193, 109, 172, 160, 159, 151, 126, 155, 126, 155, 158, 121, 154, 156, 131, 151, 161, 119, 126, 112, 190, 161, 164, 119, 156, 159, 167, 160, 94, 119, 119, 123, 122, 124, 123, 193, 169, 115, 159, 122, 122, 118, 125, 159, 148, 92, 116, 162, 175, 118, 156, 163, 153, 82, 124, 151, 165, 160, 119, 118, 116, 172, 76, 112, 174, 163, 169, 156, 163, 105, 164, 121, 171, 150, 125, 133, 160, 149, 120, 83, 152, 145, 144, 133, 142, 140, 143, 157, 147, 152, 116, 122, 143, 127, 151, 153, 129, 151, 148, 137, 146, 148, 136, 125, 152, 160, 152, 143, 152, 143, 128, 127, 155, 133, 149, 137, 125, 128, 148, 128, 145, 132, 132, 152, 147, 132, 127, 128, 162, 126, 142, 144, 144, 145, 129, 147, 131, 146, 147, 131, 137, 153, 138, 152, 153, 160, 133, 123, 169, 116, 148, 133, 145, 150, 152, 162, 119, 128, 131, 128, 130, 129, 133, 185, 156, 130, 138, 221, 130, 128, 133, 150, 184, 138, 124, 153, 148, 135, 146, 152, 145, 107, 144, 146, 148, 149, 129, 129, 129, 155, 169, 124, 168, 155, 148, 148, 147, 125, 154, 127, 155}; - model->setOperandValue(op47, op47_init, sizeof(uint8_t) * 1152); - static int32_t op49_init[] = {12653, -8529, 24113, -140, 12541, 8831, -1487, 16060, -11404, 19859, -2400, 15109, 18889, 6725, 1316, 5486, 11674, 11494, -3898, -5248, 17829, 11567, -8580, 10665, 10401, -1922, 11345, -19019, 17381, -4916, 8797, -5136, 2835, -4168, 1998, 19420, 20453, -4502, 3868, 8987, 22315, 7014, 28658, 12616, 30051, 15990, 945, 5435, 2817, 22329, 6500, 11242, 7768, 4994, 4917, 5266, 22487, -2863, 5099, 7439, 20409, 9082, 15371, 3027, 11324, -3723, 4469, -552, 4506, 14672, 8012, 7595, 5475, -12377, 12562, 8553, 7956, -5384, 17055, 18198, 19644, -27371, 4, -10482, 2560, -25736, 10030, 3921, 11295, 9291, 8032, 14893, -979, -2343, 3021, 1837, -6895, 22514, 31714, 2578, -1999, 10605, 22767, -8279, 6359, 20589, 5827, 3968, -4741, -1444, 1435, 1665, -12307, 13134, 11597, 12006, 17185, 3086, 17768, -5026, -14200, -16654, 8311, 10285, -7415, -815, -12954, 5880, 3402, 10161, -4169, 17869, 8108, 6209, 6538, 22511, -2628, 12514, 6197, 15069, 3081, -471, 17288, 564, 4521, 10750, 1720, 10659, 14208, 10787, -8546, 5895, 12084, 13825, -9637, 15126, 19276, 1468, 23091, -848, 4716, 2033, -892, 2287, -4571, -5989, -8835, -9332, 10582, 8059, 2861, 6131, -6980, 11197, 11190, 4810, 16684, 21408, 10435, 14235, -2578, -11244, 857, -1325, 12114, 8938, 20349, 4835, 6779, -2684, 10173, 5752, 11452, 9893, 5123, -1962, 5121, 26792, 9639, 4330, 9663, 9259, 1657, 18902, 24283, 2068, -3790, 12946, 3198, 7630, -11651, 2158, 5881, 7799, 17144, 9899, 17055, 6459, 6033, 15551, 54, 5965, 7067, -21070, 6809, 9412, -20587, 15542, -1148, -12815, 5175, 6771, 3980, 4002, -2081, -1257, 7702, 2627, -7320, 38581, 14712, -4, 14482, 18631, 3255, 4782, 13145, 5407, -6823, 4063, 3622, 13707, 3945, 8530, 2875, 9850}; - model->setOperandValue(op49, op49_init, sizeof(int32_t) * 256); - static uint8_t op50_init[] = {120, 125, 125, 122, 108, 113, 130, 130, 106, 127, 170, 131, 135, 138, 125, 89, 121, 112, 98, 135, 121, 134, 130, 110, 135, 93, 126, 125, 115, 132, 125, 103, 116, 120, 119, 108, 118, 108, 125, 150, 124, 125, 125, 127, 124, 129, 111, 91, 136, 109, 120, 102, 116, 132, 132, 129, 135, 116, 122, 111, 124, 118, 138, 117, 111, 123, 128, 130, 115, 120, 121, 120, 131, 131, 123, 132, 122, 132, 136, 115, 119, 147, 131, 117, 130, 131, 130, 125, 109, 120, 129, 127, 116, 119, 119, 126, 157, 119, 122, 119, 126, 122, 104, 121, 115, 118, 132, 138, 135, 124, 121, 136, 114, 98, 128, 126, 125, 128, 116, 116, 117, 137, 138, 118, 123, 127, 96, 127, 144, 117, 80, 135, 122, 136, 117, 114, 125, 121, 116, 122, 129, 118, 113, 116, 127, 124, 126, 129, 131, 118, 117, 117, 124, 123, 119, 130, 112, 122, 132, 135, 117, 128, 127, 137, 123, 124, 131, 122, 132, 99, 135, 128, 119, 131, 127, 120, 135, 132, 119, 122, 121, 137, 116, 111, 120, 119, 121, 118, 150, 118, 115, 131, 124, 139, 141, 113, 112, 159, 115, 118, 144, 125, 134, 139, 131, 133, 138, 129, 126, 120, 122, 113, 92, 126, 126, 118, 126, 162, 106, 112, 131, 136, 121, 143, 110, 123, 124, 112, 124, 122, 122, 122, 121, 134, 133, 121, 116, 123, 116, 125, 128, 107, 95, 130, 126, 124, 115, 128, 151, 127, 119, 122, 119, 120, 124, 122, 118, 123, 130, 122, 121, 121, 120, 121, 128, 123, 113, 117, 120, 118, 122, 126, 123, 122, 121, 138, 120, 127, 109, 120, 123, 133, 115, 122, 119, 122, 123, 119, 119, 118, 127, 119, 117, 118, 122, 124, 122, 123, 125, 123, 119, 117, 118, 141, 125, 116, 124, 132, 130, 124, 123, 122, 117, 118, 125, 122, 120, 127, 122, 126, 125, 125, 124, 120, 126, 121, 123, 117, 123, 104, 121, 132, 124, 116, 122, 123, 123, 120, 114, 116, 119, 128, 119, 125, 118, 121, 118, 121, 119, 123, 123, 120, 116, 123, 117, 121, 120, 123, 120, 125, 126, 117, 119, 102, 125, 119, 117, 122, 119, 125, 124, 124, 122, 118, 125, 120, 120, 122, 109, 122, 131, 125, 121, 121, 140, 116, 130, 133, 120, 132, 127, 133, 119, 118, 122, 107, 129, 110, 114, 126, 118, 123, 128, 104, 137, 123, 116, 110, 129, 123, 118, 118, 104, 136, 130, 110, 127, 123, 100, 120, 123, 134, 108, 124, 113, 125, 138, 137, 126, 133, 119, 118, 145, 115, 124, 132, 120, 134, 124, 124, 124, 112, 117, 119, 138, 121, 116, 115, 121, 109, 139, 115, 121, 119, 119, 131, 123, 106, 139, 112, 127, 138, 141, 130, 132, 117, 130, 106, 89, 123, 119, 126, 124, 185, 110, 127, 128, 117, 117, 130, 128, 130, 128, 122, 122, 121, 118, 123, 119, 119, 121, 120, 115, 116, 129, 121, 120, 124, 116, 120, 125, 123, 122, 114, 116, 126, 125, 131, 122, 127, 126, 132, 122, 121, 127, 124, 125, 126, 120, 125, 112, 116, 126, 124, 130, 122, 118, 117, 122, 128, 128, 92, 128, 114, 126, 128, 122, 118, 123, 124, 128, 121, 127, 125, 121, 120, 121, 125, 132, 125, 130, 121, 129, 123, 125, 126, 121, 116, 126, 136, 121, 115, 118, 132, 123, 124, 124, 121, 126, 116, 116, 123, 122, 126, 112, 116, 123, 123, 126, 122, 116, 119, 119, 118, 115, 129, 123, 112, 123, 124, 124, 120, 124, 122, 126, 126, 125, 124, 130, 130, 111, 124, 115, 125, 124, 121, 116, 123, 130, 124, 119, 122, 123, 123, 122, 115, 125, 120, 123, 134, 117, 122, 118, 121, 121, 129, 122, 125, 116, 119, 115, 121, 119, 121, 113, 123, 121, 123, 127, 120, 122, 122, 125, 136, 125, 122, 121, 122, 121, 116, 122, 121, 131, 124, 120, 120, 120, 123, 126, 114, 127, 124, 122, 122, 123, 123, 122, 120, 122, 126, 123, 123, 122, 117, 121, 123, 120, 128, 128, 124, 124, 121, 130, 126, 120, 122, 124, 126, 119, 111, 118, 131, 126, 124, 122, 120, 125, 119, 122, 119, 122, 121, 119, 124, 126, 124, 120, 111, 120, 120, 120, 124, 125, 124, 124, 116, 125, 125, 128, 126, 125, 121, 125, 123, 126, 128, 128, 126, 120, 125, 105, 123, 122, 128, 125, 123, 123, 125, 134, 123, 122, 125, 117, 123, 123, 118, 123, 124, 128, 122, 121, 125, 123, 121, 125, 122, 121, 127, 120, 121, 122, 121, 121, 121, 122, 123, 124, 120, 123, 115, 103, 125, 117, 122, 117, 133, 115, 132, 119, 117, 120, 117, 117, 125, 126, 116, 118, 117, 112, 116, 123, 122, 112, 124, 114, 131, 88, 125, 125, 112, 129, 122, 123, 111, 133, 126, 129, 123, 139, 123, 115, 115, 121, 117, 120, 124, 113, 145, 128, 126, 121, 128, 121, 123, 111, 131, 158, 152, 125, 113, 113, 122, 124, 135, 125, 120, 127, 153, 120, 121, 125, 124, 124, 122, 120, 136, 109, 126, 113, 117, 125, 136, 125, 124, 135, 121, 135, 103, 126, 125, 124, 121, 140, 121, 122, 116, 125, 117, 115, 126, 112, 143, 114, 113, 140, 121, 131, 120, 126, 133, 114, 121, 121, 120, 119, 125, 128, 119, 118, 160, 126, 132, 135, 128, 122, 100, 124, 122, 130, 120, 121, 120, 125, 124, 116, 123, 125, 121, 142, 118, 121, 126, 123, 124, 122, 130, 107, 126, 127, 101, 126, 123, 125, 128, 128, 119, 109, 122, 129, 126, 122, 123, 128, 125, 109, 124, 129, 120, 127, 134, 119, 116, 127, 115, 124, 124, 126, 126, 122, 122, 116, 120, 121, 126, 120, 125, 124, 121, 109, 115, 125, 124, 111, 125, 128, 129, 119, 120, 126, 123, 124, 124, 122, 124, 125, 119, 124, 124, 122, 124, 125, 125, 124, 128, 122, 128, 127, 131, 123, 123, 127, 126, 120, 117, 127, 125, 123, 124, 128, 126, 121, 123, 123, 127, 124, 125, 106, 130, 120, 129, 125, 124, 124, 126, 124, 121, 123, 127, 89, 125, 128, 122, 127, 122, 159, 136, 121, 103, 114, 146, 128, 126, 127, 123, 115, 116, 116, 129, 148, 124, 142, 130, 135, 127, 117, 99, 118, 124, 115, 132, 112, 156, 87, 127, 134, 114, 106, 122, 118, 118, 118, 123, 109, 119, 98, 115, 92, 128, 127, 124, 123, 121, 116, 132, 124, 123, 130, 133, 120, 136, 107, 133, 118, 156, 139, 119, 133, 131, 129, 128, 121, 132, 123, 136, 125, 117, 127, 121, 133, 119, 136, 128, 133, 124, 111, 120, 121, 120, 112, 107, 117, 120, 132, 124, 139, 133, 120, 118, 125, 112, 124, 122, 119, 114, 125, 120, 126, 125, 117, 124, 132, 122, 110, 145, 128, 130, 118, 135, 138, 114, 128, 123, 116, 116, 117, 125, 118, 95, 129, 129, 128, 105, 122, 120, 127, 116, 119, 118, 131, 123, 123, 127, 123, 128, 124, 120, 114, 119, 119, 120, 121, 125, 119, 119, 123, 124, 120, 126, 126, 129, 128, 113, 124, 123, 113, 123, 118, 119, 128, 122, 122, 123, 119, 120, 124, 123, 125, 118, 127, 122, 121, 125, 121, 120, 120, 123, 123, 124, 120, 136, 121, 123, 126, 123, 123, 121, 114, 124, 122, 103, 122, 126, 120, 117, 129, 128, 121, 129, 119, 121, 120, 122, 129, 129, 126, 115, 123, 120, 122, 125, 126, 125, 124, 126, 126, 125, 122, 127, 123, 123, 111, 136, 123, 124, 133, 117, 118, 117, 123, 118, 123, 100, 119, 120, 119, 121, 119, 125, 123, 122, 124, 125, 119, 120, 122, 132, 124, 132, 126, 122, 119, 178, 122, 122, 124, 123, 114, 126, 123, 114, 121, 124, 128, 120, 143, 131, 151, 113, 124, 122, 126, 117, 111, 129, 165, 137, 127, 94, 119, 135, 122, 129, 149, 115, 121, 129, 130, 108, 121, 85, 124, 132, 126, 124, 103, 124, 125, 123, 127, 131, 118, 116, 120, 120, 118, 122, 120, 125, 124, 121, 122, 133, 129, 119, 113, 125, 124, 121, 125, 129, 121, 125, 114, 122, 130, 125, 111, 115, 122, 124, 126, 76, 132, 117, 124, 135, 119, 138, 131, 121, 125, 76, 120, 115, 136, 127, 123, 126, 111, 124, 106, 120, 114, 136, 116, 123, 121, 124, 125, 134, 126, 163, 120, 121, 120, 135, 122, 123, 119, 117, 121, 130, 122, 86, 116, 71, 87, 117, 134, 130, 128, 125, 117, 104, 122, 123, 125, 117, 118, 159, 111, 119, 121, 120, 118, 121, 121, 118, 131, 103, 119, 129, 118, 122, 117, 111, 127, 138, 119, 115, 114, 137, 127, 124, 109, 121, 120, 108, 109, 106, 102, 126, 113, 117, 126, 112, 109, 128, 120, 117, 135, 112, 129, 123, 41, 123, 124, 114, 126, 137, 116, 119, 129, 139, 122, 132, 124, 120, 135, 180, 120, 143, 131, 92, 116, 170, 127, 122, 118, 121, 127, 92, 133, 104, 122, 117, 127, 133, 119, 116, 110, 128, 115, 127, 119, 140, 133, 123, 116, 128, 122, 118, 111, 123, 127, 122, 117, 122, 123, 112, 125, 122, 115, 119, 122, 124, 121, 122, 121, 127, 129, 112, 121, 124, 121, 120, 132, 116, 129, 122, 122, 120, 127, 135, 140, 123, 123, 135, 130, 127, 125, 127, 122, 122, 124, 121, 123, 124, 129, 120, 129, 122, 120, 129, 128, 116, 121, 117, 125, 123, 117, 120, 125, 123, 120, 131, 125, 127, 111, 129, 128, 124, 128, 122, 121, 150, 123, 116, 129, 118, 126, 133, 127, 121, 123, 122, 133, 125, 123, 116, 115, 121, 125, 129, 121, 116, 111, 106, 118, 113, 117, 124, 126, 126, 110, 122, 124, 108, 121, 118, 121, 122, 121, 123, 97, 120, 123, 126, 119, 125, 124, 132, 113, 132, 120, 119, 119, 125, 117, 125, 129, 120, 126, 120, 125, 122, 119, 127, 117, 116, 118, 121, 123, 118, 124, 121, 122, 130, 125, 128, 127, 117, 135, 126, 89, 109, 140, 106, 107, 137, 152, 119, 107, 123, 110, 116, 146, 121, 137, 117, 119, 116, 122, 135, 122, 156, 127, 117, 127, 122, 115, 130, 126, 118, 136, 121, 147, 109, 125, 110, 107, 125, 130, 118, 96, 89, 101, 134, 133, 140, 130, 97, 144, 142, 123, 107, 129, 98, 133, 168, 125, 112, 107, 106, 152, 117, 105, 108, 115, 109, 126, 119, 124, 116, 169, 124, 79, 119, 122, 127, 124, 150, 133, 136, 135, 126, 117, 132, 103, 112, 127, 112, 117, 132, 170, 123, 111, 136, 109, 138, 123, 121, 125, 124, 114, 118, 116, 123, 129, 141, 95, 115, 103, 151, 114, 111, 142, 123, 114, 118, 141, 120, 140, 123, 135, 131, 106, 124, 138, 124, 141, 111, 123, 118, 124, 129, 124, 124, 119, 123, 126, 128, 119, 124, 115, 127, 116, 120, 118, 122, 122, 128, 118, 127, 120, 127, 114, 121, 126, 140, 124, 115, 121, 118, 116, 117, 129, 122, 122, 118, 121, 127, 113, 117, 126, 119, 121, 126, 128, 124, 128, 126, 124, 118, 127, 124, 124, 123, 123, 137, 127, 126, 121, 127, 121, 125, 110, 119, 127, 119, 129, 120, 121, 125, 118, 122, 117, 128, 129, 118, 121, 117, 122, 133, 130, 121, 124, 111, 120, 123, 125, 124, 123, 130, 130, 135, 121, 120, 125, 124, 122, 118, 122, 125, 133, 114, 127, 115, 123, 119, 130, 134, 120, 115, 116, 124, 132, 121, 124, 121, 123, 127, 123, 122, 124, 141, 121, 135, 128, 132, 123, 129, 104, 119, 120, 132, 120, 120, 126, 120, 129, 116, 111, 126, 129, 113, 122, 118, 119, 126, 143, 129, 126, 143, 119, 123, 115, 120, 121, 121, 132, 124, 112, 126, 113, 126, 131, 130, 147, 129, 120, 120, 120, 94, 123, 129, 135, 137, 118, 116, 127, 125, 121, 119, 131, 125, 108, 127, 116, 132, 125, 123, 172, 112, 123, 122, 158, 122, 120, 100, 125, 115, 120, 130, 121, 120, 124, 126, 93, 146, 125, 120, 123, 108, 109, 124, 96, 128, 123, 114, 125, 128, 118, 116, 122, 118, 120, 127, 127, 124, 126, 119, 125, 126, 128, 132, 130, 130, 130, 120, 122, 124, 122, 110, 112, 111, 122, 128, 114, 122, 120, 120, 124, 118, 126, 126, 117, 122, 125, 118, 121, 123, 122, 123, 119, 123, 125, 130, 117, 121, 122, 118, 123, 113, 118, 121, 119, 124, 124, 123, 118, 126, 120, 124, 120, 130, 128, 108, 123, 126, 123, 125, 126, 121, 128, 120, 122, 127, 122, 122, 120, 124, 128, 120, 127, 118, 125, 127, 121, 118, 121, 119, 125, 128, 123, 121, 128, 123, 122, 135, 118, 123, 117, 119, 126, 112, 128, 122, 127, 118, 123, 121, 118, 122, 123, 127, 120, 118, 122, 131, 125, 122, 120, 113, 120, 118, 128, 119, 123, 126, 124, 151, 127, 122, 124, 120, 118, 122, 119, 123, 133, 128, 126, 118, 119, 123, 125, 123, 122, 119, 117, 122, 122, 123, 124, 119, 124, 122, 120, 126, 125, 134, 124, 132, 124, 130, 127, 127, 125, 123, 121, 122, 129, 123, 120, 120, 122, 131, 120, 110, 123, 123, 119, 123, 119, 131, 121, 126, 127, 118, 119, 127, 121, 121, 133, 116, 125, 121, 123, 122, 124, 116, 120, 121, 120, 121, 118, 122, 118, 123, 128, 127, 126, 120, 124, 134, 120, 115, 122, 118, 124, 130, 124, 118, 122, 120, 131, 120, 127, 124, 121, 129, 124, 122, 124, 125, 125, 124, 126, 125, 120, 127, 124, 122, 122, 125, 121, 125, 119, 120, 126, 122, 121, 131, 122, 119, 119, 118, 122, 124, 122, 124, 125, 124, 110, 123, 125, 123, 121, 127, 118, 124, 124, 119, 131, 118, 121, 119, 124, 126, 123, 123, 128, 127, 125, 125, 124, 123, 122, 124, 120, 124, 118, 122, 114, 124, 117, 114, 125, 130, 124, 142, 117, 124, 127, 121, 120, 119, 121, 134, 123, 122, 124, 115, 121, 120, 126, 122, 121, 126, 116, 124, 118, 136, 124, 124, 122, 124, 124, 123, 122, 124, 120, 118, 124, 120, 125, 122, 105, 131, 122, 113, 123, 123, 126, 137, 130, 125, 118, 118, 126, 113, 125, 118, 122, 102, 124, 123, 115, 124, 119, 124, 125, 116, 125, 128, 125, 130, 126, 118, 132, 118, 126, 121, 129, 123, 119, 126, 127, 130, 121, 132, 129, 127, 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107, 122, 118, 129, 130, 118, 139, 128, 138, 116, 109, 124, 113, 117, 111, 146, 122, 128, 126, 127, 124, 118, 116, 123, 113, 122, 119, 112, 114, 125, 126, 133, 118, 119, 129, 120, 107, 130, 122, 130, 122, 122, 108, 135, 127, 110, 125, 123, 110, 122, 108, 119, 165, 103, 129, 117, 103, 114, 135, 121, 119, 110, 135, 126, 109, 145, 128, 122, 112, 125, 125, 122, 117, 170, 116, 125, 112, 122, 142, 102, 128, 123, 127, 117, 113, 120, 127, 117, 122, 122, 117, 133, 146, 127, 182, 120, 157, 145, 144, 154, 131, 111, 125, 119, 113, 122, 108, 126, 119, 152, 112, 105, 118, 101, 115, 116, 133, 127, 97, 125, 103, 119, 117, 116, 110, 122, 102, 145, 146, 124, 162, 107, 117, 138, 146, 108, 120, 111, 128, 149, 131, 127, 125, 121, 109, 107, 104, 126, 140, 129, 140, 147, 92, 131, 125, 145, 113, 123, 140, 115, 127, 123, 139, 123, 133, 112, 112, 121, 119, 106, 122, 124, 157, 124, 74, 124, 99, 117, 130, 112, 131, 137, 141, 132, 123, 120, 102, 136, 131, 122, 110, 119, 129, 112, 109, 130, 125, 96, 116, 121, 127, 106, 118, 133, 113, 120, 160, 119, 122, 135, 115, 117, 118, 127, 126, 136, 111, 124, 125, 123, 120, 118, 116, 110, 125, 127, 127, 123, 121, 117, 112, 120, 121, 131, 138, 116, 126, 130, 126, 116, 119, 121, 134, 129, 122, 120, 122, 124, 123, 119, 119, 122, 124, 118, 110, 119, 127, 127, 127, 122, 130, 118, 122, 118, 119, 133, 126, 129, 124, 126, 126, 122, 123, 122, 121, 118, 117, 120, 135, 126, 122, 127, 115, 120, 129, 118, 119, 129, 109, 129, 125, 119, 127, 121, 127, 117, 124, 126, 123, 124, 134, 128, 118, 127, 123, 123, 130, 127, 125, 115, 123, 123, 130, 131, 134, 122, 123, 128, 126, 125, 119, 118, 123, 123, 115, 136, 120, 125, 121, 121, 125, 120, 133, 127, 126, 120, 125, 126, 120, 135, 123, 114, 126, 121, 128, 120, 111, 129, 116, 122, 117, 128, 124, 126, 117, 122, 123, 121, 122, 122, 124, 120, 120, 131, 123, 116, 118, 112, 127, 121, 129, 122, 123, 122, 153, 120, 123, 114, 121, 121, 128, 117, 124, 122, 130, 120, 121, 128, 126, 125, 119, 117, 120, 122, 125, 119, 124, 122, 120, 120, 139, 120, 114, 127, 127, 128, 119, 119, 125, 117, 118, 122, 124, 121, 120, 125, 122, 123, 131, 120, 121, 121, 122, 125, 131, 128, 124, 134, 112, 121, 132, 128, 118, 122, 128, 125, 125, 123, 120, 126, 125, 122, 121, 144, 125, 126, 117, 127, 122, 129, 117, 112, 121, 125, 130, 131, 122, 124, 128, 114, 132, 122, 103, 132, 119, 135, 123, 129, 117, 123, 123, 122, 121, 127, 128, 131, 122, 133, 124, 132, 124, 125, 121, 116, 117, 136, 126, 122, 119, 116, 122, 121, 123, 117, 126, 131, 118, 123, 128, 124, 128, 120, 122, 125, 125, 124, 121, 124, 126, 123, 124, 124, 121, 124, 154, 123, 117, 110, 115, 115, 126, 119, 126, 127, 111, 121, 124, 135, 128, 96, 122, 117, 121, 127, 120, 139, 119, 121, 124, 124, 119, 122, 128, 116, 129, 103, 127, 115, 112, 118, 124, 119, 117, 118, 129, 113, 115, 125, 122, 112, 125, 129, 124, 127, 120, 120, 123, 125, 119, 121, 129, 131, 126, 123, 117, 111, 114, 116, 135, 121, 120, 114, 123, 118, 116, 123, 116, 121, 121, 124, 113, 117, 124, 120, 114, 119, 126, 124, 143, 123, 117, 115, 125, 125, 124, 131, 123, 126, 124, 127, 112, 130, 81, 125, 128, 133, 130, 127, 104, 122, 128, 110, 130, 134, 146, 138, 126, 127, 139, 131, 111, 126, 108, 104, 132, 128, 111, 85, 132, 143, 125, 110, 115, 158, 135, 137, 127, 137, 103, 116, 140, 111, 104, 114, 125, 138, 126, 91, 125, 131, 134, 127, 105, 97, 148, 125, 117, 111, 120, 125, 138, 97, 129, 111, 122, 255, 116, 124, 122, 130, 125, 118, 114, 105, 117, 129, 124, 138, 118, 114, 106, 117, 121, 98, 111, 120, 119, 117, 120, 121, 123, 111, 168, 130, 118, 118, 122, 108, 111, 126, 125, 142, 124, 166, 128, 114, 111, 120, 131, 120, 125, 113, 134, 103, 118, 137, 104, 126, 134, 128, 133, 113, 124, 109, 129, 137, 129, 126, 125, 128, 128, 127, 117, 126, 120, 119, 124, 129, 116, 121, 122, 122, 114, 136, 115, 131, 127, 122, 125, 121, 121, 118, 122, 120, 128, 118, 119, 125, 105, 123, 122, 137, 123, 117, 123, 128, 117, 127, 115, 124, 128, 125, 133, 132, 122, 121, 118, 125, 127, 123, 114, 125, 120, 123, 120, 122, 135, 121, 118, 123, 131, 126, 119, 136, 123, 121, 134, 120, 122, 125, 119, 122, 117, 125, 126, 116, 125, 120, 128, 119, 117, 125, 122, 124, 127, 126, 118, 119, 120, 127, 114, 118, 120, 124, 125, 128, 124, 113, 122, 115, 120, 117, 114, 123, 132, 122, 122, 128, 130, 119, 120, 126, 123, 118, 121, 121, 122, 124, 135, 121, 124, 122, 124, 115, 118, 110, 125, 121, 123, 118, 121, 114, 129, 126, 124, 130, 129, 117, 121, 109, 123, 127, 126, 126, 124, 121, 160, 126, 121, 114, 129, 120, 128, 118, 131, 121, 118, 117, 122, 121, 134, 130, 122, 114, 121, 123, 120, 115, 119, 122, 120, 122, 131, 120, 110, 122, 144, 127, 114, 119, 121, 124, 118, 120, 122, 120, 122, 131, 118, 143, 132, 120, 126, 125, 128, 130, 128, 126, 120, 128, 117, 125, 134, 124, 125, 122, 126, 129, 125, 118, 121, 132, 116, 125, 127, 130, 125, 128, 124, 123, 124, 125, 127, 116, 125, 122, 122, 126, 127, 122, 122, 116, 134, 115, 111, 137, 126, 136, 123, 126, 121, 125, 121, 130, 125, 132, 127, 135, 126, 132, 121, 133, 125, 125, 120, 121, 126, 125, 131, 124, 122, 118, 120, 126, 123, 116, 138, 124, 119, 125, 117, 128, 125, 122, 121, 122, 124, 121, 124, 126, 131, 121, 124, 125, 109, 123, 123, 123, 122, 122, 122, 121, 122, 123, 120, 120, 123, 121, 127, 124, 122, 125, 121, 124, 124, 121, 115, 119, 124, 125, 127, 125, 135, 122, 122, 124, 121, 122, 122, 127, 126, 123, 132, 121, 122, 123, 119, 121, 124, 123, 122, 125, 119, 122, 124, 126, 124, 124, 124, 122, 126, 123, 132, 119, 121, 122, 122, 122, 122, 125, 126, 126, 114, 114, 116, 125, 121, 121, 120, 119, 123, 125, 126, 124, 124, 124, 124, 124, 124, 123, 127, 122, 121, 124, 117, 124, 120, 123, 122, 118, 123, 123, 121}; - model->setOperandValue(op50, op50_init, sizeof(uint8_t) * 32768); - static int32_t op52_init[] = {-1884, 2616, -210, 550, -35, -382, -1691, 5683, -100, -539, -2141, -164, 6676, -343, 494, 2084, 827, -61, -825, -1788, -119, 1560, -409, -792, 577, -401, 157, -731, 2754, -50, 4553, 1216, -369, -336, 2904, -595, 4462, -129, -434, -418, -2171, 4857, -144, 1306, -441, -386, 533, -575, 787, 2255, 3091, 3404, 3667, -272, 5095, -370, -103, -1000, 2933, 4342, 4233, -277, -126, -592, -68, -1615, 60, -475, 725, -768, 4874, 2, 1842, -514, 197, -2733, -512, 104, 5692, 1038, 751, 1345, -1439, -441, 1379, 1495, 2301, -1405, -408, 1437, 4501, 2220, -724, -222, 2513, -240, 692, 5183, 1577, 125, 1689, 3981, 4447, -1720, 2478, 3643, 5164, 2865, -759, 1997, 2077, 6366, 1727, -1623, -821, -120, -448, 1712, 500, -288, -300, 2786, -917, 8269, -261, -531, 2269, -421, -1191, 785, -521, -561, -63, -208, -746, -1521, -84, 112, 264, -1404, 4937, 2907, 1315, 393, 10, -598, 7132, 2318, 1271, -1888, -2319, 376, 3824, 464, -1671, 4753, 727, 6596, -752, 2540, 1856, 3008, 4146, -1702, -1343, 3173, 2241, -115, -1630, -159, 1511, -50, 1575, -317, 2710, -364, 3336, -412, -462, -133, -73, 1213, -18, -545, 4880, 1681, 2053, 3955, -97, 402, 3232, 1745, -520, -37, -833, 2292, 300, 3179, 585, 1183, 4481, -1922, -650, -21, 4456, 397, -736, -159, 829, 6986, -700, -1406, 4107, 2184, 299, -3703, 3540, 1565, 5888, 2576, 1245, 440, 7928, 3104, 4737, -253, 2132, 185, 3027, -905, -559, 1685, -561, 855, -93, 3513, 2287, -170, 27, 2200, 1498, -98, 997, 4617, 382, -274, 4951, 2198, 207, 1266, -270, -1587, 1827, 609, -195, -557}; - model->setOperandValue(op52, op52_init, sizeof(int32_t) * 256); - static uint8_t op53_init[] = {69, 67, 94, 72, 99, 6, 55, 72, 59, 83, 72, 72, 73, 75, 81, 68, 68, 60, 68, 67, 70, 64, 68, 46, 70, 63, 81, 66, 67, 73, 70, 118, 62, 68, 73, 76, 78, 75, 88, 91, 81, 77, 75, 89, 70, 72, 90, 72, 48, 64, 71, 64, 68, 84, 71, 72, 65, 78, 74, 75, 67, 61, 75, 63, 42, 68, 91, 62, 65, 70, 75, 71, 63, 69, 82, 79, 71, 75, 65, 78, 69, 57, 69, 66, 62, 64, 73, 56, 60, 85, 70, 73, 80, 95, 64, 64, 84, 69, 64, 85, 62, 62, 78, 81, 67, 75, 76, 63, 70, 59, 30, 86, 67, 65, 99, 69, 69, 75, 89, 66, 75, 62, 67, 60, 64, 65, 70, 86, 86, 74, 69, 73, 57, 67, 60, 89, 78, 94, 75, 57, 69, 67, 68, 76, 79, 87, 72, 74, 78, 50, 75, 73, 64, 66, 73, 70, 71, 71, 109, 51, 62, 57, 70, 71, 70, 75, 67, 74, 73, 66, 94, 102, 63, 83, 76, 66, 69, 56, 77, 46, 67, 71, 60, 74, 75, 67, 65, 70, 85, 74, 61, 64, 67, 72, 75, 62, 77, 74, 84, 65, 76, 71, 83, 74, 77, 53, 75, 64, 66, 56, 67, 66, 72, 60, 65, 72, 73, 78, 72, 73, 80, 80, 71, 68, 69, 59, 67, 60, 72, 68, 71, 60, 89, 75, 61, 24, 70, 85, 78, 72, 70, 65, 33, 64, 46, 62, 76, 71, 62, 104, 83, 83, 63, 106, 84, 136, 77, 63, 97, 80, 91, 106, 85, 55, 73, 52, 81, 79, 46, 52, 40, 57, 121, 71, 40, 68, 73, 66, 80, 90, 78, 125, 50, 85, 63, 52, 67, 26, 82, 49, 81, 38, 74, 81, 132, 118, 76, 60, 77, 41, 76, 76, 57, 72, 97, 71, 88, 69, 64, 89, 61, 70, 54, 79, 54, 69, 56, 71, 77, 28, 100, 79, 97, 71, 66, 79, 52, 55, 65, 72, 47, 84, 87, 61, 66, 27, 58, 72, 76, 58, 65, 65, 49, 80, 72, 81, 63, 76, 65, 50, 66, 101, 91, 70, 62, 66, 62, 76, 64, 74, 86, 61, 70, 72, 80, 65, 100, 55, 62, 116, 72, 41, 80, 54, 52, 77, 78, 66, 79, 65, 78, 32, 64, 55, 98, 113, 84, 49, 58, 37, 48, 42, 89, 112, 89, 55, 67, 66, 83, 67, 102, 54, 81, 63, 80, 79, 70, 67, 56, 82, 78, 60, 75, 62, 30, 60, 62, 114, 69, 79, 81, 73, 74, 79, 81, 81, 79, 118, 68, 119, 52, 51, 66, 87, 105, 76, 72, 72, 32, 79, 47, 58, 67, 61, 96, 59, 72, 65, 40, 73, 80, 69, 122, 55, 98, 71, 73, 82, 75, 78, 70, 59, 125, 55, 69, 57, 61, 52, 69, 60, 79, 87, 69, 87, 68, 69, 48, 111, 66, 70, 64, 53, 64, 78, 66, 50, 36, 62, 57, 83, 60, 102, 54, 54, 83, 60, 68, 72, 86, 58, 60, 75, 60, 60, 72, 72, 62, 61, 63, 61, 110, 1, 67, 71, 70, 70, 63, 101, 74, 77, 69, 74, 67, 69, 81, 69, 91, 74, 66, 66, 72, 70, 70, 67, 66, 77, 60, 68, 78, 61, 67, 68, 73, 72, 75, 63, 70, 66, 72, 68, 81, 83, 64, 75, 72, 74, 66, 70, 67, 66, 65, 64, 77, 68, 69, 78, 68, 70, 89, 60, 73, 75, 75, 67, 70, 80, 74, 68, 70, 75, 75, 68, 69, 67, 66, 70, 79, 71, 71, 67, 73, 69, 72, 56, 68, 73, 67, 68, 71, 72, 87, 68, 73, 72, 67, 67, 69, 73, 76, 68, 71, 65, 66, 78, 79, 56, 64, 74, 73, 64, 65, 70, 79, 67, 69, 75, 60, 67, 69, 63, 68, 64, 72, 65, 63, 65, 69, 71, 71, 68, 78, 80, 71, 72, 69, 65, 67, 78, 45, 67, 57, 76, 71, 69, 68, 75, 73, 68, 66, 76, 76, 71, 75, 74, 73, 76, 69, 66, 69, 73, 69, 72, 66, 62, 69, 71, 66, 70, 72, 69, 69, 65, 38, 67, 65, 75, 70, 72, 72, 68, 84, 89, 60, 71, 67, 69, 75, 72, 67, 74, 79, 69, 63, 66, 75, 69, 64, 71, 72, 77, 75, 59, 75, 69, 76, 70, 77, 69, 65, 73, 72, 58, 73, 67, 73, 70, 77, 68, 72, 72, 73, 72, 79, 79, 71, 66, 80, 72, 72, 66, 66, 70, 60, 64, 66, 74, 63, 77, 71, 67, 70, 69, 69, 69, 90, 82, 99, 78, 75, 71, 64, 41, 63, 69, 69, 68, 81, 76, 78, 73, 99, 76, 99, 138, 84, 55, 79, 112, 81, 79, 57, 70, 90, 58, 43, 45, 78, 67, 75, 66, 76, 84, 46, 56, 110, 87, 65, 63, 60, 45, 82, 90, 83, 108, 58, 59, 56, 45, 72, 59, 77, 86, 78, 77, 109, 41, 86, 69, 58, 64, 56, 48, 52, 67, 57, 81, 46, 81, 57, 43, 76, 61, 102, 42, 58, 74, 66, 80, 63, 84, 63, 71, 105, 81, 57, 92, 50, 67, 68, 65, 82, 66, 61, 62, 57, 44, 39, 47, 60, 71, 48, 133, 66, 42, 52, 72, 62, 124, 63, 51, 65, 70, 66, 61, 30, 73, 81, 58, 105, 46, 65, 45, 69, 71, 76, 74, 112, 76, 80, 67, 83, 62, 83, 68, 66, 107, 66, 61, 79, 100, 65, 92, 95, 112, 44, 68, 50, 82, 66, 64, 31, 83, 56, 116, 67, 67, 40, 65, 67, 33, 56, 50, 78, 53, 73, 64, 51, 73, 74, 44, 61, 82, 82, 73, 66, 76, 79, 77, 76, 51, 69, 67, 69, 79, 68, 47, 48, 91, 67, 73, 88, 78, 53, 62, 65, 65, 55, 38, 68, 67, 38, 76, 80, 61, 57, 66, 61, 66, 72, 75, 62, 78, 72, 54, 64, 54, 46, 58, 64, 77, 72, 66, 60, 70, 73, 48, 72, 70, 84, 77, 64, 69, 59, 46, 66, 77, 74, 86, 71, 63, 122, 52, 69, 97, 71, 124, 85, 62, 70, 52, 96, 69, 85, 15, 60, 65, 69, 59, 111, 112, 64, 90, 65, 7, 92, 56, 42, 92, 25, 44, 96, 5, 100, 28, 94, 96, 32, 99, 24, 57, 37, 48, 148, 108, 95, 73, 107, 94, 131, 47, 26, 89, 119, 117, 42, 86, 97, 92, 21, 58, 22, 91, 29, 41, 105, 27, 88, 40, 93, 88, 31, 134, 57, 130, 59, 117, 52, 40, 48, 102, 35, 102, 63, 22, 51, 129, 98, 126, 60, 122, 41, 90, 71, 101, 43, 78, 65, 105, 36, 101, 78, 60, 29, 146, 94, 76, 102, 109, 103, 59, 127, 138, 64, 30, 51, 51, 118, 7, 70, 66, 38, 65, 52, 32, 70, 61, 42, 120, 96, 40, 46, 119, 99, 63, 0, 20, 59, 42, 22, 109, 88, 46, 34, 107, 93, 77, 103, 60, 98, 77, 73, 36, 55, 22, 98, 52, 89, 91, 89, 33, 113, 25, 102, 120, 54, 70, 100, 62, 43, 33, 23, 39, 47, 129, 255, 53, 54, 70, 95, 47, 92, 45, 119, 80, 124, 62, 50, 98, 98, 51, 62, 95, 90, 102, 22, 11, 68, 47, 52, 115, 47, 92, 40, 70, 117, 100, 92, 96, 126, 81, 87, 55, 38, 118, 116, 75, 72, 96, 97, 65, 20, 55, 49, 121, 26, 93, 92, 81, 27, 95, 48, 96, 101, 56, 101, 99, 35, 61, 79, 87, 56, 35, 41, 46, 48, 41, 8, 58, 33, 79, 63, 126, 62, 102, 106, 68, 17, 47, 61, 73, 52, 39, 59, 108, 115, 106, 53, 42, 32, 108, 42, 63, 105, 64, 46, 51, 69, 49, 40, 112, 72, 67, 49, 67, 63, 35, 61, 70, 70, 75, 74, 68, 74, 63, 102, 64, 73, 56, 63, 76, 68, 67, 61, 48, 34, 88, 81, 82, 63, 66, 59, 67, 61, 62, 90, 43, 74, 80, 64, 55, 68, 73, 70, 88, 71, 70, 70, 48, 71, 56, 79, 54, 66, 57, 70, 66, 90, 72, 69, 87, 66, 56, 67, 78, 56, 57, 56, 76, 80, 70, 78, 63, 70, 68, 92, 71, 85, 65, 77, 45, 68, 65, 71, 66, 78, 66, 57, 50, 111, 71, 73, 70, 49, 72, 67, 99, 63, 68, 65, 56, 62, 83, 73, 70, 63, 77, 60, 59, 71, 70, 81, 76, 67, 103, 97, 72, 68, 127, 68, 71, 68, 65, 70, 68, 65, 66, 67, 81, 66, 60, 69, 48, 78, 58, 68, 83, 86, 66, 82, 71, 72, 69, 80, 81, 66, 74, 71, 75, 52, 60, 41, 126, 67, 86, 70, 68, 67, 74, 55, 65, 58, 88, 70, 72, 73, 63, 73, 68, 71, 64, 100, 73, 70, 67, 83, 69, 73, 74, 74, 62, 66, 64, 50, 68, 52, 75, 64, 70, 62, 65, 64, 72, 92, 66, 69, 68, 62, 69, 62, 57, 83, 69, 75, 74, 80, 80, 65, 77, 81, 64, 69, 73, 70, 71, 79, 69, 69, 74, 80, 73, 88, 45, 73, 69, 66, 86, 69, 55, 54, 70, 79, 63, 77, 57, 66, 61, 72, 61, 71, 67, 62, 59, 65, 71, 98, 92, 67, 71, 64, 87, 57, 64, 63, 55, 64, 97, 69, 70, 71, 69, 69, 64, 73, 70, 70, 70, 70, 68, 75, 68, 67, 70, 83, 103, 66, 69, 70, 65, 63, 80, 68, 85, 76, 60, 66, 71, 73, 49, 76, 64, 69, 82, 73, 57, 66, 69, 63, 76, 71, 60, 70, 69, 78, 65, 85, 65, 74, 68, 70, 80, 69, 69, 81, 64, 75, 73, 74, 70, 69, 72, 72, 70, 63, 77, 76, 68, 72, 71, 68, 69, 75, 75, 61, 80, 77, 57, 71, 59, 66, 69, 67, 69, 70, 73, 65, 64, 77, 70, 69, 74, 69, 70, 75, 70, 71, 79, 66, 83, 78, 61, 70, 72, 85, 64, 66, 72, 83, 71, 70, 80, 64, 70, 71, 74, 80, 68, 68, 63, 65, 64, 71, 77, 72, 78, 66, 68, 70, 85, 74, 69, 64, 77, 62, 72, 59, 68, 70, 69, 71, 71, 72, 81, 59, 75, 85, 72, 75, 74, 74, 68, 69, 71, 69, 74, 50, 72, 63, 96, 72, 67, 66, 70, 61, 69, 67, 65, 38, 66, 64, 62, 61, 67, 70, 62, 74, 69, 63, 71, 76, 69, 72, 75, 66, 71, 74, 66, 63, 64, 78, 69, 64, 75, 73, 76, 64, 64, 76, 67, 66, 70, 77, 69, 68, 72, 69, 58, 71, 77, 71, 69, 66, 70, 71, 85, 73, 72, 76, 51, 71, 66, 82, 77, 71, 63, 70, 64, 75, 65, 79, 76, 87, 76, 73, 79, 73, 69, 68, 70, 84, 78, 78, 67, 72, 66, 63, 47, 87, 79, 69, 70, 64, 66, 78, 74, 44, 68, 64, 77, 61, 74, 72, 74, 72, 67, 65, 68, 66, 67, 69, 96, 33, 75, 69, 69, 72, 62, 69, 38, 63, 79, 63, 52, 70, 101, 66, 75, 90, 91, 81, 60, 54, 69, 70, 73, 73, 64, 72, 70, 61, 69, 58, 56, 67, 62, 70, 86, 67, 69, 81, 65, 73, 78, 67, 66, 68, 68, 57, 73, 75, 77, 78, 69, 61, 81, 67, 71, 58, 66, 63, 78, 67, 47, 77, 66, 71, 78, 69, 62, 66, 63, 63, 125, 72, 67, 69, 72, 70, 47, 77, 68, 64, 52, 64, 77, 73, 64, 65, 73, 77, 62, 71, 71, 79, 79, 67, 49, 99, 70, 68, 50, 60, 70, 68, 67, 71, 70, 67, 107, 64, 63, 69, 77, 68, 88, 77, 87, 76, 65, 43, 60, 46, 63, 73, 68, 60, 73, 72, 65, 56, 77, 79, 71, 50, 66, 70, 61, 70, 69, 66, 74, 83, 83, 58, 50, 76, 74, 73, 61, 64, 68, 71, 61, 98, 82, 71, 57, 73, 50, 72, 82, 69, 66, 68, 64, 88, 68, 59, 81, 67, 69, 80, 76, 65, 72, 94, 66, 71, 65, 81, 58, 67, 62, 83, 76, 71, 76, 80, 80, 71, 78, 63, 63, 79, 81, 70, 64, 67, 75, 71, 103, 78, 73, 51, 70, 74, 68, 67, 85, 68, 57, 71, 67, 70, 64, 60, 87, 93, 70, 72, 63, 67, 70, 63, 69, 70, 64, 69, 72, 71, 72, 65, 83, 64, 70, 66, 64, 63, 102, 69, 72, 70, 70, 63, 76, 69, 64, 70, 74, 69, 70, 78, 71, 69, 75, 67, 89, 74, 76, 71, 67, 67, 66, 73, 74, 68, 59, 69, 70, 68, 67, 66, 77, 67, 74, 72, 71, 68, 67, 72, 74, 72, 75, 68, 71, 68, 72, 70, 67, 74, 70, 71, 78, 73, 70, 81, 68, 79, 71, 76, 67, 71, 59, 74, 68, 76, 66, 81, 68, 77, 69, 71, 70, 71, 76, 74, 72, 72, 75, 72, 63, 66, 73, 69, 71, 70, 70, 79, 65, 69, 71, 74, 67, 70, 69, 73, 65, 71, 72, 68, 67, 76, 71, 69, 73, 74, 68, 65, 69, 54, 77, 73, 73, 71, 69, 68, 75, 69, 64, 69, 64, 66, 66, 71, 77, 72, 70, 68, 73, 69, 75, 71, 71, 79, 68, 71, 75, 74, 62, 71, 70, 72, 71, 69, 69, 56, 73, 80, 60, 82, 70, 76, 71, 70, 72, 70, 71, 78, 73, 64, 65, 70, 66, 65, 68, 70, 69, 67, 68, 72, 67, 65, 67, 71, 69, 70, 69, 64, 72, 66, 72, 76, 70, 68, 75, 69, 70, 71, 68, 65, 60, 79, 72, 70, 73, 65, 78, 68, 71, 72, 69, 74, 67, 74, 73, 68, 68, 71, 58, 75, 75, 69, 70, 72, 70, 71, 64, 70, 68, 76, 76, 71, 65, 75, 78, 72, 65, 70, 68, 70, 68, 74, 76, 76, 61, 75, 64, 71, 73, 70, 70, 54, 70, 62, 70, 76, 71, 68, 79, 64, 65, 68, 69, 68, 48}; - model->setOperandValue(op53, op53_init, sizeof(uint8_t) * 2304); - static int32_t op55_init[] = {13038, 7187, -24298, -18509, -18780, 1558, -12880, 6409, 2493, -614, 3006, 17689, -15305, -15619, -7550, -7451, 6741, -7009, -9807, 4343, 9604, -17478, 17697, 13585, 12043, -10615, -13043, 5678, 603, -19885, 2479, 2669, 10616, -6847, 6035, 6746, -3640, -16645, -1685, -2293, -3217, -12684, 1057, 16536, 11521, 5124, -8610, -8002, -12629, -5272, -5030, 6394, 7465, -2211, -11773, 688, 7272, 6863, 8981, 6999, -74, -7641, 9782, -24887, 8374, -2762, 9621, 4844, -12309, -811, -15184, -1548, -16757, 18301, -14979, -3268, 414, -1826, -17006, -11037, 10889, -8445, 6257, 6089, 9536, -2040, 15106, -716, -1134, -24398, 5140, 17991, 12970, 903, -588, 17261, 334, 19344, -6391, -5587, 9229, -5262, -1242, 6141, 8087, 12221, 11573, -3360, 5190, -3270, -16340, 7408, -9833, -15706, -11246, -1534, 1398, -574, -21277, 9695, 10785, 1425, 1469, 2030, 2195, -10946, -24017, -5205, -4717, 13443, 8551, 16814, 7685, 9186, -11930, -4643, -11006, -8981, -1500, -4608, -11237, -6168, -7115, -16394, -5450, -260, 8360, 16201, 8555, 7904, 6814, 11052, -7812, 5190, -4231, 8700, -7556, -16692, -15109, 1501, -179, -15900, -21630, -840, 78, 12024, -19599, -7008, -1401, 11691, -6062, -2361, -2078, -9851, -12886, -6197, -14323, 10714, -2559, 18021, -3530, -6368, -14301, 16510, -218, 9119, 19707, 9512, 156, -5396, -8165, -10861, -8285, -14774, -1369, 1001, -18740, -4590, -11465, -11136, -12620, -12954, -6689, 5329, -9041, 2334, -11316, 2216, 2525, 3014, 129, -7723, 5379, 5859, -22827, -3197, 222, -598, -4346, -10434, -8591, 7781, 6463, -6531, -11027, 5974, 9465, -10909, 78, 6011, -9447, 10412, -3753, -2977, 17704, -13772, 13510, -4946, -18878, -5365, -9546, -6774, 433, 15195, -9317, 4092, -9579, 3417, 18249, -40, 56, -6738, -494, -7194, -1236, 1385}; - model->setOperandValue(op55, op55_init, sizeof(int32_t) * 256); - static uint8_t op56_init[] = {128, 122, 133, 119, 116, 124, 117, 128, 124, 127, 107, 176, 132, 123, 125, 117, 128, 124, 120, 127, 109, 130, 103, 119, 137, 106, 114, 115, 126, 132, 124, 128, 118, 125, 125, 109, 151, 129, 159, 113, 110, 129, 92, 113, 137, 146, 121, 114, 122, 124, 117, 146, 123, 130, 125, 120, 122, 113, 143, 128, 120, 135, 146, 124, 119, 119, 106, 115, 106, 119, 134, 130, 104, 99, 106, 131, 122, 111, 119, 162, 115, 130, 130, 118, 122, 112, 108, 130, 138, 144, 136, 120, 127, 137, 124, 115, 125, 128, 108, 108, 116, 125, 154, 123, 102, 114, 124, 134, 127, 108, 106, 118, 104, 121, 120, 145, 122, 124, 111, 120, 116, 109, 114, 138, 98, 117, 130, 118, 119, 117, 133, 115, 129, 125, 124, 116, 131, 111, 132, 138, 132, 139, 118, 114, 122, 123, 127, 120, 109, 123, 116, 109, 124, 121, 116, 123, 126, 95, 124, 127, 122, 136, 124, 104, 121, 131, 113, 133, 132, 137, 110, 131, 129, 138, 118, 124, 139, 115, 104, 115, 109, 77, 130, 131, 112, 133, 107, 118, 116, 123, 140, 119, 113, 137, 110, 134, 119, 121, 120, 116, 151, 117, 126, 129, 101, 124, 112, 128, 121, 133, 117, 121, 118, 117, 117, 124, 118, 114, 101, 121, 130, 122, 88, 130, 113, 115, 121, 129, 113, 131, 124, 100, 140, 125, 130, 144, 123, 115, 99, 138, 138, 122, 124, 134, 119, 113, 118, 123, 115, 112, 126, 120, 107, 108, 120, 117, 126, 109, 99, 126, 95, 94, 115, 126, 131, 128, 126, 143, 136, 139, 144, 121, 112, 126, 122, 110, 118, 126, 124, 114, 131, 125, 131, 124, 122, 122, 131, 105, 113, 131, 136, 143, 128, 126, 124, 128, 129, 132, 124, 112, 135, 117, 130, 121, 139, 154, 146, 119, 141, 114, 116, 123, 122, 139, 113, 113, 118, 122, 119, 110, 113, 112, 113, 113, 115, 126, 128, 136, 115, 120, 111, 116, 149, 146, 131, 130, 114, 118, 122, 110, 126, 124, 124, 118, 142, 123, 119, 128, 127, 119, 138, 141, 124, 137, 126, 126, 118, 118, 94, 136, 127, 116, 145, 112, 176, 139, 106, 101, 119, 117, 132, 113, 110, 131, 125, 130, 129, 122, 130, 131, 133, 119, 124, 105, 127, 110, 93, 123, 102, 137, 126, 142, 139, 111, 129, 117, 128, 110, 111, 121, 135, 129, 131, 120, 132, 117, 113, 129, 121, 146, 135, 130, 116, 119, 128, 124, 122, 112, 133, 131, 130, 122, 106, 122, 124, 119, 97, 129, 127, 122, 113, 120, 124, 125, 141, 122, 147, 121, 135, 133, 125, 111, 124, 111, 134, 102, 142, 113, 111, 129, 140, 112, 112, 135, 123, 104, 121, 120, 138, 121, 129, 118, 123, 99, 124, 144, 117, 123, 127, 133, 129, 141, 124, 118, 119, 132, 123, 125, 112, 130, 169, 90, 111, 121, 132, 140, 126, 124, 112, 132, 141, 147, 127, 101, 112, 131, 138, 120, 149, 134, 139, 112, 86, 126, 128, 126, 126, 119, 139, 112, 132, 124, 141, 121, 118, 124, 117, 122, 127, 126, 126, 137, 147, 137, 162, 112, 126, 127, 133, 120, 133, 127, 108, 88, 160, 122, 134, 123, 118, 148, 152, 112, 123, 121, 114, 116, 153, 110, 141, 133, 142, 110, 118, 170, 127, 128, 120, 116, 127, 118, 123, 130, 119, 132, 116, 123, 128, 117, 113, 165, 120, 101, 129, 124, 142, 131, 122, 118, 114, 123, 109, 142, 109, 127, 113, 111, 113, 133, 128, 116, 121, 142, 124, 130, 187, 129, 129, 116, 115, 127, 117, 135, 122, 118, 123, 132, 128, 117, 110, 129, 139, 120, 129, 131, 109, 116, 137, 126, 137, 118, 133, 119, 129, 157, 136, 119, 123, 135, 121, 135, 123, 117, 122, 122, 173, 102, 129, 123, 123, 129, 119, 124, 161, 118, 135, 116, 122, 131, 125, 124, 130, 123, 139, 126, 128, 126, 121, 126, 119, 126, 113, 140, 117, 131, 119, 131, 135, 116, 122, 92, 128, 137, 108, 121, 107, 148, 146, 114, 125, 134, 124, 122, 125, 132, 127, 131, 129, 118, 124, 112, 109, 126, 132, 86, 119, 134, 123, 128, 120, 142, 112, 126, 136, 132, 115, 105, 174, 121, 131, 130, 133, 122, 134, 138, 113, 134, 159, 143, 110, 130, 125, 115, 123, 117, 123, 108, 123, 108, 127, 125, 131, 115, 122, 116, 138, 129, 138, 123, 134, 117, 116, 123, 117, 129, 124, 109, 117, 128, 116, 120, 124, 122, 157, 131, 129, 121, 126, 138, 140, 112, 124, 124, 122, 127, 111, 123, 124, 120, 126, 136, 140, 130, 114, 125, 119, 127, 128, 124, 141, 114, 129, 107, 120, 119, 107, 138, 114, 120, 165, 121, 148, 124, 113, 119, 122, 121, 126, 109, 130, 121, 116, 121, 150, 124, 107, 129, 140, 118, 118, 150, 125, 124, 121, 128, 128, 121, 117, 120, 113, 128, 131, 120, 132, 122, 128, 157, 113, 100, 114, 120, 125, 104, 119, 123, 118, 111, 103, 128, 138, 131, 141, 166, 126, 111, 131, 120, 123, 124, 124, 126, 141, 125, 135, 130, 118, 113, 127, 115, 137, 134, 114, 122, 122, 126, 126, 124, 126, 121, 121, 118, 134, 121, 159, 139, 120, 124, 142, 130, 131, 147, 129, 118, 123, 109, 125, 111, 114, 144, 118, 125, 139, 122, 152, 136, 130, 131, 118, 126, 146, 122, 129, 128, 125, 122, 130, 130, 128, 113, 121, 115, 122, 150, 122, 121, 121, 121, 121, 134, 98, 125, 134, 131, 135, 135, 120, 127, 121, 127, 124, 103, 143, 151, 147, 117, 128, 119, 107, 120, 120, 112, 138, 124, 132, 117, 127, 126, 120, 123, 152, 118, 127, 112, 128, 127, 130, 117, 119, 111, 124, 123, 121, 101, 149, 122, 111, 132, 129, 129, 125, 125, 127, 138, 157, 132, 130, 122, 133, 146, 120, 115, 122, 143, 129, 124, 103, 126, 126, 114, 135, 131, 154, 121, 153, 126, 119, 123, 129, 121, 124, 127, 128, 119, 128, 107, 128, 144, 115, 126, 121, 121, 117, 133, 121, 128, 152, 130, 125, 129, 121, 123, 144, 125, 117, 123, 122, 129, 126, 122, 111, 117, 136, 132, 125, 134, 118, 118, 140, 122, 128, 129, 117, 137, 122, 121, 188, 143, 109, 137, 133, 120, 133, 128, 168, 110, 132, 123, 124, 116, 123, 117, 115, 129, 123, 122, 124, 124, 123, 123, 123, 130, 138, 129, 127, 130, 129, 97, 128, 113, 133, 126, 133, 104, 130, 117, 119, 122, 124, 124, 134, 116, 115, 123, 118, 118, 97, 127, 123, 109, 117, 128, 124, 125, 128, 116, 131, 125, 101, 127, 114, 128, 115, 133, 120, 129, 141, 116, 130, 138, 129, 121, 123, 121, 123, 124, 123, 128, 127, 121, 156, 126, 116, 114, 118, 108, 123, 135, 127, 128, 123, 119, 132, 134, 131, 170, 120, 119, 115, 126, 115, 125, 118, 131, 126, 110, 158, 120, 133, 119, 116, 114, 120, 133, 121, 122, 127, 129, 122, 152, 124, 130, 130, 123, 112, 129, 119, 130, 121, 128, 123, 125, 127, 120, 122, 134, 127, 118, 101, 102, 153, 125, 124, 122, 124, 129, 139, 115, 119, 125, 125, 120, 128, 118, 125, 122, 135, 105, 135, 121, 127, 124, 128, 116, 124, 143, 143, 128, 124, 99, 120, 119, 118, 116, 130, 121, 151, 140, 119, 123, 105, 132, 128, 133, 124, 95, 129, 119, 121, 98, 125, 126, 121, 116, 134, 125, 127, 117, 120, 129, 124, 118, 122, 123, 122, 123, 122, 134, 134, 106, 129, 139, 130, 127, 121, 128, 107, 124, 125, 123, 130, 130, 119, 124, 126, 133, 118, 130, 119, 120, 126, 130, 120, 124, 118, 136, 123, 138, 115, 124, 129, 125, 118, 121, 125, 120, 124, 124, 110, 127, 159, 125, 125, 124, 126, 122, 118, 126, 112, 114, 117, 116, 128, 111, 119, 125, 126, 122, 114, 122, 116, 117, 122, 123, 129, 132, 118, 115, 117, 136, 123, 122, 124, 128, 128, 115, 119, 124, 124, 108, 126, 141, 122, 120, 120, 140, 128, 123, 124, 112, 120, 119, 130, 126, 124, 126, 130, 119, 121, 122, 119, 125, 124, 127, 120, 120, 136, 125, 120, 135, 133, 143, 122, 125, 115, 126, 130, 125, 120, 124, 131, 129, 123, 101, 122, 130, 109, 126, 124, 125, 123, 129, 110, 115, 121, 110, 125, 143, 125, 131, 121, 113, 125, 125, 129, 131, 130, 137, 133, 113, 124, 118, 117, 126, 119, 124, 125, 134, 123, 106, 113, 124, 130, 124, 138, 127, 120, 126, 121, 124, 109, 136, 116, 123, 129, 123, 121, 126, 123, 136, 119, 78, 132, 127, 117, 124, 122, 119, 121, 123, 122, 128, 115, 95, 120, 122, 111, 122, 132, 133, 125, 136, 124, 116, 126, 126, 127, 131, 127, 123, 116, 114, 139, 117, 122, 124, 118, 117, 174, 119, 121, 119, 130, 118, 117, 120, 130, 123, 125, 125, 126, 120, 119, 118, 126, 123, 126, 122, 126, 111, 128, 127, 115, 111, 106, 120, 131, 116, 112, 129, 118, 120, 120, 132, 122, 128, 125, 81, 120, 127, 114, 122, 117, 126, 135, 117, 124, 137, 116, 116, 122, 117, 116, 123, 123, 151, 117, 143, 141, 121, 122, 125, 131, 127, 124, 121, 136, 123, 111, 116, 126, 139, 125, 118, 131, 114, 123, 126, 118, 64, 112, 125, 123, 130, 135, 108, 165, 129, 103, 115, 123, 135, 104, 109, 125, 120, 146, 123, 121, 134, 159, 120, 110, 117, 139, 136, 118, 140, 118, 172, 106, 133, 122, 121, 133, 131, 113, 138, 127, 128, 119, 137, 122, 122, 124, 118, 111, 131, 110, 138, 129, 133, 139, 136, 142, 113, 133, 126, 123, 124, 134, 140, 119, 114, 119, 125, 124, 119, 112, 119, 121, 123, 132, 132, 126, 149, 113, 127, 124, 129, 142, 123, 122, 105, 119, 123, 121, 121, 125, 105, 122, 121, 126, 130, 114, 122, 134, 139, 129, 125, 225, 106, 138, 140, 110, 146, 128, 128, 114, 139, 141, 130, 132, 120, 118, 143, 136, 135, 125, 5, 123, 145, 115, 137, 116, 117, 127, 136, 120, 120, 124, 132, 96, 128, 122, 137, 132, 118, 110, 124, 143, 121, 127, 104, 123, 133, 130, 116, 129, 129, 198, 127, 140, 121, 133, 127, 130, 116, 123, 86, 131, 132, 125, 130, 156, 186, 122, 129, 92, 109, 113, 126, 117, 119, 130, 109, 147, 122, 119, 114, 142, 153, 124, 141, 138, 149, 127, 163, 128, 129, 120, 132, 137, 110, 136, 132, 133, 108, 125, 139, 144, 106, 123, 131, 131, 100, 112, 133, 136, 111, 126, 133, 117, 128, 120, 140, 120, 126, 127, 121, 139, 109, 124, 139, 122, 118, 113, 132, 124, 129, 124, 128, 134, 121, 113, 132, 123, 130, 128, 114, 122, 123, 134, 113, 114, 117, 123, 129, 117, 123, 119, 141, 122, 121, 103, 122, 134, 120, 119, 119, 150, 145, 129, 140, 124, 168, 123, 103, 118, 120, 134, 122, 118, 143, 124, 102, 113, 126, 112, 130, 123, 139, 118, 115, 116, 124, 125, 120, 135, 124, 114, 93, 123, 151, 117, 118, 120, 135, 128, 136, 117, 129, 115, 131, 139, 124, 102, 158, 122, 122, 130, 115, 125, 122, 135, 127, 121, 129, 110, 114, 118, 123, 125, 120, 154, 112, 155, 112, 129, 120, 119, 116, 121, 111, 119, 105, 137, 116, 111, 125, 122, 151, 111, 110, 104, 122, 132, 129, 113, 124, 105, 108, 124, 146, 134, 127, 114, 132, 107, 128, 124, 136, 124, 139, 109, 146, 116, 120, 121, 127, 135, 109, 119, 134, 130, 127, 145, 122, 120, 123, 120, 125, 131, 90, 115, 133, 120, 125, 111, 134, 127, 133, 136, 97, 138, 115, 115, 178, 129, 136, 127, 124, 123, 128, 140, 140, 123, 126, 135, 122, 121, 105, 114, 128, 141, 114, 123, 121, 133, 127, 122, 100, 109, 95, 121, 114, 121, 119, 157, 105, 124, 123, 119, 93, 111, 159, 121, 104, 119, 136, 116, 119, 129, 123, 123, 127, 116, 132, 129, 130, 132, 100, 121, 118, 105, 135, 131, 123, 156, 130, 121, 127, 119, 111, 115, 118, 130, 141, 128, 119, 123, 137, 124, 122, 117, 123, 118, 95, 109, 129, 79, 108, 121, 137, 121, 134, 92, 107, 117, 122, 122, 109, 106, 125, 111, 118, 117, 127, 125, 115, 121, 119, 114, 131, 120, 122, 133, 116, 127, 128, 96, 117, 135, 118, 141, 124, 118, 110, 108, 129, 121, 139, 122, 136, 131, 118, 161, 127, 149, 118, 138, 115, 112, 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127, 132, 119, 124, 119, 116, 123, 118, 118, 104, 129, 154, 123, 115, 107, 130, 116, 136, 129, 116, 117, 115, 118, 119, 112, 132, 125, 138, 124, 106, 119, 135, 138, 124, 109, 116, 122, 117, 133, 111, 145, 116, 124, 121, 151, 104, 136, 120, 120, 139, 131, 121, 123, 154, 125, 115, 129, 118, 127, 121, 138, 116, 120, 119, 109, 129, 112, 127, 125, 120, 121, 114, 110, 125, 124, 113, 138, 125, 131, 126, 130, 119, 118, 147, 150, 122, 137, 131, 122, 117, 129, 95, 130, 124, 143, 109, 136, 126, 124, 91, 118, 99, 113, 115, 127, 130, 129, 113, 121, 135, 113, 138, 121, 136, 119, 125, 125, 132, 108, 140, 139, 110, 110, 94, 115, 125, 142, 127, 135, 122, 137, 124, 134, 131, 133, 123, 125, 125, 122, 126, 122, 126, 153, 109, 127, 110, 132, 116, 124, 136, 129, 124, 125, 106, 112, 146, 133, 138, 109, 121, 126, 133, 110, 125, 112, 117, 105, 126, 121, 130, 127, 117, 113, 91, 121, 131, 120, 115, 119, 127, 125, 124, 111, 137, 126, 125, 111, 130, 120, 105, 117, 108, 117, 105, 117, 105, 131, 126, 127, 121, 121, 91, 117, 121, 138, 137, 113, 125, 125, 122, 126, 123, 113, 120, 122, 126, 113, 126, 129, 124, 122, 126, 126, 121, 118, 149, 120, 127, 156, 118, 117, 122, 125, 111, 114, 125, 145, 121, 133, 126, 140, 123, 133, 120, 112, 108, 125, 121, 128, 116, 120, 109, 125, 127, 123, 120, 126, 121, 119, 119, 126, 128, 124, 118, 128, 126, 127, 102, 128, 134, 124, 132, 132, 116, 130, 124, 124, 115, 124, 125, 130, 137, 134, 117, 137, 120, 125, 125, 134, 124, 123, 143, 114, 125, 123, 121, 116, 118, 120, 122, 129, 126, 128, 135, 114, 121, 144, 122, 122, 128, 119, 124, 113, 126, 123, 117, 118, 128, 123, 108, 122, 107, 127, 124, 127, 112, 117, 121, 127, 126, 127, 123, 120, 122, 122, 120, 122, 121, 110, 138, 138, 118, 115, 117, 124, 124, 125, 136, 121, 129, 123, 157, 122, 127, 103, 128, 160, 107, 124, 124, 122, 110, 122, 126, 119, 118, 136, 96, 127, 130, 126, 112, 139, 119, 131, 121, 142, 103, 130, 122, 121, 113, 122, 128, 128, 125, 125, 125, 129, 118, 141, 120, 124, 111, 113, 122, 125, 125, 122, 125, 125, 106, 143, 134, 131, 123, 121, 107, 123, 123, 136, 123, 116, 126, 111, 122, 121, 120, 123, 102, 109, 117, 145, 102, 90, 121, 159, 114, 127, 136, 126, 118, 153, 129, 125, 129, 134, 112, 127, 121, 114, 129, 126, 113, 148, 122, 120, 126, 137, 114, 123, 131, 111, 132, 140, 135, 124, 124, 127, 122, 123, 102, 127, 140, 123, 127, 111, 140, 138, 116, 121, 110, 141, 121, 103, 121, 108, 141, 113, 133, 127, 128, 149, 155, 102, 138, 116, 123, 121, 131, 117, 115, 113, 138, 121, 100, 121, 119, 122, 122, 127, 133, 125, 123, 131, 132, 135, 131, 81, 123, 135, 152, 127, 119, 131, 120, 118, 120, 134, 163, 129, 124, 110, 120, 101, 121, 132, 75, 119, 124, 139, 116, 112, 106, 119, 151, 121, 137, 147, 118, 164, 118, 136, 118, 119, 111, 123, 120, 127, 117, 119, 130, 132, 145, 140, 108, 147, 134, 118, 134, 126, 128, 124, 120, 120, 101, 112, 118, 118, 130, 125, 124, 139, 114, 114, 117, 116, 118, 134, 136, 104, 138, 115, 86, 124, 101, 128, 130, 143, 116, 126, 116, 132, 111, 122, 119, 123, 123, 145, 111, 122, 121, 114, 121, 116, 120, 116, 121, 120, 113, 121, 122, 119, 117, 125, 117, 83, 152, 129, 130, 116, 113, 124, 135, 137, 129, 82, 113, 128, 115, 133, 128, 129, 127, 119, 121, 156, 113, 114, 140, 117, 115, 161, 139, 102, 136, 120, 123, 146, 145, 116, 110, 142, 124, 115, 128, 129, 120, 124, 104, 134, 134, 129, 129, 104, 117, 120, 142, 114, 145, 119, 125, 142, 82, 110, 123, 119, 123, 117, 122, 131, 138, 126, 117, 121, 133, 128, 110, 127, 110, 121, 120, 117, 125, 116, 124, 121, 125, 114, 129, 127, 133, 121, 123, 126, 117, 124, 136, 127, 114, 132, 145, 134, 126, 132, 146, 135, 122, 115, 119, 133, 139, 123, 123, 123, 129, 127, 120, 123, 125, 110, 120, 138, 125, 128, 178, 121, 143, 128, 123, 126, 135, 113, 133, 126, 135, 113, 115, 133, 114, 133, 118, 127, 130, 127, 116, 125, 116, 125, 135, 131, 104, 134, 132, 120, 124, 118, 130, 116, 127, 124, 119, 120, 124, 118, 123, 128, 127, 127, 122, 124, 135, 118, 130, 119, 106, 116, 119, 124, 112, 125, 126, 127, 125, 131, 107, 134, 130, 113, 118, 117, 126, 126, 129, 131, 126, 147, 166, 130, 124, 132, 117, 127, 131, 136, 117, 137, 121, 118, 126, 122, 130, 119, 124, 142, 135, 129, 129, 118, 111, 118, 127, 121, 121, 168, 129, 128, 128, 115, 121, 124, 115, 125, 125, 125, 124, 139, 127, 124, 124, 124, 97, 124, 124, 128, 129, 117, 131, 125, 131, 135, 119, 119, 134, 124, 126, 144, 122, 116, 118, 117, 128, 123, 114, 128, 126, 107, 118, 125, 109, 122, 127, 125, 121, 119, 123, 131, 131, 129, 124, 132, 123, 129, 112, 131, 133, 131, 124, 112, 119, 127, 124, 136, 140, 126, 125, 119, 128, 123, 152, 116, 142, 134, 140, 121, 130, 133, 116, 124, 128, 121, 156, 128, 126, 116, 129, 163, 119, 127, 119, 151, 127, 126, 119, 132, 132, 125, 134, 116, 138, 160, 109, 125, 122, 123, 126, 116, 112, 121, 118, 123, 126, 126, 126, 140, 125, 121, 146, 130, 118, 130, 135, 119, 119, 122, 139, 131, 124, 136, 111, 116, 133, 129, 130, 124, 120, 115, 121, 117, 135, 131, 117, 116, 120, 147, 120, 122, 134, 118, 131, 128, 129, 126, 123, 129, 138, 119, 131, 113, 127, 126, 120, 139, 128, 104, 127, 126, 117, 132, 121, 116, 114, 123, 143, 131, 123, 121, 128, 143, 120, 135, 123, 128, 119, 125, 124, 119, 122, 121, 122, 138, 135, 143, 141, 122, 132, 127, 112, 117, 125, 133, 133, 132, 128, 120, 139, 135, 121, 118, 141, 132, 132, 122, 121, 115, 121, 132, 132, 105, 123, 99, 124, 140, 114, 137, 119, 123, 130, 117, 125, 160, 127, 122, 111, 119, 124, 136, 122, 114, 133, 124, 132, 107, 111, 139, 115, 122, 103, 124, 110, 120, 118, 132, 142, 122, 116, 140, 135, 126, 127, 123, 118, 111, 136, 126, 120, 125, 120, 127, 122, 124, 126, 124, 135, 123, 122, 107, 112, 140, 131, 109, 122, 113, 102, 118, 138, 118, 116, 119, 125, 109, 138, 132, 102, 110, 123, 129, 120, 111, 136, 117, 130, 127, 119, 135, 127, 125, 135, 120, 129, 115, 111, 115, 123, 127, 119, 119, 121, 127, 120, 110, 123, 138, 125, 134, 132, 128, 134, 122, 142, 131, 126, 117, 130, 130, 122, 121, 126, 116, 112, 132, 123, 123, 134, 119, 117, 114, 132, 134, 115, 116, 144, 121, 134, 123, 117, 131, 126, 119, 138, 124, 110, 120, 133, 120, 129, 143, 133, 125, 119, 128, 119, 127, 122, 129, 124, 127, 141, 143, 125, 117, 121, 122, 118, 118, 132, 131, 116, 122, 125, 146, 119, 116, 123, 140, 118, 127, 125, 116, 131, 154, 128, 115, 120, 121, 127, 117, 116, 134, 126, 121, 118, 130, 125, 157, 128, 125, 124, 132, 128, 125, 123, 127, 120, 130, 117, 118, 136, 118, 117, 124, 124, 130, 117, 118, 117, 121, 118, 120, 114, 130, 127, 116, 124, 121, 119, 110, 121, 120, 130, 143, 122, 126, 126, 123, 119, 126, 178, 115, 123, 120, 123, 123, 132, 132, 139, 132, 138, 143, 113, 111, 128, 129, 118, 120, 135, 123, 126, 124, 121, 136, 118, 121, 121, 115, 138, 123, 127, 122, 163, 138, 114, 127, 136, 132, 143, 121, 128, 133, 127, 137, 132, 118, 125, 130, 155, 123, 124, 116, 140, 121, 110, 126, 128, 120, 132, 125, 119, 118, 117, 125, 121, 127, 132, 121, 121, 117, 123, 128, 123, 133, 128, 135, 123, 141, 119, 158, 131, 125, 132, 135, 126, 116, 127, 120, 125, 120, 134, 117, 166, 123, 114, 122, 128, 114, 121, 116, 146, 115, 152, 122, 126, 143, 122, 110, 117, 120, 124, 123, 120, 106, 117, 132, 114, 122, 124, 115, 127, 129, 133, 120, 129, 153, 124, 131, 131, 123, 114, 112, 110, 131, 136, 123, 120, 139, 126, 124, 108, 116, 122, 121, 115, 120, 131, 118, 117, 122, 127, 119, 130, 132, 124, 120, 119, 123, 132, 125, 113, 139, 116, 128, 119, 150, 126, 119, 119, 130, 116, 119, 114, 122, 117, 124, 131, 109, 109, 125, 118, 125, 130, 118, 114, 152, 121, 122, 134, 154, 132, 142, 122, 137, 94, 142, 119, 91, 128, 131, 131, 112, 126, 121, 120, 135, 112, 121, 117, 112, 102, 127, 126, 104, 125, 134, 109, 126, 119, 130, 114, 130, 122, 128, 129, 99, 114, 118, 121, 115, 123, 124, 129, 120, 111, 116, 112, 128, 114, 119, 116, 120, 123, 133, 123, 127, 131, 122, 129, 101, 130, 118, 145, 132, 129, 129, 123, 133, 116, 120, 107, 131, 115, 136, 134, 130, 119, 136, 122, 133, 124, 114, 126, 146, 125, 127, 126, 106, 123, 114, 118, 131, 121, 127, 117, 121, 122, 117, 114, 131, 103, 106, 133, 131, 108, 115, 133, 104, 149, 121, 134, 122, 147, 129, 116, 128, 131, 131, 126, 110, 123, 134, 116, 135, 119, 119, 124, 119, 117, 155, 109, 106, 133, 126, 121, 123, 109, 117, 110, 127, 122, 132, 122, 111, 130, 123, 117, 86, 131, 119, 124, 121, 135, 121, 125, 130, 125, 111, 125, 105, 115, 117, 115, 113, 127, 104, 122, 119, 118, 113, 111, 125, 142, 114, 113, 124, 140, 123, 115, 141, 120, 125, 111, 125, 124, 122, 123, 131, 128, 124, 117, 128, 117, 122, 107, 113, 115, 132, 112, 118, 121, 131, 123, 103, 135, 113, 128, 122, 129, 135, 129, 130, 135, 128, 115, 139, 102, 130, 122, 127, 122, 132, 120, 128, 117, 116}; - model->setOperandValue(op56, op56_init, sizeof(uint8_t) * 65536); - static int32_t op58_init[] = {8192, 10583, -634, -390, 3031, 18442, 13861, 8686, 3844, -134, -596, 16090, 8189, -439, -1761, 2197, 601, -658, 1692, 13325, 19431, 13409, 28900, 9820, -4456, 73, 564, 10928, 10775, 10520, 21773, 10722, 759, 1081, 2361, 17568, 139, 58, 23, 748, -169, 10246, 36892, -2024, -3657, 23, 16690, 689, 9339, 8827, 8516, 3383, -238, 10715, -128, 11371, 1402, 17076, 7921, 1763, 6428, 8633, -3792, -135, 12191, 971, -4242, 4471, 10561, 409, -7689, 5657, 95, 20772, 2033, -122, -644, 16590, -168, 1749, 11040, 16, 11236, 761, 2884, -540, 10755, 6725, 73, 574, -47, 22319, -1251, 8132, 16944, -2905, -294, 26607, 122, 17473, -3419, 268, 10719, 19680, -285, 1808, -1082, 15089, 8505, 22274, -1285, 9980, 248, 297, -21, 8372, 7912, 20472, 8401, 10274, 97, 705, -3609, 18, 12276, -1326, -1725, 1554, 58, 7482, 29769, 17989, 7137, 83, 9677, 14574, 751, -214, 436, 835, 7954, 36, -245, 353, -189, -1044, 2984, 26780, -1300, 8929, 3639, 1503, 4059, 9479, -852, 15017, 8355, -162, 8954, 9982, 2181, 120, -5145, 6335, -34, 37077, 11, 81, 557, 24335, 7147, -428, 3780, 706, 13473, -230, 15495, -669, -414, 7458, 584, 2840, -1885, -4488, -1361, 16221, 10598, 4656, 12683, -239, 662, 791, 321, 8543, 632, 1288, -1324, 72, 19208, -729, 45, 16619, -175, -2996, 290, 25937, 404, 1592, 1780, 8043, -1192, 1727, -53, 16790, -420, 359, 14036, 302, -1003, 14993, -295, 629, 8419, 8404, -12276, 4780, 5363, 4793, 6723, 16119, 9861, -983, -30, -868, 18024, -146, 17419, 9504, 27, -92, 911, 17520, 12707, -1608, 44, -1222, 3030, 17253, -1654, 120, -208, 7766, 11888, -123, 13024, -428}; - model->setOperandValue(op58, op58_init, sizeof(int32_t) * 256); - static uint8_t op59_init[] = {76, 112, 160, 144, 142, 76, 101, 100, 135, 142, 148, 152, 88, 150, 131, 143, 157, 135, 218, 99, 87, 94, 98, 99, 40, 134, 150, 102, 100, 96, 94, 90, 132, 138, 138, 88, 142, 154, 136, 125, 164, 100, 66, 139, 63, 147, 99, 136, 85, 99, 89, 144, 150, 100, 154, 96, 142, 95, 128, 71, 167, 100, 160, 190, 105, 127, 131, 178, 99, 130, 199, 102, 180, 96, 136, 189, 92, 95, 157, 134, 101, 143, 97, 149, 75, 128, 86, 76, 142, 143, 154, 88, 138, 84, 96, 127, 126, 99, 138, 94, 152, 148, 101, 54, 176, 133, 78, 98, 78, 79, 154, 95, 153, 146, 197, 87, 106, 91, 85, 83, 188, 147, 151, 151, 115, 145, 136, 164, 148, 99, 103, 81, 79, 175, 97, 99, 158, 150, 128, 140, 82, 139, 154, 144, 174, 140, 101, 100, 48, 84, 154, 75, 138, 101, 158, 100, 83, 152, 81, 102, 190, 142, 150, 135, 175, 74, 143, 158, 129, 84, 99, 123, 145, 138, 93, 141, 84, 139, 146, 149, 151, 140, 145, 72, 164, 97, 101, 68, 76, 136, 164, 135, 133, 97, 128, 156, 145, 153, 84, 140, 154, 85, 149, 171, 179, 74, 158, 155, 143, 97, 134, 149, 129, 94, 149, 157, 99, 165, 141, 74, 155, 51, 89, 91, 159, 102, 176, 139, 118, 84, 105, 128, 144, 22, 100, 165, 98, 102, 146, 191, 142, 80, 98, 7, 131, 169, 145, 95, 142, 129, 148, 92, 73, 141, 77, 175, 56, 94, 168, 148, 154, 62, 97, 87, 146, 160, 164, 124, 81, 161, 138, 157, 67, 149, 238, 94, 71, 87, 91, 90, 162, 142, 166, 97, 95, 93, 93, 96, 199, 164, 152, 86, 148, 162, 143, 133, 162, 95, 53, 144, 221, 169, 93, 148, 74, 93, 74, 153, 158, 92, 174, 89, 152, 85, 137, 117, 153, 89, 81, 225, 99, 133, 139, 159, 85, 135, 218, 79, 184, 86, 140, 206, 147, 85, 170, 141, 97, 149, 83, 163, 122, 144, 85, 67, 160, 155, 165, 82, 161, 76, 85, 157, 136, 92, 148, 88, 155, 160, 93, 44, 91, 147, 40, 89, 50, 62, 166, 93, 168, 152, 180, 76, 82, 79, 66, 77, 199, 159, 158, 165, 156, 160, 143, 175, 155, 156, 67, 82, 76, 184, 87, 89, 172, 165, 134, 158, 74, 152, 170, 155, 194, 148, 135, 98, 32, 85, 160, 59, 147, 95, 167, 93, 82, 160, 64, 90, 203, 161, 156, 143, 189, 46, 152, 165, 131, 72, 91, 139, 156, 146, 82, 148, 75, 133, 134, 165, 161, 157, 163, 88, 178, 90, 95, 42, 80, 144, 168, 146, 137, 85, 136, 167, 152, 169, 78, 149, 163, 76, 162, 144, 189, 67, 156, 89, 144, 96, 147, 160, 135, 82, 162, 165, 92, 168, 144, 64, 161, 43, 86, 85, 160, 157, 164, 149, 137, 80, 98, 139, 164, 136, 91, 172, 88, 95, 155, 220, 136, 77, 93, 63, 135, 193, 151, 96, 147, 137, 166, 84, 64, 149, 63, 193, 81, 96, 151, 130, 135, 85, 106, 96, 132, 144, 144, 105, 94, 139, 126, 139, 77, 134, 189, 106, 89, 103, 101, 105, 170, 127, 141, 105, 101, 102, 103, 122, 177, 146, 137, 94, 133, 140, 133, 126, 134, 104, 87, 128, 117, 150, 107, 132, 97, 102, 95, 133, 135, 104, 160, 104, 137, 101, 131, 137, 124, 96, 99, 201, 109, 124, 127, 125, 100, 124, 143, 87, 146, 104, 128, 147, 156, 100, 145, 131, 102, 134, 107, 138, 148, 139, 99, 86, 139, 137, 147, 101, 146, 89, 98, 69, 127, 104, 134, 100, 135, 140, 100, 63, 86, 69, 77, 105, 78, 90, 139, 108, 139, 136, 138, 100, 82, 95, 90, 88, 154, 137, 143, 139, 148, 143, 128, 152, 134, 152, 72, 110, 120, 158, 104, 103, 147, 140, 126, 146, 88, 134, 148, 136, 153, 132, 137, 105, 66, 95, 138, 85, 133, 106, 142, 98, 105, 138, 83, 99, 164, 142, 141, 132, 154, 76, 131, 138, 122, 98, 97, 139, 136, 130, 98, 133, 91, 115, 115, 141, 142, 141, 141, 118, 153, 103, 101, 89, 109, 126, 149, 132, 124, 101, 128, 140, 134, 142, 97, 134, 141, 95, 139, 79, 140, 96, 134, 82, 129, 99, 140, 139, 125, 94, 142, 144, 103, 127, 131, 92, 141, 64, 99, 92, 147, 163, 125, 135, 137, 91, 105, 131, 155, 150, 104, 147, 99, 107, 138, 182, 121, 104, 105, 149, 129, 164, 133, 105, 127, 127, 144, 96, 87, 139, 84, 140, 61, 98, 172, 153, 159, 66, 95, 90, 146, 159, 159, 119, 80, 159, 136, 156, 172, 152, 236, 95, 67, 86, 90, 91, 91, 150, 163, 88, 97, 93, 85, 97, 0, 162, 118, 77, 150, 158, 142, 133, 166, 94, 61, 142, 18, 163, 92, 145, 73, 93, 83, 158, 161, 92, 178, 89, 154, 86, 134, 45, 156, 89, 188, 188, 95, 131, 137, 144, 91, 140, 216, 140, 183, 90, 146, 215, 149, 87, 173, 143, 86, 153, 95, 156, 58, 146, 85, 64, 151, 154, 167, 82, 153, 76, 89, 104, 136, 95, 149, 85, 119, 161, 93, 44, 181, 149, 158, 95, 64, 62, 169, 95, 174, 153, 184, 82, 72, 80, 67, 75, 204, 157, 159, 171, 70, 162, 146, 180, 152, 56, 77, 80, 65, 183, 88, 87, 166, 164, 132, 154, 74, 152, 169, 156, 183, 151, 95, 97, 130, 89, 94, 125, 145, 94, 174, 96, 96, 160, 59, 92, 208, 154, 155, 136, 187, 49, 155, 173, 132, 75, 88, 134, 161, 146, 82, 152, 71, 151, 158, 101, 160, 150, 161, 187, 174, 90, 93, 125, 85, 142, 171, 148, 146, 90, 136, 165, 154, 156, 73, 149, 169, 77, 162, 180, 189, 58, 162, 171, 154, 96, 146, 148, 135, 78, 159, 175, 91, 167, 147, 66, 166, 14, 87, 77, 158, 53, 148, 146, 79, 78, 98, 137, 165, 15, 93, 174, 87, 95, 157, 215, 150, 77, 92, 60, 137, 190, 151, 86, 153, 139, 166, 83, 62, 129, 56, 163, 41, 86, 179, 159, 171, 45, 88, 81, 160, 177, 175, 63, 71, 186, 144, 171, 110, 165, 255, 87, 67, 76, 79, 80, 211, 161, 178, 80, 89, 85, 73, 80, 87, 181, 130, 72, 161, 179, 155, 143, 185, 88, 25, 154, 192, 180, 78, 163, 64, 85, 76, 171, 183, 81, 198, 81, 162, 73, 149, 115, 131, 76, 96, 202, 79, 142, 152, 107, 70, 147, 218, 122, 209, 75, 152, 238, 184, 69, 185, 152, 80, 165, 81, 184, 122, 164, 82, 63, 166, 168, 188, 65, 174, 68, 70, 176, 154, 86, 163, 70, 102, 175, 85, 22, 88, 126, 126, 81, 46, 50, 188, 83, 186, 171, 87, 66, 65, 59, 52, 59, 226, 169, 166, 190, 113, 184, 155, 201, 170, 104, 47, 60, 76, 203, 77, 75, 189, 180, 144, 183, 63, 171, 186, 166, 214, 161, 138, 93, 153, 88, 73, 127, 153, 84, 186, 80, 78, 172, 53, 80, 223, 174, 165, 152, 205, 16, 164, 185, 140, 57, 76, 146, 168, 157, 74, 166, 60, 155, 160, 100, 173, 173, 180, 210, 186, 80, 85, 122, 123, 149, 184, 158, 156, 76, 148, 171, 166, 183, 54, 165, 178, 59, 172, 94, 173, 34, 163, 99, 165, 78, 165, 171, 147, 59, 176, 194, 80, 188, 158, 55, 183, 111, 83, 70, 158, 126, 110, 158, 76, 70, 87, 148, 182, 171, 84, 187, 72, 85, 173, 237, 139, 50, 78, 138, 147, 219, 160, 75, 160, 149, 182, 74, 54, 151, 51, 209, 73, 100, 155, 135, 147, 68, 101, 90, 143, 150, 149, 71, 85, 153, 127, 147, 53, 142, 205, 103, 91, 97, 100, 100, 96, 134, 146, 97, 100, 95, 98, 96, 162, 154, 125, 86, 140, 155, 135, 129, 159, 100, 61, 141, 161, 155, 97, 142, 83, 102, 91, 142, 152, 104, 162, 101, 141, 92, 137, 154, 101, 95, 94, 163, 96, 129, 133, 83, 93, 129, 149, 107, 176, 99, 134, 173, 172, 87, 157, 136, 97, 144, 104, 162, 155, 144, 91, 76, 144, 145, 158, 98, 149, 81, 87, 80, 135, 100, 143, 94, 94, 149, 93, 54, 66, 49, 90, 100, 69, 81, 147, 97, 153, 146, 61, 87, 85, 89, 68, 87, 162, 143, 150, 159, 144, 153, 138, 172, 145, 154, 76, 98, 135, 168, 97, 94, 154, 146, 127, 160, 86, 145, 157, 141, 164, 140, 142, 102, 124, 90, 94, 120, 133, 102, 146, 97, 85, 146, 75, 96, 175, 140, 144, 138, 168, 60, 137, 153, 125, 87, 93, 144, 143, 134, 92, 140, 85, 130, 131, 113, 149, 152, 153, 160, 159, 97, 98, 117, 148, 127, 152, 139, 132, 96, 130, 145, 142, 155, 86, 140, 151, 89, 145, 74, 128, 67, 134, 62, 138, 86, 146, 149, 128, 88, 151, 159, 100, 154, 140, 86, 148, 158, 93, 77, 148, 160, 81, 138, 94, 81, 103, 137, 156, 186, 102, 149, 91, 104, 146, 198, 123, 76, 101, 176, 131, 174, 139, 100, 138, 135, 154, 91, 66, 147, 76, 185, 91, 100, 150, 137, 140, 93, 104, 102, 131, 143, 145, 106, 95, 133, 129, 136, 143, 136, 199, 105, 87, 98, 100, 105, 145, 135, 146, 96, 102, 104, 98, 119, 119, 145, 68, 92, 137, 129, 131, 126, 142, 104, 97, 128, 47, 144, 104, 134, 98, 102, 92, 138, 140, 100, 165, 103, 139, 100, 129, 71, 121, 96, 155, 152, 100, 124, 127, 109, 103, 127, 151, 147, 140, 106, 133, 188, 152, 103, 150, 130, 99, 141, 100, 132, 86, 140, 94, 84, 137, 135, 143, 102, 139, 91, 99, 92, 129, 104, 136, 99, 105, 137, 102, 59, 144, 136, 152, 103, 91, 91, 148, 105, 145, 134, 135, 86, 74, 101, 88, 93, 159, 142, 143, 143, 80, 148, 131, 162, 139, 78, 81, 103, 86, 156, 98, 98, 149, 141, 124, 139, 92, 136, 146, 134, 144, 133, 103, 104, 165, 102, 92, 143, 134, 99, 153, 105, 113, 143, 87, 102, 168, 143, 142, 126, 165, 76, 136, 152, 124, 101, 101, 131, 136, 132, 100, 132, 90, 145, 144, 90, 144, 134, 147, 106, 148, 102, 97, 150, 109, 126, 141, 131, 136, 103, 128, 136, 131, 125, 90, 133, 145, 95, 142, 151, 138, 90, 138, 145, 135, 104, 132, 130, 127, 89, 140, 154, 102, 142, 130, 89, 140, 206, 97, 103, 141, 71, 123, 132, 87, 99, 102, 131, 154, 81, 104, 147, 101, 108, 145, 174, 135, 110, 106, 139, 128, 152, 134, 100, 137, 132, 151, 97, 98, 116, 82, 108, 74, 102, 157, 139, 144, 73, 98, 98, 141, 142, 151, 69, 87, 152, 130, 146, 146, 144, 200, 100, 85, 92, 94, 100, 156, 140, 147, 91, 98, 96, 96, 97, 57, 147, 47, 87, 141, 145, 137, 130, 160, 98, 66, 141, 104, 148, 96, 145, 87, 97, 85, 145, 158, 97, 165, 97, 145, 89, 135, 121, 99, 89, 121, 46, 88, 129, 132, 82, 94, 130, 140, 149, 169, 100, 141, 216, 162, 92, 167, 132, 91, 153, 88, 156, 113, 144, 86, 77, 139, 144, 164, 99, 148, 84, 90, 143, 136, 99, 143, 92, 104, 149, 99, 43, 90, 101, 163, 100, 71, 79, 156, 94, 150, 143, 52, 79, 85, 90, 71, 78, 176, 152, 148, 165, 92, 154, 139, 174, 149, 86, 81, 92, 117, 169, 91, 93, 162, 148, 126, 160, 87, 148, 155, 145, 160, 142, 123, 100, 190, 97, 78, 156, 138, 99, 159, 96, 89, 150, 72, 95, 181, 145, 143, 134, 171, 64, 142, 161, 127, 86, 91, 136, 143, 136, 96, 142, 83, 147, 147, 77, 150, 142, 154, 71, 157, 95, 97, 158, 154, 124, 147, 139, 140, 93, 131, 143, 139, 142, 80, 137, 154, 88, 147, 64, 108, 61, 137, 105, 149, 88, 139, 142, 131, 77, 152, 164, 100, 156, 142, 83, 153, 220, 94, 96, 145, 89, 73, 136, 64, 86, 99, 136, 157, 156, 97, 151, 92, 102, 153, 199, 130, 79, 100, 168, 133, 168, 137, 95, 146, 132, 155, 92, 77, 127, 63, 160, 101, 116, 144, 133, 135, 78, 109, 105, 133, 127, 136, 85, 100, 139, 124, 129, 82, 132, 171, 104, 104, 104, 106, 108, 36, 124, 132, 101, 103, 104, 107, 97, 70, 124, 75, 96, 132, 139, 128, 124, 149, 106, 79, 135, 148, 128, 105, 133, 103, 108, 96, 132, 144, 110, 133, 110, 131, 97, 127, 145, 96, 107, 93, 79, 101, 125, 124, 95, 104, 122, 76, 124, 163, 110, 129, 159, 136, 101, 143, 125, 103, 136, 107, 154, 131, 125, 101, 95, 130, 130, 146, 105, 135, 94, 100, 109, 129, 107, 134, 104, 110, 133, 105, 67, 87, 87, 146, 107, 96, 98, 135, 102, 138, 132, 59, 97, 107, 97, 89, 92, 139, 136, 140, 148, 117, 133, 126, 149, 136, 123, 108, 111, 132, 145, 107, 106, 142, 125, 122, 146, 98, 135, 142, 133, 138, 130, 122, 106, 143, 103, 88, 131, 127, 110, 135, 106, 89, 132, 91, 107, 145, 128, 135, 132, 149, 86, 130, 145, 121, 100, 102, 141, 130, 126, 104, 131, 92, 129, 136, 102, 134, 129, 141, 75, 140, 106, 107, 141, 146, 122, 133, 128, 127, 105, 123, 130, 128, 140, 100, 129, 140, 100, 134, 96, 101, 99, 122, 86, 132, 100, 129, 136, 122, 97, 135, 141, 106, 141, 134, 99, 135, 135, 101, 97, 142, 122, 78, 128, 80, 101, 107, 128, 135, 169, 109, 129, 104, 109, 136, 168, 121, 84, 110, 142, 126, 153, 125, 104, 132, 125, 131, 106, 81, 134, 94, 164}; - model->setOperandValue(op59, op59_init, sizeof(uint8_t) * 2304); - static int32_t op61_init[] = {8937, 6537, 21884, 10468, 3507, 14032, 4759, -1045, 3965, 15433, -1356, -4865, 8679, -2776, 10542, -1806, 4446, 7307, 5257, 8326, 3394, 5448, 11299, 7848, 7223, 12731, 536, -11115, -3148, 13731, 6134, 4912, 9067, 5639, 3782, -4903, 57, 10165, 12833, -149, 10247, 8892, 2184, 9888, 13967, 5789, 9410, 2778, 11441, 9104, 987, 5302, 4424, 9299, 8594, -8523, 8121, 9473, -5515, 4887, 6729, 6111, 2761, -279, 6953, 5800, 8562, -5045, 1369, 3070, -14741, -6482, -16501, 1323, 28602, 6944, 17244, 4133, 8619, 2298, -2104, 4099, 11631, 6731, 6301, -2814, 19082, 5353, 13335, 1088, 13068, 5905, 14688, 6054, -10109, 27965, -10988, 1433, 7925, -9321, 9117, -6120, -4738, -3975, 6515, 4775, 10850, 8499, 8497, 10522, 5253, 5387, 7205, 14252, 6642, 11033, 654, 6191, 9670, 18442, 8265, 11132, 2898, 3425, -1201, 12747, 10945, 8954, 7663, 8573, 14019, 2349, 6683, 7720, 2377, 7271, 3764, 13685, 3275, -1397, 12473, 9157, 14790, 6429, 10448, 18103, 10124, 10625, 5135, 2469, 6960, -2339, 8602, 19515, 12958, 13660, 7951, 534, 23728, 16329, 7156, 15659, 9277, 8345, 6317, 1668, 3529, 10163, -1691, 16872, 16569, -12046, 23037, 11517, -706, 15458, 7730, 2947, 3825, 18318, 13384, 12361, 3702, -4315, 5480, 13865, 14733, 11594, 9426, 10632, 8869, -2425, 5727, 13603, -938, 8071, 11835, 7208, -2337, 6295, -172, 13667, 1327, 21014, -4853, 19988, 12823, -6207, 7475, 3977, -676, 4884, 8123, 4510, -20520, 12407, 11526, -5909, 12450, 1, 6583, 7299, 20038, 5494, 10090, 3620, 7006, 21643, 8055, 2278, 16831, 5347, -1338, 11789, 5875, 8375, 43367, -2471, 15451, -3498, 8922, -3886, 9147, 25997, 9166, 5346, 1638, 7556, -8368, 14273, 10156, 15340, 16007, 4415, 11825, 18362, 15790, 15566, 9383, 6632, 1464, 14781, 8866, 9078, 1535, 7655, 8892, 15235, 5438, 3692, 12421, -13492, 11857, 12649, 3349, 4317, 7370, -420, 11994, 15352, 7125, 5416, 2919, -4984, 6265, 8901, 10763, 11070, 10206, 15538, 2245, 5535, -9140, 5765, 5817, 11231, -549, -4533, -23493, 9124, 15043, 4110, 2180, 16154, 331, 17082, 1228, 18549, 1549, 6832, 2132, 14838, 6721, -15078, 607, -4037, 5825, 4692, 9928, 9274, 2280, 7861, 3759, 20397, 12272, 10415, 6201, 9703, 1278, 19193, 3336, 13006, 13657, 20229, 9596, 6589, 7008, 4693, 13016, 12715, -2885, 3539, 8366, 18528, 416, 16554, 6379, 6002, 5711, -1527, 13552, 3823, 4331, 8100, 12181, 7226, 16789, 3859, 9394, 14979, 7953, -6742, -4827, 5135, -112, 852, -11103, 13168, -2648, 5323, 9266, 9962, -16716, 6705, 9169, 350, 11698, -3587, 1380, 15088, 13800, -787, 5121, 6692, 13817, 14092, 11734, 11507, 9570, 7402, 12231, 5929, 8801, 23374, 8235, 9069, 1078, 7737, 17523, 10029, 7349, 10543, -654, 12417, 21654, 1116, 6791, 15846, 5110, 13203, 5020, -2780, 11306, 9143, 14691, 881, 3450, 15345, 9787, 13890, -7510, 4895, 8558, 13681, -849, 7602, 6780, -4204, 10001, -4443, 9705, 13936, 12143, 1583, 2776, 16214, 7533, 7641, 16903, 2781, 4473, 4519, -21099, 3466, 21709, 3667, 13076, 9639, 20569, 9159, 8983, 1314, 11778, 6757, 6628, 8284, 19108, 2185, 15044, 5594, 4727, 10253, 6737, 17520, 1434, 974, -11621, 16619, 4242, -1222, -10720, 8169, 7095, 8525, -4675, 10682, -2073, 3077, 18794, -5472, 13116, 13481, 1217, 11946, -2357, 10084, 24698, 19707, 884, 11580, 7002, -5687, -3247, 5673, 16892, 11375, 971, 14552, -919, 6136, 1358, 16805, 5739, 4381, -286, 10285, 16184, 3604, 11058, -10067, 3861, 11507}; - model->setOperandValue(op61, op61_init, sizeof(int32_t) * 512); - static uint8_t op62_init[] = {138, 118, 138, 127, 131, 155, 141, 160, 156, 149, 129, 148, 149, 138, 150, 139, 153, 143, 138, 139, 177, 150, 142, 137, 138, 129, 136, 144, 143, 132, 149, 138, 138, 120, 141, 137, 143, 150, 143, 139, 136, 153, 160, 202, 145, 158, 150, 147, 148, 137, 144, 141, 144, 141, 163, 136, 127, 140, 166, 138, 140, 134, 161, 149, 137, 153, 147, 149, 145, 160, 148, 155, 136, 149, 149, 148, 137, 147, 149, 157, 143, 148, 149, 154, 145, 193, 150, 146, 152, 141, 138, 151, 147, 146, 137, 156, 141, 150, 120, 139, 145, 139, 165, 156, 151, 149, 145, 150, 145, 152, 132, 142, 139, 140, 136, 149, 122, 144, 146, 150, 138, 139, 146, 144, 142, 134, 148, 144, 138, 150, 110, 142, 147, 139, 148, 149, 140, 148, 150, 161, 143, 150, 149, 141, 147, 150, 146, 145, 144, 149, 139, 148, 157, 145, 145, 140, 138, 146, 134, 158, 142, 153, 152, 111, 134, 139, 145, 137, 147, 144, 145, 136, 142, 144, 133, 123, 142, 162, 129, 152, 139, 154, 152, 149, 143, 149, 127, 143, 140, 151, 151, 130, 156, 155, 154, 157, 133, 161, 139, 144, 147, 151, 149, 138, 131, 138, 144, 153, 153, 161, 149, 143, 151, 160, 150, 136, 157, 147, 104, 134, 148, 145, 140, 142, 138, 145, 147, 165, 145, 131, 146, 106, 153, 131, 156, 140, 152, 141, 123, 136, 144, 128, 140, 138, 160, 143, 147, 144, 150, 126, 135, 140, 145, 134, 140, 138, 145, 143, 142, 149, 145, 141, 147, 146, 147, 145, 142, 140, 150, 146, 145, 149, 144, 141, 145, 144, 144, 169, 145, 146, 149, 147, 151, 129, 148, 150, 162, 148, 142, 155, 146, 148, 158, 145, 144, 134, 143, 145, 148, 144, 148, 148, 144, 157, 152, 144, 141, 150, 151, 151, 147, 146, 147, 150, 147, 148, 145, 154, 148, 152, 151, 142, 154, 148, 147, 140, 147, 150, 147, 157, 137, 146, 148, 147, 144, 142, 145, 152, 146, 146, 142, 148, 149, 138, 146, 147, 143, 133, 142, 147, 146, 147, 169, 122, 140, 148, 143, 145, 146, 142, 147, 147, 140, 144, 147, 145, 139, 145, 149, 147, 146, 146, 147, 148, 142, 144, 157, 145, 146, 142, 150, 146, 148, 147, 149, 149, 149, 150, 143, 143, 154, 142, 148, 141, 150, 148, 152, 152, 149, 143, 143, 140, 146, 135, 146, 147, 151, 147, 145, 139, 144, 139, 143, 142, 157, 150, 144, 141, 147, 147, 143, 148, 145, 144, 155, 148, 148, 147, 141, 146, 143, 147, 145, 142, 149, 144, 144, 145, 146, 145, 148, 147, 140, 148, 147, 146, 155, 145, 167, 150, 144, 147, 141, 146, 150, 142, 144, 152, 144, 143, 144, 139, 146, 147, 150, 152, 146, 151, 144, 141, 141, 130, 153, 142, 147, 146, 146, 144, 152, 147, 145, 144, 143, 144, 145, 148, 147, 145, 147, 150, 146, 147, 147, 170, 146, 147, 153, 143, 140, 149, 136, 146, 158, 125, 144, 144, 145, 143, 153, 147, 144, 141, 128, 145, 159, 139, 144, 143, 138, 146, 139, 145, 164, 142, 142, 143, 138, 142, 142, 148, 145, 146, 146, 143, 144, 126, 145, 137, 148, 145, 145, 148, 154, 140, 142, 146, 147, 147, 147, 141, 146, 157, 150, 143, 146, 144, 148, 144, 141, 142, 145, 148, 143, 141, 141, 151, 148, 146, 143, 145, 147, 146, 147, 145, 142, 145, 145, 150, 151, 145, 146, 142, 147, 144, 149, 153, 145, 154, 140, 148, 144, 144, 146, 147, 144, 138, 149, 142, 143, 145, 151, 136, 144, 143, 146, 146, 143, 147, 143, 140, 142, 155, 143, 141, 145, 136, 150, 142, 145, 141, 142, 156, 144, 147, 141, 139, 143, 134, 147, 146, 147, 135, 153, 139, 147, 137, 146, 144, 145, 152, 145, 148, 145, 151, 140, 150, 145, 148, 146, 142, 148, 147, 138, 153, 150, 143, 159, 146, 150, 148, 146, 146, 148, 149, 144, 140, 141, 142, 145, 145, 146, 150, 147, 148, 153, 149, 160, 144, 136, 146, 138, 148, 147, 147, 148, 137, 143, 144, 144, 147, 146, 148, 137, 146, 156, 148, 137, 153, 146, 144, 148, 137, 145, 135, 141, 154, 157, 153, 147, 142, 151, 143, 151, 145, 149, 140, 141, 150, 147, 147, 143, 147, 149, 145, 154, 138, 143, 146, 144, 141, 142, 145, 151, 146, 145, 146, 154, 143, 142, 145, 146, 140, 144, 142, 149, 150, 151, 139, 132, 145, 148, 153, 145, 143, 149, 147, 140, 147, 144, 141, 143, 143, 141, 141, 144, 142, 145, 137, 148, 149, 157, 152, 142, 131, 153, 140, 153, 123, 145, 141, 144, 146, 134, 145, 148, 150, 160, 152, 142, 132, 154, 154, 153, 151, 149, 155, 142, 141, 163, 144, 139, 140, 138, 139, 149, 149, 145, 149, 145, 136, 144, 137, 147, 155, 153, 135, 151, 149, 155, 151, 137, 144, 141, 147, 136, 148, 146, 150, 141, 148, 145, 145, 140, 147, 148, 136, 153, 151, 144, 139, 140, 153, 140, 172, 151, 162, 150, 146, 159, 142, 151, 154, 153, 150, 139, 154, 163, 156, 134, 145, 152, 145, 158, 145, 144, 127, 144, 133, 151, 149, 148, 148, 143, 155, 145, 149, 150, 143, 150, 138, 139, 141, 138, 151, 155, 126, 142, 147, 150, 142, 152, 137, 134, 136, 120, 140, 145, 143, 137, 146, 149, 146, 150, 147, 148, 114, 153, 111, 154, 132, 157, 143, 139, 142, 153, 151, 152, 146, 141, 148, 143, 159, 151, 148, 138, 146, 149, 130, 158, 157, 152, 142, 146, 138, 144, 168, 143, 134, 143, 150, 149, 154, 141, 139, 149, 150, 162, 138, 145, 149, 137, 146, 146, 138, 152, 136, 141, 150, 139, 143, 134, 141, 151, 142, 156, 147, 133, 141, 146, 151, 131, 149, 158, 147, 144, 145, 132, 142, 153, 138, 131, 157, 144, 135, 153, 142, 160, 147, 144, 146, 146, 140, 152, 141, 151, 138, 128, 142, 150, 141, 215, 144, 142, 135, 151, 157, 149, 182, 146, 163, 142, 152, 146, 140, 141, 140, 152, 143, 155, 147, 149, 140, 134, 151, 147, 161, 150, 140, 146, 147, 146, 155, 152, 140, 149, 159, 147, 145, 152, 145, 142, 145, 142, 144, 156, 147, 155, 146, 154, 154, 146, 146, 147, 119, 144, 132, 144, 150, 140, 140, 148, 147, 147, 143, 150, 151, 146, 137, 143, 148, 147, 144, 155, 147, 151, 147, 154, 150, 155, 147, 144, 144, 155, 149, 148, 147, 142, 149, 145, 147, 147, 137, 143, 146, 149, 141, 141, 148, 142, 141, 147, 147, 146, 138, 142, 146, 145, 135, 141, 156, 149, 145, 142, 149, 143, 146, 145, 146, 159, 140, 145, 144, 152, 145, 149, 154, 146, 146, 153, 147, 149, 144, 142, 143, 140, 156, 149, 149, 147, 146, 146, 148, 147, 142, 149, 143, 139, 149, 153, 175, 150, 146, 145, 152, 143, 147, 146, 149, 145, 143, 139, 162, 146, 142, 152, 144, 143, 145, 148, 146, 137, 147, 143, 148, 146, 149, 148, 146, 141, 146, 133, 145, 147, 144, 140, 160, 143, 145, 147, 142, 147, 153, 143, 144, 146, 149, 151, 147, 143, 134, 146, 138, 138, 148, 150, 144, 146, 146, 147, 144, 143, 146, 147, 148, 147, 143, 150, 139, 142, 141, 151, 150, 148, 139, 149, 148, 144, 157, 139, 147, 145, 144, 145, 143, 150, 146, 153, 141, 142, 151, 147, 149, 146, 147, 146, 139, 151, 145, 148, 138, 153, 139, 137, 140, 149, 156, 135, 148, 145, 150, 145, 144, 144, 146, 143, 141, 143, 146, 148, 143, 148, 149, 138, 131, 129, 143, 136, 143, 151, 149, 139, 139, 158, 132, 142, 153, 164, 131, 139, 153, 152, 139, 151, 155, 107, 147, 143, 153, 144, 145, 147, 154, 157, 149, 140, 162, 138, 144, 83, 128, 132, 152, 149, 144, 143, 125, 145, 136, 146, 150, 141, 141, 153, 147, 152, 146, 154, 149, 126, 145, 153, 146, 156, 138, 136, 151, 141, 146, 161, 149, 155, 142, 139, 134, 160, 132, 142, 147, 142, 104, 139, 147, 138, 137, 136, 140, 141, 163, 147, 110, 164, 139, 131, 139, 151, 135, 144, 150, 157, 135, 158, 150, 134, 155, 161, 143, 146, 144, 164, 153, 146, 147, 141, 141, 140, 140, 142, 146, 149, 149, 189, 133, 149, 138, 148, 150, 145, 148, 144, 148, 149, 144, 146, 147, 137, 152, 135, 144, 150, 155, 147, 133, 144, 149, 153, 158, 140, 149, 139, 130, 151, 139, 137, 156, 144, 140, 152, 145, 142, 147, 154, 148, 145, 150, 137, 137, 141, 146, 154, 138, 144, 139, 144, 155, 156, 150, 138, 140, 149, 153, 152, 144, 145, 153, 110, 165, 143, 144, 147, 150, 155, 145, 142, 140, 138, 163, 140, 138, 144, 130, 142, 169, 143, 129, 145, 150, 142, 145, 151, 131, 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143, 145, 144, 152, 149, 149, 144, 145, 151, 144, 145, 151, 147, 148, 148, 154, 136, 147, 143, 139, 145, 134, 147, 145, 143, 143, 146, 139, 143, 143, 146, 145, 147, 145, 143, 147, 147, 151, 144, 141, 141, 150, 141, 140, 145, 142, 141, 146, 146, 144, 144, 143, 135, 149, 143, 143, 143, 143, 147, 142, 151, 147, 129, 147, 144, 154, 142, 147, 153, 146, 146, 143, 146, 145, 144, 143, 145, 150, 145, 140, 148, 152, 149, 145, 147, 147, 144, 146, 144, 145, 143, 144, 144, 147, 148, 138, 153, 148, 145, 132, 147, 145, 145, 145, 147, 140, 141, 154, 150, 148, 149, 142, 149, 143, 146, 147, 147, 144, 149, 146, 143, 139, 145, 141, 139, 138, 148, 148, 146, 149, 147, 144, 147, 147, 146, 151, 147, 148, 145, 138, 151, 146, 151, 150, 145, 145, 152, 145, 151, 149, 143, 145, 146, 143, 167, 147, 146, 149, 143, 152, 145, 154, 146, 153, 143, 128, 147, 140, 147, 145, 137, 146, 146, 145, 142, 149, 144, 148, 147, 141, 144, 150, 148, 146, 147, 141, 145, 147, 150, 151, 144, 146}; - model->setOperandValue(op62, op62_init, sizeof(uint8_t) * 131072); - static int32_t op64_init[] = {-1691, 104, 5061, 660, -358, -4683, -10, 2093, -1205, -2391, 2337, -201, 305, 7238, -236, -1849, -224, 287, 8, 204, 45, 326, -725, 1155, -496, 483, -18, -2536, 520, 9077, -3042, -2994, -371, 2295, 635, -320, 3886, 9711, -68, -191, -796, 2127, 156, -3947, 6181, -1980, -510, 11, 548, -1963, -7425, 2544, -4, -93, 1424, 3005, 3725, 1282, -2086, 2705, -1690, 7843, -292, 1799, 6530, 1388, -313, 114, -375, -231, -780, -97, 2216, 345, -2884, 2715, -1881, -15, -689, -550, -1265, -4011, 7570, -86, -1190, -2096, -331, -4118, -1307, -1514, -1589, 3112, 2120, -454, -735, 2960, 2060, -101, 709, -62, -522, -1816, -1989, 2786, 2257, 2769, -1902, 46, 772, 224, -652, 169, -984, -2019, 4028, -1597, 4526, 175, -97, 2404, 2642, -609, 927, -377, 2136, 469, -31, 238, 221, 443, -338, 1162, -3274, 1306, -579, -3590, -3048, 2873, -404, -393, -302, -470, -3632, 282, 694, 2063, -6045, -939, 2961, 2490, 944, 201, 2118, -491, -619, -3895, -1078, 2492, -1160, -617, 641, 2236, -409, -835, 2887, 2521, -206, 4067, 134, 975, 794, 957, 2142, -2947, 1879, -1445, 2117, 779, 591, 397, -2073, -1206, -485, -2624, 8860, 309, 1054, 73, -2736, 2856, -251, 2635, 371, -790, -16, -92, 2485, -1917, -4039, 3404, -7462, 253, 7601, -319, -2339, -2429, -3426, -195, 3219, 8276, 2478, 2583, -174, 26, -2420, -2822, 462, -1668, -3433, -1833, 610, 2547, -1696, 299, 5909, -328, -793, -3788, 13, -110, -112, 2384, -1485, -465, -1296, 338, -783, -2147, -145, 3553, 6731, -67, 4147, 2394, 7356, -57, 1246, -339, -922, 2002, 2260, -682, 2340, -188, 2302, -1017, 561, -938, -1901, -4065, 2554, -1239, 7452, -462, 1995, -65, 5483, 2663, 371, 4160, 833, 2579, -443, 1024, -525, 26, 102, -535, 626, -950, 1721, 405, 2493, 2263, -1226, -2045, -1847, 345, 5392, -162, 197, -268, 2280, -603, 731, -2139, -2126, -1841, 1757, -422, -113, -72, 760, -1065, 2105, -1073, -424, -158, 2083, 3172, -417, -1319, 254, -1597, -528, -253, -397, 375, -235, -253, 5757, -307, 235, -362, -564, -3122, -737, 3081, -2423, 18, -2014, 1369, -47, -1258, -1579, -3709, -1615, 3505, 1859, -288, -379, -280, -290, 681, -37, 3646, 2383, -878, 3904, 924, -45, 1905, -204, 6016, -1108, -501, -431, 2534, -1303, 2120, 511, -572, -2286, 708, -270, -23, -1885, -1594, 2502, 314, 202, 1405, -2249, 577, 1076, 551, -219, -1367, 1629, -72, 1146, -235, 191, -420, 250, -2326, 1506, 3159, -951, 155, 123, 2446, 955, -16752, 224, -797, 100, -1101, -2088, 2933, 1329, -3020, -1524, -766, -1256, 2437, -186, -1357, -278, 20, 55, 3911, -635, -1523, 49, 2646, 777, 1431, 3182, -1536, -2429, 2618, 629, -299, 146, -2333, 367, 3574, 2728, -1657, 1326, -728, 1320, -1567, 329, -86, 1394, -705, 2560, 2253, 2309, 3499, -1464, 371, 219, -586, -116, -1544, -233, 2559, 2267, 1918, 2869, 510, 2208, -328, -1437, -13, -3634, -4065, 1881, -1713, -2674, 1756, 1781, 2368, -1373, 1745, -732, 28, -342, 4734, 7373, -864, 3319, -73, -178, 3220, -532, -898, 3158, 1965, -685, -4094, -2490, 2273, 2126, 295, -2108, 799, -287, -376, -1030, 158, -796, -185, 144, -1768, -78, -4335, -635, 4438, 774, -171, 646, -532, -390, 2223, -759, 2375, -633, 694}; - model->setOperandValue(op64, op64_init, sizeof(int32_t) * 512); - static uint8_t op65_init[] = {119, 111, 120, 114, 124, 109, 106, 146, 73, 114, 110, 116, 116, 113, 110, 118, 111, 126, 131, 116, 114, 100, 185, 133, 126, 129, 110, 92, 98, 127, 114, 112, 126, 111, 119, 132, 119, 119, 112, 115, 114, 88, 118, 115, 107, 119, 138, 129, 138, 142, 123, 81, 128, 115, 110, 98, 114, 139, 113, 121, 118, 120, 108, 110, 120, 114, 156, 127, 105, 128, 118, 103, 107, 124, 138, 111, 123, 114, 113, 95, 113, 141, 122, 134, 116, 122, 132, 115, 134, 131, 118, 113, 107, 134, 118, 118, 120, 109, 105, 114, 120, 108, 120, 113, 115, 114, 124, 159, 132, 128, 119, 110, 162, 126, 124, 118, 109, 109, 113, 109, 110, 114, 128, 133, 126, 108, 111, 125, 130, 179, 116, 131, 120, 102, 108, 110, 106, 109, 111, 136, 124, 112, 108, 109, 135, 114, 133, 118, 120, 117, 121, 112, 111, 122, 126, 113, 118, 110, 120, 120, 128, 104, 114, 116, 115, 117, 114, 117, 112, 112, 113, 117, 114, 130, 103, 115, 131, 108, 118, 104, 91, 112, 114, 119, 117, 106, 109, 138, 98, 114, 109, 106, 115, 110, 111, 124, 115, 125, 127, 115, 120, 132, 121, 117, 122, 132, 118, 105, 117, 119, 122, 111, 128, 119, 116, 119, 121, 115, 130, 117, 133, 118, 115, 120, 118, 141, 101, 86, 116, 118, 125, 101, 123, 126, 113, 112, 119, 122, 110, 169, 118, 103, 106, 112, 128, 118, 135, 127, 111, 116, 114, 126, 114, 125, 118, 118, 114, 117, 114, 119, 118, 113, 126, 145, 114, 116, 129, 102, 125, 113, 132, 110, 125, 116, 126, 134, 108, 109, 124, 103, 144, 131, 109, 114, 107, 104, 123, 139, 119, 128, 105, 109, 110, 120, 127, 100, 118, 106, 115, 108, 109, 135, 115, 127, 101, 110, 114, 117, 109, 120, 117, 117, 102, 123, 115, 131, 128, 122, 117, 119, 119, 115, 139, 111, 130, 124, 125, 116, 132, 118, 112, 112, 113, 117, 121, 113, 115, 168, 101, 121, 118, 115, 112, 148, 133, 117, 114, 104, 117, 132, 117, 106, 129, 125, 109, 112, 123, 111, 255, 114, 112, 118, 115, 114, 123, 121, 121, 105, 134, 115, 123, 136, 121, 111, 139, 118, 134, 119, 128, 118, 107, 109, 117, 142, 112, 86, 125, 131, 112, 133, 111, 115, 101, 118, 104, 118, 155, 117, 116, 115, 120, 110, 130, 107, 116, 108, 88, 115, 176, 109, 127, 111, 86, 119, 107, 163, 113, 114, 113, 97, 117, 125, 92, 122, 114, 106, 122, 112, 132, 109, 125, 115, 114, 118, 126, 133, 115, 121, 114, 117, 108, 118, 122, 164, 109, 113, 116, 110, 108, 115, 98, 105, 105, 135, 102, 118, 112, 150, 113, 112, 116, 116, 114, 104, 112, 110, 113, 116, 117, 133, 119, 106, 119, 123, 121, 133, 119, 108, 115, 116, 87, 101, 152, 117, 113, 119, 115, 143, 112, 111, 123, 109, 115, 116, 109, 114, 143, 116, 98, 123, 122, 100, 129, 133, 111, 127, 105, 123, 117, 111, 114, 113, 124, 83, 116, 113, 185, 122, 91, 118, 121, 123, 135, 120, 111, 99, 120, 130, 77, 176, 92, 101, 101, 100, 128, 156, 85, 183, 101, 99, 100, 83, 116, 125, 161, 106, 156, 138, 117, 99, 100, 131, 106, 102, 107, 139, 93, 122, 131, 133, 184, 87, 125, 185, 156, 106, 93, 113, 121, 179, 121, 76, 117, 103, 113, 116, 111, 97, 127, 191, 79, 159, 122, 87, 111, 154, 89, 111, 127, 95, 115, 111, 94, 186, 113, 196, 123, 130, 104, 139, 101, 136, 106, 136, 100, 166, 121, 113, 168, 87, 113, 119, 101, 119, 125, 119, 124, 105, 123, 130, 108, 158, 122, 121, 143, 130, 105, 125, 114, 103, 80, 107, 97, 113, 105, 165, 130, 180, 115, 167, 184, 107, 115, 113, 93, 98, 163, 125, 125, 114, 77, 168, 112, 119, 115, 123, 97, 104, 125, 114, 93, 105, 110, 103, 116, 165, 170, 107, 94, 114, 121, 132, 109, 115, 47, 116, 115, 99, 76, 113, 73, 86, 99, 120, 116, 109, 105, 111, 92, 103, 109, 96, 99, 81, 92, 124, 106, 88, 106, 134, 113, 112, 85, 109, 93, 109, 130, 103, 108, 91, 138, 106, 133, 187, 91, 106, 123, 105, 125, 115, 103, 92, 109, 101, 112, 111, 124, 126, 98, 125, 122, 118, 175, 109, 112, 121, 109, 102, 90, 104, 102, 86, 107, 103, 128, 162, 120, 93, 100, 120, 90, 131, 100, 86, 105, 108, 103, 106, 152, 109, 110, 111, 102, 130, 104, 161, 110, 115, 88, 70, 113, 130, 111, 118, 103, 147, 107, 84, 103, 108, 183, 110, 111, 115, 150, 128, 138, 160, 114, 117, 111, 112, 168, 169, 111, 105, 119, 112, 130, 179, 97, 159, 107, 94, 105, 114, 169, 135, 115, 127, 116, 85, 101, 86, 114, 120, 116, 119, 116, 70, 110, 122, 123, 118, 168, 128, 65, 116, 150, 88, 115, 181, 114, 103, 99, 79, 109, 111, 111, 112, 120, 111, 110, 111, 117, 123, 120, 130, 103, 115, 115, 132, 106, 123, 106, 142, 183, 113, 116, 107, 108, 161, 90, 106, 118, 101, 105, 95, 95, 112, 7, 114, 100, 120, 125, 85, 112, 117, 125, 111, 124, 94, 100, 107, 129, 100, 105, 116, 119, 127, 112, 189, 102, 81, 104, 171, 103, 194, 119, 104, 110, 146, 86, 113, 93, 151, 105, 85, 147, 123, 128, 143, 100, 95, 166, 116, 104, 108, 129, 113, 138, 95, 160, 103, 115, 97, 101, 121, 100, 108, 106, 177, 128, 128, 135, 140, 121, 97, 109, 99, 99, 99, 120, 109, 106, 126, 177, 182, 90, 103, 108, 112, 110, 121, 123, 145, 111, 114, 107, 107, 109, 104, 104, 108, 103, 109, 112, 115, 119, 167, 123, 126, 106, 120, 115, 100, 100, 111, 119, 115, 97, 113, 116, 107, 94, 118, 112, 106, 127, 114, 122, 124, 112, 105, 151, 131, 122, 123, 111, 119, 122, 88, 138, 86, 103, 120, 133, 113, 94, 114, 125, 126, 193, 97, 147, 139, 99, 115, 89, 123, 120, 112, 121, 95, 113, 113, 120, 117, 128, 108, 106, 94, 117, 113, 108, 142, 123, 111, 129, 117, 113, 126, 116, 102, 123, 126, 41, 176, 109, 139, 111, 112, 112, 127, 115, 113, 131, 113, 119, 127, 118, 118, 138, 151, 121, 90, 133, 124, 120, 115, 122, 110, 121, 118, 121, 106, 125, 110, 119, 97, 124, 132, 111, 129, 115, 122, 129, 109, 118, 113, 116, 128, 110, 129, 119, 121, 109, 161, 97, 110, 89, 99, 116, 148, 109, 134, 123, 129, 116, 119, 118, 125, 117, 99, 132, 165, 99, 113, 117, 118, 121, 112, 129, 114, 118, 109, 119, 120, 113, 114, 96, 120, 112, 122, 119, 109, 125, 124, 122, 114, 108, 114, 119, 112, 115, 118, 112, 134, 128, 144, 129, 123, 128, 79, 144, 114, 126, 150, 72, 135, 125, 110, 113, 132, 115, 130, 108, 122, 111, 116, 119, 126, 108, 116, 130, 122, 109, 130, 153, 118, 117, 110, 121, 107, 110, 105, 189, 118, 117, 116, 113, 117, 112, 119, 114, 117, 114, 111, 107, 113, 112, 112, 120, 116, 138, 115, 123, 115, 112, 109, 111, 134, 125, 115, 112, 107, 111, 110, 140, 111, 113, 106, 100, 114, 123, 122, 125, 115, 112, 113, 115, 123, 113, 120, 106, 113, 105, 154, 118, 107, 110, 118, 113, 114, 131, 117, 114, 172, 117, 102, 114, 139, 129, 141, 106, 130, 120, 129, 117, 126, 113, 123, 118, 117, 121, 91, 105, 114, 117, 100, 115, 116, 110, 116, 113, 167, 116, 127, 106, 113, 119, 112, 113, 112, 118, 112, 127, 123, 114, 116, 106, 104, 128, 116, 113, 112, 118, 109, 142, 135, 124, 128, 109, 115, 124, 142, 112, 126, 106, 119, 113, 129, 120, 129, 116, 107, 111, 107, 125, 125, 117, 105, 116, 106, 110, 117, 115, 114, 130, 118, 116, 118, 109, 119, 118, 116, 97, 120, 116, 111, 122, 106, 124, 120, 118, 115, 109, 112, 113, 99, 103, 116, 132, 118, 111, 112, 128, 117, 123, 114, 106, 97, 102, 132, 117, 114, 128, 89, 126, 117, 114, 118, 118, 125, 113, 107, 113, 129, 120, 135, 102, 114, 93, 114, 129, 120, 114, 112, 115, 115, 117, 120, 134, 116, 115, 106, 122, 110, 112, 118, 110, 112, 112, 125, 134, 111, 117, 136, 111, 115, 111, 96, 113, 131, 112, 115, 175, 117, 138, 117, 137, 116, 115, 116, 123, 112, 130, 122, 114, 107, 126, 116, 122, 110, 126, 111, 155, 129, 107, 115, 117, 114, 114, 141, 117, 129, 145, 116, 118, 112, 124, 117, 109, 120, 125, 116, 116, 116, 127, 136, 126, 112, 114, 114, 111, 118, 120, 120, 110, 114, 128, 97, 127, 115, 118, 107, 113, 109, 101, 118, 108, 127, 126, 112, 114, 115, 116, 112, 115, 114, 111, 117, 107, 108, 118, 107, 122, 113, 121, 112, 118, 117, 116, 117, 128, 105, 144, 111, 114, 123, 116, 113, 114, 111, 126, 110, 106, 112, 101, 120, 117, 119, 116, 116, 116, 98, 121, 119, 120, 112, 124, 117, 116, 109, 133, 141, 125, 113, 121, 114, 98, 125, 109, 113, 72, 122, 92, 78, 80, 95, 80, 124, 108, 107, 195, 135, 84, 98, 12, 92, 154, 103, 117, 114, 155, 90, 118, 127, 109, 107, 120, 142, 115, 98, 86, 86, 66, 95, 91, 123, 107, 126, 165, 136, 96, 211, 122, 112, 100, 120, 96, 114, 118, 100, 125, 128, 94, 107, 73, 106, 110, 103, 141, 104, 108, 114, 122, 87, 110, 84, 110, 109, 93, 143, 107, 195, 122, 95, 113, 100, 90, 120, 157, 86, 88, 163, 115, 96, 114, 115, 120, 113, 119, 115, 85, 124, 187, 129, 125, 101, 109, 110, 93, 150, 177, 114, 123, 117, 144, 120, 93, 144, 104, 106, 108, 112, 116, 112, 186, 107, 133, 120, 79, 114, 107, 117, 92, 181, 106, 112, 110, 106, 130, 113, 97, 110, 178, 70, 127, 74, 187, 106, 123, 110, 109, 161, 98, 66, 107, 106, 85, 129, 126, 107, 120, 97, 171, 115, 119, 110, 104, 114, 116, 113, 111, 97, 104, 117, 115, 121, 106, 112, 139, 101, 74, 83, 127, 112, 90, 128, 88, 112, 102, 171, 119, 110, 99, 111, 96, 139, 60, 172, 107, 112, 123, 99, 134, 99, 97, 119, 120, 116, 127, 81, 89, 94, 109, 103, 155, 109, 120, 116, 135, 130, 153, 117, 110, 109, 116, 89, 99, 161, 119, 128, 76, 109, 162, 101, 110, 113, 96, 89, 119, 112, 90, 119, 94, 155, 106, 97, 101, 175, 91, 175, 125, 109, 106, 91, 108, 112, 99, 114, 89, 123, 115, 127, 109, 101, 110, 131, 110, 114, 102, 114, 107, 101, 163, 112, 124, 109, 95, 109, 93, 74, 175, 96, 72, 109, 101, 124, 115, 109, 115, 104, 98, 112, 84, 108, 103, 117, 112, 132, 124, 131, 115, 121, 112, 166, 112, 88, 109, 101, 120, 106, 108, 118, 116, 118, 118, 117, 118, 173, 101, 93, 72, 112, 110, 111, 197, 108, 182, 122, 96, 110, 123, 115, 94, 118, 80, 122, 119, 130, 122, 112, 105, 92, 123, 125, 99, 146, 116, 113, 115, 168, 114, 115, 125, 108, 174, 105, 111, 75, 149, 100, 93, 114, 83, 120, 123, 111, 183, 175, 131, 216, 124, 110, 194, 178, 125, 102, 138, 117, 156, 133, 151, 100, 111, 111, 124, 84, 99, 114, 110, 100, 116, 106, 88, 117, 105, 140, 87, 111, 117, 124, 121, 117, 146, 114, 130, 76, 121, 110, 112, 119, 135, 104, 113, 100, 176, 91, 94, 147, 110, 108, 104, 129, 128, 78, 96, 118, 121, 130, 158, 115, 152, 125, 138, 116, 98, 126, 106, 106, 161, 117, 107, 117, 105, 117, 124, 122, 104, 118, 85, 159, 73, 118, 117, 110, 121, 161, 109, 122, 123, 133, 137, 126, 97, 122, 116, 103, 116, 103, 114, 114, 130, 155, 120, 108, 106, 184, 112, 192, 125, 111, 118, 121, 101, 103, 134, 122, 117, 145, 108, 172, 120, 111, 119, 111, 110, 167, 101, 63, 135, 102, 165, 136, 117, 101, 123, 137, 110, 172, 86, 115, 119, 113, 95, 87, 163, 112, 56, 159, 96, 171, 117, 81, 170, 158, 67, 100, 124, 73, 122, 147, 114, 97, 78, 69, 90, 92, 101, 64, 91, 79, 151, 170, 117, 69, 181, 155, 103, 102, 48, 123, 90, 48, 85, 95, 188, 107, 70, 153, 72, 157, 94, 143, 59, 42, 168, 108, 78, 68, 84, 128, 64, 92, 164, 76, 174, 59, 114, 103, 69, 88, 104, 81, 111, 113, 146, 93, 109, 81, 158, 107, 210, 77, 205, 118, 200, 94, 77, 63, 195, 161, 124, 159, 202, 98, 51, 105, 112, 106, 149, 106, 103, 115, 118, 162, 96, 154, 153, 165, 179, 97, 198, 65, 90, 125, 160, 158, 77, 162, 96, 168, 82, 182, 119, 105, 91, 184, 82, 114, 48, 56, 112, 108, 79, 87, 97, 96, 185, 95, 108, 33, 162, 114, 124, 105, 102, 120, 175, 164, 76, 105, 164, 96, 93, 102, 95, 142, 108, 109, 79, 175, 165, 105, 154, 154, 100, 119, 104, 114, 95, 174, 138, 90, 105, 88, 188, 157, 153, 149, 117, 167, 77, 185, 91, 205, 195, 189, 73, 168, 66, 117, 100, 58, 210, 165, 81, 119, 185, 155, 145, 86, 102, 189, 165, 191, 134, 78, 77, 161, 160, 181, 149, 129, 203, 68, 169, 104, 123, 94, 160, 188, 99, 152, 50, 179, 83, 103, 171, 66, 82, 106, 192, 175, 113, 201, 111, 97, 155, 111, 172, 90, 160, 166, 77, 90, 90, 134, 106, 113, 71, 102, 72, 100, 161, 175, 190, 72, 172, 108, 90, 183, 76, 201, 179, 157, 98, 190, 46, 112, 179, 133, 71, 109, 91, 161, 88, 176, 145, 159, 95, 99, 116, 116, 87, 88, 42, 78, 100, 87, 187, 184, 165, 80, 83, 117, 157, 106, 101, 94, 70, 155, 165, 161, 156, 129, 104, 54, 166, 187, 72, 195, 174, 106, 107, 87, 161, 163, 115, 160, 175, 104, 42, 90, 99, 109, 66, 95, 66, 112, 89, 192, 190, 102, 130, 167, 44, 59, 105, 153, 152, 158, 174, 24, 110, 132, 156, 148, 80, 90, 83, 104, 154, 124, 76, 104, 91, 114, 99, 64, 186, 85, 90, 95, 152, 164, 94, 158, 159, 110, 97, 108, 150, 77, 35, 88, 78, 80, 154, 79, 87, 154, 111, 160, 101, 90, 80, 107, 132, 117, 202, 114, 89, 82, 153, 125, 197, 101, 80, 172, 111, 105, 81, 154, 156, 27, 72, 201, 116, 168, 172, 111, 184, 165, 84, 97, 111, 116, 120, 201, 79, 105, 80, 183, 184, 98, 163, 88, 95, 148, 159, 182, 51, 90, 85, 183, 31, 161, 177, 153, 103, 85, 67, 194, 181, 94, 87, 91, 144, 82, 196, 162, 94, 102, 116, 93, 95, 108, 90, 113, 120, 161, 168, 57, 148, 170, 189, 164, 186, 113, 187, 96, 178, 161, 167, 117, 152, 138, 46, 92, 96, 84, 146, 98, 156, 183, 80, 102, 41, 164, 170, 27, 186, 78, 170, 101, 61, 109, 171, 47, 183, 125, 57, 164, 145, 151, 101, 119, 133, 129, 80, 109, 91, 61, 163, 109, 53, 92, 123, 112, 123, 113, 117, 125, 112, 111, 136, 124, 99, 171, 158, 97, 149, 128, 111, 110, 86, 97, 182, 164, 241, 112, 83, 100, 118, 113, 123, 96, 117, 123, 122, 107, 119, 110, 117, 95, 196, 151, 124, 98, 196, 89, 128, 124, 85, 88, 118, 113, 119, 107, 104, 101, 128, 113, 110, 98, 121, 128, 106, 112, 162, 107, 108, 108, 102, 107, 116, 115, 124, 137, 113, 126, 135, 111, 88, 85, 101, 37, 89, 103, 116, 105, 99, 120, 116, 114, 91, 102, 201, 114, 104, 96, 118, 114, 119, 117, 156, 119, 71, 129, 122, 122, 113, 113, 93, 97, 86, 114, 119, 117, 85, 119, 95, 72, 110, 104, 109, 110, 114, 116, 91, 113, 120, 103, 162, 112, 110, 117, 157, 83, 130, 160, 169, 192, 112, 113, 114, 107, 75, 163, 131, 112, 113, 107, 124, 165, 119, 83, 159, 153, 106, 117, 134, 127, 123, 110, 119, 112, 94, 118, 129, 172, 112, 113, 114, 116, 105, 99, 105, 115, 116, 88, 106, 118, 123, 111, 194, 97, 115, 126, 141, 113, 122, 112, 102, 90, 101, 112, 108, 110, 115, 133, 145, 112, 104, 130, 122, 104, 131, 106, 98, 115, 123, 132, 125, 141, 92, 102, 117, 106, 75, 157, 125, 97, 86, 123, 98, 116, 104, 108, 111, 150, 105, 90, 110, 105, 168, 45, 87, 137, 111, 118, 118, 163, 109, 115, 138, 101, 100, 102, 105, 99, 109, 104, 111, 78, 127, 112, 106, 159, 116, 117, 131, 118, 132, 127, 117, 127, 113, 100, 111, 92, 109, 107, 112, 113, 111, 119, 92, 113, 99, 78, 146, 109, 156, 161, 119, 90, 97, 95, 104, 101, 115, 115, 113, 105, 98, 111, 129, 109, 119, 95, 113, 126, 123, 124, 117, 116, 111, 86, 114, 85, 105, 94, 114, 108, 107, 121, 119, 117, 111, 119, 122, 76, 107, 118, 186, 114, 115, 108, 93, 113, 87, 114, 107, 114, 123, 113, 117, 111, 174, 123, 119, 124, 115, 140, 106, 142, 114, 121, 187, 89, 106, 114, 114, 99, 116, 113, 93, 110, 75, 105, 114, 192, 84, 110, 124, 116, 176, 119, 127, 107, 79, 71, 115, 81, 125, 102, 111, 114, 121, 125, 110, 117, 83, 121, 107, 108, 190, 115, 119, 88, 104, 113, 109, 116, 123, 119, 107, 118, 146, 149, 163, 109, 98, 123, 120, 117, 148, 117, 107, 112, 124, 113, 81, 117, 87, 110, 112, 104, 59, 110, 109, 92, 110, 107, 107, 98, 119, 83, 143, 117, 112, 126, 151, 106, 117, 109, 129, 120, 117, 123, 114, 111, 89, 104, 113, 114, 104, 119, 122, 90, 109, 117, 179, 94, 162, 119, 98, 109, 123, 87, 109, 117, 120, 98, 125, 124, 112, 117, 116, 106, 111, 103, 119, 112, 84, 74, 120, 106, 114, 74, 116, 89, 123, 115, 118, 117, 107, 106, 108, 142, 120, 151, 113, 96, 119, 167, 121, 108, 121, 154, 91, 166, 156, 102, 91, 134, 104, 104, 104, 95, 99, 76, 152, 119, 116, 113, 192, 179, 112, 126, 120, 116, 104, 111, 131, 92, 119, 114, 121, 104, 108, 120, 108, 116, 129, 116, 130, 142, 111, 105, 150, 114, 147, 115, 115, 138, 138, 120, 113, 111, 109, 114, 116, 120, 117, 121, 113, 108, 118, 84, 117, 125, 117, 113, 125, 122, 116, 132, 123, 124, 121, 211, 121, 97, 118, 115, 118, 126, 101, 125, 108, 107, 119, 133, 115, 114, 135, 104, 119, 119, 106, 110, 116, 111, 106, 158, 111, 109, 110, 109, 122, 115, 112, 123, 102, 116, 100, 125, 114, 118, 109, 108, 118, 118, 109, 124, 113, 114, 127, 108, 118, 113, 104, 118, 101, 121, 133, 110, 119, 112, 123, 116, 127, 115, 113, 109, 131, 106, 118, 115, 129, 107, 127, 112, 113, 113, 115, 103, 109, 113, 127, 106, 116, 117, 133, 110, 123, 104, 127, 102, 114, 105, 127, 109, 119, 116, 113, 131, 116, 117, 111, 110, 106, 116, 115, 117, 119, 116, 125, 103, 124, 118, 121, 113, 122, 114, 130, 133, 117, 118, 114, 113, 106, 115, 134, 114, 120, 115, 130, 117, 127, 120, 128, 130, 141, 141, 120, 114, 136, 105, 113, 103, 101, 132, 112, 116, 112, 111, 124, 112, 119, 115, 114, 105, 115, 111, 117, 117, 107, 115, 128, 107, 118, 112, 152, 118, 160, 114, 111, 117, 114, 113, 118, 137, 118, 136, 119, 124, 132, 123, 121, 113, 115, 120, 115, 124, 124, 120, 118, 134, 108, 110, 117, 128, 123, 129, 111, 116, 114, 110, 117, 108, 108, 103, 121, 107, 112, 114, 118, 121, 122, 118, 114, 123, 109, 102, 109, 115, 132, 112, 117, 124, 107, 109, 105, 110, 117, 142, 121, 107, 113, 174, 108, 118, 118, 109, 118, 110, 114, 134, 107, 102, 115, 123, 118, 114, 116, 125, 135, 149, 116, 113, 130, 110, 117, 129, 108, 118, 116, 121, 134, 114, 120, 126, 126, 102, 115, 121, 119, 124, 134, 127, 129, 103, 113, 119, 130, 118, 133, 142, 111, 116, 123, 119, 113, 88, 108, 102, 116, 115, 108, 97, 113, 117, 115, 130, 120, 116, 139, 110, 126, 121, 122, 115, 163, 112, 0, 114, 115, 119, 119, 130, 125, 121, 122, 119, 131, 127, 126, 136, 119, 126, 158, 117, 127, 116, 132, 112, 114, 132, 122, 111, 117, 127, 108, 99, 117, 106, 112, 114, 115, 116, 117, 132, 110, 117, 115, 123, 118, 118, 108, 129, 116, 109, 122, 115, 113, 133, 110, 114, 110, 133, 145, 117, 136, 112, 113, 114, 117, 134, 98, 102, 116, 113, 115, 137, 129, 114, 122, 117, 112, 118, 113, 113, 139, 110, 113, 116, 137, 118, 122, 116, 111, 110, 119, 109, 116, 128, 133, 110, 115, 130, 105, 119, 110, 123, 116, 109, 113, 117, 112, 105, 107, 100, 112, 114, 113, 128, 121, 106, 120, 127, 122, 117, 123, 123, 117, 113, 138, 111, 129, 105, 115, 114, 117, 124, 112, 120, 130, 123, 103, 119, 111, 114, 115, 117, 76, 112, 113, 100, 110, 116, 141, 127, 124, 114, 116, 109, 112, 122, 123, 164, 123, 121, 89, 124, 153, 111, 123, 125, 143, 84, 111, 95, 113, 127, 168, 82, 106, 153, 103, 106, 115, 94, 138, 93, 122, 145, 112, 98, 117, 127, 88, 108, 150, 122, 117, 88, 92, 96, 108, 95, 93, 143, 99, 122, 105, 100, 107, 94, 122, 113, 126, 114, 163, 111, 134, 94, 116, 88, 130, 105, 117, 114, 110, 174, 95, 87, 158, 81, 120, 142, 112, 96, 109, 111, 115, 145, 93, 118, 125, 128, 117, 89, 121, 123, 136, 140, 118, 95, 93, 106, 115, 116, 118, 115, 91, 151, 111, 121, 110, 120, 125, 106, 96, 114, 124, 98, 109, 81, 117, 115, 97, 123, 104, 114, 118, 110, 157, 102, 172, 106, 112, 82, 119, 128, 109, 81, 94, 113, 96, 117, 121, 99, 107, 130, 122, 119, 166, 82, 113, 98, 129, 120, 100, 110, 124, 120, 97, 125, 111, 112, 114, 98, 85, 115, 101, 123, 115, 109, 110, 112, 207, 93, 111, 108, 144, 117, 176, 181, 103, 116, 116, 140, 103, 118, 85, 106, 116, 98, 86, 98, 134, 123, 93, 154, 160, 90, 106, 111, 186, 115, 114, 101, 112, 117, 103, 101, 121, 98, 134, 116, 100, 95, 121, 85, 125, 118, 102, 106, 117, 101, 115, 86, 126, 122, 138, 120, 139, 115, 96, 109, 113, 95, 111, 113, 101, 105, 113, 108, 120, 128, 121, 82, 118, 123, 108, 126, 139, 105, 106, 122, 109, 103, 112, 116, 136, 116, 131, 114, 105, 89, 113, 84, 112, 84, 151, 101, 120, 127, 111, 109, 96, 84, 106, 145, 112, 114, 90, 110, 113, 114, 79, 116, 93, 87, 117, 112, 107, 192, 144, 106, 114, 122, 118, 121, 116, 107, 97, 84, 130, 164, 106, 179, 108, 122, 112, 154, 116, 152, 153, 142, 114, 124, 143, 117, 112, 178, 113, 119, 118, 114, 65, 116, 116, 111, 159, 164, 109, 56, 112, 172, 103, 169, 106, 117, 102, 111, 128, 115, 215, 161, 101, 120, 128, 125, 126, 132, 110, 84, 126, 119, 102, 91, 85, 116, 116, 106, 113, 114, 168, 107, 112, 105, 114, 102, 132, 110, 200, 115, 109, 120, 130, 165, 110, 113, 124, 114, 131, 176, 105, 110, 119, 151, 106, 114, 108, 122, 107, 84, 101, 165, 141, 98, 93, 110, 129, 126, 127, 102, 104, 116, 114, 151, 109, 169, 102, 116, 118, 147, 109, 109, 92, 127, 106, 111, 119, 119, 94, 165, 85, 105, 114, 98, 166, 101, 153, 102, 108, 124, 126, 109, 97, 95, 113, 107, 120, 170, 118, 102, 141, 124, 113, 119, 78, 90, 136, 103, 110, 112, 169, 118, 119, 96, 98, 116, 106, 155, 116, 142, 133, 110, 95, 107, 110, 119, 124, 93, 121, 121, 103, 121, 110, 95, 97, 106, 121, 115, 129, 109, 124, 114, 109, 113, 115, 107, 117, 119, 118, 112, 98, 113, 78, 124, 115, 113, 105, 103, 123, 115, 144, 168, 123, 110, 127, 116, 118, 131, 118, 121, 96, 106, 88, 93, 164, 117, 143, 145, 115, 114, 105, 110, 114, 125, 120, 116, 115, 113, 130, 142, 128, 114, 116, 124, 129, 124, 127, 115, 129, 115, 115, 121, 126, 149, 95, 116, 126, 112, 115, 142, 119, 116, 112, 111, 113, 114, 115, 109, 118, 122, 125, 124, 120, 87, 130, 102, 113, 113, 99, 96, 109, 112, 122, 122, 119, 110, 146, 99, 120, 113, 120, 118, 119, 122, 126, 109, 119, 131, 99, 109, 135, 106, 121, 150, 109, 115, 121, 114, 119, 120, 107, 128, 106, 105, 121, 117, 112, 122, 122, 119, 124, 107, 128, 121, 107, 111, 118, 118, 110, 126, 122, 115, 119, 110, 117, 116, 100, 118, 122, 103, 111, 110, 118, 111, 106, 116, 127, 105, 116, 111, 128, 106, 129, 114, 110, 112, 127, 117, 126, 113, 116, 129, 134, 112, 91, 146, 117, 153, 113, 110, 137, 106, 116, 131, 115, 121, 114, 116, 130, 123, 121, 117, 135, 126, 109, 111, 123, 115, 118, 115, 119, 121, 110, 103, 28, 119, 113, 114, 123, 114, 127, 125, 110, 118, 115, 110, 105, 116, 119, 108, 119, 103, 98, 112, 169, 119, 109, 131, 137, 105, 106, 115, 136, 106, 110, 107, 114, 107, 113, 120, 126, 112, 124, 108, 120, 118, 122, 112, 119, 126, 115, 113, 115, 114, 108, 117, 116, 115, 116, 116, 136, 114, 111, 116, 117, 130, 120, 109, 120, 118, 125, 127, 111, 119, 124, 112, 112, 139, 119, 115, 169, 113, 119, 122, 111, 111, 123, 119, 116, 114, 110, 116, 113, 120, 117, 111, 124, 118, 145, 109, 114, 117, 116, 123, 125, 106, 112, 119, 124, 104, 110, 118, 109, 112, 101, 105, 123, 110, 126, 128, 114, 132, 119, 109, 111, 114, 103, 113, 118, 114, 117, 111, 120, 130, 108, 154, 114, 114, 117, 114, 117, 120, 131, 125, 117, 121, 101, 120, 119, 126, 113, 117, 118, 121, 142, 116, 119, 104, 124, 149, 126, 123, 115, 126, 109, 126, 112, 124, 123, 119, 130, 118, 124, 132, 124, 117, 125, 114, 118, 132, 107, 121, 116, 117, 131, 94, 112, 117, 115, 110, 123, 115, 120, 115, 111, 120, 96, 132, 124, 115, 146, 114, 139, 119, 119, 124, 111, 109, 122, 121, 136, 125, 107, 104, 118, 146, 104, 118, 105, 112, 113, 112, 119, 128, 121, 107, 114, 119, 159, 139, 123, 102, 108, 114, 125, 120, 123, 136, 110, 118, 117, 124, 117, 119, 105, 110, 115, 109, 100, 116, 101, 133, 110, 110, 132, 107, 135, 106, 136, 113, 113, 117, 114, 131, 118, 103, 118, 114, 113, 111, 115, 115, 123, 118, 115, 117, 113, 111, 116, 122, 112, 117, 133, 119, 122, 121, 108, 110, 127, 130, 128, 128, 110, 110, 111, 107, 102, 120, 109, 108, 113, 112, 113, 119, 112, 109, 105, 109, 112, 115, 113, 108, 117, 106, 117, 115, 121, 114, 125, 114, 116, 112, 99, 111, 111, 116, 115, 123, 114, 110, 112, 132, 126, 119, 115, 119, 105, 126, 121, 106, 126, 116, 105, 98, 103, 97, 117, 108, 147, 119, 116, 110, 134, 123}; - model->setOperandValue(op65, op65_init, sizeof(uint8_t) * 4608); - static int32_t op67_init[] = {-4214, 9621, 9177, 13673, -3399, 4623, 1067, 210, 2937, 10176, 2208, -274, 1294, 23553, 7099, 9771, 403, 13543, 8182, 980, 3361, 8910, 6839, -6282, -2942, 5983, 7094, 805, 8640, -7148, 3248, -1032, 4779, -3521, 13360, 2996, 17431, 3380, -6047, -1334, -3578, -863, -7567, 18113, 10831, 21789, -8372, -2748, 15416, 2926, -2322, 2634, -4064, 12928, 2071, -755, 2017, 10921, 3937, 10959, 7032, -1828, 647, 3980, -6764, 5919, 12612, 6857, 6255, -4238, 12544, 3741, 15007, -6258, -1530, 10254, 7278, -1842, 1667, 7091, 2722, -6176, 12104, 3859, 6431, 16235, 31628, 9889, 471, -15, 10309, 15599, -8860, -9604, -2789, 3416, 1893, 12078, -989, 21591, 8362, -7187, -3033, 4972, -5602, 11204, 11500, 4086, 7970, 1197, 4093, -2159, 6313, -4407, 5760, 12772, -1199, -14735, 5800, 12302, 2561, 2751, -9133, 745, 516, -1498, 299, 10946, -7946, 18054, 13072, 2075, -4003, 3693, -1748, -3236, 4937, -10941, 10622, -4971, -4905, 6966, -1330, -1536, 1109, 3535, -2925, 255, 5214, 7204, 2781, -5078, 6673, -2948, 7297, 9914, 7403, 5959, 2757, 11753, -3890, -2293, 6262, 5589, -10244, 13348, 8237, -7001, 6496, 7394, 3636, 5873, 7017, 22007, 9987, 4647, -7801, -14036, 1112, 13463, 14389, 8300, 4837, -12923, -857, 5712, -7052, 8644, 9579, 5610, -8186, 5916, -6455, 6896, 5804, -221, 3106, 8643, 18571, 3234, 13241, 4051, 13093, 5111, 7440, 4162, 9808, 5245, 5786, -1997, 8380, 8949, -2292, 1910, 1160, 1740, 8316, 9836, 7519, 5989, 3636, 7251, 3554, 1892, 10585, 1357, -4868, 2893, 6972, 6499, 19649, 319, 10529, 4239, 8153, -5382, 3282, 4722, 5079, 7581, -3373, 377, 4248, 7658, -2987, 1735, -5732, 8079, -110, 5151, 4279, -5306, 12951, 6216, -6579, 12070, -1453, 4912, 12314, 3618, 6372, -307, 6707, 7514, 17017, 8105, 5397, 9705, 4120, 13173, 7793, 9891, 7840, 5828, -11351, 4782, 5657, 1744, 171, 4874, 1917, 3435, 2230, 895, -5880, -7738, 4351, 5505, 712, 8344, -1206, 10306, 2509, 8517, 679, 1208, 1316, -310, 9947, 3747, 330, -267, -421, -1917, 2503, 114, 5472, 4465, -986, 9854, -3928, -4528, -2415, 17972, 1681, 4169, -2884, -5997, 5233, 5019, 3393, 2296, 2333, 5062, -6723, 15911, 6052, 7590, -743, 16729, -2203, -80, 5817, 8775, 16237, -4735, 2325, -2220, 12630, 14261, 1024, 5443, 0, -1921, -1933, 4719, 7899, 631, 4078, 3147, 6832, -4773, 371, 3897, -19077, 8224, -4716, 17577, 3884, 6701, -7927, 5543, 5176, 17417, 9580, 15852, 10819, -3518, -10136, -10742, 3254, 7801, 6169, 13398, 17140, 9809, 5233, 14389, 8314, -3430, 2347, 3103, -2978, 17554, 464, -11624, 7638, 6576, 14372, -6335, -2211, 7258, 14969, 4861, 1405, 3201, 11549, 10988, -6060, 7378, 4038, -1653, 11015, 10010, 5542, -3215, -9556, 2601, 13904, -3614, 10757, 9733, 1208, -11780, 6799, -1678, 12578, -10509, 6580, 4022, 6939, 10130, 8437, -8254, 4920, 11614, -2303, 14567, 3832, 3145, -7085, 1060, -6096, -13084, -2155, 7686, 9324, 4686, -1867, 746, 5827, 6038, 4877, -10392, 4515, 2435, 2175, 17603, 11722, -412, -14021, 3669, 5465, 3738, 2191, 7507, -2873, -8475, 7100, 9624, -1725, 1249, 18807, -4320, 11018, 2264, -1323, 7588, 1392, -3662, 18561, 19745, 1843, 2797, 6860, 3145, -4468, 6726, -14893, -1214, 7007, 1407, -11027, -21833, -4331, 4698, 878, 13808, 1315, 17976, -6405, -15142, 8560, 6296, 18088, 4090, -8588, 9030, 1179, 1164, 913, 16050, 6468, -5945, 9028, 4995, -2600, 4175, 4801, 3264, -5051, -660}; - model->setOperandValue(op67, op67_init, sizeof(int32_t) * 512); - static uint8_t op68_init[] = {120, 115, 100, 109, 110, 114, 120, 116, 102, 115, 105, 112, 102, 107, 109, 115, 108, 111, 116, 115, 107, 100, 111, 114, 108, 109, 108, 112, 111, 111, 112, 118, 112, 112, 107, 112, 107, 111, 116, 112, 104, 108, 114, 107, 112, 107, 110, 105, 100, 104, 117, 123, 106, 106, 122, 108, 107, 99, 118, 106, 101, 117, 120, 103, 114, 115, 105, 110, 104, 111, 109, 104, 115, 106, 97, 114, 113, 126, 116, 113, 110, 113, 125, 111, 97, 110, 87, 102, 113, 101, 110, 104, 108, 118, 102, 107, 114, 112, 108, 115, 117, 107, 105, 108, 107, 110, 109, 104, 111, 118, 94, 109, 106, 102, 109, 104, 104, 117, 111, 109, 121, 121, 109, 112, 100, 108, 102, 109, 113, 119, 117, 117, 102, 88, 86, 117, 107, 111, 117, 113, 113, 113, 105, 116, 102, 110, 103, 97, 108, 118, 115, 108, 108, 108, 122, 107, 122, 111, 108, 101, 100, 115, 125, 92, 118, 106, 112, 115, 112, 107, 106, 105, 106, 120, 110, 106, 95, 111, 113, 115, 104, 116, 103, 125, 112, 112, 113, 105, 107, 121, 116, 118, 108, 101, 110, 106, 120, 104, 90, 107, 114, 99, 117, 91, 99, 101, 114, 105, 106, 100, 107, 118, 111, 105, 107, 110, 112, 109, 111, 107, 110, 108, 109, 115, 114, 101, 112, 109, 106, 125, 117, 98, 108, 113, 102, 96, 128, 104, 99, 108, 121, 112, 114, 109, 104, 115, 114, 107, 101, 112, 109, 110, 109, 110, 105, 106, 130, 106, 122, 105, 117, 117, 108, 108, 116, 115, 111, 117, 115, 109, 106, 106, 108, 115, 112, 102, 112, 110, 118, 103, 116, 109, 117, 116, 108, 104, 126, 108, 104, 116, 109, 113, 103, 115, 107, 104, 118, 111, 106, 108, 112, 106, 114, 113, 127, 128, 108, 112, 109, 110, 108, 132, 114, 113, 110, 107, 116, 109, 114, 121, 99, 113, 112, 111, 103, 116, 111, 112, 104, 107, 126, 106, 108, 97, 112, 104, 104, 113, 114, 120, 115, 116, 120, 109, 105, 107, 100, 112, 116, 108, 120, 114, 97, 102, 111, 110, 116, 110, 121, 119, 102, 107, 106, 112, 118, 104, 101, 114, 123, 121, 98, 108, 101, 110, 107, 95, 113, 104, 94, 107, 90, 104, 103, 105, 108, 101, 113, 106, 108, 113, 110, 115, 110, 107, 106, 102, 110, 111, 116, 112, 106, 136, 107, 109, 102, 110, 110, 123, 105, 105, 113, 108, 117, 93, 102, 106, 109, 109, 130, 105, 93, 98, 104, 116, 105, 109, 115, 111, 109, 101, 112, 96, 110, 103, 109, 106, 119, 117, 101, 111, 114, 105, 106, 104, 111, 114, 122, 118, 113, 102, 123, 107, 106, 106, 110, 97, 105, 100, 103, 111, 103, 101, 114, 107, 110, 102, 101, 104, 118, 107, 104, 109, 97, 104, 114, 98, 106, 108, 109, 101, 112, 111, 118, 128, 116, 90, 113, 109, 106, 104, 106, 113, 116, 129, 99, 105, 115, 114, 107, 98, 105, 102, 111, 106, 113, 104, 109, 119, 112, 117, 119, 111, 114, 118, 103, 113, 109, 108, 115, 103, 112, 108, 107, 106, 111, 85, 106, 119, 104, 101, 112, 107, 109, 112, 104, 109, 109, 117, 113, 116, 110, 98, 111, 102, 108, 119, 100, 121, 83, 119, 107, 113, 118, 113, 112, 114, 112, 108, 104, 112, 111, 105, 102, 115, 115, 106, 112, 97, 114, 106, 111, 107, 117, 112, 101, 112, 106, 113, 121, 100, 108, 113, 115, 105, 106, 108, 102, 122, 118, 111, 106, 105, 114, 119, 109, 120, 114, 111, 115, 108, 104, 113, 102, 112, 122, 102, 101, 111, 106, 107, 107, 92, 106, 120, 139, 122, 111, 107, 113, 105, 106, 103, 112, 116, 109, 118, 105, 118, 107, 122, 104, 102, 99, 104, 118, 111, 125, 108, 109, 110, 95, 105, 100, 109, 115, 109, 112, 116, 106, 100, 103, 109, 102, 111, 110, 118, 110, 123, 115, 115, 112, 112, 109, 110, 96, 109, 109, 101, 104, 104, 96, 121, 100, 111, 115, 115, 96, 102, 113, 105, 104, 120, 109, 104, 97, 110, 109, 117, 117, 107, 108, 121, 108, 112, 110, 108, 114, 108, 110, 115, 122, 104, 106, 87, 110, 112, 105, 107, 108, 103, 101, 96, 108, 116, 106, 115, 109, 117, 94, 116, 104, 100, 112, 104, 110, 107, 106, 110, 115, 118, 115, 87, 111, 105, 105, 104, 99, 113, 120, 106, 109, 119, 115, 115, 127, 114, 112, 112, 118, 113, 115, 114, 102, 124, 88, 89, 97, 110, 103, 110, 100, 127, 135, 109, 114, 103, 108, 119, 103, 113, 107, 112, 103, 105, 125, 112, 108, 105, 118, 108, 108, 107, 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110, 118, 116, 111, 112, 110, 120, 102, 105, 126, 121, 107, 123, 113, 87, 110, 107, 108, 118, 108, 110, 110, 106, 116, 123, 111, 103, 111, 111, 102, 117, 110, 115, 111, 110, 99, 110, 103, 107, 100, 112, 118, 92, 110, 115, 103, 103, 110, 111, 122, 107, 91, 103, 101, 114, 113, 110, 109, 113, 105, 106, 117, 111, 104, 95, 97, 107, 102, 110, 107, 116, 115, 95, 108, 113, 115, 101, 113, 116, 102, 96, 113, 101, 116, 117, 104, 111, 112, 104, 118, 114, 104, 113, 100, 103, 105, 109, 101, 110, 112, 121, 114, 104, 109, 107, 114, 111, 109, 108, 110, 110, 107, 98, 109, 111, 107, 109, 94, 106, 110, 107, 86, 120, 102, 113, 111, 95, 117, 111, 106, 104, 110, 115, 116, 109, 109, 107, 105, 107, 116, 110, 104, 109, 95, 111, 94, 111, 118, 100, 116, 109, 130, 110, 116, 124, 115, 116, 117, 96, 105, 114, 108, 111, 133, 112, 107, 98, 108, 110, 104, 107, 108, 110, 113, 112, 107, 110}; - model->setOperandValue(op68, op68_init, sizeof(uint8_t) * 262144); - static int32_t op70_init[] = {-1002, 2029, -402, -2538, 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-267, -772, 4473, 877, -1673, 1356, 148, -1181, 3232, 2643, -2289, 2126, -424, 2497, 4394, -4486, -2167, -1536, 1390, -699, 4396, -558, 2445, 1778, -1338, -673, 2304, -546, 260, -3791, -4007, -480, -1387, 4487, -930, 137, -99, 4626, 1415, -2012, -2851, -1379, 1226, -1039, -255, -1344, 2393, 4937, -2735, 4990, -1743, 2479, -2378, 2705, 4332, -1177, 6672, -2527, 800, 6449, -876, 306, 1123, 6236, -405, -326, -2256, -1799, 5133, -2199, -2405, 2961, 4845, -4547, -1576, 8816, -402, 2410, 2505, 1382, 5058, -2387, 4168, -4819, -1992, -1851, 3105, -310, -173, -1424, -289, -1582, -894, 3961, 2165, 3366, -4057, 5008, -2121, -3960, -675, -2262, -2963, -1376, 2899, -518, 904, 722, -1125, -3477, -1230, -1734, 6906, 2392, -822, -4907, 3051, 9294, 3471, -975, 5099, -879, -1189, 815, -3336, 2528, -2517, 4889, -2207, 2745, 2962, 1537, -932, -810, 4907, 4906, 2267, -3058, -1100, 150, -1992, 5509, -558, 4525, -1953, -2992, -784, 3813, -610, -1078, 4770, 6130, -691, 3576, -275, 2869, 6463, -1330, -1388, -3976, 442, 1428, 3364, -1041, -529, -555, -1341, -3156, -837, -1310, 4730, 948, -5021, 3488, 351, -688, -2116, 2909, 3363, -1947, 1706, 3399, -735, 4987, 2035, -3244, -657, -368}; - model->setOperandValue(op70, op70_init, sizeof(int32_t) * 512); - static uint8_t op71_init[] = {139, 97, 94, 101, 121, 98, 97, 114, 125, 111, 95, 110, 83, 108, 105, 84, 129, 110, 112, 108, 110, 106, 101, 96, 94, 100, 128, 118, 123, 109, 105, 91, 97, 101, 108, 113, 110, 118, 107, 111, 110, 110, 113, 94, 114, 109, 97, 101, 91, 96, 114, 122, 98, 106, 108, 117, 97, 133, 108, 108, 117, 116, 100, 120, 115, 121, 116, 114, 96, 108, 115, 105, 111, 104, 113, 103, 100, 116, 110, 119, 105, 109, 108, 96, 110, 85, 100, 107, 105, 97, 114, 104, 126, 107, 124, 122, 97, 98, 111, 108, 101, 105, 121, 109, 108, 105, 105, 108, 112, 109, 109, 111, 111, 91, 100, 95, 129, 100, 99, 148, 94, 113, 107, 110, 81, 110, 106, 107, 115, 115, 105, 103, 93, 105, 103, 108, 96, 110, 103, 109, 107, 99, 115, 107, 110, 98, 108, 111, 73, 109, 104, 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106, 103, 120, 108, 94, 105, 100, 121, 107, 130, 109, 98, 108, 93, 113, 100, 102, 103, 66, 95, 105, 106, 105, 102, 127, 110, 103, 128, 107, 124, 102, 110, 105, 104, 115, 139, 121, 89, 96, 144, 114, 106, 102, 100, 84, 107, 86, 103, 109, 110, 109, 109, 97, 110, 149, 111, 106, 122, 103, 106, 104, 100, 114, 126, 100, 105, 129, 97, 108, 120, 120, 107, 104, 103, 97, 105, 138, 101, 92, 88, 103, 121, 122, 114, 85, 88, 95, 108, 99, 111, 123, 106, 111, 124, 149, 101, 118, 102, 105, 109, 105, 154, 155, 117, 99, 97, 98, 122, 108, 115, 95, 98, 122, 89, 106, 93, 114, 82, 85, 118, 100, 107, 109, 104, 94, 82, 113, 79, 102, 101, 84, 111, 98, 115, 101, 103, 119, 95, 100, 104, 96, 97, 105, 99, 112, 103, 105, 100, 111, 72, 101, 113, 111, 99, 83, 118, 103, 106, 106, 105, 93, 106, 113, 104, 130, 105, 110, 112, 133, 80, 103, 108, 106, 110, 112, 112, 81, 82, 121, 92, 89, 91, 114, 107, 97, 100, 100, 104, 140, 96, 95, 105, 145, 82, 100, 131, 118, 88, 139, 109, 94, 93, 103, 101, 107, 104, 111, 92, 113, 112, 100, 105, 108, 97, 99, 143, 110, 79, 97, 88, 83, 118, 106, 85, 111, 107, 109, 104, 109, 93, 107, 94, 107, 76, 110, 92, 104, 120, 91, 97, 114, 100, 107, 96, 100, 108, 104, 115, 92, 116, 113, 111, 101, 102, 100, 112, 147, 114, 114, 112, 106, 114, 113, 97, 105, 108, 110, 94, 110, 108, 107, 120, 109, 111, 114, 107, 91, 104, 122, 175, 86, 116, 104, 111, 78, 104, 99, 100, 109, 103, 108, 109, 100, 109, 110, 103, 109, 103, 109, 122, 120, 113, 119, 124, 90, 90, 106, 111, 97, 113, 97, 102, 88, 106, 143, 102, 110, 98, 105, 100, 123, 98, 95, 75, 113, 109, 90, 95, 103, 111, 98, 107, 103, 89, 103, 114, 111, 110, 130, 106, 109, 100, 110, 99, 149, 102, 102, 102, 97, 108, 106, 111, 109, 85, 63, 104, 99, 108, 120, 112, 146, 107, 105, 92, 110, 108, 99, 108, 135, 96, 130, 108, 125, 91, 104, 104, 94, 108, 90, 117, 111, 99, 110, 88, 111, 103, 98, 110, 102, 112, 104, 109, 116, 107, 98, 116, 122, 108, 108, 124, 100, 91, 103, 94, 101, 100, 110, 109, 125, 110, 116, 97, 97, 106, 98, 113, 109, 114, 111, 110, 123, 112, 119, 125, 115, 108, 98, 100, 91, 99, 113, 117, 96, 104, 114, 118, 98, 122, 113, 105, 117, 105, 122, 119, 110, 119, 101, 97, 91, 114, 102, 105, 109, 119, 111, 100, 110, 123, 108, 124, 100, 109, 106, 88, 107, 99, 101, 119, 97, 92, 119, 104, 127, 108, 115, 124, 107, 97, 94, 109, 119, 117, 118, 106, 98, 106, 106, 109, 110, 102, 111, 114, 105, 92, 99, 103, 107, 98, 97, 103, 109, 118, 109, 113, 86, 119, 109, 107, 109, 119, 109, 103, 99, 110, 110, 107, 98, 106, 104, 107, 117, 107, 107, 103, 105, 110, 111, 105, 134, 103, 102, 127, 108, 111, 108, 105, 95, 102, 99, 104, 114, 102, 115, 111, 111, 105, 102, 111, 99, 98, 101, 88, 118, 107, 99, 101, 104, 114, 100, 113, 111, 109, 101, 111, 104, 111, 105, 102, 112, 101, 105, 105, 105, 117, 102, 111, 89, 110, 104, 108, 105, 113, 108, 107, 104, 106, 99, 104, 114, 116, 93, 84, 110, 112, 110, 113, 106, 109, 109, 87, 112, 114, 109, 111, 115, 110, 111, 101, 111, 101, 97, 115, 106, 111, 112, 103, 112, 95, 104, 105, 112, 115, 114, 121, 142, 104, 106, 108, 99, 92, 95, 108, 106, 114, 112, 107, 96, 98, 115, 100, 86, 101, 108, 109, 104, 93, 105, 109, 110, 96, 99, 116, 103, 104, 111, 99, 117, 114, 120, 109, 95, 102, 96, 112, 113, 95, 106, 112, 96, 107, 105, 104, 101, 108, 122, 112, 107, 98, 112, 112, 113, 119, 104, 93, 109, 112, 96, 97, 131, 105, 116, 119, 116, 96, 120, 108, 99, 116, 111, 116, 105, 95, 124, 106, 108, 106, 99, 108, 129, 104, 95, 110, 101, 123, 111, 99, 116, 107, 107, 110, 113, 109, 99, 99, 95, 108, 103, 116, 107, 109, 110, 105, 112, 113, 116, 96, 112, 103, 108, 106, 107, 98, 114, 105, 106, 115, 107, 66, 101, 111, 106, 103, 107, 101, 108, 105, 94, 103, 99, 114, 99, 113, 108, 108, 106, 107, 98, 106, 103, 113, 115, 126, 104, 115, 102, 94, 100, 99, 115, 111, 108, 87, 110, 101, 92, 106, 112, 119, 97, 112, 103, 105, 110, 108, 112, 111, 107, 99, 94, 96, 112, 97, 109, 113, 109, 117, 113, 111, 106, 105, 109, 103, 114, 117, 105, 93, 104, 100, 106, 120, 104, 103, 97, 102, 98, 111, 105, 105, 120, 98, 110, 99, 104, 104, 95, 103, 113, 111, 92, 99, 93, 106, 107, 134, 112, 95, 118, 108, 114, 111, 116, 107, 110, 120, 97, 104, 193, 104, 116, 96, 93, 113, 112, 110, 99, 132, 108, 109, 101, 110, 117, 112, 96, 106, 103, 109, 118, 84, 139, 112, 110, 104, 101, 112, 91, 103, 109, 97, 96, 115, 107, 107, 125, 98, 107, 65, 98, 115, 101, 104, 105, 102, 107, 108, 116, 100, 88, 99, 129, 110, 100, 114, 102, 89, 91, 100, 107, 101, 93, 108, 118, 108, 147, 90, 102, 112, 120, 114, 106, 109, 108, 102, 116, 100, 118, 117, 131, 113, 108, 98, 98, 102, 107, 118, 94, 95, 116, 114, 97, 103, 87, 116, 108, 119, 123, 116, 99, 114, 94, 107, 96, 137, 109, 104, 106, 147, 105, 84, 118, 105, 109, 111, 105, 105, 103, 96, 117, 113, 92, 128, 96, 86, 110, 104, 126, 109, 112, 134, 119, 112, 104, 133, 117, 119, 120, 109, 113, 108, 102, 109, 111, 86, 113, 99, 99, 109, 97, 118, 101, 114, 98, 104, 107, 108, 111, 134, 93, 128, 109, 98, 104, 142, 109, 99, 131, 119, 102, 111, 98, 102, 107, 111, 116, 119, 109, 104, 108, 91, 115, 105, 90, 93, 98, 114, 116, 102, 104, 103, 99, 100, 106, 113, 121, 132, 141, 115, 104, 102, 97, 101, 105, 88, 106, 100, 110, 109, 86, 96, 110, 109, 104, 123, 127, 143, 108, 108, 104, 102, 99, 106, 107, 80, 102, 107, 103, 133, 110, 103, 96, 104, 123, 168, 100, 121, 110, 118, 114, 100, 91, 106, 129, 123, 88, 71, 104, 153, 111, 119, 94, 101, 97, 81, 129, 115, 112, 106, 97, 124, 98, 100, 122, 95, 100, 106, 102, 98, 107, 110, 146, 97, 101, 112, 112, 103, 101, 126, 137, 104, 83, 100, 103, 100, 101, 99, 100, 124, 108, 102, 94, 101, 125, 94, 82, 87, 105, 112, 100, 111, 101, 107, 107, 93, 95, 109, 97, 92, 105, 100, 95, 107, 128, 83, 129, 120, 98, 85, 111, 96, 107, 107, 86, 106, 111, 110, 79, 95, 135, 113, 101, 94, 114, 104, 105, 106, 103, 128, 109, 124, 88, 98, 135, 104, 91, 151, 107, 93, 141, 107, 94, 119, 132, 116, 149, 106, 93, 102, 105, 97, 93, 96, 86, 96, 106, 99, 95, 109, 106, 95, 121, 104, 99, 116, 104, 117, 96, 84, 89, 100, 103, 142, 110, 87, 101, 103, 98, 108, 106, 106, 107, 94, 105, 105, 110, 96, 142, 118, 80, 95, 130, 136, 82, 96, 107, 102, 105, 97, 109, 99, 120, 88, 90, 99, 97, 108, 100, 111, 104, 98, 90, 106, 108, 120, 115, 109, 108, 113, 102, 89, 92, 134, 108, 76, 103, 82, 100, 101, 100, 113, 123, 116, 118, 108, 108, 114, 88, 110, 135, 109, 99, 101, 101, 94, 114, 97, 103, 113, 114, 114, 112, 107, 101, 112, 96, 110, 104, 124, 105, 91, 93, 98, 115, 119, 98, 78, 102, 102, 101, 90, 102, 103, 111, 93, 105, 93, 115, 112, 97, 101, 120, 126, 102, 98, 100, 105, 95, 108, 128, 98, 101, 124, 114, 117, 118, 106, 101, 98, 116, 92, 153, 97, 111, 102, 94, 108, 110, 107, 119, 122, 101, 107, 109, 158, 125, 112, 115, 100, 102, 111, 112, 169, 114, 133, 112, 99, 107, 107, 105, 93, 104, 104, 97, 108, 125, 91, 119, 99, 111, 126, 109, 113, 96, 113, 102, 114, 100, 120, 110, 106, 98, 101, 113, 107, 97, 115, 108, 107, 106, 97, 119, 112, 109, 109, 119, 124, 107, 98, 98, 105, 102, 110, 110, 114, 105, 107, 117, 114, 117, 123, 116, 109, 103, 101, 89, 96, 99, 117, 97, 106, 106, 120, 97, 95, 99, 107, 115, 107, 120, 118, 114, 117, 103, 106, 91, 128, 103, 104, 108, 118, 110, 99, 101, 111, 107, 121, 115, 111, 106, 98, 103, 110, 103, 121, 99, 95, 116, 102, 115, 108, 117, 126, 129, 93, 125, 108, 105, 106, 120, 104, 112, 106, 105, 98, 110, 103, 108, 109, 102, 91, 100, 110, 108, 117, 99, 109, 106, 117, 108, 112, 91, 117, 107, 105, 108, 112, 116, 103, 129, 107, 106, 106, 98, 107, 110, 131, 105, 109, 110, 103, 104, 95, 109, 106, 97, 103, 101, 93, 106, 110, 109, 106, 115, 109, 99, 106, 114, 130, 114, 109, 111, 107, 106, 115, 110, 100, 108, 119, 115, 106, 99, 102, 106, 114, 96, 115, 109, 104, 101, 111, 104, 109, 103, 101, 111, 101, 104, 106, 107, 113, 104, 111, 91, 109, 105, 95, 105, 107, 111, 109, 97, 106, 96, 105, 95, 101, 93, 95, 109, 107, 115, 111, 106, 99, 108, 102, 113, 121, 112, 112, 105, 122, 111, 102, 111, 100, 110, 108, 107, 91, 88, 116, 108, 97, 102, 101, 110, 108, 103, 108, 119, 103, 109, 105, 105, 95, 101, 113, 107, 117, 109, 103, 98, 98, 114, 102, 112, 100, 102, 102, 105, 130, 105, 108, 108, 97, 101, 111, 107, 105, 109, 99, 106, 109, 119, 105, 120, 104, 99, 107, 112, 96, 104, 105, 97, 109, 113, 104, 102, 109, 116, 110, 111, 99, 111, 110, 106, 103, 106, 112, 107, 109, 90, 122, 114, 107, 114, 118, 110, 96, 119, 106, 100, 107, 108, 116, 131, 93, 103, 105, 97, 107, 102, 113, 100, 105, 116, 111, 102, 89, 111, 101, 106, 102, 105, 99, 112, 110, 103, 101, 96, 107, 100, 121, 106, 106, 105, 104, 91, 109, 114, 111, 111, 103, 107, 105, 116, 90, 108, 97, 108, 101, 109, 113, 101, 112, 108, 103, 106, 102, 109, 105, 117, 105, 101, 114, 96, 113, 111, 108, 109, 109, 101, 105, 104, 126, 113, 123, 104, 120, 102, 97, 102, 100, 113, 108, 109, 88, 101, 118, 100, 116, 110, 117, 102, 112, 112, 98, 91, 108, 109, 109, 104, 100, 95, 96, 109, 98, 109, 113, 107, 114, 112, 110, 106, 110, 107, 116, 104, 113, 110, 107, 102, 102, 112, 115, 106, 104, 98, 102, 99, 109, 104, 106, 107, 130, 108, 96, 103, 102, 97, 111, 108, 105, 94, 99, 94, 109, 107, 103, 114, 97, 110, 107, 112, 105, 115, 105, 108, 88, 105, 102, 211, 106, 115, 98, 97, 107, 112, 110, 118, 105, 104, 108, 108, 106, 117, 108, 130, 106, 103, 107, 114, 80, 104, 105, 121, 104, 106, 112, 114, 95, 108, 95, 96, 118, 119, 106}; - model->setOperandValue(op71, op71_init, sizeof(uint8_t) * 4608); - static int32_t op73_init[] = {-8724, -7778, 3602, -5385, 9757, 1571, 5128, 5593, 6144, 5765, -1400, 6352, -5574, -7150, -5543, -4901, 8204, -10049, -1065, -7468, 11801, -5930, -5512, 9988, -5956, 4749, 11616, 1056, 6376, -1937, -10713, -7848, -3320, 7246, -10418, 10423, 6574, -7352, 4947, 4011, -10612, 7073, -3342, 9485, 4740, 3258, 7744, -16036, 3465, -3084, 1092, 394, 6087, 6430, 1006, 3351, -15700, 3236, -2458, -3188, -4587, -2054, -13119, -7009, -691, 985, 439, -5102, 637, 2035, 6123, 1404, 8503, 5527, 7382, -4248, 1538, 11286, 11106, 1462, 1454, 1207, -2431, -4606, 6795, 6860, 47, -13226, -4222, 346, -3845, 6998, 11043, 5421, 7011, 3344, -4951, -3207, -2417, 1171, -8000, -6263, -3081, -7534, 6157, -489, 9480, 1558, 4979, 3757, -591, 540, 7083, -5873, 8541, 3227, 7182, 269, 3500, -11198, 107, -13009, -7116, 1507, -814, 4662, 4549, 2880, -2726, 2181, 1563, -3420, -3973, 3762, -12240, 981, 6115, 6628, 2686, 3787, -1583, -2288, 11353, 2463, 2607, 1896, 7597, 1396, 10398, -1863, 5210, -4564, -313, -6099, 8502, -6120, -12866, -6588, -1912, 10251, 14670, -3739, 1731, 2527, 7462, 7118, 10408, 1462, 9669, -2844, -1537, 2230, 5997, -1359, -1920, 307, 4086, 6472, 1360, 6845, -3945, -4417, 18765, 5872, 9937, 1422, 1758, 6839, 1202, 6387, 1320, 8417, -8763, -2149, 6585, 3874, 5992, -7708, 5310, 7353, 1295, 10508, -4631, -2709, 6990, 549, 7886, -3226, 1150, 4153, -3564, 4962, -9248, -4827, 265, 5837, 12968, -6554, 7619, -1029, 3099, 5949, -925, -6549, -1023, -2418, 7441, -6620, -9092, 9193, 3092, -6543, 3922, -3113, -9131, 11549, 6574, -1482, 1866, -3089, 3226, 1275, 4993, 7245, 1672, 3740, -5147, -7093, 4606, -7485, -2585, -2150, 1976, -10188, 3690, 6516, -7454, 5254, 9291, 3156, 8396, 10940, 496, -2815, 8418, 6993, -4136, -668, 1799, 1519, 3224, -4037, -8755, -74, -13973, -2863, 330, 217, 1974, -5146, 13186, 5382, 6819, 4974, 12504, 2248, 5304, 4352, 7352, 835, 1749, 9166, 4052, 7551, 2442, -6651, 2355, -889, -1261, 2651, 8616, 8115, 2646, -4016, 1421, -4807, 1437, 1941, -6912, 90, -8981, -329, 10191, 463, 1258, -2843, 4155, 11121, 4605, 6679, -5233, -2651, 5291, 1211, 3132, -1866, 9296, 1769, -4807, -8763, 790, 6888, 6678, -5483, -6088, -4492, 2073, 618, 8919, -4644, 4544, 445, 6865, 6551, -2636, 2075, -12169, 2399, -3607, -6472, 1184, 6869, 11501, 3341, -2764, 3782, 1935, 2171, 1469, 1192, 2177, -3603, -8415, 1395, -2618, 3566, -4852, 4580, -4273, 4316, 2837, -2233, -5308, 2125, 4307, -6978, -9603, -8898, 669, 837, -3653, 11063, 4187, -14361, 1513, 12101, -2012, -1080, -1526, 6016, -3047, 10784, 7372, 7585, 772, 5052, 1566, 1894, -9372, 383, 7746, 9199, -6341, 9117, -1796, -459, 2021, 1418, -7048, -9178, -1720, -2357, 8819, 755, 6741, 1400, -7859, 14130, 11811, 8679, 981, -5256, 2956, -2891, -1955, -4086, 8937, 7855, 2640, -1682, -5689, 7548, 9995, 6458, 6280, -2786, 530, 1149, 14467, 7179, -67, 1141, 5648, 5716, 454, 3057, 10795, 6216, -975, -6650, -3162, -4871, 1844, -2571, -3195, -163, 3653, 4588, 8100, 4500, 5956, -5180, 3199, 4953, -6630, 4678, 4478, -942, -967, 15768, 4864, -6945, 206, 6450, -8064, -6508, 7290, 7527, -2398, 8697, -221, -3716, 9648, 6100, 5694, 2437, -1259, 7652, -6699, -3268, -8480, 130, 4439, -1580, 4166, 6199, -10596, -11817, 8207, 7013, -1092, -1969, 3224, 2407, 3727, 6322, -2090, -10242, 5425, 4912, 10630, -4720}; - model->setOperandValue(op73, op73_init, sizeof(int32_t) * 512); - static uint8_t op74_init[] = {154, 165, 158, 158, 155, 161, 160, 159, 161, 162, 161, 171, 159, 160, 168, 164, 169, 151, 163, 163, 154, 160, 159, 148, 162, 162, 150, 160, 159, 157, 157, 155, 168, 156, 161, 152, 151, 147, 163, 158, 160, 171, 149, 163, 166, 161, 161, 159, 170, 165, 166, 153, 170, 158, 163, 138, 167, 155, 160, 167, 148, 169, 169, 151, 158, 150, 171, 159, 158, 165, 157, 158, 151, 166, 159, 162, 159, 158, 139, 150, 159, 161, 152, 169, 151, 161, 159, 151, 165, 153, 158, 148, 155, 162, 156, 165, 164, 150, 160, 154, 157, 159, 150, 171, 161, 156, 171, 158, 152, 157, 167, 162, 170, 161, 156, 160, 163, 168, 162, 156, 154, 155, 148, 162, 163, 156, 166, 155, 172, 161, 164, 170, 165, 144, 159, 163, 163, 163, 152, 165, 162, 162, 165, 164, 150, 156, 156, 168, 160, 162, 168, 157, 147, 173, 148, 175, 160, 164, 165, 161, 159, 164, 163, 153, 156, 162, 144, 160, 163, 158, 160, 160, 158, 151, 164, 159, 160, 158, 153, 167, 155, 163, 156, 158, 156, 156, 161, 151, 146, 165, 155, 169, 146, 162, 160, 170, 158, 151, 159, 159, 152, 162, 160, 161, 164, 166, 164, 166, 170, 161, 149, 167, 162, 156, 166, 155, 157, 144, 160, 149, 155, 168, 150, 158, 152, 148, 156, 156, 156, 160, 156, 163, 165, 161, 164, 155, 164, 169, 150, 157, 156, 165, 161, 167, 159, 168, 163, 172, 152, 159, 160, 158, 147, 166, 163, 150, 157, 167, 153, 161, 163, 157, 162, 157, 159, 159, 173, 173, 155, 154, 163, 160, 165, 143, 167, 157, 166, 161, 166, 163, 163, 153, 153, 160, 161, 170, 165, 171, 156, 159, 158, 148, 162, 165, 159, 154, 159, 176, 158, 160, 157, 159, 153, 160, 162, 153, 159, 161, 160, 153, 150, 156, 155, 167, 144, 161, 158, 168, 166, 163, 154, 165, 168, 156, 162, 165, 160, 163, 159, 162, 162, 160, 165, 163, 155, 161, 162, 154, 165, 159, 154, 153, 158, 148, 146, 160, 163, 154, 162, 162, 162, 163, 158, 158, 161, 161, 156, 163, 150, 165, 163, 169, 162, 160, 172, 162, 170, 162, 153, 161, 160, 163, 176, 166, 164, 166, 169, 158, 176, 162, 161, 161, 155, 151, 167, 159, 162, 161, 151, 163, 161, 153, 167, 165, 158, 151, 166, 153, 157, 153, 172, 164, 167, 155, 173, 155, 151, 152, 154, 163, 167, 163, 159, 157, 165, 175, 157, 150, 153, 167, 179, 161, 165, 150, 153, 157, 151, 161, 158, 161, 157, 162, 160, 176, 167, 164, 158, 154, 158, 158, 170, 156, 155, 159, 160, 159, 169, 164, 150, 162, 157, 159, 149, 160, 153, 163, 152, 150, 155, 140, 159, 162, 160, 165, 155, 163, 163, 163, 162, 173, 154, 157, 168, 160, 160, 154, 162, 165, 162, 164, 162, 156, 153, 154, 165, 151, 155, 161, 165, 164, 165, 147, 161, 151, 166, 155, 157, 149, 162, 167, 171, 166, 169, 160, 158, 164, 159, 160, 153, 157, 161, 164, 164, 156, 166, 157, 156, 155, 157, 160, 158, 160, 164, 158, 164, 164, 158, 160, 160, 164, 153, 158, 147, 160, 155, 157, 165, 156, 171, 159, 157, 158, 164, 164, 151, 161, 162, 153, 159, 146, 157, 163, 154, 158, 169, 147, 161, 164, 158, 158, 164, 156, 157, 162, 161, 166, 158, 153, 165, 159, 158, 169, 163, 156, 152, 155, 159, 161, 158, 164, 163, 153, 171, 162, 163, 156, 159, 159, 158, 163, 162, 155, 157, 154, 156, 159, 158, 160, 158, 164, 162, 157, 158, 154, 162, 156, 158, 159, 158, 158, 147, 161, 160, 172, 160, 157, 166, 162, 161, 163, 161, 153, 159, 154, 164, 152, 159, 163, 160, 151, 151, 151, 159, 164, 162, 164, 153, 164, 164, 156, 158, 156, 166, 155, 168, 154, 157, 162, 154, 160, 161, 157, 167, 156, 160, 166, 158, 157, 157, 164, 164, 159, 164, 158, 158, 150, 163, 158, 159, 156, 159, 152, 153, 151, 160, 164, 159, 167, 158, 157, 166, 162, 171, 153, 163, 153, 160, 160, 162, 163, 161, 164, 156, 158, 159, 155, 151, 153, 154, 156, 164, 149, 159, 158, 154, 161, 168, 162, 158, 152, 165, 161, 154, 159, 169, 163, 158, 165, 163, 156, 158, 160, 161, 154, 168, 171, 165, 158, 156, 157, 159, 164, 158, 157, 162, 160, 154, 160, 161, 165, 152, 155, 163, 158, 156, 167, 162, 164, 169, 159, 161, 167, 158, 150, 159, 164, 162, 162, 163, 164, 160, 154, 162, 156, 159, 158, 154, 155, 156, 153, 167, 165, 154, 168, 163, 158, 163, 152, 159, 166, 160, 160, 157, 171, 156, 169, 166, 159, 155, 162, 164, 155, 157, 158, 158, 175, 148, 162, 160, 156, 161, 153, 162, 162, 155, 158, 167, 157, 154, 160, 156, 160, 164, 159, 171, 162, 153, 156, 160, 161, 164, 158, 168, 174, 160, 158, 167, 158, 158, 153, 153, 154, 161, 148, 156, 167, 159, 174, 161, 164, 159, 165, 161, 160, 155, 164, 157, 162, 158, 149, 169, 158, 155, 157, 168, 162, 161, 179, 159, 162, 171, 167, 153, 155, 155, 158, 160, 162, 160, 162, 157, 167, 160, 163, 154, 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166, 158, 164, 160, 155, 152, 152, 162, 159, 161, 164, 158, 160, 161, 152, 157, 155, 155, 158, 161, 184, 160, 158, 176, 153, 160, 160, 158, 169, 163, 164, 158, 164, 164, 129, 161, 162, 157, 158, 157, 156, 150, 155, 156, 169, 157, 157, 159, 159, 156, 160, 157, 148, 158, 164, 160, 168, 156, 160, 160, 158, 152, 152, 165, 151, 158, 166, 159, 155, 150, 159, 166, 178, 160, 166, 162, 163, 162, 157, 170, 164, 153, 159, 167, 155, 188, 160, 164, 156, 153, 146, 158, 167, 174, 165, 154, 155, 157, 162, 150, 167, 159, 152, 161, 160, 161, 154, 159, 156, 160, 157, 167, 153, 156, 158, 159, 172, 160, 151, 154, 154, 155, 161, 158, 160, 158, 166, 162, 158, 160, 161, 159, 158, 157, 154, 156, 156, 163, 166, 159, 156, 154, 158, 159, 156, 156, 161, 152, 162, 161, 162, 156, 158, 158, 162, 157, 159, 162, 162, 155, 159, 150, 153, 155, 161, 160, 163, 190, 154, 156, 157, 163, 154, 164, 160, 168, 158, 161, 166, 162, 152, 161, 170, 158, 159, 144, 163, 161, 145, 170, 159, 161, 162, 162, 153, 162, 167, 151, 155, 166, 156, 155, 158, 159, 156, 155, 161, 152, 154, 159, 153, 154, 157, 157, 158, 157, 159, 162, 156, 166, 161, 159, 163, 163, 166, 161, 162, 156, 166, 166, 155, 162, 146, 158, 173, 150, 165, 168, 150, 161, 153, 158, 158, 156, 154, 162, 154, 161, 160, 167, 153, 160, 164, 161, 162, 162, 162, 161, 160, 151, 164, 158, 161, 158, 159, 165, 165, 160, 158, 160, 161, 161, 151, 161, 160, 160, 157, 159, 158, 157, 161, 165, 155, 159, 155, 160, 161, 163, 161, 157, 159, 157, 162, 154, 155, 159, 152, 158, 160, 158, 160, 164, 151, 153, 159, 160, 162, 163, 165, 160, 159, 160, 161, 162, 156, 160, 155, 161, 158, 158, 157, 159, 155, 163, 152, 164, 164, 158, 161, 161, 157, 156, 167, 156, 156, 162, 158, 160, 165, 167, 159, 161, 155, 160, 164, 165, 158, 157, 163, 153, 159, 153, 157, 166, 162, 154, 162, 160, 164, 155, 158, 165, 167, 159, 157, 156, 162, 161, 157, 167, 160, 168, 161, 162, 158, 159, 156, 164, 162, 159, 161, 160, 158, 164, 165, 155, 163, 159, 162, 166, 158, 162, 150, 159, 155, 157, 154, 161, 163, 162, 158, 163, 157, 169, 162, 162, 158, 159, 161, 164, 159, 160, 160, 157, 163, 165, 158, 154, 157, 157, 165, 156, 156, 164, 162, 162, 159, 161, 159, 158, 163, 166, 156, 169, 162, 162, 164, 160, 161, 158, 160, 155, 153, 157, 157, 162, 157, 159, 166, 161, 163, 160, 153, 163, 156, 154, 158, 160, 168, 161, 158, 163, 161, 154, 160, 162, 162, 159, 160, 158, 163, 159, 168, 163, 158, 161, 161, 158, 163, 165, 161, 163, 160, 157, 161, 160, 160, 159, 158, 161, 159, 165, 162, 159, 158, 155, 161, 151, 163, 155, 160, 157, 159, 155, 161, 159, 162, 163, 165, 152, 161, 159, 165, 159, 149, 159, 164, 160, 158, 164, 160, 156, 158, 163, 162, 163, 160, 159, 160, 160, 162, 151, 159, 162, 165, 159, 165, 154, 167, 156, 152, 160, 157, 161, 161, 159, 160, 157, 157, 167, 154, 158, 157, 158, 153, 161, 159, 161, 167, 166, 159, 161, 160, 158, 160, 160, 173, 163, 163, 164, 162, 157, 162, 156, 166, 159, 159, 162, 158, 156, 157, 160, 164, 160, 163, 163, 164, 163, 151, 150, 157, 160, 163, 158, 152, 156, 159, 158, 158, 159, 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158, 160, 163, 164, 163, 159, 156, 163, 159, 169, 156, 161, 165, 166, 159, 155, 159, 162, 156, 168, 159, 161, 162, 159, 156, 158, 157, 155, 152, 157, 164, 152, 159, 161, 158, 165, 160, 157, 172, 160, 156, 163, 165, 157, 161, 165, 163, 155, 159, 162, 153, 158, 160, 167, 160, 153, 164, 156, 146, 158, 164, 163, 152, 156, 169, 164, 161, 162, 151, 163, 168, 161, 166, 172, 160, 164, 152, 180, 159, 166, 164, 162, 168, 165, 157, 160, 160, 162, 157, 163, 163, 146, 154, 163, 162, 160, 163, 175, 158, 166, 169, 151, 151, 163, 156, 162, 153, 160, 172, 168, 155, 162, 159, 158, 161, 156, 152, 164, 156, 155, 163, 160, 168, 162, 169, 170, 153, 160, 155, 157, 165, 165, 165, 148, 162, 164, 159, 155, 160, 152, 159, 164, 166, 157, 162, 149, 167, 157, 156, 163, 159, 161, 159, 159, 161, 158, 172, 152, 165, 155, 163, 154, 160, 162, 155, 156, 168, 160, 162, 155, 156, 159, 165, 162, 165, 163, 157, 148, 163, 150, 150, 171, 161, 159, 161, 156, 160, 157, 162, 158, 155, 158, 147, 158, 158, 164, 165, 154, 164, 155, 158, 154, 169, 164, 156, 162, 156, 155, 152, 152, 163, 170, 161, 164, 154, 162, 141, 159, 162, 163, 160, 159, 160, 166, 162, 161, 154, 163, 160, 166, 154, 160, 159, 172, 159, 169, 164, 161, 171, 163, 160, 155, 157, 158, 188, 156, 155, 151, 168, 170, 167, 169, 160, 160, 155, 161, 162, 152, 195, 162, 154, 159, 158, 160, 166, 158, 152, 157, 158, 163, 161, 153, 159, 161, 160, 164, 155, 161, 158, 156, 151, 154, 154, 161, 160, 161, 155, 161, 151, 157, 170, 164, 156, 159, 165, 151, 157, 163, 157, 160, 162, 159, 163, 160, 157, 155, 165, 150, 172, 125, 159, 167, 154, 164, 157, 161, 159, 168, 165, 152, 162, 164, 142, 154, 164, 162, 157, 171, 153, 156, 158, 152, 160, 155, 160, 160, 159, 160, 155, 165, 155, 153, 161, 161, 165, 167, 159, 158, 147, 164, 156, 165, 157, 168, 176, 169, 159, 155, 157, 161, 164, 159, 150, 153, 153, 161, 159, 170, 153, 159, 157, 165, 162, 160, 160, 160, 160, 158, 157, 156, 156, 158, 158, 155, 159, 176, 162, 154, 158, 137, 162, 190, 163, 169, 164, 154, 160, 154, 162, 140, 166, 158, 136, 164, 153, 147, 161, 158, 161, 173, 155, 163, 195, 157, 162, 159, 158, 166, 161, 158, 159, 169, 153, 160, 162, 158, 167, 156, 155, 155, 158, 163, 153, 163, 168, 161, 158, 158, 163, 159, 162, 158, 163, 172, 162, 163, 162, 161, 164, 157, 161, 159, 158, 158, 161, 159, 158, 164, 170, 167, 159, 156, 171, 164, 163, 158, 160, 165, 153}; - model->setOperandValue(op74, op74_init, sizeof(uint8_t) * 262144); - static int32_t op76_init[] = {-8479, -155, 11608, -2458, -2250, 3044, 3977, 4855, -3856, -5367, -1644, 7265, -1950, -3111, -4913, -1482, -1486, 978, 3582, 3887, -6453, -2470, 3345, -4158, 5835, 3560, 2549, -2600, -1940, 266, -567, 5359, -3347, 5558, 2029, 3921, 5215, 3025, -1615, 102, 3858, 6653, -2858, -1730, -1087, 5062, 1052, -299, 5044, -3934, 4063, 3088, -6040, 717, 5949, 6378, -1194, 5709, 75, -1205, -1460, -264, -5829, 3679, 6647, 2240, 5713, -1337, -2175, -2752, 8586, -1550, -4258, -3778, 3633, -1029, 556, -1214, -5447, -5487, 5284, 6770, 3308, 3096, -1575, -2747, 4871, 884, 5398, 4935, 231, -1612, -6543, 6353, 6411, -6364, -201, -2122, -2004, 3710, -2986, -2016, -2259, -933, 5367, -339, -886, 3327, 14295, 3076, 5733, 3889, 5147, -985, 10917, 5678, 4298, 1294, 3875, -1284, -2148, -1466, -3996, 2993, -2964, 3693, 5099, -406, 7315, 3654, 9654, -1077, 829, -3493, -5970, -605, 4949, 5971, 5707, -2364, 4284, -1188, 3850, 5937, 2720, 8587, 3980, 6813, 7638, 3935, 4789, -8064, -1579, 4672, -2225, 781, 6470, -3627, -543, 4431, 9024, 3807, -2522, 4096, 1102, -3183, 9254, 5615, 8757, 4787, -128, 5317, -1904, -2535, -302, -2241, 1622, 3869, -6468, 2016, -1618, -2332, -5187, -230, -3998, 6458, 3903, 4129, 5301, 12468, -782, 7409, -705, -3791, -1631, -2731, 4767, 6324, 6746, 4246, 4744, 7140, 1597, -1969, -6537, -1359, -504, 398, 3733, -2357, -516, 4124, 4745, -3489, 4154, 4780, 4588, -1697, 3876, 1908, 5912, -4167, 3829, -999, -80, -6562, 15736, 7352, -29, 9854, -677, -50, 15826, -1972, 2924, 4579, -10103, 4903, 3587, 5217, 3994, 4679, 4650, -1820, 432, -3026, 3362, -3376, 10626, -1476, -2404, 6563, 5721, -1936, 3509, 5221, 6899, 7501, 3928, -552, -7163, -7441, -2939, -945, 10156, -3352, 3804, 3431, 2851, 3604, -1825, -3603, 5615, 6795, -2058, 3289, 3420, 5861, 3270, 4028, 2549, -964, -2602, 1247, 5559, 5002, 3435, 543, -4442, 5583, 4872, 7151, 4377, 4410, -844, 5375, -957, 5890, -621, -313, 1289, 750, 945, 5084, -2914, 4641, -992, 3269, -2032, 132, 5724, 123, 4044, -1537, 4195, -1650, 3734, 9663, 5687, 8850, 1581, -1946, 5127, 3651, -4002, -497, -5026, 1466, -1153, -1183, 5432, -649, -8948, 4266, -1017, -1179, 4479, 45, 4066, -2254, 5609, 5726, 13045, 6018, 6281, 4681, -1547, 1169, -3227, -1542, 5964, 2669, 1208, 4710, -508, -3483, 2681, 4381, 2457, 3564, -778, -1460, 3689, 3281, 596, 4746, 1847, 9955, -1017, 6163, -1736, 3780, 2559, -234, 4047, -265, -920, 860, 3427, 4728, -1409, -950, -5040, -2623, -2261, 3159, 4478, -3951, 7484, -1248, 2436, -4901, -2672, 5248, 5730, 7760, 9232, 4805, 3348, -787, 8691, 6410, -1213, -2339, 1081, -3089, -761, -354, -1815, -775, -2310, -3415, 622, -665, -5640, -1858, -616, -1949, 5385, 3026, 8023, 4657, -1528, 4145, -890, -2765, 6168, 3809, -1156, -877, -995, -2336, 2590, -5893, -69, 6382, -3156, -19, 6887, 1783, 4670, -7374, -1851, -2257, 8090, -4856, 6253, -2493, -3278, -1536, 3759, -1758, -1354, 970, 3711, -2009, 6858, 5465, 8384, -6167, -8303, 3432, 2034, 132, -915, 10668, 9122, -2651, 3437, 3646, 3977, 3457, -1251, -272, 4728, -1021, -1037, -5393, -198, -4374, 3572, 2338, 3276, -6031, -5252, -1170, -1178, -2096, -1822, 1366, 5964, -2077, 7373, -4051, 5743, 7732, -5841, 2883, 7882, -155, -1130, 5697, 4918, 204, -1854, -4629, 4240, -905, 5480, -357, 10978, -2732}; - model->setOperandValue(op76, op76_init, sizeof(int32_t) * 512); - static uint8_t op77_init[] = {140, 158, 104, 137, 131, 113, 139, 144, 128, 142, 134, 127, 133, 137, 155, 132, 128, 127, 119, 129, 138, 155, 124, 137, 117, 125, 128, 137, 145, 143, 146, 113, 129, 112, 126, 117, 129, 116, 127, 102, 118, 121, 133, 132, 91, 129, 128, 143, 132, 134, 122, 115, 136, 141, 140, 113, 135, 161, 110, 134, 135, 136, 113, 123, 154, 144, 139, 147, 183, 168, 127, 120, 147, 143, 120, 116, 132, 139, 131, 133, 117, 119, 123, 122, 142, 144, 119, 108, 123, 126, 129, 136, 143, 116, 119, 122, 140, 189, 136, 127, 138, 160, 132, 138, 138, 141, 118, 129, 130, 134, 126, 127, 127, 139, 135, 116, 108, 154, 125, 147, 120, 128, 134, 157, 139, 127, 121, 147, 137, 165, 133, 166, 185, 152, 157, 184, 115, 146, 108, 135, 131, 127, 141, 112, 140, 129, 112, 117, 132, 129, 106, 131, 133, 123, 129, 130, 124, 124, 128, 123, 136, 122, 142, 116, 133, 131, 131, 120, 135, 162, 114, 143, 134, 149, 152, 141, 125, 135, 134, 131, 147, 134, 169, 164, 149, 123, 121, 110, 121, 127, 130, 134, 137, 139, 136, 65, 133, 115, 133, 159, 123, 130, 152, 170, 154, 111, 139, 133, 116, 138, 164, 158, 127, 133, 134, 119, 148, 105, 129, 129, 125, 152, 130, 147, 131, 148, 122, 131, 135, 139, 155, 193, 127, 134, 121, 129, 150, 131, 157, 139, 101, 120, 133, 148, 119, 133, 131, 150, 133, 152, 139, 131, 117, 161, 118, 126, 129, 121, 125, 135, 140, 134, 138, 145, 135, 143, 136, 106, 127, 130, 135, 147, 133, 103, 133, 90, 116, 124, 124, 140, 130, 143, 133, 151, 129, 142, 119, 165, 127, 109, 112, 132, 124, 137, 108, 154, 176, 101, 134, 126, 181, 132, 145, 167, 154, 112, 136, 120, 134, 108, 123, 140, 127, 149, 127, 152, 132, 138, 132, 126, 161, 142, 145, 119, 142, 126, 140, 142, 93, 124, 158, 148, 152, 103, 148, 133, 125, 130, 129, 137, 120, 104, 125, 136, 130, 118, 182, 148, 170, 137, 120, 129, 156, 125, 149, 135, 180, 141, 130, 129, 134, 180, 144, 130, 150, 114, 90, 128, 134, 128, 133, 130, 111, 135, 109, 129, 119, 144, 115, 126, 139, 131, 140, 125, 151, 118, 115, 137, 129, 133, 183, 149, 136, 134, 107, 133, 136, 123, 117, 131, 133, 103, 127, 150, 171, 164, 133, 132, 137, 153, 147, 139, 130, 131, 157, 130, 137, 126, 131, 119, 132, 145, 145, 126, 132, 131, 118, 127, 97, 141, 136, 126, 129, 130, 143, 132, 136, 139, 134, 154, 122, 145, 112, 68, 114, 149, 117, 139, 136, 137, 127, 131, 137, 140, 124, 140, 121, 120, 131, 144, 149, 123, 128, 132, 130, 131, 125, 146, 127, 113, 129, 115, 139, 131, 152, 139, 120, 133, 160, 133, 94, 138, 130, 136, 138, 135, 155, 95, 124, 111, 119, 139, 117, 128, 126, 135, 140, 138, 129, 128, 134, 118, 114, 170, 136, 112, 120, 135, 123, 129, 124, 137, 148, 117, 102, 126, 122, 116, 151, 148, 146, 167, 161, 120, 142, 137, 179, 130, 129, 120, 113, 125, 153, 179, 142, 149, 109, 120, 121, 137, 162, 206, 161, 130, 124, 127, 115, 124, 112, 120, 125, 112, 106, 87, 131, 94, 143, 127, 114, 144, 139, 132, 126, 118, 130, 125, 150, 113, 148, 105, 118, 105, 156, 130, 203, 114, 128, 182, 125, 178, 140, 151, 132, 128, 141, 169, 124, 136, 136, 100, 140, 151, 117, 131, 123, 94, 115, 120, 131, 93, 138, 117, 117, 136, 148, 108, 139, 182, 148, 102, 137, 119, 137, 127, 155, 165, 133, 132, 129, 127, 107, 115, 129, 119, 140, 143, 102, 127, 117, 163, 118, 157, 131, 137, 158, 189, 144, 117, 120, 138, 136, 131, 136, 159, 155, 132, 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167, 104, 135, 140, 149, 137, 138, 129, 146, 129, 124, 107, 119, 130, 138, 133, 132, 140, 136, 127, 128, 129, 120, 131, 117, 127, 133, 80, 154, 118, 130, 130, 113, 135, 143, 126, 128, 145, 131, 131, 132, 104, 128, 130, 142, 143, 156, 122, 157, 130, 126, 140, 135, 137, 135, 161, 134, 138, 124, 143, 142, 123, 126, 124, 130, 143, 112, 130, 94, 126, 129, 130, 129, 165, 136, 120, 132, 146, 118, 149, 130, 134, 127, 127, 149, 137, 126, 131, 142, 140, 117, 144, 128, 120, 131, 138, 111, 118, 127, 129, 115, 112, 124, 125, 128, 135, 130, 114, 126, 124, 116, 119, 116, 122, 117, 142, 130, 136, 144, 123, 131, 121, 129, 148, 132, 135, 134, 132, 148, 117, 129, 116, 136, 158, 128, 114, 137, 143, 126, 120, 119, 135, 85, 133, 124, 135, 122, 118, 133, 179, 147, 115, 125, 131, 134, 100, 135, 122, 130, 133, 122, 134, 105, 137, 115, 134, 136, 145, 141, 116, 162, 111, 138, 148, 155, 128, 130, 140, 131, 146, 129, 155, 133, 128, 133, 128, 126, 141, 137, 141, 136, 121, 136, 121, 138, 141, 116, 133, 118, 130, 103, 136, 124, 119, 147, 148, 135, 132, 146, 127, 118, 139, 127, 134, 177, 130, 135, 125, 135, 123, 136, 139, 120, 111, 134, 141, 150, 146, 138, 152, 137, 135, 144, 156, 134, 140, 145, 148, 153, 138, 131, 133, 128, 116, 125, 129, 127, 120, 131, 125, 123, 150, 128, 133, 132, 130, 131, 134, 122, 126, 129, 137, 134, 120, 124, 142, 130, 134, 126, 134, 129, 145, 140, 139, 149, 144, 132, 128, 123, 139, 131, 115, 136, 133, 179, 129, 148, 128, 165, 120, 119, 152, 130, 112, 130, 126, 137, 130, 131, 132, 125, 125, 135, 140, 63, 132, 129, 141, 147, 136, 130, 128, 155, 145, 126, 137, 129, 147, 159, 122, 133, 129, 140, 134, 142, 135, 123, 111, 132, 129, 129, 133, 123, 143, 115, 129}; - model->setOperandValue(op77, op77_init, sizeof(uint8_t) * 4608); - static int32_t op79_init[] = {-15709, 4131, -4227, -5060, 2469, 12246, -19310, -8550, -9589, 12285, -7295, -15468, 11009, -7912, 5778, -20369, -3115, -18818, -3064, 2549, -12244, 28189, -771, 11366, 10671, 1860, 6985, -17783, -8928, -14702, -4247, -19396, -2501, 10280, 2728, -14644, 13925, 2380, -6508, -6569, 695, -12303, -362, -3622, -26612, 7296, -712, 3120, -573, 5385, 7673, -10656, 9944, 4715, -2813, 14568, -20969, 18686, -10451, -11382, -19825, -10149, -6873, 17940, -1107, 8045, -3687, 9594, -24737, 3873, 1520, -13797, -6282, -5740, 15157, 15136, 14698, -15312, -1085, -10619, 553, -4692, -8701, 17841, -17061, 3846, -19712, 251, -9151, 2983, -1555, 6642, -2927, -4289, 8981, -14765, 18048, -22482, -5925, 8598, 221, -13452, 8187, 14091, -14916, -12484, 12265, 1094, 11626, -14723, -14811, -3572, -5172, 5325, -25788, -7053, 8015, 5654, 4936, 11112, -1846, -17838, -2018, 6719, -9349, 2825, 7283, 14865, 6798, -5312, -601, -13297, 4397, -11312, 2146, 9295, -6071, 5773, -21407, -8507, -23790, 10532, 2137, -2871, 1030, -693, 23772, 18374, -17991, -3732, 3438, 2846, 11278, -3337, 8266, -11105, 7936, 11428, 6500, -6636, -22631, -5567, 5003, 15060, -8927, 18526, 8434, 3600, 12771, 5914, -29740, -4939, 8408, -13006, 8024, -10686, 3498, 4919, 14427, -2665, -10006, 25887, -31827, 28376, 5430, -293, 9483, 16461, -8344, 2527, 13569, -176, 16371, 8827, 16757, -8652, 15813, -12070, 2613, -1607, 5296, -32302, -7878, -12248, -15939, 17740, 12075, 15927, 14608, 2832, 12405, 14022, 5738, -4134, -22407, -13522, -2698, -10719, 11108, -5238, 14045, -3512, -20879, 10529, 8586, -7680, -6382, -9137, 9572, 1048, -3262, 10972, 2059, 19554, 12647, -28701, -17721, -19013, -1563, -5998, -1815, -10005, -14449, -16462, 30240, -3446, 15076, -1551, -2160, -11720, 13836, 12902, 16321, -11785, -9941, 126, -5831, -13604, -13463, -18182, 3920, -5227, -23801, -22028, 5987, -9911, 7162, 5850, -12745, 6060, 33703, 4533, 1699, -13885, -12335, -6369, 15926, -9638, 19855, -8524, -39, -9352, 1021, -6630, 3875, -6846, -134, -2620, 19, -14629, -1484, 9025, 12694, 16409, 8964, 9614, -32362, 3036, -9107, -13590, 20788, -6593, 16806, 2325, -5755, -31367, -5682, 1950, 9876, -4913, 2061, 14208, -1988, 11241, 9114, 14270, -4746, -3943, 2455, 4810, 3148, 5044, -1212, -1350, -14076, -15664, -4032, 1575, 1386, 16482, 6230, 14366, -29026, -4054, 4768, -788, -11222, 17241, -20631, 10649, -610, -5172, -5640, -13145, -7901, 1909, -5276, 17840, -14676, -3130, 8765, -7049, 2887, 4771, -5342, -8374, 4959, -1487, -2564, -13403, 10136, 13499, -7934, 6443, 2967, -8260, -7565, -18021, 5570, 8064, -10804, -3881, 18203, -7242, 1886, -12058, -4871, 895, 10453, 29461, 12491, -1507, 12237, 10251, 14659, 18957, -5478, -12698, -14890, -12834, 8764, 2284, 1618, 10989, -27920, -6622, -2389, -21486, 1788, -13233, -10561, -10419, -21887, 25949, -14537, 12799, -10535, -10532, 4729, 10402, 507, 2919, 12062, -5174, 2022, -9594, 20526, -6706, -18210, 3738, 3938, 2633, 13965, 300, -16966, -8638, 1281, 7235, -2840, 5671, 19536, 9603, -26095, 4173, 11441, -15425, -9934, -11685, 11459, 12218, -16117, -6083, 7361, 10819, 12725, -29310, -1634, 13373, -548, 14769, 1649, 38, 13320, -5953, -8049, 12228, 8733, 1225, 10642, 3554, 17369, -4237, 28871, -5764, 7381, 6123, -8698, -11423, -14187, 1342, -5802, -1008, 11484, 5197, -23915, -22464, 17764, 2070, 8901, 4514, -3589, -1151, 15309, -16064, -876, 3226, 1276, 9193, 927, 1615, -18942, -13356, 9804, 5069, -8493, -18616, -969, 6259, -17856, 27312, 2379, 14479, -8654, 12897, -3924, -8140, 254, 6570, -14154, 7708, -10344, 3417}; - model->setOperandValue(op79, op79_init, sizeof(int32_t) * 512); - static uint8_t op80_init[] = {99, 128, 95, 117, 120, 113, 108, 111, 125, 93, 131, 109, 119, 107, 90, 120, 128, 123, 121, 130, 121, 105, 106, 117, 121, 107, 120, 106, 88, 117, 129, 99, 116, 131, 120, 107, 122, 91, 101, 119, 98, 92, 125, 123, 136, 113, 115, 105, 117, 84, 104, 112, 101, 116, 120, 130, 119, 123, 120, 107, 117, 114, 106, 111, 138, 113, 131, 122, 96, 122, 123, 128, 105, 120, 124, 115, 127, 96, 97, 109, 120, 118, 115, 121, 111, 135, 117, 108, 111, 114, 109, 111, 115, 113, 123, 112, 115, 106, 118, 110, 130, 117, 116, 107, 114, 118, 104, 123, 116, 114, 102, 100, 118, 112, 104, 109, 120, 109, 124, 108, 143, 125, 117, 135, 114, 108, 125, 104, 130, 132, 126, 128, 112, 112, 116, 124, 121, 128, 107, 114, 108, 105, 123, 123, 133, 126, 108, 124, 111, 117, 104, 104, 123, 108, 94, 110, 130, 112, 117, 111, 122, 99, 112, 107, 135, 116, 108, 115, 110, 121, 114, 110, 110, 103, 109, 121, 116, 131, 109, 112, 111, 99, 138, 97, 116, 120, 114, 115, 106, 107, 118, 118, 129, 130, 112, 151, 124, 101, 109, 105, 115, 88, 113, 110, 89, 129, 110, 115, 116, 106, 107, 130, 117, 104, 118, 116, 100, 131, 123, 125, 112, 105, 122, 126, 119, 97, 116, 110, 129, 117, 129, 118, 119, 119, 113, 118, 132, 111, 113, 102, 114, 128, 114, 124, 128, 105, 101, 116, 105, 116, 120, 115, 100, 108, 112, 115, 107, 128, 117, 126, 114, 124, 110, 101, 103, 122, 98, 123, 125, 127, 110, 113, 126, 100, 128, 136, 114, 133, 104, 124, 112, 119, 118, 113, 126, 119, 117, 114, 120, 113, 130, 119, 116, 116, 147, 135, 130, 89, 123, 108, 108, 108, 119, 102, 103, 106, 121, 108, 109, 110, 116, 139, 131, 127, 111, 109, 118, 110, 113, 107, 128, 115, 122, 110, 105, 137, 127, 128, 100, 124, 109, 112, 121, 121, 88, 122, 101, 99, 106, 103, 109, 123, 117, 111, 108, 114, 117, 101, 135, 117, 96, 118, 98, 114, 116, 108, 93, 105, 134, 106, 112, 98, 120, 107, 113, 119, 143, 118, 118, 120, 119, 93, 113, 129, 114, 124, 122, 116, 113, 123, 108, 118, 122, 119, 100, 114, 113, 126, 140, 102, 124, 101, 121, 117, 138, 119, 109, 122, 106, 120, 123, 129, 102, 104, 98, 125, 107, 108, 104, 117, 103, 115, 105, 112, 103, 109, 107, 125, 112, 116, 123, 133, 107, 114, 111, 116, 116, 107, 116, 113, 121, 128, 116, 122, 123, 119, 115, 108, 109, 148, 121, 115, 131, 125, 122, 114, 125, 106, 114, 115, 117, 128, 103, 90, 110, 113, 112, 122, 119, 130, 132, 96, 117, 114, 111, 118, 121, 103, 97, 108, 125, 120, 117, 109, 115, 121, 134, 108, 118, 113, 105, 115, 124, 106, 137, 122, 110, 131, 110, 118, 113, 113, 116, 110, 112, 119, 117, 121, 115, 116, 122, 97, 121, 116, 128, 114, 125, 121, 133, 126, 117, 125, 122, 133, 126, 117, 116, 114, 115, 126, 98, 106, 104, 133, 105, 105, 95, 110, 127, 107, 106, 120, 109, 121, 115, 106, 120, 103, 123, 118, 118, 126, 116, 123, 112, 126, 111, 133, 120, 112, 117, 108, 107, 124, 111, 116, 121, 96, 110, 102, 112, 135, 104, 97, 115, 118, 117, 113, 123, 110, 131, 104, 101, 94, 117, 125, 120, 122, 125, 127, 115, 124, 120, 113, 104, 117, 126, 113, 118, 122, 112, 139, 105, 117, 100, 118, 119, 126, 114, 123, 98, 120, 125, 130, 114, 125, 107, 119, 111, 101, 142, 123, 128, 108, 124, 134, 113, 123, 118, 112, 130, 112, 116, 117, 124, 124, 111, 128, 111, 111, 105, 121, 122, 120, 122, 115, 104, 113, 107, 112, 125, 115, 129, 139, 130, 113, 107, 118, 135, 113, 105, 99, 123, 119, 108, 112, 120, 113, 104, 131, 130, 114, 117, 110, 116, 99, 111, 120, 112, 129, 120, 109, 85, 142, 118, 113, 114, 123, 105, 108, 92, 115, 120, 114, 89, 108, 117, 126, 110, 111, 102, 111, 113, 123, 123, 122, 106, 105, 113, 95, 117, 133, 110, 119, 145, 105, 116, 130, 120, 111, 118, 110, 118, 114, 124, 109, 110, 123, 108, 96, 110, 101, 120, 115, 122, 117, 128, 128, 122, 134, 115, 124, 119, 108, 124, 116, 120, 117, 127, 110, 120, 122, 115, 114, 110, 101, 119, 118, 118, 108, 114, 112, 110, 120, 115, 115, 104, 108, 109, 137, 111, 97, 108, 124, 97, 110, 122, 106, 102, 116, 116, 109, 110, 109, 132, 114, 118, 122, 117, 121, 121, 122, 132, 121, 98, 121, 115, 108, 118, 115, 113, 121, 100, 119, 132, 111, 107, 111, 121, 118, 113, 111, 129, 113, 112, 117, 111, 119, 119, 123, 133, 129, 103, 115, 111, 117, 112, 129, 129, 129, 129, 123, 116, 116, 100, 108, 112, 108, 113, 119, 123, 123, 120, 117, 113, 108, 119, 118, 120, 116, 109, 114, 112, 109, 119, 108, 106, 116, 124, 106, 112, 116, 116, 128, 95, 137, 130, 122, 117, 125, 120, 117, 112, 119, 94, 131, 118, 112, 102, 114, 126, 103, 144, 114, 116, 115, 128, 120, 116, 125, 102, 115, 110, 113, 121, 111, 116, 111, 123, 115, 108, 122, 118, 121, 107, 101, 129, 114, 120, 130, 118, 117, 109, 137, 98, 113, 117, 116, 110, 117, 120, 122, 103, 121, 133, 123, 112, 102, 115, 126, 109, 118, 98, 98, 115, 118, 123, 123, 114, 114, 135, 126, 114, 124, 118, 105, 122, 124, 112, 119, 124, 120, 113, 130, 113, 123, 120, 110, 143, 121, 123, 109, 115, 115, 123, 113, 113, 99, 107, 95, 103, 125, 123, 127, 136, 120, 102, 116, 115, 124, 120, 113, 119, 97, 118, 118, 128, 104, 126, 119, 131, 110, 110, 103, 104, 99, 123, 110, 107, 114, 118, 104, 121, 126, 122, 117, 128, 115, 112, 122, 128, 119, 112, 108, 106, 90, 122, 125, 105, 114, 117, 99, 104, 118, 124, 121, 107, 109, 109, 122, 120, 116, 137, 111, 167, 114, 97, 110, 118, 112, 122, 127, 125, 117, 103, 124, 141, 120, 105, 113, 118, 129, 101, 110, 118, 118, 103, 108, 113, 106, 110, 118, 115, 103, 124, 106, 126, 114, 108, 109, 113, 139, 113, 120, 119, 124, 117, 128, 121, 112, 106, 133, 115, 100, 123, 116, 104, 114, 108, 105, 124, 118, 126, 124, 128, 113, 116, 113, 117, 116, 118, 108, 115, 126, 110, 99, 104, 103, 103, 116, 117, 118, 108, 119, 112, 117, 112, 113, 122, 109, 130, 114, 117, 111, 112, 103, 100, 123, 121, 120, 112, 114, 115, 112, 114, 113, 112, 113, 110, 116, 117, 109, 121, 113, 109, 117, 101, 125, 103, 116, 108, 127, 103, 114, 133, 120, 119, 117, 119, 111, 117, 121, 104, 114, 122, 130, 111, 124, 108, 110, 125, 104, 120, 125, 141, 112, 115, 113, 118, 127, 121, 122, 126, 110, 100, 119, 116, 118, 106, 121, 111, 126, 107, 118, 117, 124, 110, 106, 89, 126, 104, 118, 100, 117, 121, 129, 124, 118, 118, 118, 122, 103, 123, 127, 113, 113, 122, 116, 125, 113, 128, 98, 133, 110, 145, 123, 124, 123, 104, 116, 101, 109, 119, 98, 125, 111, 120, 120, 129, 113, 118, 135, 115, 134, 116, 118, 102, 110, 123, 115, 110, 120, 120, 140, 113, 117, 112, 112, 108, 141, 113, 118, 116, 99, 129, 121, 116, 122, 119, 105, 118, 123, 112, 120, 112, 127, 117, 133, 113, 126, 116, 92, 108, 118, 113, 118, 110, 123, 103, 116, 134, 111, 110, 119, 121, 124, 104, 133, 113, 128, 102, 101, 123, 127, 124, 109, 119, 101, 122, 119, 111, 115, 123, 104, 109, 116, 116, 113, 122, 112, 114, 122, 112, 122, 123, 119, 116, 122, 118, 111, 115, 124, 124, 119, 105, 125, 129, 114, 129, 114, 124, 122, 114, 108, 112, 121, 115, 114, 122, 108, 116, 117, 112, 129, 110, 125, 116, 112, 123, 116, 115, 102, 118, 136, 106, 127, 116, 112, 108, 122, 118, 109, 122, 118, 119, 123, 119, 140, 110, 124, 113, 110, 96, 115, 118, 111, 142, 123, 105, 119, 116, 114, 118, 93, 113, 114, 131, 130, 118, 120, 109, 111, 123, 129, 97, 111, 107, 106, 115, 131, 118, 118, 121, 121, 106, 121, 117, 115, 118, 105, 111, 91, 116, 128, 122, 120, 118, 124, 110, 128, 123, 109, 114, 115, 113, 116, 110, 106, 117, 103, 109, 118, 111, 112, 118, 108, 97, 111, 124, 100, 104, 103, 113, 117, 129, 96, 125, 105, 111, 107, 113, 134, 108, 129, 108, 115, 122, 125, 111, 119, 112, 119, 121, 105, 117, 125, 119, 111, 136, 128, 121, 137, 126, 103, 117, 112, 126, 137, 124, 108, 118, 113, 130, 114, 112, 126, 102, 128, 108, 111, 111, 85, 116, 110, 123, 120, 119, 117, 118, 114, 112, 94, 125, 119, 119, 124, 121, 120, 120, 123, 119, 116, 108, 114, 118, 109, 123, 116, 118, 111, 103, 112, 119, 112, 108, 124, 116, 108, 101, 119, 108, 117, 96, 133, 104, 112, 100, 127, 102, 119, 113, 128, 121, 124, 101, 106, 117, 121, 106, 133, 110, 123, 119, 131, 107, 124, 91, 103, 114, 120, 112, 130, 122, 118, 103, 117, 109, 120, 113, 121, 118, 117, 136, 122, 116, 129, 127, 123, 119, 112, 146, 106, 113, 120, 103, 118, 136, 113, 104, 102, 122, 125, 131, 146, 106, 108, 115, 112, 122, 101, 125, 106, 102, 120, 99, 112, 121, 117, 108, 115, 117, 142, 105, 112, 110, 111, 117, 112, 119, 107, 88, 121, 125, 109, 119, 115, 92, 121, 86, 87, 109, 102, 111, 98, 116, 123, 108, 113, 99, 120, 108, 117, 122, 116, 118, 105, 126, 120, 116, 114, 115, 132, 124, 112, 113, 113, 122, 111, 105, 110, 123, 109, 122, 124, 118, 122, 121, 123, 122, 120, 115, 117, 119, 122, 114, 112, 109, 117, 139, 125, 119, 115, 119, 95, 122, 111, 111, 112, 117, 115, 124, 117, 117, 117, 107, 115, 126, 119, 121, 108, 118, 115, 120, 111, 125, 103, 121, 101, 110, 107, 114, 112, 119, 120, 130, 109, 111, 114, 130, 119, 112, 122, 112, 116, 130, 116, 115, 116, 109, 114, 121, 135, 121, 115, 123, 111, 105, 105, 108, 113, 122, 107, 126, 119, 125, 122, 106, 109, 118, 130, 120, 102, 115, 121, 121, 106, 110, 71, 112, 117, 104, 118, 110, 112, 117, 126, 104, 122, 108, 111, 115, 107, 91, 113, 134, 108, 100, 139, 130, 132, 121, 118, 116, 117, 158, 105, 118, 110, 118, 105, 127, 114, 110, 124, 114, 99, 123, 100, 119, 112, 109, 124, 116, 113, 108, 118, 130, 110, 122, 125, 128, 118, 114, 116, 114, 106, 120, 116, 105, 125, 115, 114, 119, 113, 141, 84, 127, 111, 122, 115, 117, 139, 120, 110, 115, 119, 111, 111, 122, 119, 114, 112, 99, 126, 117, 118, 125, 116, 111, 139, 116, 113, 106, 111, 129, 107, 107, 124, 120, 117, 129, 103, 112, 111, 108, 124, 136, 127, 118, 94, 113, 115, 110, 101, 101, 124, 125, 127, 119, 118, 110, 124, 104, 116, 118, 111, 125, 97, 119, 98, 120, 125, 86, 111, 95, 122, 121, 96, 119, 116, 119, 105, 112, 119, 109, 121, 124, 103, 105, 115, 108, 122, 105, 107, 108, 110, 120, 104, 116, 105, 111, 107, 120, 114, 124, 115, 127, 125, 112, 118, 136, 123, 126, 126, 113, 113, 114, 145, 107, 117, 129, 110, 114, 120, 122, 108, 119, 124, 114, 72, 106, 112, 119, 115, 118, 121, 117, 94, 114, 115, 123, 101, 122, 109, 111, 123, 114, 112, 116, 133, 114, 108, 127, 122, 105, 114, 113, 95, 89, 134, 117, 121, 122, 99, 118, 108, 113, 123, 117, 95, 116, 120, 86, 118, 116, 98, 117, 115, 114, 117, 110, 119, 102, 103, 108, 124, 123, 98, 122, 117, 112, 104, 113, 119, 114, 110, 104, 104, 124, 107, 115, 124, 98, 137, 106, 112, 112, 120, 105, 121, 135, 130, 120, 113, 125, 116, 125, 116, 112, 124, 116, 119, 117, 114, 124, 96, 123, 117, 119, 118, 114, 114, 110, 119, 120, 113, 125, 110, 113, 121, 118, 123, 120, 120, 114, 111, 116, 111, 119, 124, 114, 114, 119, 121, 123, 119, 123, 116, 116, 112, 109, 104, 109, 107, 126, 123, 120, 113, 117, 119, 115, 115, 117, 125, 111, 115, 114, 106, 102, 125, 119, 121, 120, 109, 120, 116, 114, 120, 122, 114, 115, 115, 118, 114, 110, 119, 120, 117, 120, 118, 122, 114, 122, 115, 113, 117, 121, 123, 114, 122, 116, 114, 117, 108, 113, 120, 116, 119, 110, 121, 133, 133, 116, 103, 121, 117, 120, 120, 111, 118, 121, 114, 115, 111, 125, 115, 112, 113, 125, 117, 118, 116, 115, 127, 119, 113, 112, 109, 112, 118, 128, 115, 116, 114, 117, 115, 114, 123, 114, 117, 117, 127, 117, 117, 119, 118, 117, 124, 116, 115, 124, 125, 110, 117, 118, 115, 112, 116, 115, 115, 114, 110, 113, 116, 114, 118, 121, 112, 126, 113, 121, 131, 127, 114, 122, 113, 121, 123, 123, 117, 121, 130, 120, 116, 115, 119, 116, 132, 118, 113, 115, 121, 126, 115, 121, 118, 118, 116, 114, 114, 122, 112, 114, 114, 123, 111, 115, 119, 116, 111, 116, 115, 118, 119, 109, 122, 115, 122, 113, 117, 118, 130, 114, 116, 122, 121, 118, 119, 115, 118, 117, 121, 111, 112, 115, 117, 113, 126, 120, 120, 117, 110, 114, 109, 129, 115, 112, 117, 126, 120, 111, 108, 120, 98, 106, 116, 132, 113, 114, 117, 113, 121, 111, 114, 116, 109, 121, 100, 119, 118, 109, 114, 108, 121, 123, 109, 111, 113, 113, 115, 113, 114, 119, 120, 113, 110, 115, 113, 124, 120, 119, 118, 120, 110, 121, 120, 122, 112, 112, 125, 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156, 122, 109, 102, 117, 118, 112, 117, 119, 118, 133, 100, 122, 123, 133, 128, 111, 126, 118, 116, 101, 102, 111, 113, 125, 117, 111, 117, 115, 124, 112, 120, 111, 121, 111, 117, 107, 119, 120, 115, 121, 107, 118, 105, 91, 112, 108, 113, 127, 117, 102, 116, 113, 89, 100, 112, 110, 111, 118, 96, 119, 128, 122, 118, 114, 126, 118, 106, 128, 96, 115, 121, 118, 115, 115, 118, 116, 114, 110, 121, 127, 117, 108, 115, 114, 110, 125, 119, 101, 125, 114, 112, 130, 116, 113, 111, 122, 116, 112, 113, 131, 111, 114, 111, 120, 112, 115, 117, 119, 108, 123, 131, 116, 118, 114, 111, 118, 120, 117, 122, 122, 132, 121, 115, 126, 102, 102, 117, 118, 117, 122, 109, 117, 115, 118, 112, 105, 110, 119, 112, 117, 116, 109, 132, 110, 113, 117, 123, 115, 119, 121, 113, 111, 110, 126, 102, 126, 108, 120, 106, 110, 124, 114, 131, 111, 105, 116, 121, 103, 119, 109, 111, 107, 92, 102, 78, 112, 111, 105, 122, 108, 119, 122, 114, 119, 112, 123, 120, 112, 121, 110, 99, 102, 130, 121, 103, 110, 118, 118, 118, 120, 131, 103, 115, 119, 118, 123, 115, 116, 127, 104, 113, 124, 114, 95, 128, 100, 104, 117, 113, 114, 104, 108, 121, 103, 125, 74, 109, 97, 109, 118, 124, 108, 94, 104, 106, 104, 110, 123, 111, 118, 110, 120, 113, 133, 122, 111, 111, 124, 124, 124, 120, 108, 113, 127, 138, 108, 120, 112, 128, 120, 121, 107, 122, 107, 115, 122, 117, 113, 111, 117, 112, 123, 109, 107, 120, 115, 130, 124, 92, 137, 108, 110, 121, 110, 101, 124, 115, 118, 119, 126, 117, 109, 114, 105, 100, 111, 99, 126, 111, 104, 109, 126, 124, 125, 101, 118, 114, 112, 123, 104, 116, 107, 128, 118, 113, 128, 113, 105, 102, 121, 121, 99, 109, 121, 128, 111, 114, 113, 108, 113, 133, 109, 114, 100, 121, 103, 115, 132, 128, 99, 126, 113, 114, 121, 127, 132, 132, 126, 111, 114, 106, 113, 120, 117, 119, 119, 122, 107, 114, 140, 124, 102, 110, 118, 109, 111, 121, 111, 104, 119, 108, 107, 118, 112, 107, 112, 121, 118, 124, 109, 112, 123, 123, 126, 90, 113, 117, 126, 138, 123, 136, 134, 120, 127, 120, 103, 114, 117, 111, 138, 129, 106, 121, 113, 107, 114, 118, 131, 106, 107, 114, 107, 104, 100, 136, 132, 118, 106, 137, 108, 112, 107, 110, 105, 113, 79, 117, 113, 121, 105, 125, 114, 118, 111, 140, 94, 106, 117, 117, 118, 116, 123, 99, 118, 122, 131, 136, 120, 115, 115, 130, 96, 114, 91, 118, 111, 122, 126, 99, 98, 114, 110, 110, 116, 126, 114, 120, 125, 115, 114, 111, 126, 119, 114, 127, 143, 106, 119, 104, 108, 114, 117, 129, 122, 125, 119, 105, 141, 100, 123, 97, 102, 123, 117, 132, 120, 135, 135, 94, 123, 115, 131, 118, 115, 106, 119, 112, 114, 115, 105, 113, 128, 121, 114, 127, 112, 118, 115, 114, 133, 111, 135, 128, 127, 106, 147, 109, 104, 123, 106, 113, 127, 116, 107, 112, 95, 109, 110, 102, 128, 133, 109, 105, 114, 101, 116, 118, 98, 105, 108, 124, 110, 138, 121, 116, 98, 125, 111, 123, 130, 112, 125, 104, 127, 115, 97, 108, 124, 138, 126, 104, 109, 114, 114, 115, 110, 107, 106, 103, 116, 132, 110, 89, 110, 117, 114, 104, 116, 101, 116, 114, 112, 115, 116, 103, 112, 122, 99, 125, 110, 115, 121, 115, 111, 129, 104, 126, 121, 109, 115, 105, 124, 102, 116, 118, 125, 113, 122, 124, 114, 114, 107, 115, 100, 102, 122, 114, 111, 123, 121, 118, 117, 104, 121, 106, 111, 148, 108, 117, 116, 111, 117, 125, 130, 152, 147, 103, 123, 118, 109, 114, 119, 104, 104, 125, 123, 103, 121, 120, 109, 115, 116, 123, 101, 125, 98, 102, 104, 118, 112, 113, 99, 104, 131, 120, 118, 120, 112, 118, 119, 136, 114, 122, 121, 121, 115, 110, 104, 112, 118, 97, 119, 112, 126, 105, 93, 115, 104, 127, 107, 127, 108, 109, 113, 110, 128, 109, 117, 120, 163, 117, 98, 109, 91, 131, 130, 91, 126, 111, 122, 119, 107, 120, 102, 108, 117, 101, 128, 116, 120, 115, 111, 123, 122, 139, 120, 125, 120, 118, 124, 112, 113, 107, 125, 111, 109, 131, 137, 122, 102, 127, 122, 130, 127, 116, 114, 128, 94, 123, 115, 111, 117, 98, 112, 116, 129, 102, 89, 125, 123, 131, 141, 126, 112, 119, 129, 128, 123, 120, 136, 126, 109, 114, 116, 141, 108, 118, 118, 114, 119, 87, 116, 117, 112, 116, 121, 109, 115, 119, 122, 121, 106, 116, 139, 120, 131, 117, 133, 103, 103, 133, 122, 101, 116, 124, 127, 114, 121, 119, 85, 133, 107, 118, 142, 113, 109, 155, 120, 94, 119, 120, 112, 99, 99, 109, 118, 127, 112, 116, 130, 105, 112, 129, 116, 119, 107, 117, 102, 107, 114, 126, 136, 117, 128, 114, 106, 113, 119, 98, 104, 124, 110, 119, 107, 137, 122, 99, 124, 117, 118, 120, 118, 93, 110, 111, 130, 111, 95, 113, 125, 122, 101, 127, 111, 131, 106}; - model->setOperandValue(op80, op80_init, sizeof(uint8_t) * 262144); - static int32_t op83_init[] = {-4628, -2934, 1194, -4381, -7082, 2581, -2454, -471, -2961, -6593, -7812, -3592, -7396, -2142, -1082, -625, -696, -4648, -5302, -1783, -5261, -1369, 2343, -2216, 426, -2891, -594, -476, 1138, 277, -3851, 548, -720, 2505, -5856, -595, -6603, 1293, -2881, -676, 1556, 624, 2128, 981, -4575, 3327, 2164, 3142, 354, -6065, -673, 3220, -9618, 7095, 4169, 4332, 2419, 1777, -2618, 6553, 6394, 2998, 4845, 769, -817, 3180, 1109, -1080, 2713, 1365, -3032, -3135, -1297, -1483, -1523, -841, 155, -1295, 972, 4761, 2553, -8008, -2033, 368, -14412, -6709, -2727, -3975, -1662, -3303, 1786, -3532, -3764, -1591, -2645, -3037, -5524, -1286, -8199, -8220, -5688, -7443, -5917, -899, 3561, 1799, -1877, 129, 248, 633, -2704, -4421, 2452, 2015, -1323, 5102, -3120, -42, -5990, 2163, 1832, -1115, -1634, -1324, -5337, 2440, -3066, -4100, -1675, -4275, -1555, -6653, -6817, -5934, 1841, -9346, -5108, -3929, -8298, -5621, -5565, -7759, -4927, -6129, -5658, -6660, -6747, -1909, -3746, -5484, -4428, 149, 2149, -6093, 3139, -2266, -364, -1559, -5926, -789, -1709, -2984, 901, 5180, 285, 689, 1407, -2742, -2092, 2991, -2354, 1410, 5182, 1087, 1137, 1963, -237, -4070, 1529, 5108, -1070, -630, -2106, 1742, -3862, 4631, 2674, -774, 2865, -2089, 2484, -3221, 1109, 2732, -2720, -1492, -13, -277, -1679, -4892, 744, -3213, -2327, 1325, 3471, 3833, -326, -1649, 1459, 1805, 5115, -4089, 4642, -1139, -375, 1255, 760, -4348, 3026, -1436, 3728, -2445, -4350, -2613, 7480, -4343, 1558, -1419, 1276, -6539, 1541, -2500, -828, 4065, 1681, 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-5710, -1367, 2062, -8691, -6561, -5459, -3561, -7031, -4874, -103, 2030, -3070, 2275, 1083, -5497, -7414, -875, 214, -998, -9100, 714, 662, -1542, 5503, 380, 3351, 2744, 3908, -3264, 5720, 11648, -2728, 4997, -4761, 4220, 6026, -62, -6441, 2359, 1593, -105, -1919, -534, -1455, 3262, 1643, 1533, 2747, -4187, 1812, -5331, 3473, 2109, -2289, -216, -2436, 7393, 4863, 163, 6070, 1800, -635, 245, -92, 3470, 3668, 5685, 1101, 3601, 820, 5039, 2243, 1235, 2374, 4984, 11643, -175, -3471, 562, 6748, -6998, 1575, -1606, -2922, 2210, -615, -239, -9301, 1507, 5768, 2501, 5403, 1765, 1585, -872, -2896, -441, -2565, 9615, 2026, 7678, -3265, 100, 1980, 1185, -2288, -834, -857, -3410, -2018, 1286, -759, 2900, 1294, -2892, 4258, 263, 5384, -749, 4332, 1843, -4352, 1829, -880, 1625, -1803, 7728, 783, -3608, -1169, 6987, -4352, -2128, 1544, 8367, -6268, -3862, 335, -2077, -3999, 7192, 4464, -2705, 257, 1538, 4067, -1275, -3142, -5383, 1376, 8806, -7403, 3991, 4207, -546, -942, -1073, -3538, -7994, -3799, 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-1753, 1310, 5660, 2729, 2062, 4785, -6511, 7187, -958, 4346, 3431, 3234, 2887, 8839, -1736, -5764, -1007, -3370, -7742, 4920, 1921, -2891, 7142, -1985, 3954, -5282, -186, 1036, 2830, 4441, -411, 359, 3677, -1326, 8244, 1162, 2649, 1072, -1234, 1258, 2506, 3138, 3671, 1695, 2645, 1418, -2893, 1453, -3968, -143, -23, 5791, 5162, 1999, 184, 4527, 2905, -4165, 3505, 687, 7384, 446, 2027, 4122, 1851, 2726, -1071, 288, 1770, -3483, -3478, -3251, -3578, 3312, 1885, 3437, 3908, 529, -2020, 2644, 634, -6579, 3714, 3184, 975, 5794, -2631, -1078, 4390, -1247, 6716, -5793, 332, -5787, -2474, 630, 3999, 28, -2157, 3522, 2511, -2710, -952, 1193, 1954, -4560, 5154, 409, -4706, 2393, -53, -7592, -2070, 1445, 7151, 6877, 6615, 2066, 884, 8126, -4531, 2274, 131, -4773, -4713, 5844, -3936, 7323, 10151, 7921, -691, 1488, 7411, 818, 1451, 4508, 10638, -1999, -2475, 3971, -193, 3565, 3371, 4528, -3569, -2711, 4687, -6700, -6017, 4296, 4158, -1190, 1108, -2640, 1533, -1879, 1047, -2490, -1554, 8773, -257, 1229, -4238, 2964, 14, -10224, -2028, 777, 1006, -3939, 6428, 4139, 1799, 2747, 2312, 636, 9735, -3070, -5282, 104, -914, -1612, -2058, 3302, 4238, 655, -469, 2834, -244, 6558, 1260, -4228, -1588, 4197, 7835, 9766, 7238, 3974, 5035, -5768, -1770, 3435, 7952, -515, 13, -5245, -1036, -199, -797, -576, 6356, 4743, 5163, 2549, 2080, -1068, -5, -5517, -2960, -616, 7591, 4146, 2780, 2665, -5790, 1727, -1257, 2844, 1848, -910, -185, -916, -3648, -833, 1329, -4269, 1616, -3497, 1107, 3807, 3039, 3702, 1039, 468, 2160, 3104, -71, -1102, 918, -997, -1922, 3350, 2401, -2955, -58, -429, 819, 4478, 3886, 631, 1391, 5752, 5937, 4683, 3843, 508, 10267, 4684, 11635, 5454, 5185, 2818, 2977, 2906, -2776, 3943, 361, -2178, 5216, 849, 1668, 4607, 590, -7587, -8202, -3721, -1972, -1450, -1112, 3620, 3713}; - model->setOperandValue(op83, op83_init, sizeof(int32_t) * 1001); - static uint8_t op84_init[] = {65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 65, 65, 65, 66, 66, 65, 65, 65, 66, 66, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 66, 65, 66, 65, 65, 65, 65, 65, 66, 66, 65, 65, 66, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 66, 65, 65, 65, 65, 66, 65, 65, 66, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 66, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 65, 65, 65, 66, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 65, 65, 66, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 66, 65, 66, 65, 65, 66, 65, 65, 66, 65, 65, 66, 66, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 66, 65, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 66, 65, 65, 65, 65, 65, 65, 66, 65, 65, 65, 66, 65, 65, 65, 66, 65, 65, 66, 65, 65, 65, 65, 66, 66, 65, 65, 65, 65, 65, 66, 65, 65, 65, 65, 65, 65, 65, 66, 65, 65, 66, 65, 65, 65, 65, 66, 65, 66, 65, 65, 66, 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59, 64, 75, 74, 64, 63, 59, 66, 69, 64, 67, 70, 72, 60, 69, 138, 73, 63, 64, 62, 75, 59, 74, 94, 69, 84, 58, 61, 76, 65, 95, 60, 126, 61, 60, 76, 74, 79, 65, 65, 69, 70, 80, 59, 57, 65, 52, 88, 58, 55, 76, 79, 66, 49, 51, 66, 62, 80, 60, 77, 49, 96, 54, 56, 53, 52, 48, 80, 71, 55, 60, 86, 68, 73, 65, 65, 71, 79, 59, 69, 76, 86, 73, 98, 63, 45, 73, 106, 61, 63, 61, 84, 105, 80, 53, 50, 63, 103, 51, 92, 66, 50, 71, 66, 64, 65, 65, 59, 60, 72, 82, 64, 74, 65, 77, 105, 69, 60, 59, 79, 51, 72, 49, 75, 77, 58, 66, 60, 77, 63, 65, 68, 69, 58, 74, 55, 95, 83, 56, 67, 65, 68, 59, 53, 75, 65, 63, 60, 67, 68, 76, 68, 80, 56, 68, 45, 52, 64, 78, 66, 77, 75, 72, 106, 47, 78, 76, 77, 63, 60, 64, 75, 89, 123, 66, 68, 57, 71, 77, 60, 47, 76, 54, 60, 64, 65, 61, 100, 60, 72, 54, 49, 62, 80, 87, 62, 55, 51, 58, 57, 51, 77, 59, 65, 58, 60, 57, 93, 52, 69, 70, 64, 63, 63, 62, 87, 63, 75, 51, 60, 68, 59, 68, 60, 52, 67, 69, 75, 51, 65, 59, 68, 64, 51, 79, 85, 104, 50, 59, 62, 72, 71, 65, 95, 56, 62, 58, 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55, 77, 66, 51, 53, 52, 76, 78, 84, 102, 73, 57, 65, 67, 62, 87, 71, 69, 97, 61, 108, 77, 80, 55, 75, 72, 58, 66, 70, 53, 70, 59, 96, 57, 82, 58, 65, 63, 58, 64, 42, 67, 57, 57, 85, 62, 87, 64, 71, 59, 55, 55, 54, 59, 53, 69, 67, 92, 65, 99, 55, 90, 71, 73, 93, 79, 53, 65, 75, 113, 73, 73, 79, 67, 65, 54, 66, 58, 70, 60, 60, 57, 85, 65, 53, 63, 56, 96, 55, 60, 50, 48, 74, 74, 67, 66, 59, 51, 71, 63, 59, 54, 113, 67, 61, 64, 73, 58, 72, 48, 77, 61, 67, 65, 77, 55, 67, 57, 65, 55, 51, 57, 63, 74, 56, 82, 46, 72, 90, 54, 106, 56, 73, 73, 51, 52, 65, 57, 56, 77, 55, 69, 74, 70, 64, 59, 48, 63, 66, 53, 105, 47, 63, 81, 77, 63, 74, 67, 53, 65, 58, 80, 59, 89, 63, 57, 93, 74, 36, 55, 55, 54, 74, 59, 50, 58, 63, 81, 70, 70, 61, 67, 60, 63, 70, 66, 63, 51, 59, 49, 67, 75, 50, 76, 58, 86, 93, 58, 67, 74, 49, 80, 71, 70, 62, 62, 56, 66, 51, 50, 94, 66, 53, 58, 63, 50, 68, 45, 58, 64, 72, 59, 69, 51, 40, 53, 63, 85, 58, 119, 55, 72, 85, 71, 45, 52, 42, 63, 66, 58, 54, 60, 39, 80, 68, 75, 60, 65, 65, 53, 56, 92, 56, 63, 76, 61, 69, 63, 37, 68, 76, 71, 63, 52, 57, 64, 72, 81, 85, 45, 64, 57, 60, 65, 57, 61, 108, 64, 76, 55, 47, 54, 76, 50, 79, 65, 69, 71, 70, 53, 82, 52, 63, 50, 61, 77, 63, 55, 72, 68, 58, 51, 71, 72, 57, 71, 74, 64, 61, 44, 84, 70, 51, 101, 85, 59, 58, 77, 75, 73, 53, 55, 53, 67, 76, 62, 55, 70, 60, 97, 71, 64, 63, 70, 71, 51, 57, 56, 55, 67, 71, 77, 65, 76, 53, 83, 63, 64, 56, 75, 54, 64, 66, 55, 53, 59, 50, 72, 53, 59, 63, 76, 58, 70, 94, 68, 62, 73, 49, 54, 74, 78, 44, 72, 60, 76, 51, 57, 65, 68, 74, 59, 56, 68, 73, 62, 56, 47, 71, 82, 85, 73, 60, 64, 49, 52, 52, 74, 77, 74, 66, 68, 67, 77, 62, 65, 53, 47, 91, 58, 66, 56, 65, 88, 82, 68, 71, 82, 56, 54, 52, 64, 55, 38, 78, 59, 42, 63, 62, 65, 72, 65, 90, 82, 57, 58, 57, 75, 62, 50, 51, 63, 49, 71, 96, 68, 69, 76, 56, 60, 71, 72, 57, 66, 56, 81, 55, 57, 73, 60, 59, 80, 53, 60, 81, 59, 71, 58, 46, 73, 86, 54, 65, 78, 74, 56, 62, 64, 56, 49, 64, 74, 71, 62, 72, 60, 89, 55, 61, 56, 76, 55, 67, 56, 83, 74, 62, 64, 81, 69, 61, 69, 75, 82, 55, 62, 79, 64, 93, 72, 72, 64, 81, 80, 65, 86, 55, 67, 74, 76, 65, 103, 60, 49, 66, 63, 78, 55, 89, 58, 54, 95, 71, 59, 54, 50, 58, 63, 56, 73, 70, 71, 56, 67, 85, 62, 89, 81, 63, 50, 51, 76, 51, 43, 75, 77, 70, 98, 68, 61, 47, 83, 59, 38, 46, 63, 65, 61, 42, 62, 50, 83, 51, 80, 64, 75, 55, 52, 56, 62, 63, 65, 67, 57, 81, 81, 57, 55, 45, 48, 54, 63, 69, 67, 89, 54, 121, 46, 77, 81, 58, 60, 54, 60, 68, 93, 65, 73, 65, 74, 80, 53, 57, 58, 71, 74, 71, 53, 49, 52, 75, 58, 58, 57, 61, 49, 81, 56, 54, 66, 91, 68, 80, 63, 69, 46, 75, 58, 94, 55, 57, 73, 49, 49, 53, 89, 60, 73, 64, 60, 75, 57, 66, 76, 49, 46, 152, 59, 52, 48, 50, 71, 70, 84, 68, 59, 49, 64, 70, 84, 78, 58, 83, 63, 53, 49, 62, 63, 60, 50, 55, 49, 49, 71, 56, 56, 59, 62, 55, 48, 73, 118, 65, 50, 89, 57, 52, 65, 60, 73, 55, 56, 53, 81, 74, 60, 64, 57, 78, 55, 55, 81, 52, 60, 69, 78, 50, 82, 72, 90, 90, 87, 68, 59, 55, 76, 50, 58, 57, 58, 118, 81, 68, 72, 107, 62, 63, 100, 48, 70, 53, 61, 71, 60, 57, 53, 72, 57, 53, 89, 104, 66, 110, 56, 66, 61, 36, 60, 60, 57, 72, 64, 100, 52, 55, 63, 71, 62, 87, 88, 72, 68, 51, 60, 61, 67, 81, 125, 43, 62, 67, 75, 80, 69, 69, 62, 57, 40, 58, 50, 58, 66, 63, 61, 79, 69, 64, 68, 51, 71, 63, 99, 98, 59, 66, 45, 67, 50, 45, 68, 79, 61, 58, 72, 74, 67, 76, 57, 91, 56, 67, 87, 75, 79, 93, 77, 60, 56, 63, 68, 66, 78, 60, 53, 67, 79, 56, 50, 53, 66, 68, 76, 76, 56, 64, 68, 56, 49, 49, 87, 67, 53, 41, 56, 81, 63, 67, 56, 86, 57, 57, 45, 68, 63, 53, 75, 54, 57, 79, 70, 59, 76, 76, 68, 62, 57, 74, 67, 63, 62, 85, 49, 73, 61, 82, 54, 73, 72, 64, 57, 58, 113, 65, 54, 55, 60, 75, 54, 80, 77, 32, 67, 71, 67, 67, 69, 68, 60, 46, 82, 66, 121, 54, 62, 58, 56, 54, 67, 53, 61, 70, 52, 72, 50, 76, 71, 62, 73, 62, 77, 54, 53, 79, 70, 52, 62, 50, 66, 43, 68, 49, 61, 59, 52, 78, 61, 93, 52, 85, 85, 56, 62, 49, 48, 57, 86, 69, 52, 73, 69, 57, 55, 63, 67, 81, 95, 64, 67, 67, 54, 69, 57, 77, 56, 59, 78, 49, 58, 57, 62, 61, 70, 69, 45, 63, 59, 63, 66, 57, 66, 79, 52, 61, 52, 54, 51, 60, 74, 55, 59, 69, 52, 58, 65, 57, 71, 68, 55, 77, 85, 54, 72, 69, 82, 78, 78, 63, 59, 60, 63, 87, 75, 61, 77, 57, 83, 48, 64, 48, 58, 68, 69, 70, 79, 61, 65, 60, 64, 53, 72, 83, 69, 73, 85, 82, 52, 84, 62, 60, 84, 91, 73, 66, 79, 83, 65, 86, 57, 65, 90, 55, 59, 79, 54, 56, 67, 81, 66, 88, 71, 66, 58, 61, 62, 70, 70, 65, 61, 50, 77, 67, 76, 60, 70, 67, 57, 64, 84, 84, 41, 75, 66, 86, 71, 67, 51, 72, 83, 58, 88, 67, 74, 79, 63, 63, 76, 78, 52, 82, 93, 57, 84, 65, 58, 56, 79, 62, 61, 77, 61, 78, 71, 43, 73, 42, 56, 78, 64, 69, 63, 94, 65, 52, 74, 82, 55, 76, 72, 54, 59, 63, 56, 44, 45, 59, 69, 62, 49, 62, 72, 86, 80, 51, 55, 51, 62, 97, 77, 63, 77, 64, 58, 59, 49, 42, 46, 83, 71, 56, 61, 71, 61, 70, 91, 45, 55, 66, 96, 56, 94, 48, 48, 64, 71, 57, 76, 69, 57, 72, 65, 87, 44, 45, 64, 65, 68, 81, 59, 63, 57, 63, 76, 49, 66, 52, 69, 200, 62, 71, 66, 66, 53, 73, 43, 70, 93, 57, 54, 45, 69, 60, 53, 53, 129, 73, 68, 64, 50, 55, 84, 78, 71, 51, 60, 109, 57, 75, 85, 71, 63, 55, 61, 51, 59, 52, 61, 72, 61, 59, 59, 80, 70, 71, 84, 59, 58, 65, 41, 57, 67, 61, 61, 69, 68, 74, 47, 57, 62, 66, 65, 54, 43, 65, 48, 50, 77, 85, 68, 59, 71, 100, 67, 56, 53, 89, 55, 63, 76, 59, 47, 49, 69, 65, 57, 94, 79, 64, 63, 59, 64, 65, 75, 62, 53, 53, 66, 84, 101, 78, 74, 75, 50, 69, 62, 64, 65, 64, 54, 67, 75, 59, 76, 70, 69, 78, 68, 67, 74, 60, 93, 67, 56, 66, 55, 59, 52, 42, 88, 79, 68, 76, 46, 50, 42, 55, 61, 71, 55, 63, 66, 71, 62, 61, 67, 65, 78, 79, 91, 72, 57, 50, 73, 77, 81, 68, 81, 68, 65, 69, 59, 57, 67, 77, 58, 69, 65, 62, 71, 69, 86, 62, 63, 84, 50, 73, 60, 68, 85, 61, 70, 52, 63, 54, 66, 106, 71, 80, 47, 79, 60, 96, 60, 69, 71, 48, 64, 64, 48, 85, 77, 71, 60, 53, 59, 59, 75, 64, 88, 78, 59, 52, 58, 49, 129, 53, 72, 72, 83, 70, 94, 40, 78, 71, 91, 59, 60, 63, 70, 57, 45, 65, 41, 69, 62, 60, 59, 69, 88, 78, 70, 53, 131, 50, 43, 77, 70, 65, 76, 59, 78, 64, 62, 51, 59, 67, 60, 71, 46, 75, 58, 54, 65, 40, 53, 42, 64, 50, 79, 69, 71, 72, 73, 68, 77, 67, 77, 76, 46, 61, 55, 57, 59, 70, 63, 67, 42, 62, 63, 58, 65, 76, 59, 58, 48, 54, 61, 52, 72, 70, 51, 72, 71, 68, 76, 96, 60, 80, 70, 70, 69, 51, 57, 53, 68, 91, 61, 65, 68, 43, 60, 88, 60, 47, 64, 60, 66, 81, 57, 45, 73, 76, 77, 64, 59, 61, 45, 75, 54, 56, 85, 63, 72, 45, 84, 89, 66, 58, 86, 58, 74, 58, 61, 73, 59, 80, 69, 62, 63, 57, 46, 40, 70, 76, 58, 68, 88, 55, 59, 81, 84, 60, 66, 66, 53, 68, 58, 64, 60, 66, 78, 84, 55, 68, 61, 108, 65, 67, 113, 74, 106, 69, 77, 59, 51, 66, 55, 65, 59, 38, 48, 68, 85, 66, 80, 44, 65, 79, 82, 60, 61, 73, 61, 69, 76, 62, 58, 75, 47, 82, 72, 94, 71, 76, 58, 69, 51, 66, 59, 76, 59, 65, 49, 61, 79, 65, 61, 75, 65, 55, 81, 63, 59, 82, 65, 59, 71, 61, 54, 75, 63, 61, 68, 85, 41, 56, 65, 74, 78, 56, 78, 77, 66, 76, 44, 62, 64, 75, 50, 71, 74, 87, 95, 77, 56, 34, 61, 78, 63, 55, 63, 63, 53, 66, 91, 56, 62, 69, 63, 64, 64, 109, 52, 63, 64, 96, 46, 65, 86, 79, 86, 64, 80, 54, 83, 97, 93, 73, 86, 65, 47, 124, 57, 60, 61, 79, 56, 52, 60, 67, 57, 53, 66, 66, 48, 60, 68, 79, 61, 75, 56, 70, 66, 63, 74, 68, 86, 57, 72, 84, 66, 87, 60, 80, 42, 69, 71, 58, 68, 61, 56, 50, 54, 71, 62, 59, 44, 69, 73, 64, 72, 104, 81, 55, 60, 77, 64, 82, 92, 54, 75, 70, 68, 59, 59, 69, 61, 54, 78, 83, 66, 62, 50, 80, 72, 52, 81, 61, 68, 75, 78, 58, 49, 57, 49, 78, 49, 51, 69, 44, 48, 57, 106, 66, 68, 79, 77, 63, 64, 61, 82, 68, 55, 50, 55, 79, 69, 71, 71, 49, 42, 77, 86, 90, 74, 64, 77, 63, 74, 85, 52, 75, 85, 67, 115, 51, 55, 55, 53, 80, 61, 60, 48, 69, 49, 74, 76, 44, 44, 79, 64, 83, 57, 60, 57, 65, 60, 54, 59, 56, 70, 73, 46, 71, 82, 58, 77, 98, 60, 72, 69, 85, 88, 47, 141, 54, 78, 63, 75, 75, 66, 64, 50, 61, 72, 59, 49, 65, 49, 55, 63, 69, 59, 65, 66, 71, 66, 59, 58, 69, 83, 71, 61, 63, 58, 59, 72, 77, 66, 64, 57, 81, 59, 52, 76, 77, 65, 81, 61, 52, 68, 87, 67, 61, 63, 81, 61, 56, 64, 84, 67, 63, 68, 57, 69, 66, 78, 63, 58, 70, 57, 80, 82, 75, 66, 75, 60, 69, 64, 67, 77, 59, 64, 60, 58, 69, 59, 90, 55, 76, 61, 70, 61, 76, 60, 59, 65, 62, 75, 65, 66, 54, 56, 68, 59, 80, 78, 52, 135, 58, 63, 73, 114, 67, 62, 47, 63, 67, 54, 58, 59, 81, 55, 72, 76, 43, 73, 74, 61, 49, 68, 67, 92, 69, 57, 39, 54, 52, 78, 85, 77, 55, 57, 67, 53, 57, 86, 78, 108, 76, 70, 65, 52, 81, 55, 68, 49, 57, 66, 64, 62, 68, 86, 65, 83, 73, 56, 96, 45, 73, 59, 66, 60, 86, 57, 61, 56, 63, 57, 72, 51, 52, 44, 61, 76, 48, 53, 57, 83, 47, 76, 76, 75, 53, 67, 56, 54, 62, 60, 72, 50, 87, 56, 64, 64, 35, 60, 56, 74, 44, 71, 67, 68, 67, 55, 92, 73, 62, 57, 71, 71, 47, 65, 64, 67, 63, 44, 67, 86, 59, 66, 82, 54, 61, 77, 62, 64, 72, 28, 107, 64, 54, 62, 72, 48, 61, 90, 52, 80, 48, 73, 66, 69, 86, 71, 68, 71, 69, 66, 98, 54, 82, 73, 86, 73, 67, 79, 93, 84, 66, 73, 40, 58, 50, 63, 39, 35, 63, 76, 73, 114, 45, 44, 86, 73, 61, 38, 60, 49, 79, 135, 59, 54, 60, 75, 69, 74, 56, 52, 59, 65, 74, 58, 74, 70, 60, 86, 67, 67, 57, 57, 76, 60, 54, 52, 77, 62, 65, 51, 53, 58, 73, 57, 56, 71, 56, 88, 78, 68, 96, 55, 77, 65, 55, 65, 66, 80, 67, 69, 58, 52, 72, 43, 66, 71, 59, 69, 71, 64, 67, 96, 67, 69, 78, 55, 101, 50, 88, 51, 55, 80, 70, 69, 67, 75, 96, 50, 49, 58, 69, 65, 53, 67, 66, 70, 93, 68, 79, 60, 68, 103, 76, 69, 54, 56, 71, 72, 55, 55, 55, 54, 60, 83, 57, 58, 52, 68, 50, 76, 65, 71, 57, 78, 78, 39, 72, 74, 74, 74, 61, 120, 81, 70, 61, 55, 77, 63, 60, 60, 55, 64, 63, 74, 65, 67, 56, 67, 76, 64, 67, 41, 50, 74, 84, 77, 54, 69, 54, 90, 55, 58, 62, 66, 46, 66, 63, 84, 74, 60, 52, 58, 78, 73, 68, 115, 55, 75, 56, 71, 46, 48, 44, 91, 52, 70, 69, 77, 83, 82, 61, 50, 36, 72, 64, 74, 67, 52, 65, 67, 60, 74, 49, 54, 43, 76, 68, 79, 56, 55, 74, 86, 103, 78, 50, 69, 69, 60, 59, 57, 67, 84, 84, 68, 35, 70, 72, 61, 62, 61, 62, 68, 79, 69, 49, 69, 78, 85, 67, 57, 65, 67, 128, 70, 80, 61, 87, 44, 45, 62, 49, 59, 77, 84, 62, 53, 61, 58, 79, 78, 103, 117, 85, 121, 55, 61, 87, 67, 68, 89, 76, 62, 44, 56, 65, 66, 57, 83, 70, 53, 75, 51, 73, 58, 59, 102, 75, 76, 60, 68, 83, 68, 59, 53, 30, 82, 62, 47, 57, 58, 74, 102, 49, 67, 54, 91, 54, 59, 66, 49, 77, 71, 50, 105, 60, 65, 57, 84, 69, 95, 56, 76, 67, 46, 74, 64, 66, 59, 74, 59, 116, 67, 85, 45, 68, 61, 77, 68, 55, 85, 91, 64, 86, 45, 58, 62, 60, 57, 58, 79, 55, 64, 60, 68, 74, 72, 97, 57, 65, 69, 56, 64, 58, 44, 43, 42, 59, 57, 78, 53, 51, 61, 73, 104, 76, 69, 80, 45, 87, 71, 62, 57, 64, 66, 113, 54, 77, 90, 76, 62, 70, 76, 72, 95, 51, 43, 94, 76, 60, 57, 62, 53, 49, 68, 76, 68, 57, 49, 55, 51, 71, 70, 76, 56, 55, 62, 62, 82, 70, 67, 77, 48, 64, 33, 49, 53, 52, 65, 64, 56, 53, 56, 90, 64, 86, 79, 54, 58, 63, 69, 46, 65, 149, 84, 65, 53, 55, 50, 50, 67, 73, 44, 49, 82, 60, 84, 51, 80, 61, 76, 60, 64, 78, 53, 43, 72, 95, 77, 64, 69, 43, 75, 59, 109, 72, 62, 71, 65, 72, 69, 66, 65, 75, 60, 93, 63, 73, 56, 54, 52, 67, 68, 92, 75, 90, 70, 67, 51, 56, 65, 109, 70, 101, 88, 67, 66, 56, 61, 71, 65, 69, 38, 65, 70, 80, 78, 64, 75, 46, 66, 39, 66, 70, 63, 70, 53, 59, 44, 45, 54, 79, 84, 102, 64, 87, 66, 58, 49, 74, 47, 61, 69, 62, 75, 62, 90, 59, 43, 60, 40, 55, 79, 66, 61, 51, 69, 54, 107, 46, 51, 69, 54, 67, 51, 57, 55, 62, 61, 61, 75, 63, 59, 62, 77, 78, 87, 56, 94, 64, 61, 57, 80, 64, 69, 58, 82, 64, 47, 93, 39, 53, 47, 74, 56, 72, 58, 68, 43, 55, 60, 72, 63, 85, 66, 54, 71, 61, 55, 65, 65, 103, 61, 68, 80, 103, 81, 60, 56, 66, 93, 80, 77, 53, 55, 107, 52, 47, 56, 65, 99, 56, 54, 80, 65, 43, 74, 54, 62, 50, 35, 50, 72, 100, 50, 69, 42, 50, 69, 38, 60, 52, 63, 83, 63, 41, 57, 63, 66, 63, 74, 54, 66, 66, 54, 54, 65, 68, 65, 59, 64, 109, 58, 67, 38, 72, 68, 59, 67, 48, 94, 79, 86, 122, 82, 83, 60, 55, 96, 58, 60, 57, 52, 65, 69, 29, 61, 64, 57, 73, 87, 42, 81, 68, 62, 52, 54, 71, 42, 63, 55, 57, 52, 46, 72, 44, 52, 64, 65, 73, 70, 52, 57, 42, 55, 50, 49, 52, 55, 45, 126, 43, 68, 54, 58, 56, 53, 62, 64, 53, 61, 43, 71, 54, 85, 79, 68, 69, 88, 59, 52, 91, 54, 75, 64, 63, 51, 72, 79, 85, 67, 84, 66, 63, 63, 75, 57, 64, 78, 93, 80, 81, 68, 56, 54, 60, 84, 43, 79, 105, 61, 44, 47, 55, 78, 85, 68, 87, 69, 49, 66, 52, 72, 40, 104, 77, 59, 64, 87, 60, 56, 67, 53, 56, 84, 78, 90, 61, 55, 58, 63, 74, 58, 40, 55, 50, 51, 50, 66, 42, 91, 73, 48, 87, 76, 88, 83, 19, 61, 66, 69, 42, 79, 68, 43, 70, 62, 73, 63, 34, 66, 87, 67, 81, 74, 86, 64, 61, 59, 60, 93, 65, 70, 54, 48, 56, 55, 55, 62, 59, 52, 55, 69, 81, 61, 54, 66, 70, 92, 51, 61, 67, 118, 53, 55, 85, 75, 79, 53, 73, 58, 54, 89, 52, 37, 85, 86, 61, 66, 57, 80, 53, 62, 57, 56, 70, 87, 65, 54, 50, 47, 84, 58, 50, 102, 61, 47, 71, 61, 46, 59, 72, 76, 63, 58, 49, 60, 57, 92, 52, 85, 76, 57, 68, 75, 75, 71, 70, 67, 69, 48, 59, 56, 58, 76, 52, 50, 29, 66, 63, 68, 78, 62, 22, 93, 59, 50, 53, 71, 60, 44, 48, 66, 85, 120, 61, 88, 113, 79, 137, 67, 77, 52, 54, 111, 54, 73, 55, 47, 66, 71, 69, 69, 61}; - model->setOperandValue(op84, op84_init, sizeof(uint8_t) * 1025024); - static int32_t op87_init[] = {1, 1001}; - model->setOperandValue(op87, op87_init, sizeof(int32_t) * 2); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op88, op2, op1, b104, b105, b106, b107}, {op0}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op0, op29, op28, b108, b109, b110, b111, b112}, {op27}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op27, op32, op31, b113, b114, b115, b116}, {op30}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op30, op35, op34, b117, b118, b119, b120, b121}, {op33}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op33, op38, op37, b122, b123, b124, b125}, {op36}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op36, op41, op40, b126, b127, b128, b129, b130}, {op39}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op39, op44, op43, b131, b132, b133, b134}, {op42}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op42, op47, op46, b135, b136, b137, b138, b139}, {op45}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op45, op50, op49, b140, b141, b142, b143}, {op48}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op48, op53, op52, b144, b145, b146, b147, b148}, {op51}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op51, op56, op55, b149, b150, b151, b152}, {op54}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op54, op59, op58, b153, b154, b155, b156, b157}, {op57}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op57, op62, op61, b158, b159, b160, b161}, {op60}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op60, op65, op64, b162, b163, b164, b165, b166}, {op63}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op63, op68, op67, b167, b168, b169, b170}, {op66}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op66, op71, op70, b171, b172, b173, b174, b175}, {op69}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op69, op74, op73, b176, b177, b178, b179}, {op72}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op72, op77, op76, b180, b181, b182, b183, b184}, {op75}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op75, op80, op79, b185, b186, b187, b188}, {op78}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op78, op5, op4, b189, b190, b191, b192, b193}, {op3}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op3, op8, op7, b194, b195, b196, b197}, {op6}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op6, op11, op10, b198, b199, b200, b201, b202}, {op9}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op9, op14, op13, b203, b204, b205, b206}, {op12}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op12, op17, op16, b207, b208, b209, b210, b211}, {op15}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op15, op20, op19, b212, b213, b214, b215}, {op18}); - model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op18, op23, op22, b216, b217, b218, b219, b220}, {op21}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op21, op26, op25, b221, b222, b223, b224}, {op24}); - model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {op24, b225, b226, b227, b228, b229, b230}, {op81}); - model->addOperation(ANEURALNETWORKS_CONV_2D, {op81, op84, op83, b231, b232, b233, b234}, {op82}); - model->addOperation(ANEURALNETWORKS_RESHAPE, {op82, op87}, {op86}); - model->addOperation(ANEURALNETWORKS_SOFTMAX, {op86, b235}, {op85}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op88}, - {op85}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul.model.cpp deleted file mode 100644 index 9ab5c9a37..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: mul.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_1_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_1_nnfw.model.cpp deleted file mode 100644 index b8a4120f1..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_1_nnfw.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: mul_broadcast_3D_1D_1_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_2_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_2_nnfw.model.cpp deleted file mode 100644 index c5d215fb8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_3D_1D_2_nnfw.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: mul_broadcast_3D_1D_2_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {3, 2, 4}); - OperandType type1(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_quant8.model.cpp deleted file mode 100644 index 06917efda..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul_broadcast_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: mul_broadcast_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 1.0, 0); - OperandType type1(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 1.0, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 2.0, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul_quant8.model.cpp deleted file mode 100644 index 5670845d9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul_quant8.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: mul_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2}, 2.0, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/mul_relu.model.cpp b/runtimes/tests/neural_networks_test/generated/models/mul_relu.model.cpp deleted file mode 100644 index ece7fd0d2..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/mul_relu.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: mul_relu.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {1}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_MUL, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/pad.model.cpp b/runtimes/tests/neural_networks_test/generated/models/pad.model.cpp deleted file mode 100644 index 97e173e21..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/pad.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: pad.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 4, 1}); - OperandType type1(Type::TENSOR_INT32, {4, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t op2_init[] = {0, 0, 1, 1, 1, 1, 0, 0}; - model->setOperandValue(op2, op2_init, sizeof(int32_t) * 8); - model->addOperation(ANEURALNETWORKS_PAD, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/pad_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/pad_float_1.model.cpp deleted file mode 100644 index 61ae0b766..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/pad_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: pad_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 3, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 4, 7, 1}); - OperandType type1(Type::TENSOR_INT32, {4, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t op2_init[] = {0, 0, 0, 2, 1, 3, 0, 0}; - model->setOperandValue(op2, op2_init, sizeof(int32_t) * 8); - model->addOperation(ANEURALNETWORKS_PAD, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu1_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu1_float_1.model.cpp deleted file mode 100644 index 7bb424ed1..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu1_float_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu1_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU1, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu1_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu1_float_2.model.cpp deleted file mode 100644 index cbf2e7d1d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu1_float_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu1_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 30, 24, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU1, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_1.model.cpp deleted file mode 100644 index 905c87bd4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu1_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU1, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_2.model.cpp deleted file mode 100644 index 8e58e339c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu1_quant8_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu1_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 64, 64, 2}, 1.f, 128); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU1, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu6_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu6_float_1.model.cpp deleted file mode 100644 index 45c632b12..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu6_float_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu6_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU6, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu6_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu6_float_2.model.cpp deleted file mode 100644 index c59232ba6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu6_float_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu6_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 26, 40, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU6, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_1.model.cpp deleted file mode 100644 index 7be2b9ad6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu6_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU6, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_2.model.cpp deleted file mode 100644 index 87b17cc73..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu6_quant8_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu6_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 128, 20, 2}, 1.f, 128); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU6, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu_float_1.model.cpp deleted file mode 100644 index f2a27183c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu_float_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu_float_2.model.cpp deleted file mode 100644 index 13e857547..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu_float_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 64, 40, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu_quant8_1.model.cpp deleted file mode 100644 index e5ebe01fc..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu_quant8_1.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.f, 128); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/relu_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/relu_quant8_2.model.cpp deleted file mode 100644 index fefba7af3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/relu_quant8_2.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: relu_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 32, 60, 2}, 1.f, 128); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto output = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RELU, {input}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/reshape.model.cpp b/runtimes/tests/neural_networks_test/generated/models/reshape.model.cpp deleted file mode 100644 index e7efea38d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/reshape.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: reshape.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 3, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {9}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t op2_init[] = {-1}; - model->setOperandValue(op2, op2_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/reshape_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/reshape_quant8.model.cpp deleted file mode 100644 index cb8932087..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/reshape_quant8.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: reshape_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 1, 3, 3}, 1.f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {9}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t op2_init[] = {-1}; - model->setOperandValue(op2, op2_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/reshape_quant8_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/reshape_quant8_weights_as_inputs.model.cpp deleted file mode 100644 index 9f6dafd10..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/reshape_quant8_weights_as_inputs.model.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape_quant8_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 1, 3, 3}, 1.f, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {9}, 1.f, 0); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/reshape_weights_as_inputs.model.cpp b/runtimes/tests/neural_networks_test/generated/models/reshape_weights_as_inputs.model.cpp deleted file mode 100644 index cf4396a3a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/reshape_weights_as_inputs.model.cpp +++ /dev/null @@ -1,22 +0,0 @@ -// Generated file (from: reshape_weights_as_inputs.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 3, 3}); - OperandType type2(Type::TENSOR_FLOAT32, {9}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_RESHAPE, {op1, op2}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/resize_bilinear.model.cpp b/runtimes/tests/neural_networks_test/generated/models/resize_bilinear.model.cpp deleted file mode 100644 index f291f1d44..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/resize_bilinear.model.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// Generated file (from: resize_bilinear.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 3, 3, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto height = model->addOperand(&type2); - auto width = model->addOperand(&type2); - // Phase 2, operations - static int32_t height_init[] = {3}; - model->setOperandValue(height, height_init, sizeof(int32_t) * 1); - static int32_t width_init[] = {3}; - model->setOperandValue(width, width_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RESIZE_BILINEAR, {op1, height, width}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/resize_bilinear_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/resize_bilinear_2.model.cpp deleted file mode 100644 index b0f0768ec..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/resize_bilinear_2.model.cpp +++ /dev/null @@ -1,27 +0,0 @@ -// Generated file (from: resize_bilinear_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - OperandType type1(Type::TENSOR_FLOAT32, {1, 3, 3, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto height = model->addOperand(&type2); - auto width = model->addOperand(&type2); - // Phase 2, operations - static int32_t height_init[] = {3}; - model->setOperandValue(height, height_init, sizeof(int32_t) * 1); - static int32_t width_init[] = {3}; - model->setOperandValue(width, width_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RESIZE_BILINEAR, {op1, height, width}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/rnn.model.cpp b/runtimes/tests/neural_networks_test/generated/models/rnn.model.cpp deleted file mode 100644 index 4eaba28ba..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/rnn.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: rnn.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type5(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); - OperandType type3(Type::TENSOR_FLOAT32, {16}); - OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto weights = model->addOperand(&type1); - auto recurrent_weights = model->addOperand(&type2); - auto bias = model->addOperand(&type3); - auto hidden_state_in = model->addOperand(&type4); - auto activation_param = model->addOperand(&type5); - auto hidden_state_out = model->addOperand(&type4); - auto output = model->addOperand(&type4); - // Phase 2, operations - static int32_t activation_param_init[] = {1}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, weights, recurrent_weights, bias, hidden_state_in}, - {hidden_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp deleted file mode 100644 index 72fcb6703..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp +++ /dev/null @@ -1,32 +0,0 @@ -// Generated file (from: rnn_state.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type5(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {16, 16}); - OperandType type1(Type::TENSOR_FLOAT32, {16, 8}); - OperandType type3(Type::TENSOR_FLOAT32, {16}); - OperandType type4(Type::TENSOR_FLOAT32, {2, 16}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 8}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto weights = model->addOperand(&type1); - auto recurrent_weights = model->addOperand(&type2); - auto bias = model->addOperand(&type3); - auto hidden_state_in = model->addOperand(&type4); - auto activation_param = model->addOperand(&type5); - auto hidden_state_out = model->addOperand(&type4); - auto output = model->addOperand(&type4); - // Phase 2, operations - static int32_t activation_param_init[] = {1}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, weights, recurrent_weights, bias, hidden_state_in}, - {hidden_state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/softmax_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/softmax_float_1.model.cpp deleted file mode 100644 index d189002bb..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/softmax_float_1.model.cpp +++ /dev/null @@ -1,23 +0,0 @@ -// Generated file (from: softmax_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::FLOAT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto beta = model->addOperand(&type1); - auto output = model->addOperand(&type0); - // Phase 2, operations - static float beta_init[] = {1e-06f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/softmax_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/softmax_float_2.model.cpp deleted file mode 100644 index 8f575cfa7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/softmax_float_2.model.cpp +++ /dev/null @@ -1,23 +0,0 @@ -// Generated file (from: softmax_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::FLOAT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto beta = model->addOperand(&type1); - auto output = model->addOperand(&type0); - // Phase 2, operations - static float beta_init[] = {1.0f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_1.model.cpp deleted file mode 100644 index 4e7eb8ae1..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: softmax_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::FLOAT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.00390625f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 4}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto beta = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static float beta_init[] = {1e-05f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_2.model.cpp deleted file mode 100644 index e24d6f265..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/softmax_quant8_2.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: softmax_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::FLOAT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 5}, 0.00390625f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 5}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto beta = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static float beta_init[] = {1.0f}; - model->setOperandValue(beta, beta_init, sizeof(float) * 1); - model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch.model.cpp deleted file mode 100644 index 4064c94a9..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - OperandType type3(Type::TENSOR_FLOAT32, {4, 1, 1, 2}); - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {0, 0, 0, 0}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_1.model.cpp deleted file mode 100644 index f4dfab99f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_1.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 4, 1}); - OperandType type3(Type::TENSOR_FLOAT32, {4, 2, 2, 1}); - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {0, 0, 0, 0}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_2.model.cpp deleted file mode 100644 index 44dee00ce..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_2.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 5, 2, 1}); - OperandType type3(Type::TENSOR_FLOAT32, {6, 2, 2, 1}); - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {3, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {1, 0, 2, 0}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_3.model.cpp deleted file mode 100644 index f2fa99042..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_float_3.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 2, 1}); - OperandType type3(Type::TENSOR_FLOAT32, {6, 2, 4, 1}); - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {3, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {1, 1, 2, 4}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_1.model.cpp deleted file mode 100644 index cfd56c2e7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_1.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 1.0, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 1}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {2, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {0, 0, 0, 0}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_2.model.cpp deleted file mode 100644 index 8ab61a116..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_2.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 5, 2, 1}, 1.0, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {6, 2, 2, 1}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {3, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {1, 0, 2, 0}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_3.model.cpp deleted file mode 100644 index 7ee388441..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_batch_quant8_3.model.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Generated file (from: space_to_batch_quant8_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::TENSOR_INT32, {2, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 4, 2, 1}, 1.0, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {6, 2, 4, 1}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto paddings = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t block_size_init[] = {3, 2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 2); - static int32_t paddings_init[] = {1, 1, 2, 4}; - model->setOperandValue(paddings, paddings_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_SPACE_TO_BATCH_ND, {input, block_size, paddings}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_1.model.cpp deleted file mode 100644 index 764318627..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: space_to_depth_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 1, 1, 8}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SPACE_TO_DEPTH, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_2.model.cpp deleted file mode 100644 index 8fdd5357e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_2.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: space_to_depth_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 2, 2, 4}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 4, 1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SPACE_TO_DEPTH, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_3.model.cpp deleted file mode 100644 index 176546497..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_float_3.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: space_to_depth_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 2, 2, 8}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 4, 4, 2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto block_size = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t block_size_init[] = {2}; - model->setOperandValue(block_size, block_size_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SPACE_TO_DEPTH, {input, block_size}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_1.model.cpp deleted file mode 100644 index 92d080b67..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: space_to_depth_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 8}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t radius_init[] = {2}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SPACE_TO_DEPTH, {input, radius}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_2.model.cpp deleted file mode 100644 index 4e91914df..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/space_to_depth_quant8_2.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: space_to_depth_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 4}, 0.5f, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.5f, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto radius = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t radius_init[] = {2}; - model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SPACE_TO_DEPTH, {input, radius}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/squeeze.model.cpp b/runtimes/tests/neural_networks_test/generated/models/squeeze.model.cpp deleted file mode 100644 index 806a10c61..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/squeeze.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: squeeze.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {4, 1, 1, 2}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 2}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto squeezeDims = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t squeezeDims_init[] = {1, 2}; - model->setOperandValue(squeezeDims, squeezeDims_init, sizeof(int32_t) * 2); - model->addOperation(ANEURALNETWORKS_SQUEEZE, {input, squeezeDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/squeeze_2D_float_1_nnfw.model.cpp b/runtimes/tests/neural_networks_test/generated/models/squeeze_2D_float_1_nnfw.model.cpp deleted file mode 100644 index 4d6621e21..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/squeeze_2D_float_1_nnfw.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: squeeze_2D_float_1_nnfw.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {4, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto squeezeDims = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t squeezeDims_init[] = {1}; - model->setOperandValue(squeezeDims, squeezeDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SQUEEZE, {input, squeezeDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/squeeze_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/squeeze_float_1.model.cpp deleted file mode 100644 index 2277e38e6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/squeeze_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: squeeze_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 24, 1}); - OperandType type2(Type::TENSOR_FLOAT32, {1, 24}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto squeezeDims = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t squeezeDims_init[] = {2}; - model->setOperandValue(squeezeDims, squeezeDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SQUEEZE, {input, squeezeDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/squeeze_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/squeeze_quant8_1.model.cpp deleted file mode 100644 index f122d43f5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/squeeze_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: squeeze_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 24, 1}, 1.0, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {1, 24}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto squeezeDims = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t squeezeDims_init[] = {2}; - model->setOperandValue(squeezeDims, squeezeDims_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SQUEEZE, {input, squeezeDims}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice.model.cpp deleted file mode 100644 index 5f1b875c2..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {0, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {2, 2}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_1.model.cpp deleted file mode 100644 index 1385693c8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_1.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_10.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_10.model.cpp deleted file mode 100644 index 5da568959..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_10.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_10.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {2}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_2.model.cpp deleted file mode 100644 index 84c3fe65d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_2.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-3}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_3.model.cpp deleted file mode 100644 index a6067409d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_3.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-5}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_4.model.cpp deleted file mode 100644 index 497ed4ba7..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_4.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_5.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_5.model.cpp deleted file mode 100644 index 568e03013..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_5.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_5.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_6.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_6.model.cpp deleted file mode 100644 index 8333a4334..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_6.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_6.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {1}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_7.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_7.model.cpp deleted file mode 100644 index 34f2d13fa..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_7.model.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// Generated file (from: strided_slice_ex_float_7.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {3}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t begins_init[] = {-1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {-1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_8.model.cpp deleted file mode 100644 index 6027abb1c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_8.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, -1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, -4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {2, -1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_9.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_9.model.cpp deleted file mode 100644 index de18b9d76..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_ex_float_9.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_ex_float_9.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_STRIDED_SLICE_EX, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_1.model.cpp deleted file mode 100644 index fcd2f6dac..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_1.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_10.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_10.model.cpp deleted file mode 100644 index 1463f13ab..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_10.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_10.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {2}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_11.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_11.model.cpp deleted file mode 100644 index 2197b502a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_11.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_11.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {0, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {1, 3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {1}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_2.model.cpp deleted file mode 100644 index 47179ca53..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_2.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-3}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_3.model.cpp deleted file mode 100644 index 113c775a3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_3.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-5}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_4.model.cpp deleted file mode 100644 index af5ffa891..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_4.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_5.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_5.model.cpp deleted file mode 100644 index a0280d3a8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_5.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_5.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_6.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_6.model.cpp deleted file mode 100644 index cb40c8527..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_6.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_6.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {3}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {1}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_7.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_7.model.cpp deleted file mode 100644 index 1580128a1..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_7.model.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// Generated file (from: strided_slice_float_7.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {3}); - OperandType type1(Type::TENSOR_INT32, {1}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t begins_init[] = {-1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {-1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_8.model.cpp deleted file mode 100644 index 0dd388435..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_8.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {1, 3}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, -1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, -4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {2, -1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_9.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_9.model.cpp deleted file mode 100644 index 22e0e7028..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_float_9.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_float_9.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type3(Type::TENSOR_FLOAT32, {2, 2}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type1(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_10.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_10.model.cpp deleted file mode 100644 index a6eec78a4..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_10.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_qaunt8_10.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {2}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_11.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_11.model.cpp deleted file mode 100644 index 170dc7e0f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_qaunt8_11.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_qaunt8_11.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 1.0, 0); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {0, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {1, 3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {1}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_1.model.cpp deleted file mode 100644 index 7f8e602eb..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_1.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_2.model.cpp deleted file mode 100644 index e6042147e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_2.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-3}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_3.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_3.model.cpp deleted file mode 100644 index 2cc75a461..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_3.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_3.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {-5}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_4.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_4.model.cpp deleted file mode 100644 index 2fe2277d6..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_4.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_4.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_5.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_5.model.cpp deleted file mode 100644 index 1ed3ed107..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_5.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_5.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_6.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_6.model.cpp deleted file mode 100644 index 73da2fc3a..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_6.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_6.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {3}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {3}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {1}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_7.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_7.model.cpp deleted file mode 100644 index 089388bf2..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_7.model.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// Generated file (from: strided_slice_quant8_7.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {1}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {3}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t begins_init[] = {-1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 1); - static int32_t ends_init[] = {-4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 1); - static int32_t strides_init[] = {-1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 1); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_8.model.cpp deleted file mode 100644 index ef55fc15f..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_8.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, -1}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, -4}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {2, -1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {0}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_9.model.cpp b/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_9.model.cpp deleted file mode 100644 index 37bb2898e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/strided_slice_quant8_9.model.cpp +++ /dev/null @@ -1,40 +0,0 @@ -// Generated file (from: strided_slice_quant8_9.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type1(Type::TENSOR_INT32, {2}); - OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 1.0, 0); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto begins = model->addOperand(&type1); - auto ends = model->addOperand(&type1); - auto strides = model->addOperand(&type1); - auto beginMask = model->addOperand(&type2); - auto endMask = model->addOperand(&type2); - auto shrinkAxisMask = model->addOperand(&type2); - auto output = model->addOperand(&type3); - // Phase 2, operations - static int32_t begins_init[] = {1, 0}; - model->setOperandValue(begins, begins_init, sizeof(int32_t) * 2); - static int32_t ends_init[] = {2, 2}; - model->setOperandValue(ends, ends_init, sizeof(int32_t) * 2); - static int32_t strides_init[] = {1, 1}; - model->setOperandValue(strides, strides_init, sizeof(int32_t) * 2); - static int32_t beginMask_init[] = {1}; - model->setOperandValue(beginMask, beginMask_init, sizeof(int32_t) * 1); - static int32_t endMask_init[] = {0}; - model->setOperandValue(endMask, endMask_init, sizeof(int32_t) * 1); - static int32_t shrinkAxisMask_init[] = {0}; - model->setOperandValue(shrinkAxisMask, shrinkAxisMask_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_STRIDED_SLICE, {input, begins, ends, strides, beginMask, endMask, shrinkAxisMask}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/sub.model.cpp b/runtimes/tests/neural_networks_test/generated/models/sub.model.cpp deleted file mode 100644 index 40a0247c8..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/sub.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: sub.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - auto act = model->addOperand(&type1); - auto op3 = model->addOperand(&type0); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SUB, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/sub_broadcast_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/sub_broadcast_float.model.cpp deleted file mode 100644 index cf1f61a85..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/sub_broadcast_float.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: sub_broadcast_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type2(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {1, 2}); - OperandType type1(Type::TENSOR_FLOAT32, {2, 2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type1); - auto act = model->addOperand(&type2); - auto op3 = model->addOperand(&type1); - // Phase 2, operations - static int32_t act_init[] = {0}; - model->setOperandValue(act, act_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SUB, {op1, op2, act}, {op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1, op2}, - {op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/svdf.model.cpp b/runtimes/tests/neural_networks_test/generated/models/svdf.model.cpp deleted file mode 100644 index 30632a9b5..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/svdf.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: svdf.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type5(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {2, 40}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 4}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 10}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto weights_feature = model->addOperand(&type1); - auto weights_time = model->addOperand(&type2); - auto bias = model->addOperand(&type3); - auto state_in = model->addOperand(&type4); - auto rank_param = model->addOperand(&type5); - auto activation_param = model->addOperand(&type5); - auto state_out = model->addOperand(&type4); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t rank_param_init[] = {1}; - model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); - static int32_t activation_param_init[] = {0}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, weights_feature, weights_time, bias, state_in}, - {state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/svdf2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/svdf2.model.cpp deleted file mode 100644 index 2b1ae214d..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/svdf2.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: svdf2.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type5(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 4}); - OperandType type4(Type::TENSOR_FLOAT32, {2, 80}); - OperandType type3(Type::TENSOR_FLOAT32, {4}); - OperandType type2(Type::TENSOR_FLOAT32, {8, 10}); - OperandType type1(Type::TENSOR_FLOAT32, {8, 3}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto weights_feature = model->addOperand(&type1); - auto weights_time = model->addOperand(&type2); - auto bias = model->addOperand(&type3); - auto state_in = model->addOperand(&type4); - auto rank_param = model->addOperand(&type5); - auto activation_param = model->addOperand(&type5); - auto state_out = model->addOperand(&type4); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t rank_param_init[] = {2}; - model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); - static int32_t activation_param_init[] = {0}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, weights_feature, weights_time, bias, state_in}, - {state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {0}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/svdf_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/svdf_state.model.cpp deleted file mode 100644 index 3f0b2040c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/svdf_state.model.cpp +++ /dev/null @@ -1,36 +0,0 @@ -// Generated file (from: svdf_state.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type5(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); - OperandType type4(Type::TENSOR_FLOAT32, {2, 40}); - OperandType type6(Type::TENSOR_FLOAT32, {2, 4}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 10}); - OperandType type1(Type::TENSOR_FLOAT32, {4, 3}); - OperandType type3(Type::TENSOR_FLOAT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto weights_feature = model->addOperand(&type1); - auto weights_time = model->addOperand(&type2); - auto bias = model->addOperand(&type3); - auto state_in = model->addOperand(&type4); - auto rank_param = model->addOperand(&type5); - auto activation_param = model->addOperand(&type5); - auto state_out = model->addOperand(&type4); - auto output = model->addOperand(&type6); - // Phase 2, operations - static int32_t rank_param_init[] = {1}; - model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); - static int32_t activation_param_init[] = {0}; - model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); - model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input, weights_feature, weights_time, bias, state_in}, - {state_out, output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/tanh.model.cpp b/runtimes/tests/neural_networks_test/generated/models/tanh.model.cpp deleted file mode 100644 index bc3cd4a01..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/tanh.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: tanh.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_TANH, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/tanh_.model.cpp b/runtimes/tests/neural_networks_test/generated/models/tanh_.model.cpp deleted file mode 100644 index c221ea627..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/tanh_.model.cpp +++ /dev/null @@ -1,19 +0,0 @@ -// Generated file (from: tanh_.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto op2 = model->addOperand(&type0); - // Phase 2, operations - model->addOperation(ANEURALNETWORKS_TANH, {op1}, {op2}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_float.model.cpp deleted file mode 100644 index 7d365de9e..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_float.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: tensorflowmax_ex_2D_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_FLOAT32, {3, 4}); - OperandType type2(Type::TENSOR_FLOAT32, {3}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t axis_init[] = {1}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TENSORFLOW_MAX_EX, {input, axis}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_int32.model.cpp deleted file mode 100644 index efb5923ae..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/tensorflowmax_ex_2D_int32.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: tensorflowmax_ex_2D_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type0(Type::TENSOR_INT32, {3, 4}); - OperandType type2(Type::TENSOR_INT32, {3}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto axis = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t axis_init[] = {1}; - model->setOperandValue(axis, axis_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TENSORFLOW_MAX_EX, {input, axis}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_float.model.cpp deleted file mode 100644 index 23168320c..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_float.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: topk_v2_1D_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {2}); - OperandType type0(Type::TENSOR_FLOAT32, {4}); - OperandType type3(Type::TENSOR_INT32, {2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_int32.model.cpp deleted file mode 100644 index 5d11fed89..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_int32.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: topk_v2_1D_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {2}); - OperandType type0(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_quant8.model.cpp deleted file mode 100644 index ff60c1d74..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_1D_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: topk_v2_1D_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type3(Type::TENSOR_INT32, {2}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2}, 0.5f, 1); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {4}, 0.5f, 1); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_float.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_float.model.cpp deleted file mode 100644 index 17097cd11..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_float.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: topk_v2_2D_float.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_FLOAT32, {3,2}); - OperandType type0(Type::TENSOR_FLOAT32, {3,4}); - OperandType type3(Type::TENSOR_INT32, {3,2}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_int32.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_int32.model.cpp deleted file mode 100644 index 36e137e46..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_int32.model.cpp +++ /dev/null @@ -1,25 +0,0 @@ -// Generated file (from: topk_v2_2D_int32.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type2(Type::TENSOR_INT32, {3,2}); - OperandType type0(Type::TENSOR_INT32, {3,4}); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type2); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_quant8.model.cpp b/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_quant8.model.cpp deleted file mode 100644 index a0ffc8946..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/topk_v2_2D_quant8.model.cpp +++ /dev/null @@ -1,26 +0,0 @@ -// Generated file (from: topk_v2_2D_quant8.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::INT32, {}); - OperandType type3(Type::TENSOR_INT32, {3,2}); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {3,2}, 0.5f, 1); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {3,4}, 0.5f, 1); - // Phase 1, operands - auto op1 = model->addOperand(&type0); - auto k = model->addOperand(&type1); - auto op2 = model->addOperand(&type2); - auto op3 = model->addOperand(&type3); - // Phase 2, operations - static int32_t k_init[] = {2}; - model->setOperandValue(k, k_init, sizeof(int32_t) * 1); - model->addOperationEx(ANEURALNETWORKS_TOPK_V2_EX, {op1, k}, {op2, op3}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {op1}, - {op2, op3}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/transpose.model.cpp b/runtimes/tests/neural_networks_test/generated/models/transpose.model.cpp deleted file mode 100644 index e4c741456..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/transpose.model.cpp +++ /dev/null @@ -1,23 +0,0 @@ -// Generated file (from: transpose.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1}); - OperandType type1(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto perms = model->addOperand(&type1); - auto output = model->addOperand(&type0); - // Phase 2, operations - static int32_t perms_init[] = {0, 2, 1, 3}; - model->setOperandValue(perms, perms_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_TRANSPOSE, {input, perms}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/transpose_float_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/transpose_float_1.model.cpp deleted file mode 100644 index f6d0d08e3..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/transpose_float_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: transpose_float_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type0(Type::TENSOR_FLOAT32, {2, 3, 4, 5}); - OperandType type2(Type::TENSOR_FLOAT32, {4, 2, 3, 5}); - OperandType type1(Type::TENSOR_INT32, {4}); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto perms = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t perms_init[] = {2, 0, 1, 3}; - model->setOperandValue(perms, perms_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_TRANSPOSE, {input, perms}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/generated/models/transpose_quant8_1.model.cpp b/runtimes/tests/neural_networks_test/generated/models/transpose_quant8_1.model.cpp deleted file mode 100644 index 808ad2b38..000000000 --- a/runtimes/tests/neural_networks_test/generated/models/transpose_quant8_1.model.cpp +++ /dev/null @@ -1,24 +0,0 @@ -// Generated file (from: transpose_quant8_1.mod.py). Do not edit -void CreateModel(Model *model) { - OperandType type1(Type::TENSOR_INT32, {4}); - OperandType type0(Type::TENSOR_QUANT8_ASYMM, {2, 3, 4, 5}, 1.0, 0); - OperandType type2(Type::TENSOR_QUANT8_ASYMM, {4, 2, 3, 5}, 1.0, 0); - // Phase 1, operands - auto input = model->addOperand(&type0); - auto perms = model->addOperand(&type1); - auto output = model->addOperand(&type2); - // Phase 2, operations - static int32_t perms_init[] = {2, 0, 1, 3}; - model->setOperandValue(perms, perms_init, sizeof(int32_t) * 4); - model->addOperation(ANEURALNETWORKS_TRANSPOSE, {input, perms}, {output}); - // Phase 3, inputs and outputs - model->identifyInputsAndOutputs( - {input}, - {output}); - assert(model->isValid()); -} - -bool is_ignored(int i) { - static std::set<int> ignore = {}; - return ignore.find(i) != ignore.end(); -} diff --git a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux b/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux deleted file mode 100644 index 69a47fd03..000000000 --- a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux +++ /dev/null @@ -1,85 +0,0 @@ -GeneratedTests.avg_pool_quant8_4 -GeneratedTests.batch_to_space -GeneratedTests.batch_to_space_float_1 -GeneratedTests.batch_to_space_quant8_1 -GeneratedTests.depth_to_space_float_1 -GeneratedTests.depth_to_space_float_2 -GeneratedTests.depth_to_space_float_3 -GeneratedTests.depth_to_space_quant8_1 -GeneratedTests.depth_to_space_quant8_2 -GeneratedTests.hashtable_lookup_float -GeneratedTests.hashtable_lookup_quant8 -GeneratedTests.l2_normalization -GeneratedTests.l2_normalization_2 -GeneratedTests.l2_normalization_large -GeneratedTests.local_response_norm_float_1 -GeneratedTests.local_response_norm_float_2 -GeneratedTests.local_response_norm_float_3 -GeneratedTests.local_response_norm_float_4 -GeneratedTests.lsh_projection -GeneratedTests.lsh_projection_2 -GeneratedTests.lsh_projection_weights_as_inputs -GeneratedTests.lstm -GeneratedTests.lstm2 -GeneratedTests.lstm2_state -GeneratedTests.lstm2_state2 -GeneratedTests.lstm3 -GeneratedTests.lstm3_state -GeneratedTests.lstm3_state2 -GeneratedTests.lstm3_state3 -GeneratedTests.lstm_state -GeneratedTests.lstm_state2 -GeneratedTests.max_pool_quant8_3 -GeneratedTests.mean -GeneratedTests.mean_float_1 -GeneratedTests.mean_float_2 -GeneratedTests.mean_quant8_1 -GeneratedTests.mean_quant8_2 -GeneratedTests.mobilenet_quantized -GeneratedTests.pad_float_1 -GeneratedTests.space_to_batch -GeneratedTests.space_to_batch_float_1 -GeneratedTests.space_to_batch_float_2 -GeneratedTests.space_to_batch_float_3 -GeneratedTests.space_to_batch_quant8_1 -GeneratedTests.space_to_batch_quant8_2 -GeneratedTests.space_to_batch_quant8_3 -GeneratedTests.space_to_depth_float_1 -GeneratedTests.space_to_depth_float_2 -GeneratedTests.space_to_depth_float_3 -GeneratedTests.space_to_depth_quant8_1 -GeneratedTests.space_to_depth_quant8_2 -GeneratedTests.svdf -GeneratedTests.svdf2 -GeneratedTests.svdf_state -GeneratedTests.tensorflowmax_ex_2D_int32 -GeneratedTests.transpose -GeneratedTests.transpose_float_1 -GeneratedTests.transpose_quant8_1 -ValidationTestCompilation.CreateExecution -ValidationTestCompilation.SetPreference -ValidationTestIdentify.DuplicateInputs -ValidationTestIdentify.DuplicateOutputs -ValidationTestIdentify.InputIsOutput -ValidationTestIdentify.OutputIsInput -ValidationTestModel.AddOperand -ValidationTestModel.CreateCompilation -ValidationTestModel.Finish -ValidationTestModel.IdentifyInputsAndOutputs -ValidationTestModel.SetOperandValue -ValidationTestModel.SetOperandValueFromMemory -ValidationTestCompilation.Finish -ValidationTestExecution.SetInput -ValidationTestExecution.SetOutput -ValidationTestExecution.SetInputFromMemory -ValidationTestExecution.SetOutputFromMemory -ValidationTestExecution.StartCompute -ValidationTestExecution.EventWait -# failed after submodule change to public armcl -GeneratedTests.add_broadcast_quant8 -GeneratedTests.add_quant8 -GeneratedTests.logistic_quant8_1 -GeneratedTests.logistic_quant8_2 -GeneratedTests.mul_broadcast_quant8 -GeneratedTests.mul_quant8 -GeneratedTests.sub_broadcast_float diff --git a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux.neurun b/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux.neurun deleted file mode 100644 index 0a7a26f04..000000000 --- a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-linux.neurun +++ /dev/null @@ -1,105 +0,0 @@ -# -# Following tests will be skipped on armv7l-linux -# -# Segmentation fault -ValidationTestModel.SetOptionalOperand -ValidationTestModel.IdentifyInputsAndOutputs -ValidationTestModel.CreateCompilation -# Not support operations -TrivialTest.AddTwo -TrivialTest.AddThree -TrivialTest.BroadcastAddTwo -TrivialTest.BroadcastMulTwo -ValidationTestModel.AddOperation -ValidationTestCompilation.SetPreference -ValidationTestCompilation.CreateExecution -ValidationTestCompilation.Finish -ValidationTestIdentify.InputIsOutput -ValidationTestIdentify.OutputIsInput -ValidationTestIdentify.DuplicateInputs -ValidationTestIdentify.DuplicateOutputs -ValidationTestIdentify.Ok -ValidationTestExecution.SetInput -ValidationTestExecution.SetOutput -ValidationTestExecution.SetInputFromMemory -ValidationTestExecution.SetOutputFromMemory -ValidationTestExecution.StartCompute -ValidationTestExecution.EventWait -GeneratedTests.add* -GeneratedTests.avg_pool_float_1 -GeneratedTests.avg_pool_float_2 -GeneratedTests.avg_pool_float_3 -GeneratedTests.avg_pool_float_4 -GeneratedTests.avg_pool_quant8_1 -GeneratedTests.avg_pool_quant8_2 -GeneratedTests.avg_pool_quant8_3 -GeneratedTests.avg_pool_quant8_4 -GeneratedTests.conv_float_channels -GeneratedTests.conv_float_channels_weights_as_inputs -GeneratedTests.conv_float_large -GeneratedTests.conv_float_large_weights_as_inputs -GeneratedTests.conv_float -GeneratedTests.conv_float_weights_as_inputs -GeneratedTests.conv_quant8_channels -GeneratedTests.conv_quant8_channels_weights_as_inputs -GeneratedTests.conv_quant8_large -GeneratedTests.conv_quant8_large_weights_as_inputs -GeneratedTests.conv_quant8 -GeneratedTests.conv_quant8_overflow -GeneratedTests.conv_quant8_overflow_weights_as_inputs -GeneratedTests.conv_quant8_weights_as_inputs -GeneratedTests.depth_to_space* -GeneratedTests.depthwise_conv2d* -GeneratedTests.depthwise_conv -GeneratedTests.dequantize -GeneratedTests.embedding_lookup -GeneratedTests.embedding_lookup_2d_nnfw -GeneratedTests.embedding_lookup_4d_nnfw -GeneratedTests.floor_ -GeneratedTests.hashtable_lookup* -GeneratedTests.l2_normalization* -GeneratedTests.l2_pool* -GeneratedTests.local_response_norm* -GeneratedTests.logistic* -GeneratedTests.lsh_projection* -GeneratedTests.lstm* -GeneratedTests.max_pool_float_1 -GeneratedTests.max_pool_float_2 -GeneratedTests.max_pool_float_3 -GeneratedTests.max_pool_quant8_1 -GeneratedTests.max_pool_quant8_2 -GeneratedTests.max_pool_quant8_3 -GeneratedTests.mobilenet* -GeneratedTests.mul* -GeneratedTests.relu1* -GeneratedTests.relu6* -GeneratedTests.relu* -GeneratedTests.resize_bilinear* -GeneratedTests.rnn* -GeneratedTests.mean* -GeneratedTests.pad* -GeneratedTests.space_to_depth* -GeneratedTests.svdf* -GeneratedTests.tanh_ -GeneratedTests.batch_to_space* -GeneratedTests.div_* -GeneratedTests.space_to_batch* -GeneratedTests.squeeze* -GeneratedTests.strided_slice* -GeneratedTests.sub* -GeneratedTests.transpose* -GeneratedTests.cast_ex* -GeneratedTests.gather_1D* -GeneratedTests.gather_2D* -GeneratedTests.strided_slice_ex* -GeneratedTests.tensorflowmax_ex* -GeneratedTests.topk_v2* -# Unhandled exception -GeneratedTests.concat* -GeneratedTests.fully_connected* -GeneratedTests.reshape* -# Unexpected result -GeneratedTests.avg_pool_quant8_5 -GeneratedTests.conv_quant8_2 -GeneratedTests.max_pool_quant8_4 -GeneratedTests.softmax* diff --git a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-tizen b/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-tizen deleted file mode 100644 index e12147f88..000000000 --- a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.armv7l-tizen +++ /dev/null @@ -1,95 +0,0 @@ -GeneratedTests.avg_pool_quant8_4 -GeneratedTests.batch_to_space -GeneratedTests.batch_to_space_float_1 -GeneratedTests.batch_to_space_quant8_1 -GeneratedTests.concat_float_3 -GeneratedTests.concat_quant8_1 -GeneratedTests.concat_quant8_3 -GeneratedTests.depth_to_space_float_1 -GeneratedTests.depth_to_space_float_2 -GeneratedTests.depth_to_space_float_3 -GeneratedTests.depth_to_space_quant8_1 -GeneratedTests.depth_to_space_quant8_2 -GeneratedTests.hashtable_lookup_float -GeneratedTests.hashtable_lookup_quant8 -GeneratedTests.l2_normalization -GeneratedTests.l2_normalization_2 -GeneratedTests.l2_normalization_large -GeneratedTests.l2_pool_float -GeneratedTests.l2_pool_float_2 -GeneratedTests.l2_pool_float_large -GeneratedTests.local_response_norm_float_1 -GeneratedTests.local_response_norm_float_2 -GeneratedTests.local_response_norm_float_3 -GeneratedTests.local_response_norm_float_4 -GeneratedTests.lsh_projection -GeneratedTests.lsh_projection_2 -GeneratedTests.lsh_projection_weights_as_inputs -GeneratedTests.lstm -GeneratedTests.lstm2 -GeneratedTests.lstm2_state -GeneratedTests.lstm2_state2 -GeneratedTests.lstm3 -GeneratedTests.lstm3_state -GeneratedTests.lstm3_state2 -GeneratedTests.lstm3_state3 -GeneratedTests.lstm_state -GeneratedTests.lstm_state2 -GeneratedTests.max_pool_quant8_3 -GeneratedTests.mean -GeneratedTests.mean_float_1 -GeneratedTests.mean_float_2 -GeneratedTests.mean_quant8_1 -GeneratedTests.mean_quant8_2 -GeneratedTests.mobilenet_quantized -GeneratedTests.pad -GeneratedTests.pad_float_1 -GeneratedTests.space_to_batch -GeneratedTests.space_to_batch_float_1 -GeneratedTests.space_to_batch_float_2 -GeneratedTests.space_to_batch_float_3 -GeneratedTests.space_to_batch_quant8_1 -GeneratedTests.space_to_batch_quant8_2 -GeneratedTests.space_to_batch_quant8_3 -GeneratedTests.space_to_depth_float_1 -GeneratedTests.space_to_depth_float_2 -GeneratedTests.space_to_depth_float_3 -GeneratedTests.space_to_depth_quant8_1 -GeneratedTests.space_to_depth_quant8_2 -GeneratedTests.svdf -GeneratedTests.svdf2 -GeneratedTests.svdf_state -GeneratedTests.tensorflowmax_ex_2D_int32 -GeneratedTests.transpose -GeneratedTests.transpose_float_1 -GeneratedTests.transpose_quant8_1 -ValidationTestCompilation.CreateExecution -ValidationTestCompilation.SetPreference -ValidationTestIdentify.DuplicateInputs -ValidationTestIdentify.DuplicateOutputs -ValidationTestIdentify.InputIsOutput -ValidationTestIdentify.OutputIsInput -ValidationTestModel.AddOperand -ValidationTestModel.CreateCompilation -ValidationTestModel.Finish -ValidationTestModel.IdentifyInputsAndOutputs -ValidationTestModel.SetOperandValue -ValidationTestModel.SetOperandValueFromMemory -ValidationTestCompilation.Finish -ValidationTestExecution.SetInput -ValidationTestExecution.SetOutput -ValidationTestExecution.SetInputFromMemory -ValidationTestExecution.SetOutputFromMemory -ValidationTestExecution.StartCompute -ValidationTestExecution.EventWait -GeneratedTests.concat_float_1 -GeneratedTests.concat_float_2 -GeneratedTests.concat_quant8_2 -# failed after submodule change to public armcl -GeneratedTests.add_broadcast_quant8 -GeneratedTests.add_quant8 -GeneratedTests.logistic_quant8_1 -GeneratedTests.logistic_quant8_2 -GeneratedTests.mul_broadcast_quant8 -GeneratedTests.mul_quant8 -GeneratedTests.sub_broadcast_float diff --git a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.x86_64-linux b/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.x86_64-linux deleted file mode 100644 index bcd67b807..000000000 --- a/runtimes/tests/neural_networks_test/runtime_run_android_nn_test.skip.x86_64-linux +++ /dev/null @@ -1,72 +0,0 @@ -# -# Following tests will be skipped on x86_64-linux -# -# ===================== -# Fails in CPU fallback -# ===================== -GeneratedTests.depth_to_space_float_1 -GeneratedTests.depth_to_space_float_2 -GeneratedTests.depth_to_space_float_3 -GeneratedTests.depth_to_space_quant8_1 -GeneratedTests.depth_to_space_quant8_2 -GeneratedTests.dequantize -GeneratedTests.embedding_lookup -GeneratedTests.floor_ -GeneratedTests.hashtable_lookup_float -GeneratedTests.hashtable_lookup_quant8 -GeneratedTests.l2_normalization_2 -GeneratedTests.l2_normalization_large -GeneratedTests.l2_normalization -GeneratedTests.local_response_norm_float_1 -GeneratedTests.local_response_norm_float_2 -GeneratedTests.local_response_norm_float_3 -GeneratedTests.local_response_norm_float_4 -GeneratedTests.lsh_projection_2 -GeneratedTests.lsh_projection -GeneratedTests.lsh_projection_weights_as_inputs -GeneratedTests.lstm2 -GeneratedTests.lstm2_state2 -GeneratedTests.lstm2_state -GeneratedTests.lstm3 -GeneratedTests.lstm3_state2 -GeneratedTests.lstm3_state3 -GeneratedTests.lstm3_state -GeneratedTests.lstm -GeneratedTests.lstm_state2 -GeneratedTests.lstm_state -GeneratedTests.relu1_float_1 -GeneratedTests.relu1_float_2 -GeneratedTests.relu1_quant8_1 -GeneratedTests.relu1_quant8_2 -GeneratedTests.rnn -GeneratedTests.rnn_state -GeneratedTests.space_to_depth_float_1 -GeneratedTests.space_to_depth_float_2 -GeneratedTests.space_to_depth_float_3 -GeneratedTests.space_to_depth_quant8_1 -GeneratedTests.space_to_depth_quant8_2 -GeneratedTests.svdf2 -GeneratedTests.svdf -GeneratedTests.svdf_state -GeneratedTests.strided_slice_float_10 -GeneratedTests.strided_slice_float_1 -GeneratedTests.strided_slice_float_2 -GeneratedTests.strided_slice_float_3 -GeneratedTests.strided_slice_float_4 -GeneratedTests.strided_slice_float_5 -GeneratedTests.strided_slice_float_6 -GeneratedTests.strided_slice_float_7 -GeneratedTests.strided_slice_float_8 -GeneratedTests.strided_slice_float_9 -GeneratedTests.strided_slice -GeneratedTests.tensorflowmax_ex_2D_float -GeneratedTests.tensorflowmax_ex_2D_int32 -GeneratedTests.sub_broadcast_float -TrivialTest.* -ValidationTest.* -ValidationTestModel.* -ValidationTestCompilation.* -ValidationTestIdentify.* -ValidationTestExecution.* -GeneratedTests.gather_1D_quant8 -GeneratedTests.gather_2D_quant8 diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_float.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_float.mod.py deleted file mode 100644 index 228a52ad7..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_float.mod.py +++ /dev/null @@ -1,19 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_FLOAT32", "{4}") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_FLOAT32", "{2}") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [3.0, 4.0, 5.0, 6.0], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [5.0, 3.0]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_int32.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_int32.mod.py deleted file mode 100644 index 36aa02937..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_int32.mod.py +++ /dev/null @@ -1,19 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_INT32", "{4}") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_INT32", "{2}") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [40000, 41000, 50000, 60000], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [50000, 40000]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_quant8.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_quant8.mod.py deleted file mode 100644 index 7e8bfb0a7..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_1D_quant8.mod.py +++ /dev/null @@ -1,19 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{4}, 0.5f, 1") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2}, 0.5f, 1") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [7, 9, 11, 13], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [11, 7]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_float.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_float.mod.py deleted file mode 100644 index 5f678c643..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_float.mod.py +++ /dev/null @@ -1,22 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_FLOAT32", "{2,4}") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, - 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, - 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, - 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_int32.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_int32.mod.py deleted file mode 100644 index 357367b26..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_int32.mod.py +++ /dev/null @@ -1,22 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_INT32", "{3,4}") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_INT32", "{2,4}") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [40000, 41000, 50000, 60000, - 70000, 80000, 90000, 79000, - 170000, 180000, 190000, 110000], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [170000, 180000, 190000, 110000, - 40000, 41000, 50000, 60000]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_quant8.mod.py b/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_quant8.mod.py deleted file mode 100644 index 7e07d042c..000000000 --- a/runtimes/tests/neural_networks_test/specs/Ex/gather_2D_quant8.mod.py +++ /dev/null @@ -1,22 +0,0 @@ -# model -model = Model() -i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{3,4}, 0.5f, 1") # a vector of 2 float32s -i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s -axis = Int32Scalar("axis", 0) -i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2,4}, 0.5f, 1") -model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) - -# Example 1. Input in operand 0, -input0 = {i1: # input 0 - [7, 9, 11, 13, - 15, 17, 19, 3, - 5, 37, 39, 23], - i2: # input 1 - [2, 0]} - -output0 = {i3: # output 0 - [5, 37, 39, 23, - 7, 9, 11, 13]} - -# Instantiate an example -Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/generate_test.sh b/runtimes/tests/neural_networks_test/specs/generate_test.sh deleted file mode 100755 index c0676cf3c..000000000 --- a/runtimes/tests/neural_networks_test/specs/generate_test.sh +++ /dev/null @@ -1,118 +0,0 @@ -# -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# Copyright (C) 2017 The Android Open Source Project -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -NNAPI_VERSION=" -V1_0 -V1_1 -Ex -" - -# Process one test spec, and optionally provide the log file argument -# for the slicing tool. The first argument is the test spec file; the -# second optional argument specifies the log file this test should dump -# results into. Only used by the test slicing tool to collect reference -# outputs from the CPU. Also, it outputs the right #includes in the -# test harness so the test would be invoked by TestGenerated.cpp -# -# This function shouldn't be directly called from other scripts. Use -# generate_wrapper below for generating models and examples and updating the -# test framework in one shot. - -spec_dir=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd ) -export ANDROID_BUILD_TOP=$(readlink -f "$spec_dir/../../../../..") -export NNAPI_BASE=$ANDROID_BUILD_TOP/nnfw -[ -z "$TEST_DIR" ] && TEST_DIR="nnfw/runtimes/tests/neural_networks_test" - -function generate_one_testcase { - # Generate one testcase - local LOGFILE=$2 - if [ -n "$2" ]; then - local LOGFILE=", \"$2\"" - fi - local BASENAME=`basename -s .mod.py $1` - local EXAMPLE="-e $ANDROID_BUILD_TOP/$TEST_DIR/generated/examples/$BASENAME.example.cpp" - - $NNAPI_BASE/externals/nnapi_test_generator/test_generator.py ./`basename $1`\ - -m $ANDROID_BUILD_TOP/$TEST_DIR/generated/models/$BASENAME.model.cpp $EXAMPLE - ret=$? - # Paste these lines into TestGenerated.cpp - echo - echo namespace $BASENAME { - echo std::vector\<MixedTypedExample\> examples \= { - echo // Generated $BASENAME test - echo \#include \"generated/examples/$BASENAME.example.cpp\" - echo }\; - echo // Generated model constructor - echo \#include \"generated/models/$BASENAME.model.cpp\" - echo } // namespace $BASENAME - echo TEST_F\(GeneratedTests\, $BASENAME\) { - echo ' execute'\($BASENAME\:\:CreateModel\, - echo ' '$BASENAME\:\:is_ignored\, - echo ' '$BASENAME\:\:examples${LOGFILE}\)\; - echo } - return $ret -} - -# Driver for generate_one_testcase. Append the output of generate_one_testcase -# (which are C++ snippets that invokes the test harness) to the -# all_generated_tests.cpp -# Optionally, the "LOG" file ($2), only used by the slicing tool, would be -# passed to generate_one_testcase. -# -# This function should be called to process one test spec from other scripts. -function generate_wrapper { - local LOGFILE="" - if [ $1 = "log" ]; then - local LOGFILE=$2 - shift - shift - fi - cd $ANDROID_BUILD_TOP/$TEST_DIR/specs - OUTFILE=$ANDROID_BUILD_TOP/$TEST_DIR/generated/all_generated_tests.cpp - echo "// DO NOT EDIT;" > $OUTFILE - echo "// Generated by ${TEST_DIR}/specs/generate_test.sh" >> $OUTFILE - - for ver in $NNAPI_VERSION; - do - VER_DIR=$ANDROID_BUILD_TOP/$TEST_DIR/specs/$ver - [ ! -d $VER_DIR ] && continue - cd $VER_DIR - for f in $@; - do - generate_one_testcase $f $LOGFILE >> $OUTFILE - if [ $? -ne 0 ]; then - echo "Failed processing $f" - return $? - fi - done - done - return $? -} - -# Only run the following when not sourced by another script -if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then - if [ $# -eq 0 ]; then - FILES=*.mod.py - else - FILES="$@" - fi - generate_wrapper $FILES - if [ $? -ne 0 ]; then - exit $? - fi - echo "Generated file in ${TEST_DIR}/generated/"`basename $OUTFILE` -fi # [[ "${BASH_SOURCE[0]}" == "${0}" ]] - diff --git a/scripts/command/build b/scripts/command/build index 5219001c3..1e8b9b731 100644 --- a/scripts/command/build +++ b/scripts/command/build @@ -1,7 +1,25 @@ #!/bin/bash -# NOTE 'run' sets NNFW_PROJECT_PATH and invokes this script +# NOTE 'run' sets NNFW_SCRIPT_PATH and invokes this script +source "${NNFW_SCRIPT_PATH}/config/build.configuration" -# TODO Support command-line options -# TODO Implement build steps inside this script -make -C "${NNFW_PROJECT_PATH}" +if [[ ! -d "${BUILD_ALIAS}" ]]; then + echo "'${BUILD_ALIAS}' does not exist. Please run 'configure' first" + exit 255 +fi + +# Set parallel build +# TODO Use argument instead of environment variable +HOST_OS=${HOST_OS:-linux} +NPROCS=${NPROCS:-1} +PARALLEL_BUILD=${PARALLEL_BUILD:-1} + +if [ "${PARALLEL_BUILD}" == "1" ]; then + # Get number of processors (linux only for now) + if [ "${HOST_OS}" == "linux" ]; then + NPROCS="$(grep -c ^processor /proc/cpuinfo)" + fi +fi + +cd ${BUILD_ALIAS} +make -j ${NPROCS} "$@" diff --git a/scripts/command/build-acl b/scripts/command/build-acl new file mode 100644 index 000000000..a9957555b --- /dev/null +++ b/scripts/command/build-acl @@ -0,0 +1,77 @@ +#!/bin/bash + +# NOTE 'run' sets NNFW_SCRIPT_PATH and invokes this script +source "${NNFW_SCRIPT_PATH}/config/build.configuration" + +# Set parallel build +# TODO: use argument instead of environment variable and merge with build command +TARGET_ARCH=${TARGET_ARCH:-armv7l} +BUILD_TYPE=${BUILD_TYPE:-Debug} +NPROCS=${NPROCS:-1} +PARALLEL_BUILD=${PARALLEL_BUILD:-1} +HOST_OS=${HOST_OS:-linux} +TARGET_OS=${TARGET_OS:-linux} + +# make TARGET and TYPE to lowercase +TARGET_ARCH_LC="$(echo ${TARGET_ARCH} | tr A-Z a-z)" +BUILD_TYPE_LC="$(echo ${BUILD_TYPE} | tr A-Z a-z)" +# we need base name 'arm` for all arm arch +TARGET_ARCH_BASE="${TARGET_ARCH_LC}" + +if [ "${TARGET_ARCH_BASE}" == *"arm64"* ]; then + # For now Android is the only option for arm64 + TARGET_ARCH_BASE=arm64 + TARGET_OS=android +fi + +if [ "${PARALLEL_BUILD}" == "1" ]; then + # Get number of processors (linux only for now) + if [ "${HOST_OS}" == "linux" ]; then + NPROCS="$(grep -c ^processor /proc/cpuinfo)" + fi +fi + +ACL_FOLDER="externals/acl" +ACL_COMMAND="scons -j${NPROCS} neon=1 opencl=1 examples=0 embed_kernels=1 os=${TARGET_OS}" +if [ "${TARGET_ARCH_LC}" == "armv7l" ]; then + ACL_COMMAND+=" arch=armv7a" + ACL_BUILD_OUT="armv7a-${TARGET_OS}" +elif [ "${TARGET_ARCH_LC}" == "aarch64" ]; then + ACL_COMMAND+=" arch=arm64-v8a" + ACL_BUILD_OUT="arm64-v8a-${TARGET_OS}" +elif [ "${TARGET_ARCH_BASE}" == "arm64" ]; then + ACL_COMMAND+=" arch=arm64-v8a" + ACL_BUILD_OUT="arm64-v8a-${TARGET_OS}" +fi + +if [ "${TARGET_OS}" == "android" ]; then + ACL_COMMAND+=" Werror=0" + ANDROID_GNUSTL_PATH="${ROOTFS_ARM64}/bin:${ROOTFS_ARM64}/aarch64-linux-android/bin:$$PATH" +else + ACL_COMMAND+=" Werror=1" +fi + +if [ "${BUILD_TYPE_LC}" == "debug" ]; then + ACL_COMMAND+=" debug=1 asserts=1" +fi + +ACL_FOLDER_NAME="${ACL_BUILD_OUT}.${BUILD_TYPE_LC}" +ACL_COMMAND+=" build_dir=${ACL_FOLDER_NAME}" +ACL_FOLDER_BUILD="${ACL_FOLDER}/build/${ACL_FOLDER_NAME}" + +pushd ${ACL_FOLDER} +if [ "${TARGET_OS}" == "android" ]; then + CXX=clang++ CC=clang PATH=${ANDROID_GNUSTL_PATH} ${ACL_COMMAND} +else + ${ACL_COMMAND} +fi +popd + +WORK_FOLDER=${TARGET_ARCH_LC}-${TARGET_OS}.${BUILD_TYPE_LC} +WORK_PATH=${NNFW_PROJECT_PATH}/${WORKSPACE_RPATH}/${WORK_FOLDER} +INSTALL_PATH=${NNFW_INSTALL_PATH:-${WORK_PATH}/out} + +mkdir -vp ${INSTALL_PATH}/lib +cp -v ${ACL_FOLDER_BUILD}/libarm_compute_core.so ${INSTALL_PATH}/lib/. +cp -v ${ACL_FOLDER_BUILD}/libarm_compute_graph.so ${INSTALL_PATH}/lib/. +cp -v ${ACL_FOLDER_BUILD}/libarm_compute.so ${INSTALL_PATH}/lib/. diff --git a/scripts/command/build-docker b/scripts/command/build-docker new file mode 100644 index 000000000..2da70be78 --- /dev/null +++ b/scripts/command/build-docker @@ -0,0 +1,52 @@ +#!/bin/bash + +function Usage() +{ + echo "Usage: $0 $(basename ${BASH_SOURCE[0]}) [OPTIONS]" + echo "" + echo "Options:" + echo " --tizen Build docker image for tizen build" + echo "Options can use as docker build option:" + docker build --help +} + +DOCKER_FILE_RPATH="docker/Dockerfile" +DOCKER_BUILD_ARGS=() + +# Handle argument for this script +# Set default docker image name, tag +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --tizen) + DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME:-nnfw_docker_tizen} + DOCKER_FILE_RPATH="docker/Dockerfile_tizen" + ;; + esac +done +source "$NNFW_SCRIPT_PATH/config/image_name.configuration" +DOCKER_BUILD_ARGS+="-t ${DOCKER_IMAGE_NAME}" + +# Argument for docker build commands +for i in "$@" +do + case $i in + -h|--help|help) + --tizen) + # Already handled argument + ;; + *) + DOCKER_BUILD_ARGS+=($1) + ;; + esac + shift +done + +docker build --build-arg http_proxy="$http_proxy" \ + --build-arg https_proxy="$https_proxy" \ + ${DOCKER_BUILD_ARGS[@]} \ + - < $NNFW_SCRIPT_PATH/$DOCKER_FILE_RPATH diff --git a/scripts/command/common.sh b/scripts/command/common.sh deleted file mode 100755 index ec2ca7bb8..000000000 --- a/scripts/command/common.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -export DOCKER_IMAGE_NAME=nnfw_docker diff --git a/scripts/command/configure b/scripts/command/configure new file mode 100644 index 000000000..3b654d677 --- /dev/null +++ b/scripts/command/configure @@ -0,0 +1,85 @@ +#!/bin/bash + +source "${NNFW_SCRIPT_PATH}/config/build.configuration" + +# Set target platform using environment variable +# TODO: use argument instead of environment variable +TARGET_ARCH=${TARGET_ARCH:-$(uname -p)} +BUILD_TYPE=${BUILD_TYPE:-Debug} +CROSS_BUILD=${CROSS_BUILD:-0} +HOST_OS=${HOST_OS:-linux} +TARGET_OS=${TARGET_OS:-linux} +## TODO: fix obs build break +OBS_BUILD=${OBS_BUILD:-OFF} +COVERAGE_BUILD=${COVERAGE_BUILD:-0} +BENCHMARK_ACL_BUILD=${BENCHMARK_ACL_BUILD:-0} +OPTIONS=${OPTIONS:-} + +# make TARGET and TYPE to lowercase +TARGET_ARCH_LC="$(echo ${TARGET_ARCH} | tr A-Z a-z)" +BUILD_TYPE_LC="$(echo ${BUILD_TYPE} | tr A-Z a-z)" + +if [ "${TARGET_ARCH_LC}" == *"arm64"* ]; then + # arm64 as target-arch comes from Android + if [ ! -z ${ROOTFS_DIR} ]; then + ROOTFS_ARM64="${ROOTFS_DIR}" + export ROOTFS_ARM64 + fi + # For now Android is the only option for arm64 + TARGET_OS=android +elif [ "${TARGET_ARCH_LC}" == *"arm"* ]; then + if [ ! -z ${ROOTFS_DIR} ] ; then + ROOTFS_ARM="${ROOTFS_DIR}" + export ROOTFS_ARM + fi +elif [ "${TARGET_ARCH_LC}" == *"aarch64"* ]; then + # aarch64 as target-arch comes from all except for Android + if [ ! -z ${ROOTFS_DIR} ] ; then + ROOTFS_ARM64="${ROOTFS_DIR}" + export ROOTFS_ARM64 + fi +fi + +# Todo: we may set CROSS_BUILD=1 when ROOTFS_DIR is given +# the toolchain file, only for cross build +if [ "${CROSS_BUILD}" == "1" ]; then + TOOLCHAIN_FILE="cmake/config/config_${TARGET_ARCH_LC}-${TARGET_OS}.cmake" + OPTION_TOOLCHAIN="-DCMAKE_TOOLCHAIN_FILE=${TOOLCHAIN_FILE}" +else + OPTION_TOOLCHAIN= +fi + +if [ "${COVERAGE_BUILD}" == "1" ]; then + OPTIONS+=" -DCOVERAGE_BUILD=1" +else + OPTIONS+=" -DCOVERAGE_BUILD=0" +fi + +if [ "${BENCHMARK_ACL_BUILD}" == "1" ]; then + OPTIONS+=" -DBUILD_BENCHMARK_ACL=1" +fi + +WORK_FOLDER=${TARGET_ARCH_LC}-${TARGET_OS}.${BUILD_TYPE_LC} +WORK_PATH=${NNFW_PROJECT_PATH}/${WORKSPACE_RPATH}/${WORK_FOLDER} +BUILD_PATH=${WORK_PATH}/obj +INSTALL_PATH=${NNFW_INSTALL_PATH:-${WORK_PATH}/out} + +mkdir -vp ${BUILD_PATH} +mkdir -vp ${INSTALL_PATH} +rm -rf ${BUILD_ALIAS} +rm -rf ${INSTALL_ALIAS} +ln -s ${BUILD_PATH} ${BUILD_ALIAS} +ln -s ${INSTALL_PATH} ${INSTALL_ALIAS} + +cd "${BUILD_PATH}" + +cmake \ + -B${BUILD_PATH} -H${NNFW_PROJECT_PATH} \ + -DCMAKE_INSTALL_PREFIX=${INSTALL_PATH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE_LC} -DTARGET_ARCH=${TARGET_ARCH_LC} \ + -DHOST_OS=${HOST_OS} \ + -DTARGET_OS=${TARGET_OS} \ + -DOBS_BUILD=${OBS_BUILD} \ + ${OPTION_TOOLCHAIN} \ + ${OPTIONS} \ + "${NNFW_PROJECT_PATH}" "$@" diff --git a/scripts/command/docker-run b/scripts/command/docker-run index 386c68145..ff0881967 100644 --- a/scripts/command/docker-run +++ b/scripts/command/docker-run @@ -1,22 +1,6 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -source $SCRIPT_ROOT/common.sh - -HOST_HOME=$SCRIPT_ROOT/../.. -DOCKER_HOME=/home - -GIT_SSL_NO_VERIFY=1 - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e http_proxy" -DOCKER_ENV_VARS+=" -e no_proxy" -DOCKER_ENV_VARS+=" -e GIT_SSL_NO_VERIFY" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" +source "${NNFW_SCRIPT_PATH}/config/docker.configuration" docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME "$@" EXITCODE=$? diff --git a/scripts/command/docker-run-user b/scripts/command/docker-run-user new file mode 100644 index 000000000..d5b74db39 --- /dev/null +++ b/scripts/command/docker-run-user @@ -0,0 +1,10 @@ +#!/bin/bash + +source "${NNFW_SCRIPT_PATH}/config/docker.configuration" + +DOCKER_RUN_OPTS+=" -u $(id -u):$(id -g)" + +docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME "$@" +EXITCODE=$? + +exit $EXITCODE diff --git a/scripts/command/docker-shell b/scripts/command/docker-shell index f4834f2ff..1f942cedb 100755..100644 --- a/scripts/command/docker-shell +++ b/scripts/command/docker-shell @@ -1,24 +1,11 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +source "${NNFW_SCRIPT_PATH}/config/docker.configuration" -source $SCRIPT_ROOT/common.sh - -HOST_HOME=$SCRIPT_ROOT/../.. -DOCKER_HOME=/home - -GIT_SSL_NO_VERIFY=1 - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e http_proxy" -DOCKER_ENV_VARS+=" -e no_proxy" -DOCKER_ENV_VARS+=" -e GIT_SSL_NO_VERIFY" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" DOCKER_RUN_OPTS+=" -it" +docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME "/bin/bash" +EXITCODE=$? -CMD="/bin/bash" +docker_cleanup -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME $CMD +exit $EXITCODE diff --git a/scripts/command/docker_build.sh b/scripts/command/docker_build.sh deleted file mode 100755 index c5545f6c7..000000000 --- a/scripts/command/docker_build.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -source $SCRIPT_ROOT/common.sh - -docker build --build-arg http_proxy="$http_proxy" \ - --build-arg https_proxy="$https_proxy" \ - -t $DOCKER_IMAGE_NAME \ - - < $SCRIPT_ROOT/../docker/Dockerfile diff --git a/scripts/command/docker_build_cross_arm_neurun.sh b/scripts/command/docker_build_cross_arm_neurun.sh deleted file mode 100755 index 4e1022a34..000000000 --- a/scripts/command/docker_build_cross_arm_neurun.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" -DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" -DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -DOCKER_ENV_VARS+=" -e TARGET_OS=linux" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - -# prepare rootfs -if [[ ! -d $ROOTFS_DIR ]]; then - echo "cannot find rootfs" - exit 1 -fi - -DOCKER_VOLUMES+=" -v $ROOTFS_DIR/:/opt/rootfs" - -CMD="export OPTIONS=\" -DBUILD_NEURUN=ON -DBUILD_NN_RUNTIME=OFF \" && make acl && make && make install && make build_test_suite" - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME - -exit $BUILD_RESULT diff --git a/scripts/command/docker_build_cross_arm_ubuntu.sh b/scripts/command/docker_build_cross_arm_ubuntu.sh index 5da2bd6c3..29f27260e 100755 --- a/scripts/command/docker_build_cross_arm_ubuntu.sh +++ b/scripts/command/docker_build_cross_arm_ubuntu.sh @@ -1,44 +1,30 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - # prepare rootfs if [[ ! -d $ROOTFS_DIR ]]; then - echo "cannot find rootfs" - exit 1 -fi - -if [[ ! -z $ENV_FILE ]]; then - DOCKER_ENV_VARS+=" --env-file ${ENV_FILE} " + echo "cannot find rootfs" + exit 1 fi DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" +export DOCKER_ENV_VARS +export DOCKER_VOLUMES + +pushd $NNFW_PROJECT_PATH + if [ -n "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" + source run docker-shell else CMD="export BENCHMARK_ACL_BUILD=1 && make acl && make && make install && make build_test_suite" + source run docker-run-user bash -c "${CMD}" fi +EXITCODE=$? -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME +popd -exit $BUILD_RESULT +exit $EXITCODE diff --git a/scripts/command/docker_build_cross_arm_ubuntu_without_aclbuild.sh b/scripts/command/docker_build_cross_arm_ubuntu_without_aclbuild.sh deleted file mode 100755 index b75be173d..000000000 --- a/scripts/command/docker_build_cross_arm_ubuntu_without_aclbuild.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" -DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" -DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -DOCKER_ENV_VARS+=" -e EXT_ACL_FOLDER=/opt/libarmcl" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - -# prepare armcl library -if [[ ! -d $ARMCL_DIR ]]; then - echo "cannot find armcl" - exit 1 -fi - -# prepare rootfs -if [[ ! -d $ROOTFS_DIR ]]; then - echo "cannot find rootfs" - exit 1 -fi - -DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" -DOCKER_VOLUMES+=" -v $ARMCL_DIR:/opt/libarmcl" - -if [ -n "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" -else - CMD="make external_acl && export BENCHMARK_ACL_BUILD=1 && make && make install && make build_test_suite" -fi - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME - -exit $BUILD_RESULT diff --git a/scripts/command/docker_build_tizen_cross.sh b/scripts/command/docker_build_tizen_cross.sh index 3a45cfd4f..6d750b1a9 100755 --- a/scripts/command/docker_build_tizen_cross.sh +++ b/scripts/command/docker_build_tizen_cross.sh @@ -1,22 +1,10 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" DOCKER_ENV_VARS+=" -e TARGET_OS=tizen" -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - # prepare rootfs if [[ ! -d $ROOTFS_DIR ]]; then echo "cannot find rootfs" @@ -25,12 +13,15 @@ fi DOCKER_VOLUMES+=" -v $ROOTFS_DIR/:/opt/rootfs" -CMD="make && make install && make build_test_suite" +export NNFW_PROJECT_PATH +export NNFW_SCRIPT_PATH -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? +pushd $NNFW_PROJECT_PATH + +CMD="make && make install && make build_test_suite" +source run docker-run-user bash -c "${CMD}" +EXITCODE=$? -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME +popd -exit $BUILD_RESULT +exit $EXITCODE diff --git a/scripts/command/docker_build_ubuntu_coverity.sh b/scripts/command/docker_build_ubuntu_coverity.sh deleted file mode 100755 index 455303257..000000000 --- a/scripts/command/docker_build_ubuntu_coverity.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/bin/bash - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" -DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" -DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -DOCKER_ENV_VARS+=" -e EXT_ACL_FOLDER=/opt/libarmcl" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - -TMP_DIR=$HOST_HOME/tmp - -if [ ! -d $COVERITY_ANALYZER_DIR ]; then - echo "cannot find coverity-analyzer" - exit 1 -fi - -which $COVERITY_ANALYZER_DIR/bin/cov-analyze -if [[ $? -ne 0 ]]; then - echo "cannot find coverity-analyzer" - exit 1 -fi - -pushd $HOST_HOME - -# prepare rootfs -if [[ ! -d $ROOTFS_DIR ]]; then - echo "cannot find rootfs" - exit 1 -fi - -DOCKER_VOLUMES+=" -v $COVERITY_ANALYZER_DIR:/opt/cov-analyzer" -DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" -DOCKER_VOLUMES+=" -v $COVERITY_OUT:/opt/cov-out" - -if [ -n "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" -else - CMD="make acl tflite && /opt/cov-analyzer/bin/cov-configure --template --compiler arm-linux-gnueabihf-gcc && /opt/cov-analyzer/bin/cov-build --dir /opt/cov-out make runtime testbuild" -fi - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME - -# Reset host name (configuration of coverity is done in docker container) -$COVERITY_ANALYZER_DIR/bin/cov-manage-emit --dir $COVERITY_OUT reset-host-name - -# Analyze build result -$COVERITY_ANALYZER_DIR/bin/cov-analyze \ - --dir $COVERITY_OUT --jobs auto --all --disable-test-metrics --security \ - --concurrency --enable-constraint-fpp --fnptr-models --enable SIZECHECK \ - --checker-option STRING_OVERFLOW:report_fixed_size_dest:no --checker-option OVERRUN:allow_symbol:no \ - --checker-option PASS_BY_VALUE:unmodified_threshold:1024 --checker-option PASS_BY_VALUE:size_threshold:1024 \ - -co NULL_RETURNS:stat_threshold:70 --enable INTEGER_OVERFLOW --enable INCOMPATIBLE_CAST \ - --enable IDENTICAL_BRANCHES --enable UNINTENDED_INTEGER_DIVISION - -popd - -exit $BUILD_RESULT diff --git a/scripts/command/docker_build_ubuntu_svace.sh b/scripts/command/docker_build_ubuntu_svace.sh deleted file mode 100755 index 49f1ee336..000000000 --- a/scripts/command/docker_build_ubuntu_svace.sh +++ /dev/null @@ -1,67 +0,0 @@ -#!/bin/bash - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" -DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" -DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -DOCKER_ENV_VARS+=" -e EXT_ACL_FOLDER=/opt/libarmcl" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - -TMP_DIR=$HOST_HOME/tmp - -if [ ! -d $SVACE_ANALYZER_DIR ]; then - echo "cannot find svace-analyzer" - exit 1 -fi - -which $SVACE_ANALYZER_DIR/bin/svace -if [[ $? -ne 0 ]]; then - echo "cannot find svace-analyzer" - exit 1 -fi - -pushd $HOST_HOME - -# prepare rootfs -if [[ ! -d $ROOTFS_DIR ]]; then - echo "cannot find rootfs" - exit 1 -fi - -# prepare svace -if [[ ! -f $SVACE_POLICY_FILE ]]; then - echo "cannot find svace policy" - exit 1 -fi - -DOCKER_VOLUMES+=" -v $SVACE_ANALYZER_DIR:/opt/svace-analyzer" -DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" - -if [ -n "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" -else - CMD="make acl tflite && /opt/svace-analyzer/bin/svace init && /opt/svace-analyzer/bin/svace build make runtime testbuild" -fi - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME - -$SVACE_ANALYZER_DIR/bin/svace analyze --warning $SVACE_POLICY_FILE - -popd - -exit $BUILD_RESULT diff --git a/scripts/command/docker_coverage_report.sh b/scripts/command/docker_coverage_report.sh index c9294fe48..e4d3a92aa 100755 --- a/scripts/command/docker_coverage_report.sh +++ b/scripts/command/docker_coverage_report.sh @@ -1,24 +1,9 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +DOCKER_ENV_VARS+=" -e SRC_PREFIX=${NNFW_PROJECT_PATH}" -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker +pushd ${NNFW_PROJECT_PATH} > /dev/null -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" -DOCKER_ENV_VARS+=" -e DOCKER_HOME=${DOCKER_HOME}" - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "./scripts/command/gen_coverage_report.sh" - -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME - -exit $BUILD_RESULT +source run docker-run-user bash -c "./run gen_coverage_report.sh" +popd > /dev/null diff --git a/scripts/command/docker_cross_test_coverage_build.sh b/scripts/command/docker_cross_test_coverage_build.sh index 784b84605..8d8e10835 100755 --- a/scripts/command/docker_cross_test_coverage_build.sh +++ b/scripts/command/docker_cross_test_coverage_build.sh @@ -1,22 +1,10 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker - -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" DOCKER_ENV_VARS+=" -e COVERAGE_BUILD=1" -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - # prepare rootfs if [[ ! -d $ROOTFS_DIR ]]; then echo "cannot find rootfs" @@ -25,17 +13,9 @@ fi DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" -if [ -n "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" -else - CMD="make acl && make && make install && make build_coverage_suite" -fi - -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME sh -c "$CMD" -BUILD_RESULT=$? +pushd $NNFW_PROJECT_PATH > /dev/null -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME +CMD="make acl && make && make install && make build_coverage_suite" +source run docker-run-user bash -c "${CMD}" -exit $BUILD_RESULT +popd > /dev/null diff --git a/scripts/command/docker_gbs_build.sh b/scripts/command/docker_gbs_build.sh index dcd989ebb..047d9eff8 100755 --- a/scripts/command/docker_gbs_build.sh +++ b/scripts/command/docker_gbs_build.sh @@ -1,38 +1,26 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker_tizen - -HOST_HOME=$SCRIPT_ROOT/../.. -DOCKER_NNFW_HOME=${DOCKER_HOME} -DOCKER_RPM_HOME=/home/rpm +DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME:-nnfw_docker_tizen} +DOCKER_RPM_HOME=$NNFW_PROJECT_PATH/rpm if [ "${GBS_RPM_DIR}" == "" ]; then - GBS_RPM_DIR=$HOST_HOME/Product/out/rpm + GBS_RPM_DIR=$NNFW_PROJECT_PATH/Product/out/rpm mkdir -p ${GBS_RPM_DIR} fi -if [ -z ${DOCKER_IMAGE} ]; -then - # use default docker image - DOCKER_IMAGE=nnfw_docker_tizen:latest -fi +DOCKER_VOLUMES+=" -v ${GBS_RPM_DIR}:${DOCKER_RPM_HOME}" +export NNFW_PROJECT_PATH +export NNFW_SCRIPT_PATH -DOCKER_VOLUMES+=" -v ${GBS_RPM_DIR}:${DOCKER_RPM_HOME} -v $HOST_HOME:${DOCKER_NNFW_HOME}" -DOCKER_RUN_OPTS+=" --rm" -DOCKER_RUN_OPTS+=" -w ${DOCKER_NNFW_HOME}" +pushd $NNFW_PROJECT_PATH -CMD="gbs -c ${DOCKER_NNFW_HOME}/scripts/command/gbs.conf build -A armv7l --profile=profile.tizen --clean --include-all --define '${GBS_DEFINE}' && +CMD="gbs -c ${NNFW_PROJECT_PATH}/scripts/config/gbs.conf build -A armv7l --profile=profile.tizen --clean --include-all --define '${GBS_DEFINE}' && cp -rf /home/GBS-ROOT/local/repos/tizen/armv7l/RPMS/*.rpm ${DOCKER_RPM_HOME}/." -docker run $DOCKER_RUN_OPTS $DOCKER_VOLUMES ${DOCKER_ENV_VARS:-} ${DOCKER_IMAGE} sh -c "$CMD" -BUILD_RESULT=$? +source run docker-run bash -c "${CMD}" +EXITCODE=$? -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_NNFW_HOME -restore_ownership $HOST_HOME $DOCKER_RPM_HOME +popd -exit $BUILD_RESULT +exit $EXITCODE diff --git a/scripts/command/docker_run_test.sh b/scripts/command/docker_run_test.sh index f6605b144..dadd19f8a 100755 --- a/scripts/command/docker_run_test.sh +++ b/scripts/command/docker_run_test.sh @@ -1,30 +1,18 @@ #!/bin/bash -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +export DOCKER_ENV_VARS +export DOCKER_VOLUMES -# DOCKER_HOME and DOCKER_ENV_VARS -source $SCRIPT_ROOT/nnfw_docker +pushd $NNFW_PROJECT_PATH -HOST_HOME=$SCRIPT_ROOT/../.. - -DOCKER_VOLUMES=" -v /dev/null:/dev/raw1394" -DOCKER_VOLUMES+=" -v $HOST_HOME:$DOCKER_HOME" - -DOCKER_RUN_OPTS="--rm" -DOCKER_RUN_OPTS+=" -w $DOCKER_HOME" - -CMD="make install" - -if [ "$DOCKER_INTERACTIVE" ]; then - DOCKER_RUN_OPTS+=" -it" - CMD="/bin/bash" +if [ -n "$DOCKER_INTERACTIVE" ]; then + source run docker-shell +else + CMD="make install" + source run docker-run-user bash -c "${CMD}" fi +EXITCODE=$? -docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME $CMD - -BUILD_RESULT=$? - -source $SCRIPT_ROOT/../docker_helper -restore_ownership $HOST_HOME $DOCKER_HOME +popd -exit $BUILD_RESULT +exit $EXITCODE diff --git a/scripts/command/format-checker.sh b/scripts/command/format-checker.sh index 75c345dd0..2e947ec70 100755 --- a/scripts/command/format-checker.sh +++ b/scripts/command/format-checker.sh @@ -1,39 +1,77 @@ #!/bin/bash -function pushd () { +INVALID_EXIT=0 + +pushd () { command pushd "$@" > /dev/null } -function popd () { +popd () { command popd "$@" > /dev/null } -function check_cpp_tool() { - which clang-format-3.9 - if [[ $? -ne 0 ]]; then +command_exists() { + command -v $1 > /dev/null 2>&1 +} + +check_cpp_tool() { + if ! command_exists clang-format-3.9; then echo "Error: clang-format-3.9 is not available." echo " Please install clang-format-3.9." exit 1 fi } -function check_python_tool() { - which yapf - if [[ $? -ne 0 ]]; then +check_python_tool() { + if ! command_exists yapf; then echo "Error: yapf is not available." echo " Please install yapf." exit 1 fi } -function check_cpp_files() { +check_newline() { + # Check all files (CMakeLists.txt, *.cl, ... not only for C++, Python) + FILES_TO_CHECK=$(git ls-files) + if [[ ${#FILES_TO_CHECK} -ne 0 ]]; then + CRCHECK=$(file $FILES_TO_CHECK | grep 'with CR') + fi + FILES_TO_FIX=($(echo "$CRCHECK" | grep "with CRLF line" | cut -d':' -f1)) + for f in ${FILES_TO_FIX[@]}; do + tr -d '\r' < $f > $f.fixed && cat $f.fixed > $f && rm $f.fixed + done + FILES_TO_FIX=($(echo "$CRCHECK" | grep "with CR line" | cut -d':' -f1)) + for f in ${FILES_TO_FIX[@]}; do + tr '\r' '\n' < $f > $f.fixed && cat $f.fixed > $f && rm $f.fixed + done +} + +check_permission() { + # Check all files except script + FILES_TO_CHECK=() + for NON_SCRIPT_FILE in $(git ls-files -- . ':!:run' ':!:scripts/git-hooks/*' ':!:*.sh' ':!:*.py'); do + FILES_TO_CHECK+=("${NON_SCRIPT_FILE}") + done + + if [[ ${#FILES_TO_CHECK} -eq 0 ]]; then + return + fi + for FILE_TO_CHECK in ${FILES_TO_CHECK[@]}; do + RESULT=$(stat -c '%A' ${FILE_TO_CHECK} | grep 'x') + if [ "${RESULT}" != "" ]; then + chmod a-x ${FILE_TO_CHECK} + fi + done +} + +check_cpp_files() { DIRECTORIES_TO_BE_TESTED=$1 DIRECTORIES_NOT_TO_BE_TESTED=$2 # Check c++ files for TEST_DIR in ${DIRECTORIES_TO_BE_TESTED[@]}; do pushd $TEST_DIR - CPP_FILES_TO_CHECK=$(git ls-files '*.h' '*.cpp' '*.cc') + CPP_FILES_TO_CHECK=$(git ls-files '*.h' '*.cpp' '*.cc' ':!:NeuralNetworks.h') ARR=($CPP_FILES_TO_CHECK) for s in ${DIRECTORIES_NOT_TO_BE_TESTED[@]}; do if [[ $s = $TEST_DIR* ]]; then @@ -44,12 +82,16 @@ function check_cpp_files() { CPP_FILES_TO_CHECK=${ARR[*]} if [[ ${#CPP_FILES_TO_CHECK} -ne 0 ]]; then clang-format-3.9 -i $CPP_FILES_TO_CHECK + EXIT_CODE=$? + if [[ $EXIT_CODE -ne 0 ]]; then + INVALID_EXIT=$EXIT_CODE + fi fi popd done } -function check_python_files() { +check_python_files() { DIRECTORIES_TO_BE_TESTED=$1 DIRECTORIES_NOT_TO_BE_TESTED=$2 @@ -67,6 +109,10 @@ function check_python_files() { PYTHON_FILES_TO_CHECK=${ARR[*]} if [[ ${#PYTHON_FILES_TO_CHECK} -ne 0 ]]; then yapf -i --style='{based_on_style: pep8, column_limit: 90}' $PYTHON_FILES_TO_CHECK + EXIT_CODE=$? + if [[ $EXIT_CODE -ne 0 ]]; then + INVALID_EXIT=$EXIT_CODE + fi fi popd done @@ -93,6 +139,9 @@ if [[ ${#DIRECTORIES_TO_BE_TESTED[@]} -eq 0 ]]; then exit 0 fi +check_newline +check_permission + if [[ $__Check_CPP -ne 0 ]]; then check_cpp_tool check_cpp_files $DIRECTORIES_TO_BE_TESTED $DIRECTORIES_NOT_TO_BE_TESTED @@ -105,14 +154,25 @@ fi git diff --ignore-submodules > format.patch PATCHFILE_SIZE=$(stat -c%s format.patch) + +if [[ -z "${CRCHECK}" ]] && [[ $PATCHFILE_SIZE -eq 0 ]] && [[ $INVALID_EXIT -eq 0 ]]; then + echo "[PASSED] Format checker succeed." + exit 0 +fi + +# Something went wrong + +if [[ ! -z "${CRCHECK}" ]]; then + echo "[FAILED] Please use LF for newline for following files." + echo "$CRCHECK" +fi + if [[ $PATCHFILE_SIZE -ne 0 ]]; then echo "[FAILED] Format checker failed and update code to follow convention." echo " You can find changes in format.patch" - exit 1 -else - echo "[PASSED] Format checker succeed." - exit 0 fi -echo "Error: Something went wrong." +if [[ $INVALID_EXIT -ne 0 ]]; then + echo "[[FAILED] Invalid format checker exit." +fi exit 1 diff --git a/scripts/command/gen_coverage_report.sh b/scripts/command/gen_coverage_report.sh index cff7594cd..fa72717cf 100755 --- a/scripts/command/gen_coverage_report.sh +++ b/scripts/command/gen_coverage_report.sh @@ -47,13 +47,11 @@ COVERTURA_PATH="${OUTPUT_PATH}/nnfw_coverage.xml" "${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${LIBS_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/libs/*" "${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${INCLUDE_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/include/*" "${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${RUNTIMES_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/runtimes/*" -"${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${TOOLS_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/tools/*" +"${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${TOOLS_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/tests/tools/*" "${LCOV_PATH}" -a "${LIBS_COVERAGE_INFO_PATH}" -a "${INCLUDE_COVERAGE_INFO_PATH}" \ -a "${RUNTIMES_COVERAGE_INFO_PATH}" -a "${TOOLS_COVERAGE_INFO_PATH}" \ -o "${FINAL_COVERAGE_INFO_PATH}" -"${LCOV_PATH}" -r "${FINAL_COVERAGE_INFO_PATH}" -o "${FINAL_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/runtimes/tests/*" -"${LCOV_PATH}" -r "${FINAL_COVERAGE_INFO_PATH}" -o "${FINAL_COVERAGE_INFO_PATH}" "${SRC_PREFIX}/runtimes/nn/depend/*" "${GENHTML_PATH}" "${FINAL_COVERAGE_INFO_PATH}" --output-directory "${HTML_PATH}" ${GENHTML_FLAG:-} tar -zcf "${OUTPUT_PATH}"/coverage_report.tar.gz "${HTML_PATH}" -$SCRIPT_ROOT/lcov-to-covertura-xml.sh "${FINAL_COVERAGE_INFO_PATH}" -o "${COVERTURA_PATH}" +python ${HOST_HOME}/externals/lcov-to-cobertura-xml/lcov_cobertura.py "${FINAL_COVERAGE_INFO_PATH}" -o "${COVERTURA_PATH}" diff --git a/scripts/command/imported_url.txt b/scripts/command/imported_url.txt deleted file mode 100644 index b3a27cff5..000000000 --- a/scripts/command/imported_url.txt +++ /dev/null @@ -1,3 +0,0 @@ -# This file contains urls of files which is imported from public origin. - -1. lcov-to-covertura-xml.sh : https://github.com/eriwen/lcov-to-cobertura-xml diff --git a/scripts/command/install b/scripts/command/install new file mode 100644 index 000000000..a40268a50 --- /dev/null +++ b/scripts/command/install @@ -0,0 +1,16 @@ +#!/bin/bash + +source "${NNFW_SCRIPT_PATH}/config/build.configuration" + +if [[ ! -d "${BUILD_ALIAS}" ]]; then + echo "'${BUILD_ALIAS}' does not exist. Please run 'configure' first" + exit 255 +fi + +if [[ ! -d "${INSTALL_ALIAS}" ]]; then + echo "'${INSTALL_ALIAS}' does not exist. Please run 'configure' first" + exit 255 +fi + +cd ${BUILD_ALIAS} +make install diff --git a/scripts/command/run_coverity.sh b/scripts/command/run_coverity.sh deleted file mode 100755 index 2c4f9514b..000000000 --- a/scripts/command/run_coverity.sh +++ /dev/null @@ -1,60 +0,0 @@ -#!/bin/bash - -# _COVERITY_USER, _COVERITY_PASSWORD, _COVERITY_STREAM and COVERITY_SCRIPT_DOWNLOAD_URL should be defined. -# XXX are used only in this script and _XXX are used in Coverity script. -if [ -z ${_COVERITY_STREAM+x} ]; then - echo "_COVERITY_STREAM is unset"; - exit 1 -else - echo "_COVERITY_STREAM is set to '$_COVERITY_STREAM'"; -fi - -if [ -z ${_COVERITY_USER+x} ]; then - echo "_COVERITY_USER is unset"; - exit 1 -else - echo "_COVERITY_USER is set to '$_COVERITY_USER'"; -fi - -if [ -z ${_COVERITY_PASSWORD+x} ]; then - echo "_COVERITY_PASSWORD is unset"; - exit 1 -else - echo "_COVERITY_PASSWORD is set to '$_COVERITY_PASSWORD'"; -fi - -if [ -z ${COVERITY_SCRIPT_DOWNLOAD_URL+x} ]; then - echo "COVERITY_SCRIPT_DOWNLOAD_URL is unset"; - exit 1 -else - echo "COVERITY_SCRIPT_DOWNLOAD_URL is set to '$COVERITY_SCRIPT_DOWNLOAD_URL'"; -fi - -# -# Set variables required for Coverity script -# - -SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" -HOST_HOME=$(readlink -f $SCRIPT_ROOT/../..) - -export _BUILD_PATH=$HOST_HOME -export _RUN_COVERITY_ROOT=$_BUILD_PATH/ci_coverity -mkdir -p $_RUN_COVERITY_ROOT - -export GBS_BUILDROOT=$_RUN_COVERITY_ROOT/GBS-ROOT/ -mkdir -p $GBS_BUILDROOT - -# Prepare gbs.conf for Coverity -sed -- 's/^buildroot = .*/buildroot = '${GBS_BUILDROOT//\//\\/}'/' < $SCRIPT_ROOT/gbs.conf > $SCRIPT_ROOT/gbs_coverity.conf -export _BUILD_CMD="gbs -c ${SCRIPT_ROOT}/gbs_coverity.conf build -A armv7l --profile=profile.tizen --clean-repo" -export _PROD_LOCATION=$GBS_BUILDROOT/local/repos/tizen/armv7l/RPMS - -export _COVERITY_BINARY_DIR=$_RUN_COVERITY_ROOT/coverity-binary -mkdir -p $_COVERITY_BINARY_DIR - -# Invoke Coverity script -pushd $_RUN_COVERITY_ROOT -wget $COVERITY_SCRIPT_DOWNLOAD_URL -popd -chmod +x $_RUN_COVERITY_ROOT/${COVERITY_SCRIPT_DOWNLOAD_URL##*/} -$_RUN_COVERITY_ROOT/${COVERITY_SCRIPT_DOWNLOAD_URL##*/} diff --git a/scripts/command/tizen_xu4_test.sh b/scripts/command/tizen_xu4_test.sh index d56dae2b5..38894df0f 100755 --- a/scripts/command/tizen_xu4_test.sh +++ b/scripts/command/tizen_xu4_test.sh @@ -131,17 +131,17 @@ fi # run unittest if [ "$UNITTEST" == "on" ]; then - $SDB_CMD shell $TEST_ROOT/tools/test_driver/test_driver.sh --unittest --artifactpath=$TEST_ROOT + $SDB_CMD shell $TEST_ROOT/tests/scripts/test_driver.sh --unittest --artifactpath=$TEST_ROOT fi # run framework test if [ "$FRAMEWORK" == "on" ]; then - $SDB_CMD shell $TEST_ROOT/tools/test_driver/test_driver.sh --frameworktest --artifactpath=$TEST_ROOT + $SDB_CMD shell $TEST_ROOT/tests/scripts/test_driver.sh --frameworktest --artifactpath=$TEST_ROOT fi # run verification if [ "$VERIFICATION" == "on" ]; then - $SDB_CMD shell $TEST_ROOT/tools/test_driver/test_driver.sh --verification --artifactpath=$TEST_ROOT + $SDB_CMD shell $TEST_ROOT/tests/scripts/test_driver.sh --verification --artifactpath=$TEST_ROOT fi # pull gcov files diff --git a/scripts/config/build.configuration b/scripts/config/build.configuration new file mode 100644 index 000000000..cdbf3bf4b --- /dev/null +++ b/scripts/config/build.configuration @@ -0,0 +1,5 @@ +WORKSPACE_RPATH=${NNFW_WORKSPACE:-Product} + +# Soft link path to build and install directory +BUILD_ALIAS=${NNFW_PROJECT_PATH}/${WORKSPACE_RPATH}/obj +INSTALL_ALIAS=${NNFW_PROJECT_PATH}/${WORKSPACE_RPATH}/out diff --git a/scripts/config/docker.configuration b/scripts/config/docker.configuration new file mode 100644 index 000000000..806ffb32e --- /dev/null +++ b/scripts/config/docker.configuration @@ -0,0 +1,44 @@ +#!/bin/bash + +source "$NNFW_SCRIPT_PATH/config/image_name.configuration" + +echo "Using docker image $DOCKER_IMAGE_NAME" + +if [ -z "`docker images | grep $DOCKER_IMAGE_NAME`" ]; then + echo "Need docker image!" + exit 1 +fi + +HOST_PATH="$NNFW_PROJECT_PATH" +DOCKER_PATH="$NNFW_PROJECT_PATH" + +export GIT_SSL_NO_VERIFY=1 + +DOCKER_VOLUMES+=" -v $HOST_PATH:$DOCKER_PATH" + +if [[ ! -z $ENV_FILE ]]; then + DOCKER_ENV_VARS+=" --env-file ${ENV_FILE} " +fi + +DOCKER_ENV_VARS+=" -e http_proxy" +DOCKER_ENV_VARS+=" -e no_proxy" +DOCKER_ENV_VARS+=" -e GIT_SSL_NO_VERIFY" +DOCKER_ENV_VARS+=" -e EXTERNAL_DOWNLOAD_SERVER" + +DOCKER_RUN_OPTS="${DOCKER_OPTS}" +DOCKER_RUN_OPTS+=" --rm" +DOCKER_RUN_OPTS+=" -w $DOCKER_PATH" + +function docker_cleanup() +{ + # Newly created files during during docker run can have different ownership. + # This may cause some problems, for example, some jenkins slaves or developers + # can't remove built files due to lack of permission. + # To address this issue, let's change owner of all files + # in nncc to owner of nncc. + NNFW_OWNER_UID=$(stat -c "%u" $HOST_PATH) + NNFW_OWNER_GID=$(stat -c "%g" $HOST_PATH) + + CMD="chown -R $NNFW_OWNER_UID:$NNFW_OWNER_GID $DOCKER_PATH" + docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME $CMD +} diff --git a/scripts/command/gbs.conf b/scripts/config/gbs.conf index af555c399..af555c399 100644 --- a/scripts/command/gbs.conf +++ b/scripts/config/gbs.conf diff --git a/scripts/config/image_name.configuration b/scripts/config/image_name.configuration new file mode 100644 index 000000000..507f154c7 --- /dev/null +++ b/scripts/config/image_name.configuration @@ -0,0 +1,3 @@ +#!/bin/bash + +DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME:-nnfw_docker} diff --git a/scripts/docker/Dockerfile b/scripts/docker/Dockerfile index 420c3b61a..37e4e1ebf 100644 --- a/scripts/docker/Dockerfile +++ b/scripts/docker/Dockerfile @@ -10,9 +10,10 @@ RUN apt-get update && apt-get --yes --force-yes install git RUN apt-get update && apt-get --yes --force-yes install gcc-5-arm-linux-gnueabi g++-5-arm-linux-gnueabi RUN apt-get update && apt-get --yes --force-yes install lcov RUN apt-get update && apt-get --yes --force-yes install clang-format-3.9 python-pip -RUN pip install yapf +RUN pip install yapf==0.22.0 RUN apt-get update && apt-get --yes --force-yes install doxygen graphviz RUN apt-get update && apt-get --yes --force-yes install wget RUN wget https://releases.linaro.org/components/toolchain/binaries/6.3-2017.02/arm-linux-gnueabihf/gcc-linaro-6.3.1-2017.02-x86_64_arm-linux-gnueabihf.tar.xz -O gcc.tar.xz -nv RUN tar -xf gcc.tar.xz -C /opt/ && rm -rf gcc.tar.xz ENV PATH "/opt/gcc-linaro-6.3.1-2017.02-x86_64_arm-linux-gnueabihf/bin:$PATH" +RUN apt-get update && apt-get --yes --force-yes install python3 diff --git a/scripts/docker_helper b/scripts/docker_helper deleted file mode 100644 index 37ec9fcbd..000000000 --- a/scripts/docker_helper +++ /dev/null @@ -1,33 +0,0 @@ -# Newly created files during docker run can have different ownership. -# This may cause some problems, for example, some jenkins slaves or developers -# can't remove built files due to lack of permission. -# To address this issue, let's change owner of all files and subdirector -# to owner of host directory. -# -# Parameters: -# $1 : host directory -# $2 : docker directory -# -# Requires following variables defined -# DOCKER_RUN_OPTS, DOCKER_ENV_VARS, DOCKER_VOLUMES, DOCKER_IMAGE_NAME -function restore_ownership() { - if [ -z "$1" ] - then - echo "Requires host path" - exit 1 - fi - _HOST_DIR=$1 - - if [ -z "$2" ] - then - echo "Requires docker path" - exit 1 - fi - - _DOCKER_DIR=$2 - - _OWNER_UID=$(stat -c "%u" $_HOST_DIR) - _OWNER_GID=$(stat -c "%g" $_HOST_DIR) - CMD="chown -R $_OWNER_UID:$_OWNER_GID $_DOCKER_DIR" - docker run $DOCKER_RUN_OPTS $DOCKER_ENV_VARS $DOCKER_VOLUMES $DOCKER_IMAGE_NAME $CMD -} diff --git a/tests/.FORMATCHECKED b/tests/.FORMATCHECKED new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/tests/.FORMATCHECKED diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt new file mode 100644 index 000000000..23d09d13e --- /dev/null +++ b/tests/CMakeLists.txt @@ -0,0 +1,2 @@ +add_subdirectory(nnapi) +add_subdirectory(tools) diff --git a/tests/framework/tests/custom/tensorflowmax/config.sh b/tests/framework/tests/custom/tensorflowmax/config.sh index 122c459db..058799935 100644 --- a/tests/framework/tests/custom/tensorflowmax/config.sh +++ b/tests/framework/tests/custom/tensorflowmax/config.sh @@ -1 +1,2 @@ MODELFILE_NAME="custom_max_test.tflite" +STATUS="disabled" diff --git a/tests/framework/tests/embedding_lookup/config.sh b/tests/framework/tests/embedding_lookup/config.sh index c882b15eb..5e5e4ad92 100644 --- a/tests/framework/tests/embedding_lookup/config.sh +++ b/tests/framework/tests/embedding_lookup/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="embedding_lookup_test1.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/exp/config.sh b/tests/framework/tests/exp/config.sh new file mode 100644 index 000000000..47f878783 --- /dev/null +++ b/tests/framework/tests/exp/config.sh @@ -0,0 +1,2 @@ +MODELFILE_NAME="exp_4d.tflite" +STATUS="enabled" diff --git a/tests/framework/tests/l2_normalization/config.sh b/tests/framework/tests/l2_normalization/config.sh index 2cd9d0e00..47801240f 100644 --- a/tests/framework/tests/l2_normalization/config.sh +++ b/tests/framework/tests/l2_normalization/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="l2_normalization_test1.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/l2_pool_2d/config.sh b/tests/framework/tests/l2_pool_2d/config.sh index 1d5a977f0..a77aa66cf 100644 --- a/tests/framework/tests/l2_pool_2d/config.sh +++ b/tests/framework/tests/l2_pool_2d/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="l2pool2d_test.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/logistic/config.sh b/tests/framework/tests/logistic/config.sh index 632c3629e..456773aa9 100644 --- a/tests/framework/tests/logistic/config.sh +++ b/tests/framework/tests/logistic/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="sigmoid_test.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/neg/config.sh b/tests/framework/tests/neg/config.sh new file mode 100644 index 000000000..8cde1a258 --- /dev/null +++ b/tests/framework/tests/neg/config.sh @@ -0,0 +1,2 @@ +MODELFILE_NAME="neg_4d.tflite" +STATUS="enabled" diff --git a/tests/framework/tests/rsqrt/config.sh b/tests/framework/tests/rsqrt/config.sh new file mode 100644 index 000000000..c97f8af6e --- /dev/null +++ b/tests/framework/tests/rsqrt/config.sh @@ -0,0 +1,2 @@ +MODELFILE_NAME="rsqrt_4d.tflite" +STATUS="disabled" diff --git a/tests/framework/tests/sqrt/config.sh b/tests/framework/tests/sqrt/config.sh new file mode 100644 index 000000000..cf3bf19fa --- /dev/null +++ b/tests/framework/tests/sqrt/config.sh @@ -0,0 +1,2 @@ +MODELFILE_NAME="sqrt_4d.tflite" +STATUS="disabled" diff --git a/tests/framework/tests/squeeze/config.sh b/tests/framework/tests/squeeze/config.sh index e2b0ecf98..5bcc67716 100644 --- a/tests/framework/tests/squeeze/config.sh +++ b/tests/framework/tests/squeeze/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="squeeze_test.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/tanh/config.sh b/tests/framework/tests/tanh/config.sh index 76a0abad2..a9dde4923 100644 --- a/tests/framework/tests/tanh/config.sh +++ b/tests/framework/tests/tanh/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="tanh_test.tflite" -STATUS="disabled" diff --git a/tests/framework/tests/tranpose/config.sh b/tests/framework/tests/tranpose/config.sh index 07a489f41..9adb85e70 100644 --- a/tests/framework/tests/tranpose/config.sh +++ b/tests/framework/tests/tranpose/config.sh @@ -1,2 +1 @@ MODELFILE_NAME="transpose_4d.tflite" -STATUS="disabled" diff --git a/tests/nnapi/.FORMATDENY b/tests/nnapi/.FORMATDENY new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/tests/nnapi/.FORMATDENY diff --git a/tests/nnapi/CMakeLists.txt b/tests/nnapi/CMakeLists.txt new file mode 100644 index 000000000..d3c9cab4c --- /dev/null +++ b/tests/nnapi/CMakeLists.txt @@ -0,0 +1,56 @@ +if (NOT BUILD_RUNTIME_NNAPI_TEST) + return() +endif(NOT BUILD_RUNTIME_NNAPI_TEST) + +# Executable `runtime_run` (Dummy runner executable using NN API) +set(RUNTIME_NNAPI_TEST nnapi_gtest) +set(RUNTIME_NNAPI_TEST_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/TestMain.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/src/TestTrivialModel.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/src/TestValidation.cpp) + +set(GENERATED_TEST_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/TestGenerated_V1_0.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/src/TestGenerated_V1_1.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/src/TestGenerated_Ex.cpp) + +# g++-6.3 can build TestGenerated_XXX.cpp. +if (CMAKE_COMPILER_IS_GNUCC AND NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 6.3) + set(RUNTIME_NNAPI_TEST_SRC ${RUNTIME_NNAPI_TEST_SRC} ${GENERATED_TEST_SRC}) +endif() + +# Both gbs and cross for tizen support building generated test +if (OBS_BUILD) + set(RUNTIME_NNAPI_TEST_SRC ${RUNTIME_NNAPI_TEST_SRC} ${GENERATED_TEST_SRC}) +endif() + +add_executable(${RUNTIME_NNAPI_TEST} ${RUNTIME_NNAPI_TEST_SRC}) + +nnfw_find_package(GTest) + +set(RUNTIME_NNAPI_TEST_SRC_INC ${NNFW_INCLUDE_DIR} + ${CMAKE_CURRENT_SOURCE_DIR}/include + ${CMAKE_CURRENT_SOURCE_DIR}/src) +target_include_directories(${RUNTIME_NNAPI_TEST} PRIVATE ${RUNTIME_NNAPI_TEST_SRC_INC}) + +if (BUILD_NEURUN) + set(SKIPLIST_FILE_NAME ${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM}.neurun) +elseif (BUILD_PURE_ARM_COMPUTE) + set(SKIPLIST_FILE_NAME ${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM}) +endif() +target_link_libraries(${RUNTIME_NNAPI_TEST} gtest) +target_link_libraries(${RUNTIME_NNAPI_TEST} ${LIB_PTHREAD} dl) + +install(TARGETS nnapi_gtest DESTINATION unittest) + +install(FILES ${SKIPLIST_FILE_NAME} + DESTINATION unittest + RENAME ${RUNTIME_NNAPI_TEST}.skip + OPTIONAL) + +# If build both runtime (pure_arm_compute and neurun) at once, +# install both skiplist file as backup +if (BUILD_NEURUN AND BUILD_PURE_ARM_COMPUTE) + set(SKIPLIST_ALL_RUNTIME + ${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM} + ${RUNTIME_NNAPI_TEST}.skip.${TARGET_PLATFORM}.neurun) + install(FILES ${SKIPLIST_ALL_RUNTIME} DESTINATION unittest OPTIONAL) +endif() diff --git a/tests/nnapi/include/NeuralNetworksWrapper.h b/tests/nnapi/include/NeuralNetworksWrapper.h new file mode 100644 index 000000000..71468e212 --- /dev/null +++ b/tests/nnapi/include/NeuralNetworksWrapper.h @@ -0,0 +1,355 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// Provides C++ classes to more easily use the Neural Networks API. + +#ifndef __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ +#define __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ + +#include "NeuralNetworksShim.h" +#include "NeuralNetworksExShim.h" + +#include <math.h> +#include <vector> + +namespace nnfw { +namespace rt { +namespace wrapper { + +enum class Type { + FLOAT32 = ANEURALNETWORKS_FLOAT32, + INT32 = ANEURALNETWORKS_INT32, + UINT32 = ANEURALNETWORKS_UINT32, + TENSOR_FLOAT32 = ANEURALNETWORKS_TENSOR_FLOAT32, + TENSOR_INT32 = ANEURALNETWORKS_TENSOR_INT32, + TENSOR_QUANT8_ASYMM = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, +}; + +enum class ExecutePreference { + PREFER_LOW_POWER = ANEURALNETWORKS_PREFER_LOW_POWER, + PREFER_FAST_SINGLE_ANSWER = ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER, + PREFER_SUSTAINED_SPEED = ANEURALNETWORKS_PREFER_SUSTAINED_SPEED +}; + +enum class Result { + NO_ERROR = ANEURALNETWORKS_NO_ERROR, + OUT_OF_MEMORY = ANEURALNETWORKS_OUT_OF_MEMORY, + INCOMPLETE = ANEURALNETWORKS_INCOMPLETE, + UNEXPECTED_NULL = ANEURALNETWORKS_UNEXPECTED_NULL, + BAD_DATA = ANEURALNETWORKS_BAD_DATA, +}; + +struct OperandType { + ANeuralNetworksOperandType operandType; + // int32_t type; + std::vector<uint32_t> dimensions; + + OperandType(Type type, const std::vector<uint32_t>& d, float scale = 0.0f, + int32_t zeroPoint = 0) + : dimensions(d) { + operandType.type = static_cast<int32_t>(type); + operandType.scale = scale; + operandType.zeroPoint = zeroPoint; + + operandType.dimensionCount = static_cast<uint32_t>(dimensions.size()); + operandType.dimensions = dimensions.data(); + } +}; + +class Memory { +public: + Memory(size_t size, int protect, int fd, size_t offset) { + mValid = ANeuralNetworksMemory_createFromFd(size, protect, fd, offset, &mMemory) == + ANEURALNETWORKS_NO_ERROR; + } + + ~Memory() { ANeuralNetworksMemory_free(mMemory); } + + // Disallow copy semantics to ensure the runtime object can only be freed + // once. Copy semantics could be enabled if some sort of reference counting + // or deep-copy system for runtime objects is added later. + Memory(const Memory&) = delete; + Memory& operator=(const Memory&) = delete; + + // Move semantics to remove access to the runtime object from the wrapper + // object that is being moved. This ensures the runtime object will be + // freed only once. + Memory(Memory&& other) { *this = std::move(other); } + Memory& operator=(Memory&& other) { + if (this != &other) { + mMemory = other.mMemory; + mValid = other.mValid; + other.mMemory = nullptr; + other.mValid = false; + } + return *this; + } + + ANeuralNetworksMemory* get() const { return mMemory; } + bool isValid() const { return mValid; } + +private: + ANeuralNetworksMemory* mMemory = nullptr; + bool mValid = true; +}; + +class Model { +public: + Model() { + // TODO handle the value returned by this call + ANeuralNetworksModel_create(&mModel); + } + ~Model() { ANeuralNetworksModel_free(mModel); } + + // Disallow copy semantics to ensure the runtime object can only be freed + // once. Copy semantics could be enabled if some sort of reference counting + // or deep-copy system for runtime objects is added later. + Model(const Model&) = delete; + Model& operator=(const Model&) = delete; + + // Move semantics to remove access to the runtime object from the wrapper + // object that is being moved. This ensures the runtime object will be + // freed only once. + Model(Model&& other) { *this = std::move(other); } + Model& operator=(Model&& other) { + if (this != &other) { + mModel = other.mModel; + mNextOperandId = other.mNextOperandId; + mValid = other.mValid; + other.mModel = nullptr; + other.mNextOperandId = 0; + other.mValid = false; + } + return *this; + } + + Result finish() { return static_cast<Result>(ANeuralNetworksModel_finish(mModel)); } + + uint32_t addOperand(const OperandType* type) { + if (ANeuralNetworksModel_addOperand(mModel, &(type->operandType)) != + ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + return mNextOperandId++; + } + + void setOperandValue(uint32_t index, const void* buffer, size_t length) { + if (ANeuralNetworksModel_setOperandValue(mModel, index, buffer, length) != + ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + } + + void setOperandValueFromMemory(uint32_t index, const Memory* memory, uint32_t offset, + size_t length) { + if (ANeuralNetworksModel_setOperandValueFromMemory(mModel, index, memory->get(), offset, + length) != ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + } + + void addOperation(ANeuralNetworksOperationType type, const std::vector<uint32_t>& inputs, + const std::vector<uint32_t>& outputs) { + if (ANeuralNetworksModel_addOperation(mModel, type, static_cast<uint32_t>(inputs.size()), + inputs.data(), static_cast<uint32_t>(outputs.size()), + outputs.data()) != ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + } + + void addOperationEx(ANeuralNetworksOperationTypeEx type, const std::vector<uint32_t>& inputs, + const std::vector<uint32_t>& outputs) { + if (ANeuralNetworksModel_addOperationEx(mModel, type, static_cast<uint32_t>(inputs.size()), + inputs.data(), static_cast<uint32_t>(outputs.size()), + outputs.data()) != ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + } + + void identifyInputsAndOutputs(const std::vector<uint32_t>& inputs, + const std::vector<uint32_t>& outputs) { + if (ANeuralNetworksModel_identifyInputsAndOutputs( + mModel, static_cast<uint32_t>(inputs.size()), inputs.data(), + static_cast<uint32_t>(outputs.size()), + outputs.data()) != ANEURALNETWORKS_NO_ERROR) { + mValid = false; + } + } + ANeuralNetworksModel* getHandle() const { return mModel; } + bool isValid() const { return mValid; } + +private: + ANeuralNetworksModel* mModel = nullptr; + // We keep track of the operand ID as a convenience to the caller. + uint32_t mNextOperandId = 0; + bool mValid = true; +}; + +class Event { +public: + Event() {} + ~Event() { ANeuralNetworksEvent_free(mEvent); } + + // Disallow copy semantics to ensure the runtime object can only be freed + // once. Copy semantics could be enabled if some sort of reference counting + // or deep-copy system for runtime objects is added later. + Event(const Event&) = delete; + Event& operator=(const Event&) = delete; + + // Move semantics to remove access to the runtime object from the wrapper + // object that is being moved. This ensures the runtime object will be + // freed only once. + Event(Event&& other) { *this = std::move(other); } + Event& operator=(Event&& other) { + if (this != &other) { + mEvent = other.mEvent; + other.mEvent = nullptr; + } + return *this; + } + + Result wait() { return static_cast<Result>(ANeuralNetworksEvent_wait(mEvent)); } + + // Only for use by Execution + void set(ANeuralNetworksEvent* newEvent) { + ANeuralNetworksEvent_free(mEvent); + mEvent = newEvent; + } + +private: + ANeuralNetworksEvent* mEvent = nullptr; +}; + +class Compilation { +public: + Compilation(const Model* model) { + int result = ANeuralNetworksCompilation_create(model->getHandle(), &mCompilation); + if (result != 0) { + // TODO Handle the error + } + } + + ~Compilation() { ANeuralNetworksCompilation_free(mCompilation); } + + Compilation(const Compilation&) = delete; + Compilation& operator=(const Compilation&) = delete; + + Compilation(Compilation&& other) { *this = std::move(other); } + Compilation& operator=(Compilation&& other) { + if (this != &other) { + mCompilation = other.mCompilation; + other.mCompilation = nullptr; + } + return *this; + } + + Result setPreference(ExecutePreference preference) { + return static_cast<Result>(ANeuralNetworksCompilation_setPreference( + mCompilation, static_cast<int32_t>(preference))); + } + + Result finish() { return static_cast<Result>(ANeuralNetworksCompilation_finish(mCompilation)); } + + ANeuralNetworksCompilation* getHandle() const { return mCompilation; } + +private: + ANeuralNetworksCompilation* mCompilation = nullptr; +}; + +class Execution { +public: + Execution(const Compilation* compilation) { + int result = ANeuralNetworksExecution_create(compilation->getHandle(), &mExecution); + if (result != 0) { + // TODO Handle the error + } + } + + ~Execution() { ANeuralNetworksExecution_free(mExecution); } + + // Disallow copy semantics to ensure the runtime object can only be freed + // once. Copy semantics could be enabled if some sort of reference counting + // or deep-copy system for runtime objects is added later. + Execution(const Execution&) = delete; + Execution& operator=(const Execution&) = delete; + + // Move semantics to remove access to the runtime object from the wrapper + // object that is being moved. This ensures the runtime object will be + // freed only once. + Execution(Execution&& other) { *this = std::move(other); } + Execution& operator=(Execution&& other) { + if (this != &other) { + mExecution = other.mExecution; + other.mExecution = nullptr; + } + return *this; + } + + Result setInput(uint32_t index, const void* buffer, size_t length, + const ANeuralNetworksOperandType* type = nullptr) { + return static_cast<Result>( + ANeuralNetworksExecution_setInput(mExecution, index, type, buffer, length)); + } + + Result setInputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, + uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { + return static_cast<Result>(ANeuralNetworksExecution_setInputFromMemory( + mExecution, index, type, memory->get(), offset, length)); + } + + Result setOutput(uint32_t index, void* buffer, size_t length, + const ANeuralNetworksOperandType* type = nullptr) { + return static_cast<Result>( + ANeuralNetworksExecution_setOutput(mExecution, index, type, buffer, length)); + } + + Result setOutputFromMemory(uint32_t index, const Memory* memory, uint32_t offset, + uint32_t length, const ANeuralNetworksOperandType* type = nullptr) { + return static_cast<Result>(ANeuralNetworksExecution_setOutputFromMemory( + mExecution, index, type, memory->get(), offset, length)); + } + + Result startCompute(Event* event) { + ANeuralNetworksEvent* ev = nullptr; + Result result = static_cast<Result>(ANeuralNetworksExecution_startCompute(mExecution, &ev)); + event->set(ev); + return result; + } + + Result compute() { + ANeuralNetworksEvent* event = nullptr; + Result result = + static_cast<Result>(ANeuralNetworksExecution_startCompute(mExecution, &event)); + if (result != Result::NO_ERROR) { + return result; + } + // TODO how to manage the lifetime of events when multiple waiters is not + // clear. + result = static_cast<Result>(ANeuralNetworksEvent_wait(event)); + ANeuralNetworksEvent_free(event); + return result; + } + +private: + ANeuralNetworksExecution* mExecution = nullptr; +}; + +} // namespace wrapper +} // namespace rt +} // namespace nnfw + +#endif // __NNFW_RT_NEURAL_NETWORKS_WRAPPER_H__ diff --git a/runtimes/tests/neural_networks_test/include/TestHarness.h b/tests/nnapi/include/TestHarness.h index 1fcb0d661..1fcb0d661 100644 --- a/runtimes/tests/neural_networks_test/include/TestHarness.h +++ b/tests/nnapi/include/TestHarness.h diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux b/tests/nnapi/nnapi_gtest.skip.armv7l-linux new file mode 100644 index 000000000..1c85418a9 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux @@ -0,0 +1,44 @@ +GeneratedTests.add_broadcast_quant8 +GeneratedTests.add_quant8 +GeneratedTests.argmax_ex_quant8* +GeneratedTests.logical_not_ex* +GeneratedTests.logistic_quant8_1 +GeneratedTests.logistic_quant8_2 +GeneratedTests.lsh_projection +GeneratedTests.lsh_projection_2 +GeneratedTests.lsh_projection_weights_as_inputs +GeneratedTests.lstm +GeneratedTests.lstm2 +GeneratedTests.lstm2_state +GeneratedTests.lstm2_state2 +GeneratedTests.lstm3 +GeneratedTests.lstm3_state +GeneratedTests.lstm3_state2 +GeneratedTests.lstm3_state3 +GeneratedTests.lstm_state +GeneratedTests.lstm_state2 +GeneratedTests.mul_broadcast_quant8 +GeneratedTests.mul_quant8 +GeneratedTests.pack* +GeneratedTests.svdf +GeneratedTests.svdf2 +GeneratedTests.svdf_state +ValidationTestCompilation.CreateExecution +ValidationTestCompilation.Finish +ValidationTestCompilation.SetPreference +ValidationTestExecution.EventWait +ValidationTestExecution.SetInput +ValidationTestExecution.SetInputFromMemory +ValidationTestExecution.SetOutput +ValidationTestExecution.SetOutputFromMemory +ValidationTestExecution.StartCompute +ValidationTestIdentify.DuplicateInputs +ValidationTestIdentify.DuplicateOutputs +ValidationTestIdentify.InputIsOutput +ValidationTestIdentify.OutputIsInput +ValidationTestModel.AddOperand +ValidationTestModel.CreateCompilation +ValidationTestModel.Finish +ValidationTestModel.IdentifyInputsAndOutputs +ValidationTestModel.SetOperandValue +ValidationTestModel.SetOperandValueFromMemory diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-linux.neurun b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.neurun new file mode 100644 index 000000000..59957ba60 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-linux.neurun @@ -0,0 +1,117 @@ +# +# Following tests will be skipped on armv7l-linux +# +# Not support operations +TrivialTest.AddTwo +TrivialTest.AddThree +TrivialTest.BroadcastAddTwo +TrivialTest.BroadcastMulTwo +ValidationTestCompilation.SetPreference +ValidationTestCompilation.CreateExecution +ValidationTestCompilation.Finish +ValidationTestIdentify.InputIsOutput +ValidationTestIdentify.OutputIsInput +ValidationTestIdentify.DuplicateInputs +ValidationTestIdentify.DuplicateOutputs +ValidationTestIdentify.Ok +ValidationTestExecution.SetInput +ValidationTestExecution.SetOutput +ValidationTestExecution.SetInputFromMemory +ValidationTestExecution.SetOutputFromMemory +ValidationTestExecution.StartCompute +ValidationTestExecution.EventWait +GeneratedTests.add* +GeneratedTests.argmax* +GeneratedTests.avg_pool_float_1 +GeneratedTests.avg_pool_float_2 +GeneratedTests.avg_pool_float_3 +GeneratedTests.avg_pool_float_4 +GeneratedTests.avg_pool_quant8_1 +GeneratedTests.avg_pool_quant8_2 +GeneratedTests.avg_pool_quant8_3 +GeneratedTests.avg_pool_quant8_4 +GeneratedTests.conv_float_channels +GeneratedTests.conv_float_channels_weights_as_inputs +GeneratedTests.conv_float_large +GeneratedTests.conv_float_large_weights_as_inputs +GeneratedTests.conv_float +GeneratedTests.conv_float_weights_as_inputs +GeneratedTests.conv_quant8_channels +GeneratedTests.conv_quant8_channels_weights_as_inputs +GeneratedTests.conv_quant8_large +GeneratedTests.conv_quant8_large_weights_as_inputs +GeneratedTests.conv_quant8 +GeneratedTests.conv_quant8_overflow +GeneratedTests.conv_quant8_overflow_weights_as_inputs +GeneratedTests.conv_quant8_weights_as_inputs +GeneratedTests.depth_to_space* +GeneratedTests.depthwise_conv2d* +GeneratedTests.depthwise_conv +GeneratedTests.dequantize +GeneratedTests.embedding_lookup +GeneratedTests.embedding_lookup_2d_nnfw +GeneratedTests.embedding_lookup_4d_nnfw +GeneratedTests.equal_ex* +GeneratedTests.exp_ex* +GeneratedTests.floor_ +GeneratedTests.hashtable_lookup* +GeneratedTests.l2_normalization* +GeneratedTests.l2_pool* +GeneratedTests.local_response_norm* +GeneratedTests.logical_and_ex* +GeneratedTests.logical_or_ex* +GeneratedTests.logistic* +GeneratedTests.lsh_projection* +GeneratedTests.lstm* +GeneratedTests.max_pool_float_1 +GeneratedTests.max_pool_float_2 +GeneratedTests.max_pool_float_3 +GeneratedTests.max_pool_quant8_1 +GeneratedTests.max_pool_quant8_2 +GeneratedTests.max_pool_quant8_3 +GeneratedTests.mobilenet* +GeneratedTests.mul* +GeneratedTests.neg* +GeneratedTests.notequal* +GeneratedTests.prelu_ex* +GeneratedTests.reduce_min* +GeneratedTests.relu1* +GeneratedTests.relu6* +GeneratedTests.relu* +GeneratedTests.resize_bilinear* +GeneratedTests.rnn* +GeneratedTests.rsqrt* +GeneratedTests.mean* +GeneratedTests.pad* +GeneratedTests.space_to_depth* +GeneratedTests.sqrt_ex* +GeneratedTests.squared_difference_ex* +GeneratedTests.svdf* +GeneratedTests.tanh_ +GeneratedTests.batch_to_space* +GeneratedTests.div_* +GeneratedTests.space_to_batch* +GeneratedTests.squeeze* +GeneratedTests.strided_slice* +GeneratedTests.sub* +GeneratedTests.transpose* +GeneratedTests.cast_ex* +GeneratedTests.gather_ex* +GeneratedTests.strided_slice_ex* +GeneratedTests.tensorflowmax_ex* +GeneratedTests.reduce_sum_ex* +GeneratedTests.topk_v2* +# Unhandled exception +GeneratedTests.concat* +GeneratedTests.fully_connected* +GeneratedTests.reshape* +# Unexpected result +GeneratedTests.avg_pool_quant8_5 +GeneratedTests.conv_quant8_2 +GeneratedTests.max_pool_quant8_4 +GeneratedTests.softmax* +GeneratedTests.split* +GeneratedTests.transpose_conv* +GeneratedTests.pack* +GeneratedTests.unpack* +GeneratedTests.logical_not_ex* diff --git a/tests/nnapi/nnapi_gtest.skip.armv7l-tizen b/tests/nnapi/nnapi_gtest.skip.armv7l-tizen new file mode 100644 index 000000000..ebb51de94 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.armv7l-tizen @@ -0,0 +1,47 @@ +GeneratedTests.lsh_projection +GeneratedTests.lsh_projection_2 +GeneratedTests.lsh_projection_weights_as_inputs +GeneratedTests.lstm +GeneratedTests.lstm2 +GeneratedTests.lstm2_state +GeneratedTests.lstm2_state2 +GeneratedTests.lstm3 +GeneratedTests.lstm3_state +GeneratedTests.lstm3_state2 +GeneratedTests.lstm3_state3 +GeneratedTests.lstm_state +GeneratedTests.lstm_state2 +GeneratedTests.notequal* +GeneratedTests.prelu_ex* +GeneratedTests.svdf +GeneratedTests.svdf2 +GeneratedTests.svdf_state +GeneratedTests.logical_not_ex* +ValidationTestCompilation.CreateExecution +ValidationTestCompilation.SetPreference +ValidationTestIdentify.DuplicateInputs +ValidationTestIdentify.DuplicateOutputs +ValidationTestIdentify.InputIsOutput +ValidationTestIdentify.OutputIsInput +ValidationTestModel.AddOperand +ValidationTestModel.CreateCompilation +ValidationTestModel.Finish +ValidationTestModel.IdentifyInputsAndOutputs +ValidationTestModel.SetOperandValue +ValidationTestModel.SetOperandValueFromMemory +ValidationTestCompilation.Finish +ValidationTestExecution.SetInput +ValidationTestExecution.SetOutput +ValidationTestExecution.SetInputFromMemory +ValidationTestExecution.SetOutputFromMemory +ValidationTestExecution.StartCompute +ValidationTestExecution.EventWait +GeneratedTests.add_broadcast_quant8 +GeneratedTests.add_quant8 +GeneratedTests.logical_and_ex* +GeneratedTests.logical_or_ex* +GeneratedTests.logistic_quant8_1 +GeneratedTests.logistic_quant8_2 +GeneratedTests.mul_broadcast_quant8 +GeneratedTests.mul_quant8 +GeneratedTests.pack* diff --git a/tests/nnapi/nnapi_gtest.skip.x86_64-linux b/tests/nnapi/nnapi_gtest.skip.x86_64-linux new file mode 100644 index 000000000..e87c4acf6 --- /dev/null +++ b/tests/nnapi/nnapi_gtest.skip.x86_64-linux @@ -0,0 +1,88 @@ +# +# Following tests will be skipped on x86_64-linux +# +# ===================== +# Fails in CPU fallback +# ===================== +GeneratedTests.depth_to_space_float_1 +GeneratedTests.depth_to_space_float_2 +GeneratedTests.depth_to_space_float_3 +GeneratedTests.depth_to_space_quant8_1 +GeneratedTests.depth_to_space_quant8_2 +GeneratedTests.dequantize +GeneratedTests.embedding_lookup +GeneratedTests.exp_ex_1D_float +GeneratedTests.equal_ex* +GeneratedTests.exp_ex_2D_float +GeneratedTests.floor_ +GeneratedTests.hashtable_lookup_float +GeneratedTests.hashtable_lookup_quant8 +GeneratedTests.l2_normalization_2 +GeneratedTests.l2_normalization_large +GeneratedTests.l2_normalization +GeneratedTests.local_response_norm_float_1 +GeneratedTests.local_response_norm_float_2 +GeneratedTests.local_response_norm_float_3 +GeneratedTests.local_response_norm_float_4 +GeneratedTests.logical_and_ex* +GeneratedTests.lsh_projection_2 +GeneratedTests.lsh_projection +GeneratedTests.lsh_projection_weights_as_inputs +GeneratedTests.lstm2 +GeneratedTests.lstm2_state2 +GeneratedTests.lstm2_state +GeneratedTests.lstm3 +GeneratedTests.lstm3_state2 +GeneratedTests.lstm3_state3 +GeneratedTests.lstm3_state +GeneratedTests.lstm +GeneratedTests.lstm_state2 +GeneratedTests.lstm_state +GeneratedTests.neg* +GeneratedTests.notequal* +GeneratedTests.prelu_ex* +GeneratedTests.reduce_min_ex* +GeneratedTests.relu1_float_1 +GeneratedTests.relu1_float_2 +GeneratedTests.relu1_quant8_1 +GeneratedTests.relu1_quant8_2 +GeneratedTests.rnn +GeneratedTests.rnn_state +GeneratedTests.rsqrt_ex_float_1 +GeneratedTests.space_to_depth_float_1 +GeneratedTests.space_to_depth_float_2 +GeneratedTests.space_to_depth_float_3 +GeneratedTests.space_to_depth_quant8_1 +GeneratedTests.space_to_depth_quant8_2 +GeneratedTests.sqrt_ex* +GeneratedTests.squared_difference_ex* +GeneratedTests.svdf2 +GeneratedTests.svdf +GeneratedTests.svdf_state +GeneratedTests.strided_slice_float_10 +GeneratedTests.strided_slice_float_1 +GeneratedTests.strided_slice_float_2 +GeneratedTests.strided_slice_float_3 +GeneratedTests.strided_slice_float_4 +GeneratedTests.strided_slice_float_5 +GeneratedTests.strided_slice_float_6 +GeneratedTests.strided_slice_float_7 +GeneratedTests.strided_slice_float_8 +GeneratedTests.strided_slice_float_9 +GeneratedTests.strided_slice +GeneratedTests.tensorflowmax_ex* +GeneratedTests.reduce_sum_ex* +GeneratedTests.sub_broadcast_float +GeneratedTests.logical_not_ex* +TrivialTest.* +ValidationTest.* +ValidationTestModel.* +ValidationTestCompilation.* +ValidationTestIdentify.* +ValidationTestExecution.* +GeneratedTests.gather_ex_1D_quant8 +GeneratedTests.gather_ex_2D_quant8 +GeneratedTests.split* +GeneratedTests.transpose_conv* +GeneratedTests.pack* +GeneratedTests.unpack* diff --git a/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py b/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py new file mode 100644 index 000000000..e2255e004 --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_float_1.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [1]) +output = Output("output", "TENSOR_INT32", "{1, 1, 2, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 4.0, + 2.0, 3.0]} + +output0 = {output: # output 0 + [1, + 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py b/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py new file mode 100644 index 000000000..6f06bfd0b --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_float_2.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 2, 2, 2}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [3]) +output = Output("output", "TENSOR_INT32", "{1, 2, 2, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 7.0, 8.0, + 1.0, 9.0, 7.0, 3.0]} + +output0 = {output: # output 0 + [1,1, + 1,0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py b/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py new file mode 100644 index 000000000..f7f98afe2 --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_int32.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_INT32", "{1, 2, 2, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [1]) +output = Output("output", "TENSOR_INT32", "{1, 1, 2, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 4, + 2, 3]} + +output0 = {output: # output 0 + [1, + 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_neg_axis_float.mod.py b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_float.mod.py new file mode 100644 index 000000000..b29cf1eb6 --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_float.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 2, 4, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [-3]) +output = Output("output", "TENSOR_INT32", "{1, 1, 4, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 7.0, 8.0, + 1.0, 9.0, 7.0, 3.0]} + +output0 = {output: # output 0 + [0, 1, 0, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_neg_axis_int32.mod.py b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_int32.mod.py new file mode 100644 index 000000000..ddc27b35a --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_neg_axis_int32.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_INT32", "{1, 2, 4, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [-3]) +output = Output("output", "TENSOR_INT32", "{1, 1, 4, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 7, 8, + 1, 9, 7, 3]} + +output0 = {output: # output 0 + [0, 1, 0, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py b/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py new file mode 100644 index 000000000..fd3c5230b --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_quant8.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 0.5f, 2") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [1]) +output = Output("output", "TENSOR_INT32", "{1, 1, 2, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 4, + 2, 3]} + +output0 = {output: # output 0 + [1, + 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/argmax_ex_quant8_neg_axis.mod.py b/tests/nnapi/specs/Ex/argmax_ex_quant8_neg_axis.mod.py new file mode 100644 index 000000000..c7ddc0e5d --- /dev/null +++ b/tests/nnapi/specs/Ex/argmax_ex_quant8_neg_axis.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{1, 2, 4, 1}, 0.5f, 5") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [-3]) +output = Output("output", "TENSOR_INT32", "{1, 1, 4, 1}") + +model = model.Operation("ARGMAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 7, 8, + 1, 9, 7, 3]} + +output0 = {output: # output 0 + [0, 1, 0, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/cast_ex_float32_to_int32.mod.py b/tests/nnapi/specs/Ex/cast_ex_float32_to_int32.mod.py index 8f94fb8ae..8f94fb8ae 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/cast_ex_float32_to_int32.mod.py +++ b/tests/nnapi/specs/Ex/cast_ex_float32_to_int32.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/cast_ex_int32_to_float32.mod.py b/tests/nnapi/specs/Ex/cast_ex_int32_to_float32.mod.py index 4a85dc9c5..4a85dc9c5 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/cast_ex_int32_to_float32.mod.py +++ b/tests/nnapi/specs/Ex/cast_ex_int32_to_float32.mod.py diff --git a/tests/nnapi/specs/Ex/equal_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/equal_ex_1D_float.mod.py new file mode 100644 index 000000000..f2ec16789 --- /dev/null +++ b/tests/nnapi/specs/Ex/equal_ex_1D_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{3}") # a vector of input +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of output +model = model.Operation("EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2.0, 3.254232, 5.1232], + i2: # input 1 + [2.0, 3.254111, 5.1232]} + +output0 = {i3: # output 0 + [255, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/equal_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/equal_ex_4D_float.mod.py new file mode 100644 index 000000000..f804a5066 --- /dev/null +++ b/tests/nnapi/specs/Ex/equal_ex_4D_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of input +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") # a vector of output +model = model.Operation("EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 1543.25454532, 5.1232, 10.1], + i2: # input 1 + [0, 5313.25414521, 5.1, 10.1]} + +output0 = {i3: # output 0 + [255, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/equal_ex_broadcast_4D_2D_float.mod.py b/tests/nnapi/specs/Ex/equal_ex_broadcast_4D_2D_float.mod.py new file mode 100644 index 000000000..5e31698a9 --- /dev/null +++ b/tests/nnapi/specs/Ex/equal_ex_broadcast_4D_2D_float.mod.py @@ -0,0 +1,30 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3, 2, 2, 2}, 1.f, 0") +model = model.Operation("EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.89, 11.0, 9.75, 10.20, + 8.25, 2.0, 1.15, 0.0, + 3.0, 1.0, 8.25, 6.0, + 8.45, 3.0, 8.25, 1.2, + 0.0, 3.0, 2.0, 7.34, + 4.3, 9.56, 11.0, 3.0], + i2: # input 1 + [8.25, 3.0, 2.0, 10.20]} + +output0 = {i3: # output 0 + [0, 0, 0, 255, + 255, 0, 0, 0, + 0, 0, 0, 0, + 0, 255, 0, 0, + 0, 255, 255, 0, + 0, 0, 0, 0] + } + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/equal_ex_broadcast_float.mod.py b/tests/nnapi/specs/Ex/equal_ex_broadcast_float.mod.py new file mode 100644 index 000000000..279c000ba --- /dev/null +++ b/tests/nnapi/specs/Ex/equal_ex_broadcast_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2}") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +model = model.Operation("EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [7.45, 3.21, 2.0, 7.67], + i2: # input 1 + [0.0, 7.67]} + +output0 = {i3: # output 0 + [0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/equal_ex_quant8.mod.py b/tests/nnapi/specs/Ex/equal_ex_quant8.mod.py new file mode 100644 index 000000000..a799c6f42 --- /dev/null +++ b/tests/nnapi/specs/Ex/equal_ex_quant8.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of input +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of input +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of output +model = model.Operation("EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2, 3, 0], + i2: # input 1 + [2, 9, 0]} + +output0 = {i3: # output 0 + [255, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/exp_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/exp_ex_1D_float.mod.py new file mode 100644 index 000000000..1979b0aec --- /dev/null +++ b/tests/nnapi/specs/Ex/exp_ex_1D_float.mod.py @@ -0,0 +1,15 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{4}") # a vector of 4 float32s +i2 = Output("op2", "TENSOR_FLOAT32", "{4}") # a vector of 4 float32s +model = model.Operation("EXP_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.0, 4.0, 5.0, 6.0]} + +output0 = {i2: # output 0 + [20.085537, 54.59815, 148.41316, 403.4288]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/exp_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/exp_ex_2D_float.mod.py new file mode 100644 index 000000000..a79ca9a8a --- /dev/null +++ b/tests/nnapi/specs/Ex/exp_ex_2D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") # a vector of 12 float32s +i2 = Output("op2", "TENSOR_FLOAT32", "{3,4}") # a vector of 12 float32s +model = model.Operation("EXP_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789]} + +output0 = {i2: # output 0 + [2.2724796e+01, 6.1772419e+01, 1.6791484e+02, 4.5643985e+02, + 1.2407322e+03, 3.3726597e+03, 9.1678398e+03, 3.0754671e+00, + 8.3599854e+00, 7.4287776e+07, 2.0193510e+08, 6.7741680e+04]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/exp_ex_3D_float.mod.py b/tests/nnapi/specs/Ex/exp_ex_3D_float.mod.py new file mode 100644 index 000000000..c9e30e099 --- /dev/null +++ b/tests/nnapi/specs/Ex/exp_ex_3D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,2}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2,3,2}") +model = model.Operation("EXP_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789]} + +output0 = {i2: # output 0 + [2.2724796e+01, 6.1772419e+01, 1.6791484e+02, 4.5643985e+02, + 1.2407322e+03, 3.3726597e+03, 9.1678398e+03, 3.0754671e+00, + 8.3599854e+00, 7.4287776e+07, 2.0193510e+08, 6.7741680e+04]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/exp_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/exp_ex_4D_float.mod.py new file mode 100644 index 000000000..d7b777d95 --- /dev/null +++ b/tests/nnapi/specs/Ex/exp_ex_4D_float.mod.py @@ -0,0 +1,31 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,2,3}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2,3,2,3}") +model = model.Operation("EXP_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789]} + +output0 = {i2: # output 0 + [2.2724796e+01, 6.1772419e+01, 1.6791484e+02, 4.5643985e+02, + 1.2407322e+03, 3.3726597e+03, 9.1678398e+03, 3.0754671e+00, + 8.3599854e+00, 7.4287776e+07, 2.0193510e+08, 6.7741680e+04, + 1.2407322e+03, 3.3726597e+03, 9.1678398e+03, 3.0754671e+00, + 2.2724796e+01, 6.1772419e+01, 1.6791484e+02, 4.5643985e+02, + 2.2724796e+01, 6.1772419e+01, 1.6791484e+02, 4.5643985e+02, + 8.3599854e+00, 7.4287776e+07, 2.0193510e+08, 6.7741680e+04, + 1.2407322e+03, 3.3726597e+03, 9.1678398e+03, 3.0754671e+00, + 8.3599854e+00, 7.4287776e+07, 2.0193510e+08, 6.7741680e+04]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/gather_ex_1D_float.mod.py new file mode 100644 index 000000000..eed3bf92d --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_1D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{4}") # a vector of 4 float32s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{2}") # a vector of 2 float32s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.0, 4.0, 5.0, 6.0], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [5.0, 3.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_1D_int32.mod.py b/tests/nnapi/specs/Ex/gather_ex_1D_int32.mod.py new file mode 100644 index 000000000..0eefbea58 --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_1D_int32.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{4}") # a vector of 4 int32s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_INT32", "{2}") # a vector of 2 int32s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [40000, 41000, 50000, 60000], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [50000, 40000]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_1D_quant8.mod.py b/tests/nnapi/specs/Ex/gather_ex_1D_quant8.mod.py new file mode 100644 index 000000000..f3276162f --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_1D_quant8.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{4}, 0.5f, 1") # a vector of 4 qasymm8s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2}, 0.5f, 1") # another vector of 2 qasymm8s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [7, 9, 11, 13], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [11, 7]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_float.mod.py new file mode 100644 index 000000000..52f7bf12f --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_float.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") # a vector of 12 float32s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{2,4}") # a vector of 8 float32s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_int32.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_int32.mod.py new file mode 100644 index 000000000..357efd08f --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_int32.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{3,4}") # a vector of 12 int32s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_INT32", "{2,4}") # a vector of 8 int32s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [40000, 41000, 50000, 60000, + 70000, 80000, 90000, 79000, + 170000, 180000, 190000, 110000], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [170000, 180000, 190000, 110000, + 40000, 41000, 50000, 60000]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/gather_ex_2D_quant8.mod.py b/tests/nnapi/specs/Ex/gather_ex_2D_quant8.mod.py new file mode 100644 index 000000000..8e4eb04ec --- /dev/null +++ b/tests/nnapi/specs/Ex/gather_ex_2D_quant8.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{3,4}, 0.5f, 1") # a vector of 12 qasymm8s +i2 = Input("op2", "TENSOR_INT32", "{2}") # another vector of 2 int32s +axis = Int32Scalar("axis", 0) +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2,4}, 0.5f, 1") # a vector of 8 qasymm8s +model = model.Operation("GATHER_EX", i1, i2, axis).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [7, 9, 11, 13, + 15, 17, 19, 3, + 5, 37, 39, 23], + i2: # input 1 + [2, 0]} + +output0 = {i3: # output 0 + [5, 37, 39, 23, + 7, 9, 11, 13]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_1D.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_1D.mod.py new file mode 100644 index 000000000..0f474f94e --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_1D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 255 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_2D.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_2D.mod.py new file mode 100644 index 000000000..eea915d69 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_2D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 1 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_3D.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_3D.mod.py new file mode 100644 index 000000000..dccc4f0de --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_3D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, 0, 255, 0, 255], + i2: # input 1 + [0, 255, 0, 255, 0, 0, 255, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255, 0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_4D.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_4D.mod.py new file mode 100644 index 000000000..dceb275c7 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_4D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, 0, 255, 0, 255], + i2: # input 1 + [0, 255, 0, 255, 0, 0, 255, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255, 0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_broadcast.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_broadcast.mod.py new file mode 100644 index 000000000..45d899235 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_broadcast.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1, 2, 1}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 1 + [0, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_and_ex_broadcast_4D_2D.mod.py b/tests/nnapi/specs/Ex/logical_and_ex_broadcast_4D_2D.mod.py new file mode 100644 index 000000000..9235f7fdb --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_and_ex_broadcast_4D_2D.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_AND_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, + 255, 255, 0, 0, + 0, 0, 0, 0, + 255, 255, 255, 255], + i2: # input 1 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 0, 0, 255, + 0, 255, 0, 0, + 0, 0, 0, 0, + 0, 255, 0, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_not_ex_1D.mod.py b/tests/nnapi/specs/Ex/logical_not_ex_1D.mod.py new file mode 100644 index 000000000..a913b1997 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_not_ex_1D.mod.py @@ -0,0 +1,16 @@ +# model +model = Model() +i = Input("op1", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") + +o = Output("op2", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") +model = model.Operation("LOGICAL_NOT_EX", i).To(o) + +# Example 1. Input +input0 = {i: # input + [255, 0, 255, 255]} + +output0 = {o: # output + [0, 255, 0, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_not_ex_4D.mod.py b/tests/nnapi/specs/Ex/logical_not_ex_4D.mod.py new file mode 100644 index 000000000..49306fc51 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_not_ex_4D.mod.py @@ -0,0 +1,16 @@ +# model +model = Model() +i = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") # a vector of input + +o = Output("op2", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") # a vector of output +model = model.Operation("LOGICAL_NOT_EX", i).To(o) + +# Example 1. Input +input0 = {i: # input + [0, 255, 255, 255]} + +output0 = {o: # output + [255, 0, 0, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_1D.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_1D.mod.py new file mode 100644 index 000000000..48e6d8ec3 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_1D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{4}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 255 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_2D.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_2D.mod.py new file mode 100644 index 000000000..5a6517170 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_2D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 1 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_3D.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_3D.mod.py new file mode 100644 index 000000000..06fbbe65d --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_3D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, 0, 255, 0, 255], + i2: # input 1 + [0, 255, 0, 255, 0, 0, 255, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255, 0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_4D.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_4D.mod.py new file mode 100644 index 000000000..f6833c96e --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_4D.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 1, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, 0, 255, 0, 255], + i2: # input 1 + [0, 255, 0, 255, 0, 0, 255, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255, 0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_broadcast.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_broadcast.mod.py new file mode 100644 index 000000000..89c8310bb --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_broadcast.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1, 2, 1}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255], + i2: # input 1 + [0, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/logical_or_ex_broadcast_4D_2D.mod.py b/tests/nnapi/specs/Ex/logical_or_ex_broadcast_4D_2D.mod.py new file mode 100644 index 000000000..a673494e5 --- /dev/null +++ b/tests/nnapi/specs/Ex/logical_or_ex_broadcast_4D_2D.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2, 2, 2, 2}, 1.f, 0") +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2, 2, 2}, 1.f, 0") +model = model.Operation("LOGICAL_OR_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 0, 255, 255, + 255, 255, 0, 0, + 0, 0, 0, 0, + 255, 255, 255, 255], + i2: # input 1 + [0, 255, 0, 255]} + +output0 = {i3: # output 0 + [0, 255, 255, 255, + 255, 255, 0, 255, + 0, 255, 0, 255, + 255, 255, 255, 255]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/neg_ex_1D_float.mod.py new file mode 100644 index 000000000..d66298099 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_1D_float.mod.py @@ -0,0 +1,15 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{4}") # a vector of input +i2 = Output("op2", "TENSOR_FLOAT32", "{4}") # a vector of output +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.0, 4.0, 5.0, 6.0]} + +output0 = {i2: # output 0 + [-3.0, -4.0, -5.0, -6.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/neg_ex_2D_float.mod.py new file mode 100644 index 000000000..829fedea8 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_2D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3,4}") +i2 = Output("op2", "TENSOR_FLOAT32", "{3,4}") +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789]} + +output0 = {i2: # output 0 + [-3.123456789123456789, -4.123456789123456789, -5.123456789123456789, -6.123456789123456789, + -7.123456789123456789, -8.123456789123456789, -9.123456789123456789, -1.123456789123456789, + -2.123456789123456789, -18.123456789123456789, -19.123456789123456789, -11.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_3D_float.mod.py b/tests/nnapi/specs/Ex/neg_ex_3D_float.mod.py new file mode 100644 index 000000000..1f57c49f2 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_3D_float.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,4}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2,3,4}") +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 40.123456789123456789, 42.123456789123456789, 4.123456789123456789, 12.123456789123456789, + 22.123456789123456789, 32.123456789123456789, 62.123456789123456789, 52.123456789123456789, + 92.123456789123456789, 59.123456789123456789, 69.123456789123456789, 312.123456789123456789]} + +output0 = {i2: # output 0 + [-3.123456789123456789, -4.123456789123456789, -5.123456789123456789, -6.123456789123456789, + -7.123456789123456789, -8.123456789123456789, -9.123456789123456789, -1.123456789123456789, + -2.123456789123456789, -18.123456789123456789, -19.123456789123456789, -11.123456789123456789, + -40.123456789123456789, -42.123456789123456789, -4.123456789123456789, -12.123456789123456789, + -22.123456789123456789, -32.123456789123456789, -62.123456789123456789, -52.123456789123456789, + -92.123456789123456789, -59.123456789123456789, -69.123456789123456789, -312.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_3D_int.mod.py b/tests/nnapi/specs/Ex/neg_ex_3D_int.mod.py new file mode 100644 index 000000000..56dbec1d7 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_3D_int.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,3,4}") +i2 = Output("op2", "TENSOR_INT32", "{2,3,4}") +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3, 4, 5, 6, + 7, 8, 9, 1, + 2, 18, 19, 11, + 40, 42, 4, 12, + 22, 32, 62, 52, + 92, 59, 69, 312]} + +output0 = {i2: # output 0 + [-3, -4, -5, -6, + -7, -8, -9, -1, + -2, -18, -19, -11, + -40, -42, -4, -12, + -22, -32, -62, -52, + -92, -59, -69, -312]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/neg_ex_4D_float.mod.py new file mode 100644 index 000000000..4d1b10d08 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_4D_float.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,3,2,2}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2,3,2,2}") +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.123456789123456789, 4.123456789123456789, 5.123456789123456789, 6.123456789123456789, + 7.123456789123456789, 8.123456789123456789, 9.123456789123456789, 1.123456789123456789, + 2.123456789123456789, 18.123456789123456789, 19.123456789123456789, 11.123456789123456789, + 40.123456789123456789, 42.123456789123456789, 4.123456789123456789, 12.123456789123456789, + 22.123456789123456789, 32.123456789123456789, 62.123456789123456789, 52.123456789123456789, + 92.123456789123456789, 59.123456789123456789, 69.123456789123456789, 312.123456789123456789]} + +output0 = {i2: # output 0 + [-3.123456789123456789, -4.123456789123456789, -5.123456789123456789, -6.123456789123456789, + -7.123456789123456789, -8.123456789123456789, -9.123456789123456789, -1.123456789123456789, + -2.123456789123456789, -18.123456789123456789, -19.123456789123456789, -11.123456789123456789, + -40.123456789123456789, -42.123456789123456789, -4.123456789123456789, -12.123456789123456789, + -22.123456789123456789, -32.123456789123456789, -62.123456789123456789, -52.123456789123456789, + -92.123456789123456789, -59.123456789123456789, -69.123456789123456789, -312.123456789123456789]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/neg_ex_4D_int.mod.py b/tests/nnapi/specs/Ex/neg_ex_4D_int.mod.py new file mode 100644 index 000000000..ac90eb361 --- /dev/null +++ b/tests/nnapi/specs/Ex/neg_ex_4D_int.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,3,2,2}") +i2 = Output("op2", "TENSOR_INT32", "{2,3,2,2}") +model = model.Operation("NEG_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3, 4, 5, 6, + -7, 8,-9, 1, + 2, 18, 19, 11, + -40, 42, 4, 12, + 22, -32, 62, 52, + 92, 59, 69, -312]} + +output0 = {i2: # output 0 + [-3, -4, -5, -6, + 7, -8, 9, -1, + -2, -18, -19, -11, + 40, -42, -4, -12, + -22, 32, -62, -52, + -92, -59, -69, 312]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/notequal_ex_broadcast_4D_2D_float.mod.py b/tests/nnapi/specs/Ex/notequal_ex_broadcast_4D_2D_float.mod.py new file mode 100644 index 000000000..ac900511c --- /dev/null +++ b/tests/nnapi/specs/Ex/notequal_ex_broadcast_4D_2D_float.mod.py @@ -0,0 +1,30 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3, 2, 2, 2}, 1.f, 0") +model = model.Operation("NOT_EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.25, 11.0, 2.2, 10.3, + 8.5, 2.1, 1.0, 0.5, + 3.1, 1.0, 8.5, 6.5, + 11.2, 3.0, 8.5, 1.0, + 0.3, 3.0, 2.1, 7.5, + 4.3, 9.2, 11.1, 3.0], + i2: # input 1 + [8.5, 3.0, 2.1, 10.3]} + +output0 = {i3: # output 0 + [255, 255, 255, 0, + 0, 255, 255, 255, + 255, 255, 255, 255, + 255, 0, 255, 255, + 255, 0, 0, 255, + 255, 255, 255, 255] + } + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/notequal_ex_broadcast_float.mod.py b/tests/nnapi/specs/Ex/notequal_ex_broadcast_float.mod.py new file mode 100644 index 000000000..69acc6dea --- /dev/null +++ b/tests/nnapi/specs/Ex/notequal_ex_broadcast_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2}") + +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{2, 2}, 1.f, 0") +model = model.Operation("NOT_EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0.2, 3.21, 2.4, 7.44], + i2: # input 1 + [0.21, 7.44]} + +output0 = {i3: # output 0 + [255, 255, 255, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/notequal_ex_float.mod.py b/tests/nnapi/specs/Ex/notequal_ex_float.mod.py new file mode 100644 index 000000000..b3deef7a6 --- /dev/null +++ b/tests/nnapi/specs/Ex/notequal_ex_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{3}") # a vector of input +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of output +model = model.Operation("NOT_EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2.0, 3.254232, 5.1232], + i2: # input 1 + [2.0, 3.254111, 5.1232]} + +output0 = {i3: # output 0 + [0, 255, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/notequal_ex_quant8.mod.py b/tests/nnapi/specs/Ex/notequal_ex_quant8.mod.py new file mode 100644 index 000000000..d5115a611 --- /dev/null +++ b/tests/nnapi/specs/Ex/notequal_ex_quant8.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of input +i2 = Input("op2", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of input +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{3}, 1.f, 0") # a vector of output +model = model.Operation("NOT_EQUAL_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2, 3, 0], + i2: # input 1 + [2, 9, 0]} + +output0 = {i3: # output 0 + [0, 255, 0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_float_1.mod.py b/tests/nnapi/specs/Ex/pack_ex_3D_float_1.mod.py new file mode 100644 index 000000000..3f3ea683f --- /dev/null +++ b/tests/nnapi/specs/Ex/pack_ex_3D_float_1.mod.py @@ -0,0 +1,54 @@ +# Sample Stack or Pack model +model = Model() +i1 = Input("input1", "TENSOR_FLOAT32", "{2, 3, 4}") +i2 = Input("input2", "TENSOR_FLOAT32", "{2, 3, 4}") +i3 = Input("input3", "TENSOR_FLOAT32", "{2, 3, 4}") +num = Int32Scalar("num_tensors", 3) +axis = Int32Scalar("axis", 0) +out = Output("output", "TENSOR_FLOAT32", "{3, 2, 3, 4}") +model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) + +input0 = {i1: # input 0 + [0.3, 1.0, 2.0, 3.0, + 4.0, 5.5, 6.3, 7.2, + 8.22, 9.8, 10.3, 11.0, + 12.22, 13.2, 14.44, 15.32, + 16.55, 17.33, 18.1, 19.0, + 20.32, 21.9, 22.1, 23.22], + i2: # input 1 + [24.22, 25.1, 26.0, 27.12, + 28.32, 29.11, 30.0, 31.98, + 32.99, 33.11, 34.1, 35.123, + 36.21, 37.22, 38.23, 39.76, + 40.1, 41.43, 42.34, 43.1, + 44.123, 45.43, 46.1, 47.1], + i3: # input 2 + [48.0, 49.76, 50.0, 51.1, + 52.22, 53.12, 54.1, 55.5, + 56.5, 57.4, 58.1, 59.23, + 60.2, 61.12, 62.11, 63.34, + 64.11, 65.1, 66.43, 67.1, + 68.1, 69.34, 70.11, 71.45]} + +output0 = {out: # output 0 + [0.3, 1.0, 2.0, 3.0, + 4.0, 5.5, 6.3, 7.2, + 8.22, 9.8, 10.3, 11.0, + 12.22, 13.2, 14.44, 15.32, + 16.55, 17.33, 18.1, 19.0, + 20.32, 21.9, 22.1, 23.22, + 24.22, 25.1, 26.0, 27.12, + 28.32, 29.11, 30.0, 31.98, + 32.99, 33.11, 34.1, 35.123, + 36.21, 37.22, 38.23, 39.76, + 40.1, 41.43, 42.34, 43.1, + 44.123, 45.43, 46.1, 47.1, + 48.0, 49.76, 50.0, 51.1, + 52.22, 53.12, 54.1, 55.5, + 56.5, 57.4, 58.1, 59.23, + 60.2, 61.12, 62.11, 63.34, + 64.11, 65.1, 66.43, 67.1, + 68.1, 69.34, 70.11, 71.45]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_float_2.mod.py b/tests/nnapi/specs/Ex/pack_ex_3D_float_2.mod.py new file mode 100644 index 000000000..d0caa02cc --- /dev/null +++ b/tests/nnapi/specs/Ex/pack_ex_3D_float_2.mod.py @@ -0,0 +1,54 @@ +# Sample Stack or Pack model +model = Model() +i1 = Input("input1", "TENSOR_FLOAT32", "{2, 3, 4}") +i2 = Input("input2", "TENSOR_FLOAT32", "{2, 3, 4}") +i3 = Input("input3", "TENSOR_FLOAT32", "{2, 3, 4}") +num = Int32Scalar("num_tensors", 3) +axis = Int32Scalar("axis", 2) +out = Output("output", "TENSOR_FLOAT32", "{2, 3, 3, 4}") +model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) + +input0 = {i1: # input 0 + [0.3, 1.0, 2.0, 3.0, + 4.0, 5.5, 6.3, 7.2, + 8.22, 9.8, 10.3, 11.0, + 12.22, 13.2, 14.44, 15.32, + 16.55, 17.33, 18.1, 19.0, + 20.32, 21.9, 22.1, 23.22], + i2: # input 1 + [24.22, 25.1, 26.0, 27.12, + 28.32, 29.11, 30.0, 31.98, + 32.99, 33.11, 34.1, 35.123, + 36.21, 37.22, 38.23, 39.76, + 40.1, 41.43, 42.34, 43.1, + 44.123, 45.43, 46.1, 47.1], + i3: # input 2 + [48.0, 49.76, 50.0, 51.1, + 52.22, 53.12, 54.1, 55.5, + 56.5, 57.4, 58.1, 59.23, + 60.2, 61.12, 62.11, 63.34, + 64.11, 65.1, 66.43, 67.1, + 68.1, 69.34, 70.11, 71.45]} + +output0 = {out: # output 0 + [0.3, 1.0, 2.0, 3.0, + 24.22, 25.1, 26.0, 27.12, + 48.0, 49.76, 50.0, 51.1, + 4.0, 5.5, 6.3, 7.2, + 28.32, 29.11, 30.0, 31.98, + 52.22, 53.12, 54.1, 55.5, + 8.22, 9.8, 10.3, 11.0, + 32.99, 33.11, 34.1, 35.123, + 56.5, 57.4, 58.1, 59.23, + 12.22, 13.2, 14.44, 15.32, + 36.21, 37.22, 38.23, 39.76, + 60.2, 61.12, 62.11, 63.34, + 16.55, 17.33, 18.1, 19.0, + 40.1, 41.43, 42.34, 43.1, + 64.11, 65.1, 66.43, 67.1, + 20.32, 21.9, 22.1, 23.22, + 44.123, 45.43, 46.1, 47.1, + 68.1, 69.34, 70.11, 71.45]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_int_1.mod.py b/tests/nnapi/specs/Ex/pack_ex_3D_int_1.mod.py new file mode 100644 index 000000000..23b365957 --- /dev/null +++ b/tests/nnapi/specs/Ex/pack_ex_3D_int_1.mod.py @@ -0,0 +1,25 @@ +# Sample Stack or Pack model +model = Model() +i1 = Input("input1", "TENSOR_INT32", "{2, 3, 4}") +i2 = Input("input2", "TENSOR_INT32", "{2, 3, 4}") +i3 = Input("input3", "TENSOR_INT32", "{2, 3, 4}") +num = Int32Scalar("num_tensors", 3) +axis = Int32Scalar("axis", 0) +out = Output("output", "TENSOR_INT32", "{3, 2, 3, 4}") +model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) + +input0 = {i1: # input 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], + i2: # input 1 + [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], + i3: # input 2 + [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +output0 = {out: # output 0 + [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, + 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/pack_ex_3D_int_2.mod.py b/tests/nnapi/specs/Ex/pack_ex_3D_int_2.mod.py new file mode 100644 index 000000000..747fcc628 --- /dev/null +++ b/tests/nnapi/specs/Ex/pack_ex_3D_int_2.mod.py @@ -0,0 +1,25 @@ +# Sample Stack or Pack model +model = Model() +i1 = Input("input1", "TENSOR_INT32", "{2, 3, 4}") +i2 = Input("input2", "TENSOR_INT32", "{2, 3, 4}") +i3 = Input("input3", "TENSOR_INT32", "{2, 3, 4}") +num = Int32Scalar("num_tensors", 3) +axis = Int32Scalar("axis", 2) +out = Output("output", "TENSOR_INT32", "{2, 3, 3, 4}") +model = model.Operation("PACK_EX", i1, i2, i3, num, axis).To(out) + +input0 = {i1: # input 0 + [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], + i2: # input 1 + [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], + i3: # input 2 + [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +output0 = {out: # output 0 + [ 0, 1, 2, 3, 24, 25, 26, 27, 48, 49, 50, 51, 4, 5, 6, 7, 28, 29, + 30, 31, 52, 53, 54, 55, 8, 9, 10, 11, 32, 33, 34, 35, 56, 57, 58, 59, + 12, 13, 14, 15, 36, 37, 38, 39, 60, 61, 62, 63, 16, 17, 18, 19, 40, 41, + 42, 43, 64, 65, 66, 67, 20, 21, 22, 23, 44, 45, 46, 47, 68, 69, 70, 71]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/prelu_ex_float_1.mod.py b/tests/nnapi/specs/Ex/prelu_ex_float_1.mod.py new file mode 100644 index 000000000..d2ebd2a0b --- /dev/null +++ b/tests/nnapi/specs/Ex/prelu_ex_float_1.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of alpha +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of output +model = model.Operation("PRELU_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.0, -2.0, + -1.0, -2.0 + ], + i2: # input 1 + [0.0, 1.0, + 1.0, 2.0]} + +output0 = {i3: # output 0 + [3.0, -2.0, + -1.0, -4.0 + ]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/prelu_ex_float_2.mod.py b/tests/nnapi/specs/Ex/prelu_ex_float_2.mod.py new file mode 100644 index 000000000..d2ebd2a0b --- /dev/null +++ b/tests/nnapi/specs/Ex/prelu_ex_float_2.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of input +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of alpha +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # a vector of output +model = model.Operation("PRELU_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [3.0, -2.0, + -1.0, -2.0 + ], + i2: # input 1 + [0.0, 1.0, + 1.0, 2.0]} + +output0 = {i3: # output 0 + [3.0, -2.0, + -1.0, -4.0 + ]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_min_ex_float.mod.py b/tests/nnapi/specs/Ex/reduce_min_ex_float.mod.py new file mode 100644 index 000000000..a58c2dba5 --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_min_ex_float.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [2]) +output = Output("output", "TENSOR_FLOAT32", "{1, 2, 1}") + +model = model.Operation("REDUCE_MIN_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2.0, 1.0, + 3.0, 4.0]} + +output0 = {output: # output 0 + [1.0, + 3.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_min_ex_float_1.mod.py b/tests/nnapi/specs/Ex/reduce_min_ex_float_1.mod.py new file mode 100644 index 000000000..50b23bb8a --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_min_ex_float_1.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{4, 3, 2}") +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 0, -3, -3]) +output = Output("output", "TENSOR_FLOAT32", "{2}") + +model = model.Operation("REDUCE_MIN_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [23.0, 24.0, 13.0, 22.0, 5.0, 18.0, 7.0, 8.0, 9.0, 15.0, 11.0, 12.0, + 3.0, 14.0, 10.0, 16.0, 17.0, 6.0, 19.0, 20.0, 21.0, 4.0, 1.0, 2.0]} + +output0 = {output: # output 0 + [1.0, 2.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_min_ex_float_2.mod.py b/tests/nnapi/specs/Ex/reduce_min_ex_float_2.mod.py new file mode 100644 index 000000000..087ce63b0 --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_min_ex_float_2.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{4, 3, 2}") +axis = Parameter("axis", "TENSOR_INT32", "{2}", [0, 2]) +output = Output("output", "TENSOR_FLOAT32", "{1, 3, 1}") + +model = model.Operation("REDUCE_MIN_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [20.0, 2.0, 22.0, 4.0, 24.0, 18.0, 7.0, 8.0, 19.0, 10.0, 14.0, 12.0, + 13.0, 11.0, 15.0, 16.0, 17.0, 6.0, 9.0, 1.0, 21.0, 3.0, 23.0, 5.0]} + +output0 = {output: # output 0 + [1.0, 3.0, 5.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_sum_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/reduce_sum_ex_2D_float.mod.py new file mode 100644 index 000000000..1368f66c5 --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_sum_ex_2D_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{3, 4}") +axis = Int32Scalar("axis", 1) +out1 = Output("output", "TENSOR_FLOAT32", "{3}") +model = model.Operation("REDUCE_SUM_EX", i1, axis).To(out1) + +# Example 1. Input in operand 0, 1 +input0 = {i1: # input 0 + [3.2, 11.47, 3.8, 5.76, + 28.2, 0.999, -1.3, -13.5, + -3.4, -22.1, -2.2, -49.7]} + +output0 = {out1: # output 0 + [24.23, 14.399002, -77.4]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float.mod.py new file mode 100644 index 000000000..15b8dd7b2 --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 3, 4, 1}") +axis = Int32Scalar("axis", 1) +out1 = Output("output", "TENSOR_FLOAT32", "{1, 4, 1}") +model = model.Operation("REDUCE_SUM_EX", i1, axis).To(out1) + +# Example 1. Input in operand 0, 1 +input0 = {i1: # input 0 + [6.4, 7.3, 19.3, -2.3, + 8.3, 2.0, 11.8, -3.4, + 22.8, 3.0, -28.7, 4.9]} + +output0 = {out1: # output 0 + [37.5, 12.3, 2.3999977, -0.7999997]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_C.mod.py b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_C.mod.py new file mode 100644 index 000000000..4bfea0f97 --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_C.mod.py @@ -0,0 +1,32 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] + +output_table = [0 for x in range(batch * rows * cols)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + # The value of output_table is the depthwise sum of input_table. + output_table[i * rows * cols + j * cols + k] += input_table[i * rows * cols * depth + j * cols * depth + k * depth + l]; + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +# Axis value should be in the range [-(rank), rank). And '-n' is the same axis with 'rank - n'. So '3' and '-1' are the same axis. +axis = Parameter("axis", "TENSOR_INT32", "{2}", [3, -1]) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (batch, rows, cols)) + +model = model.Operation("REDUCE_SUM_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_HW.mod.py b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_HW.mod.py new file mode 100644 index 000000000..d94bcf79c --- /dev/null +++ b/tests/nnapi/specs/Ex/reduce_sum_ex_4D_float_reducing_HW.mod.py @@ -0,0 +1,32 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] + +output_table = [0 for x in range(batch * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + # The value of output_table is the rowwise sum and colwise sum of input_table. + output_table[i * depth + l] += input_table[i * rows * cols * depth + j * cols * depth + k * depth + l]; + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +# Axis value should be in the range [-(rank), rank). And '-n' is the same axis with 'rank - n'. So this test's axis value are the same [1, 2]. +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 2, -3, -2]) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batch, depth)) + +model = model.Operation("REDUCE_SUM_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/rsqrt_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/rsqrt_ex_1D_float.mod.py new file mode 100644 index 000000000..f052708cf --- /dev/null +++ b/tests/nnapi/specs/Ex/rsqrt_ex_1D_float.mod.py @@ -0,0 +1,13 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{4}") #A vector of inputs +i2 = Output("op2", "TENSOR_FLOAT32", "{4}") #A vector of outputs +model = model.Operation("RSQRT_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [36.0, 90.0, 43.0, 36.0]} +output0 = {i2: # output 0 + [0.166667, 0.105409, 0.152499, 0.166667]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/rsqrt_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/rsqrt_ex_2D_float.mod.py new file mode 100644 index 000000000..5f475ae58 --- /dev/null +++ b/tests/nnapi/specs/Ex/rsqrt_ex_2D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() + +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2}") +model = model.Operation("RSQRT_EX", i1).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 4.0, 8.0]} + +output0 = {i3: # output 0 + [1.0, + 0.70710678118, + 0.5, + 0.35355339059]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/rsqrt_ex_3D_float.mod.py b/tests/nnapi/specs/Ex/rsqrt_ex_3D_float.mod.py new file mode 100644 index 000000000..48abf8c18 --- /dev/null +++ b/tests/nnapi/specs/Ex/rsqrt_ex_3D_float.mod.py @@ -0,0 +1,16 @@ +# model +model = Model() + +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2}") +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2, 2}") +model = model.Operation("RSQRT_EX", i1).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 36.0, 2.0, 90, 4.0, 43.0, 8.0, 36.0]} + +output0 = {i3: # output 0 + [1.0, 0.166667, 0.70710678118, 0.105409, 0.5, 0.152499, 0.35355339059, 0.166667]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/rsqrt_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/rsqrt_ex_4D_float.mod.py new file mode 100644 index 000000000..6a1ae3c00 --- /dev/null +++ b/tests/nnapi/specs/Ex/rsqrt_ex_4D_float.mod.py @@ -0,0 +1,18 @@ +# model +model = Model() + +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +model = model.Operation("RSQRT_EX", i1).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 36.0, 2.0, 90, 4.0, 16.0, 25.0, 100.0, + 23.0, 19.0, 40.0, 256.0, 4.0, 43.0, 8.0, 36.0]} + +output0 = {i3: # output 0 + [1.0, 0.166667, 0.70710678118, 0.105409, 0.5, 0.25, 0.2, 0.1, + 0.208514, 0.229416, 0.158114, 0.0625, 0.5, 0.152499, 0.35355339059, 0.166667]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py new file mode 100644 index 000000000..97b61462e --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_1D_float.mod.py @@ -0,0 +1,39 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{8}") +axis = Int32Scalar("axis", 0) +i2 = Output("op2", "TENSOR_FLOAT32", "{1}") +i3 = Output("op3", "TENSOR_FLOAT32", "{1}") +i4 = Output("op4", "TENSOR_FLOAT32", "{1}") +i5 = Output("op5", "TENSOR_FLOAT32", "{1}") +i6 = Output("op6", "TENSOR_FLOAT32", "{1}") +i7 = Output("op7", "TENSOR_FLOAT32", "{1}") +i8 = Output("op8", "TENSOR_FLOAT32", "{1}") +i9 = Output("op9", "TENSOR_FLOAT32", "{1}") + +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3, i4, i5, i6, i7, i8, i9]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]} + +output0 = { + i2: # output 0 + [1.0], + i3: # output 1 + [2.0], + i4: # output 2 + [3.0], + i5: # output 3 + [4.0], + i6: # output 4 + [5.0], + i7: # output 5 + [6.0], + i8: # output 6 + [7.0], + i9: # output 7 + [8.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py b/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py new file mode 100644 index 000000000..378b37707 --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_1D_int32.mod.py @@ -0,0 +1,39 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{8}") +axis = Int32Scalar("axis", 0) +i2 = Output("op2", "TENSOR_INT32", "{1}") +i3 = Output("op3", "TENSOR_INT32", "{1}") +i4 = Output("op4", "TENSOR_INT32", "{1}") +i5 = Output("op5", "TENSOR_INT32", "{1}") +i6 = Output("op6", "TENSOR_INT32", "{1}") +i7 = Output("op7", "TENSOR_INT32", "{1}") +i8 = Output("op8", "TENSOR_INT32", "{1}") +i9 = Output("op9", "TENSOR_INT32", "{1}") + +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3, i4, i5, i6, i7, i8, i9]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = { + i2: # output 0 + [1], + i3: # output 1 + [2], + i4: # output 2 + [3], + i5: # output 3 + [4], + i6: # output 4 + [5], + i7: # output 5 + [6], + i8: # output 6 + [7], + i9: # output 7 + [8]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_float_1.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_float_1.mod.py new file mode 100644 index 000000000..3059fae7c --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_1.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 0) +i2 = Output("op2", "TENSOR_FLOAT32", "{1,2,2,2}") +i3 = Output("op3", "TENSOR_FLOAT32", "{1,2,2,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]} + +output0 = { + i2: # output 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], + i3: # output 1 + [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_float_2.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_float_2.mod.py new file mode 100644 index 000000000..faa59bc3c --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_2.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 3) +i2 = Output("op2", "TENSOR_FLOAT32", "{2,2,2,1}") +i3 = Output("op3", "TENSOR_FLOAT32", "{2,2,2,1}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]} + +output0 = { + i2: # output 0 + [1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0], + i3: # output 1 + [2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_float_3.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_float_3.mod.py new file mode 100644 index 000000000..2091e4b4a --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_float_3.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2,2,2,2}") +axis = Int32Scalar("axis", -4) # Negative axis +i2 = Output("op2", "TENSOR_FLOAT32", "{1,2,2,2}") +i3 = Output("op3", "TENSOR_FLOAT32", "{1,2,2,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]} + +output0 = { + i2: # output 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], + i3: # output 1 + [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_int32_1.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_int32_1.mod.py new file mode 100644 index 000000000..3e9438f50 --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_1.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 0) +i2 = Output("op2", "TENSOR_INT32", "{1,2,2,2}") +i3 = Output("op3", "TENSOR_INT32", "{1,2,2,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 2, 3, 4, 5, 6, 7, 8], + i3: # output 1 + [9, 10, 11, 12, 13, 14, 15, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_int32_2.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_int32_2.mod.py new file mode 100644 index 000000000..83253cb14 --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_2.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 1) +i2 = Output("op2", "TENSOR_INT32", "{2,1,2,2}") +i3 = Output("op3", "TENSOR_INT32", "{2,1,2,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 2, 3, 4, 9, 10, 11, 12], + i3: # output 1 + [5, 6, 7, 8, 13, 14, 15, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_int32_3.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_int32_3.mod.py new file mode 100644 index 000000000..b20b2053b --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_3.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 2) +i2 = Output("op2", "TENSOR_INT32", "{2,2,1,2}") +i3 = Output("op3", "TENSOR_INT32", "{2,2,1,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 2, 5, 6, 9, 10, 13, 14], + i3: # output 1 + [3, 4, 7, 8, 11, 12, 15, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_int32_4.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_int32_4.mod.py new file mode 100644 index 000000000..3c255cc48 --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_4.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") +axis = Int32Scalar("axis", 3) +i2 = Output("op2", "TENSOR_INT32", "{2,2,2,1}") +i3 = Output("op3", "TENSOR_INT32", "{2,2,2,1}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 3, 5, 7, 9, 11, 13, 15], + i3: # output 1 + [2, 4, 6, 8, 10, 12, 14, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_int32_5.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_int32_5.mod.py new file mode 100644 index 000000000..30e5e245d --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_int32_5.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_INT32", "{2,2,2,2}") +axis = Int32Scalar("axis", -4) # Negative axis +i2 = Output("op2", "TENSOR_INT32", "{1,2,2,2}") +i3 = Output("op3", "TENSOR_INT32", "{1,2,2,2}") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 2, 3, 4, 5, 6, 7, 8], + i3: # output 1 + [9, 10, 11, 12, 13, 14, 15, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py b/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py new file mode 100644 index 000000000..46cb3ab78 --- /dev/null +++ b/tests/nnapi/specs/Ex/split_ex_4D_quant8.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{2,2,2,2}, 0.5f, 1") +axis = Int32Scalar("axis", 0) +i2 = Output("op2", "TENSOR_QUANT8_ASYMM", "{1,2,2,2}, 0.5f, 1") +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1,2,2,2}, 0.5f, 1") +model = model.Operation("SPLIT_EX", axis, i1).To([i2, i3]) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]} + +output0 = { + i2: # output 0 + [1, 2, 3, 4, 5, 6, 7, 8], + i3: # output 1 + [9, 10, 11, 12, 13, 14, 15, 16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/sqrt_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/sqrt_ex_1D_float.mod.py new file mode 100644 index 000000000..fa6a01c54 --- /dev/null +++ b/tests/nnapi/specs/Ex/sqrt_ex_1D_float.mod.py @@ -0,0 +1,15 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{4}") # a vector of input +i2 = Output("op2", "TENSOR_FLOAT32", "{4}") # a vector of output +model = model.Operation("SQRT_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [62.0, 5.0, 73.0, 34.0]} + +output0 = {i2: # output 0 + [7.87400787, 2.23606798, 8.54400375, 5.83095189]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/sqrt_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/sqrt_ex_2D_float.mod.py new file mode 100644 index 000000000..d5e816fd4 --- /dev/null +++ b/tests/nnapi/specs/Ex/sqrt_ex_2D_float.mod.py @@ -0,0 +1,13 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2, 2}") +model = model.Operation("SQRT_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [36, 2, 9, 12]} +output0 = {i2: # output 0 + [6.0, 1.41421356, 3.0, 3.46410162]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/sqrt_ex_3D_float.mod.py b/tests/nnapi/specs/Ex/sqrt_ex_3D_float.mod.py new file mode 100644 index 000000000..d96a89653 --- /dev/null +++ b/tests/nnapi/specs/Ex/sqrt_ex_3D_float.mod.py @@ -0,0 +1,14 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2, 2, 2}") +model = model.Operation("SQRT_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [90, 36, 22, 10, 9, 80, 40, 18]} +output0 = {i2: # output 0 + [9.48683298, 6.0, 4.69041576, 3.16227766, + 3.0, 8.94427191, 6.32455532, 4.24264069]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/sqrt_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/sqrt_ex_4D_float.mod.py new file mode 100644 index 000000000..55561ba0f --- /dev/null +++ b/tests/nnapi/specs/Ex/sqrt_ex_4D_float.mod.py @@ -0,0 +1,14 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +i2 = Output("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +model = model.Operation("SQRT_EX", i1).To(i2) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [36, 90, 43, 36, 2, 22, 19, 10, 9, 80, 40, 90, 15, 56, 18, 12]} +output0 = {i2: # output 0 + [6.0, 9.48683298, 6.55743852, 6.0, 1.41421356, 4.69041576, 4.35889894, 3.16227766, + 3.0, 8.94427191, 6.32455532, 9.48683298, 3.87298335, 7.48331477, 4.24264069, 3.46410162]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_1D_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_1D_float.mod.py new file mode 100644 index 000000000..ef7d9f62e --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_1D_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3}") +i2 = Input("op2", "TENSOR_FLOAT32", "{3}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{3}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [2.0, 10.8, 19.4212], + i2: # input 1 + [2.0, 4.4, 15.9856]} + +output0 = {i3: # output 0 + [0.0, 40.96, 11.80334736]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_2D_float.mod.py new file mode 100644 index 000000000..1753f1960 --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_2D_float.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.0, 7.8, + 3.1432, 28.987456], + i2: # input 1 + [2.0, 3.2, + 1.9856, 8.167952]} + +output0 = {i3: # output 0 + [4.0, 21.16, + 1.34003776, 433.451746806016]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_3D_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_3D_float.mod.py new file mode 100644 index 000000000..f91043636 --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_3D_float.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2, 2}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2, 2}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.0, 7.8, 3.1432, 28.987456, 2.0, 10.8, 19.4212, 10.0], + i2: # input 1 + [2.0, 3.2, 1.9856, 8.167952, 2.0, 4.4, 15.9856, 6.0]} + +output0 = {i3: # output 0 + [4.0, 21.16, 1.34003776, 433.451746806016, + 0.0, 40.96, 11.80334736, 16.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_4D_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_4D_float.mod.py new file mode 100644 index 000000000..571c6ce99 --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_4D_float.mod.py @@ -0,0 +1,28 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.0, 7.8, 3.1432, 28.987456, + 9.0, 6.0, 5.0, 12.0, + 2.0, 10.8, 19.4212, 10.0, + 27.0, 36.0, 64.0, 128.0], + i2: # input 1 + [2.0, 3.2, 1.9856, 8.167952, + 1.0, 2.0, 5.0, 14.0, + 2.0, 4.4, 15.9856, 6.0, + 18.0, 30.0, 61.0, 120.0]} + +output0 = {i3: # output 0 + [4.0, 21.16, 1.34003776, 433.451746806016, + 64.0, 16.0, 0, 4.0, + 0.0, 40.96, 11.80334736, 16.0, + 81.0, 36.0, 9.0, 64.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_4D_2D_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_4D_2D_float.mod.py new file mode 100644 index 000000000..8404f4fcd --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_4D_2D_float.mod.py @@ -0,0 +1,29 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [74.0, 63.0, 103.0, 88.0, + 57.0, 9.0, 68.0, 20.0, + 121.0, 38.0, 54.0, 119.0, + 56.0, 106.0, 98.0, 98.0, + 7.0, 89.0, 108.0, 104.0, + 20.0, 81.0, 4.0, 124.0], + i2: # input 1 + [56.0, 115.0, 115.0, 116.0]} + +output0 = {i3: # output 0 + [324.0, 2704.0, 144.0, 784.0, + 1.0, 11236.0, 2209.0, 9216.0, + 4225.0, 5929.0, 3721.0, 9.0, + 0.0, 81.0, 289.0, 324.0, + 2401.0, 676.0, 49.0, 144.0, + 1296.0, 1156.0, 12321.0, 64.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_float.mod.py b/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_float.mod.py new file mode 100644 index 000000000..0926426e6 --- /dev/null +++ b/tests/nnapi/specs/Ex/squared_difference_ex_broadcast_float.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +i2 = Input("op2", "TENSOR_FLOAT32", "{1, 1, 2, 1}") + +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +model = model.Operation("SQUARED_DIFFERENCE_EX", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [4.0, 8.0, 10.0, 14.0], + i2: # input 1 + [1.0, 2.0]} + +output0 = {i3: # output 0 + [9.0, 36.0, 81.0, 144.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/tensorflowmax_ex_2D_float.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_float.mod.py index 6cf8d83e7..6cf8d83e7 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/tensorflowmax_ex_2D_float.mod.py +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_float.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/tensorflowmax_ex_2D_int32.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_int32.mod.py index 940dab3c3..940dab3c3 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/tensorflowmax_ex_2D_int32.mod.py +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_2D_int32.mod.py diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_C.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_C.mod.py new file mode 100644 index 000000000..82a5dbb93 --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_C.mod.py @@ -0,0 +1,34 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + input_table[i * rows * cols * depth + j * cols * depth + k * depth + l] = i * rows * cols * depth + j * cols * depth + k * depth + l; + +output_table = [x for x in range(batch * rows * cols)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + output_table[i * rows * cols + j * cols + k] = i * rows * cols * depth + j * cols * depth + k * depth + depth - 1; + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +axis = Parameter("axis", "TENSOR_INT32", "{2}", [3, -1]) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (batch, rows, cols)) + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_HW.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_HW.mod.py new file mode 100644 index 000000000..a12762055 --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_4D_float_reducing_HW.mod.py @@ -0,0 +1,34 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + input_table[i * rows * cols * depth + j * cols * depth + k * depth + l] = i * rows * cols * depth + j * cols * depth + k * depth + l; + +# Since the axises to be reduced are {rows, cols} and the value of the input always increases in here, the output's values are i * rows * cols * depth + (rows - 1) * cols * depth + (cols - 1) * depth + l. +output_table = [x for x in range(batch * depth)] +for i in range(batch): + for l in range(depth): + output_table[i * depth + l] = i * rows * cols * depth + (rows - 1) * cols * depth + (cols - 1) * depth + l; + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 2, -3, -2]) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batch, depth)) + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_float.mod.py new file mode 100644 index 000000000..6de220721 --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_float.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +axis = Parameter("axis", "TENSOR_INT32", "{1}", [2]) +output = Output("output", "TENSOR_FLOAT32", "{1, 2, 1}") + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0]} + +output0 = {output: # output 0 + [2.0, + 4.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_1.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_float_1.mod.py new file mode 100644 index 000000000..42dc9d94b --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_float_1.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{4, 3, 2}") +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 0, -3, -3]) +output = Output("output", "TENSOR_FLOAT32", "{2}") + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0]} + +output0 = {output: # output 0 + [23.0, 24.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_float_2.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_float_2.mod.py new file mode 100644 index 000000000..98c0d9d03 --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_float_2.mod.py @@ -0,0 +1,17 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{4, 3, 2}") +axis = Parameter("axis", "TENSOR_INT32", "{2}", [0, 2]) +output = Output("output", "TENSOR_FLOAT32", "{1, 3, 1}") + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0]} + +output0 = {output: # output 0 + [20, 22, 24]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_1.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_1.mod.py new file mode 100644 index 000000000..8e85633b3 --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_1.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{4, 3, 2}, 0.8, 5") +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 0, -3, -3]) +output = Output("output", "TENSOR_QUANT8_ASYMM", "{2}, 0.8, 5") + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24]} + +output0 = {output: # output 0 + [23, 24]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_2.mod.py b/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_2.mod.py new file mode 100644 index 000000000..75138b14a --- /dev/null +++ b/tests/nnapi/specs/Ex/tensorflowmax_ex_quant8_2.mod.py @@ -0,0 +1,18 @@ +model = Model() +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{4, 3, 2}, 0.8, 5") +axis = Parameter("axis", "TENSOR_INT32", "{2}", [0, 2]) +output = Output("output", "TENSOR_QUANT8_ASYMM", "{1, 3, 1}, 0.8, 5") + +model = model.Operation("TENSORFLOW_MAX_EX", i1, axis).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, + 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24]} + +output0 = {output: # output 0 + [20, 22, 24]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_float.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_1D_float.mod.py index 1e0ed21d6..1e0ed21d6 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_float.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_1D_float.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_int32.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_1D_int32.mod.py index d2bd39adf..d2bd39adf 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_int32.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_1D_int32.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_quant8.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_1D_quant8.mod.py index 6f36ce41f..6f36ce41f 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_1D_quant8.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_1D_quant8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_float.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_2D_float.mod.py index 204bc143f..204bc143f 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_float.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_2D_float.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_int32.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_2D_int32.mod.py index b90a35488..b90a35488 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_int32.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_2D_int32.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_quant8.mod.py b/tests/nnapi/specs/Ex/topk_v2_ex_2D_quant8.mod.py index d8b5c6075..d8b5c6075 100644 --- a/runtimes/tests/neural_networks_test/specs/Ex/topk_v2_2D_quant8.mod.py +++ b/tests/nnapi/specs/Ex/topk_v2_ex_2D_quant8.mod.py diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_float_1.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_float_1.mod.py new file mode 100644 index 000000000..82b59c3cf --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_float_1.mod.py @@ -0,0 +1,30 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +i1 = Input("in", "TENSOR_FLOAT32", "{1, 4, 4, 1}" ) +pad = Int32Scalar("pad_same", 1) +s_x = Int32Scalar("stride_x", 1) +s_y = Int32Scalar("stride_y", 1) +i2 = Output("op", "TENSOR_FLOAT32", "{1, 4, 4, 1}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 4, 4, 1], + i1: # input 0 + [1.0, 2.0, 3.0, 4.0, + 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0], + weights: # input 1 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]} + +output0 = {i2: # output 0 + [29.0, 62.0, 83.0, 75.0, + 99.0, 192.0, 237.0, 198.0, + 207.0, 372.0, 417.0, 330.0, + 263.0, 446.0, 485.0, 365.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_float_2.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_float_2.mod.py new file mode 100644 index 000000000..e209345c6 --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_float_2.mod.py @@ -0,0 +1,28 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_FLOAT32", "{1, 3, 3, 1}") +i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}" ) +pad = Int32Scalar("pad_valid", 2) +s_x = Int32Scalar("stride_x", 2) +s_y = Int32Scalar("stride_y", 2) +i2 = Output("op", "TENSOR_FLOAT32", "{1, 5, 5, 1}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 5, 5, 1], + i1: # input 0 + [1.0, 2.0, 3.0, 4.0], + weights: # input 1 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]} + +output0 = {i2: # output 0 + [1.0, 2.0, 5.0, 4.0, 6.0, + 4.0, 5.0, 14.0, 10.0, 12.0, + 10.0, 14.0, 36.0, 24.0, 30.0, + 12.0, 15.0, 34.0, 20.0, 24.0, + 21.0, 24.0, 55.0, 32.0, 36.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_float_3.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_float_3.mod.py new file mode 100644 index 000000000..056c5809d --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_float_3.mod.py @@ -0,0 +1,35 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_FLOAT32", "{2, 3, 3, 1}") +i1 = Input("in", "TENSOR_FLOAT32", "{1, 2, 2, 1}" ) +pad = Int32Scalar("pad_valid", 2) +s_x = Int32Scalar("stride_x", 2) +s_y = Int32Scalar("stride_y", 2) +i2 = Output("op", "TENSOR_FLOAT32", "{1, 5, 5, 2}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 5, 5, 2], + i1: # input 0 + [1.0, 2.0, 3.0, 4.0], + weights: # input 1 + [1.0, 3.0, 5.0, 7.0, 9.0, 11.0, + 13.0, 15.0, 17.0, 2.0, 4.0, 6.0, + 8.0, 10.0, 12.0, 14.0, 16.0, 18.0]} + +output0 = {i2: # output 0 + [1.0, 2.0, 3.0, 4.0, 7.0, + 10.0, 6.0, 8.0, 10.0, 12.0, + 7.0, 8.0, 9.0, 10.0, 25.0, + 28.0, 18.0, 20.0, 22.0, 24.0, + 16.0, 20.0, 24.0, 28.0, 62.0, + 72.0, 42.0, 48.0, 54.0, 60.0, + 21.0, 24.0, 27.0, 30.0, 61.0, + 68.0, 36.0, 40.0, 44.0, 48.0, + 39.0, 42.0, 45.0, 48.0, 103.0, + 110.0, 60.0, 64.0, 68.0, 72.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_int_1.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_int_1.mod.py new file mode 100644 index 000000000..fb8eb72be --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_int_1.mod.py @@ -0,0 +1,24 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_INT32", "{1, 3, 3, 1}") +i1 = Input("in", "TENSOR_INT32", "{1, 4, 4, 1}" ) +pad = Int32Scalar("pad_same", 1) +s_x = Int32Scalar("stride_x", 1) +s_y = Int32Scalar("stride_y", 1) +i2 = Output("op", "TENSOR_INT32", "{1, 4, 4, 1}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 4, 4, 1], + i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], + weights: # input 1 + [1, 2, 3, 4, 5, 6, 7, 8, 9]} + +output0 = {i2: # output 0 + [29, 62, 83, 75, 99, 192, 237, 198, 207, 372, 417, 330, 263, 446, 485, 365]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_int_2.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_int_2.mod.py new file mode 100644 index 000000000..9bf4b113d --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_int_2.mod.py @@ -0,0 +1,25 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_INT32", "{1, 3, 3, 1}") +i1 = Input("in", "TENSOR_INT32", "{1, 2, 2, 1}" ) +pad = Int32Scalar("pad_valid", 2) +s_x = Int32Scalar("stride_x", 2) +s_y = Int32Scalar("stride_y", 2) +i2 = Output("op", "TENSOR_INT32", "{1, 5, 5, 1}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 5, 5, 1], + i1: # input 0 + [1, 2, 3, 4], + weights: # input 1 + [1, 2, 3, 4, 5, 6, 7, 8, 9]} + +output0 = {i2: # output 0 + [1, 2, 5, 4, 6, 4, 5, 14, 10, 12, 10, 14, 36, + 24, 30, 12, 15, 34, 20, 24, 21, 24, 55, 32, 36]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/transpose_conv_ex_int_3.mod.py b/tests/nnapi/specs/Ex/transpose_conv_ex_int_3.mod.py new file mode 100644 index 000000000..e44480968 --- /dev/null +++ b/tests/nnapi/specs/Ex/transpose_conv_ex_int_3.mod.py @@ -0,0 +1,27 @@ +# model +model = Model() +i0 = Input("op_shape", "TENSOR_INT32", "{4}") +weights = Input("ker", "TENSOR_INT32", "{2, 3, 3, 1}") +i1 = Input("in", "TENSOR_INT32", "{1, 2, 2, 1}" ) +pad = Int32Scalar("pad_valid", 2) +s_x = Int32Scalar("stride_x", 2) +s_y = Int32Scalar("stride_y", 2) +i2 = Output("op", "TENSOR_INT32", "{1, 5, 5, 2}") +model = model.Operation("TRANSPOSE_CONV_EX", i0, weights, i1, pad, s_x, s_y).To(i2) + +# Example 1. Input in operand 0, +input0 = {i0: # output shape + [1, 5, 5, 2], + i1: # input 0 + [1, 2, 3, 4], + weights: # input 1 + [1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, 8, 10, 12, 14, 16, 18]} + +output0 = {i2: # output 0 + [1, 2, 3, 4, 7, 10, 6, 8, 10, 12, 7, 8, 9, + 10, 25, 28, 18, 20, 22, 24, 16, 20, 24, 28, 62, 72, + 42, 48, 54, 60, 21, 24, 27, 30, 61, 68, 36, 40, 44, + 48, 39, 42, 45, 48, 103, 110, 60, 64, 68, 72]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/unpack_ex_3D_float_1.mod.py b/tests/nnapi/specs/Ex/unpack_ex_3D_float_1.mod.py new file mode 100644 index 000000000..e5cb38e4b --- /dev/null +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_float_1.mod.py @@ -0,0 +1,53 @@ +# Sample UnPack model, axis = 0 +model = Model() +input = Input("input", "TENSOR_FLOAT32", "{3, 2, 3, 4}") +axis = Int32Scalar("axis", 0) +num_splits = Int32Scalar("num_splits", 3) +out1 = Output("output1", "TENSOR_FLOAT32", "{2, 3, 4}") +out2 = Output("output2", "TENSOR_FLOAT32", "{2, 3, 4}") +out3 = Output("output3", "TENSOR_FLOAT32", "{2, 3, 4}") +model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) + +input0 = {input: # input 0 + [0.0, 1.0, 2.0, 3.0, + 4.0, 5.0, 6.0, 7.0, + 8.0, 9.0, 10.0, 11.0, + 12.0, 13.0, 14.0, 15.0, + 16.0, 17.0, 18.0, 19.0, + 20.0, 21.0, 22.0, 23.0, + 24.0, 25.0, 26.0, 27.0, + 28.0, 29.0, 30.0, 31.0, + 32.0, 33.0, 34.0, 35.0, + 36.0, 37.0, 38.0, 39.0, + 40.0, 41.0, 42.0, 43.0, + 44.0, 45.0, 46.0, 47.0, + 48.0, 49.0, 50.0, 51.0, + 52.0, 53.0, 54.0, 55.0, + 56.0, 57.0, 58.0, 59.0, + 60.0, 61.0, 62.0, 63.0, + 64.0, 65.0, 66.0, 67.0, + 68.0, 69.0, 70.0, 71.0]} + +output0 = {out1: # output 0 + [0.0, 1.0, 2.0, 3.0, + 4.0, 5.0, 6.0, 7.0, + 8.0, 9.0, 10.0, 11.0, + 12.0, 13.0, 14.0, 15.0, + 16.0, 17.0, 18.0, 19.0, + 20.0, 21.0, 22.0, 23.0], + out2: # output 1 + [24.0, 25.0, 26.0, 27.0, + 28.0, 29.0, 30.0, 31.0, + 32.0, 33.0, 34.0, 35.0, + 36.0, 37.0, 38.0, 39.0, + 40.0, 41.0, 42.0, 43.0, + 44.0, 45.0, 46.0, 47.0], + out3: # output 2 + [48.0, 49.0, 50.0, 51.0, + 52.0, 53.0, 54.0, 55.0, + 56.0, 57.0, 58.0, 59.0, + 60.0, 61.0, 62.0, 63.0, + 64.0, 65.0, 66.0, 67.0, + 68.0, 69.0, 70.0, 71.0]} +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/unpack_ex_3D_float_2.mod.py b/tests/nnapi/specs/Ex/unpack_ex_3D_float_2.mod.py new file mode 100644 index 000000000..1d1045ae9 --- /dev/null +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_float_2.mod.py @@ -0,0 +1,54 @@ +# Sample UnPack model, axis = 2 +model = Model() +input = Input("input", "TENSOR_FLOAT32", "{3, 2, 3, 4}") +axis = Int32Scalar("axis", 2) +num_splits = Int32Scalar("num_splits", 3) +out1 = Output("output1", "TENSOR_FLOAT32", "{3, 2, 4}") +out2 = Output("output2", "TENSOR_FLOAT32", "{3, 2, 4}") +out3 = Output("output3", "TENSOR_FLOAT32", "{3, 2, 4}") +model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) + +input0 = {input: # input 0 + [0.3, 1.2, 2.1, 3.454, + 4.12, 5.23, 6.34, 7.12, + 8.2, 9.23, 10.11, 11.34, + 12.3, 13.12, 14.122, 15.1, + 16.11, 17.234, 18.2, 19.1, + 20.45, 21.5, 22.5, 23.64, + 24.2, 25.76, 26.4, 27.34, + 28.43, 29.112, 30.45, 31.5, + 32.45, 33.65, 34.6, 35.34, + 36.3, 37.54, 38.6743, 39.56, + 40.451, 41.56, 42.55, 43.511, + 44.4, 45.45, 46.5641, 47.122, + 48.1, 49.456, 50.56, 51.11, + 52.1, 53.34, 54.62, 55.1, + 56.12, 57.4, 58.2, 59.1, + 60.465, 61.1, 62.3, 63.45, + 64.1, 65.11, 66.4, 67.9, + 68.123, 69.65, 70.89, 71.987]} + +output0 = {out1: # output 0 + [0.3, 1.2, 2.1, 3.454, + 12.3, 13.12, 14.122, 15.1, + 24.2, 25.76, 26.4, 27.34, + 36.3, 37.54, 38.6743, 39.56, + 48.1, 49.456, 50.56, 51.11, + 60.465, 61.1, 62.3, 63.45], + out2: # output 1 + [4.12, 5.23, 6.34, 7.12, + 16.11, 17.234, 18.2, 19.1, + 28.43, 29.112, 30.45, 31.5, + 40.451, 41.56, 42.55, 43.511, + 52.1, 53.34, 54.62, 55.1, + 64.1, 65.11, 66.4, 67.9], + out3: # output 2 + [8.2, 9.23, 10.11, 11.34, + 20.45, 21.5, 22.5, 23.64, + 32.45, 33.65, 34.6, 35.34, + 44.4, 45.45, 46.5641, 47.122, + 56.12, 57.4, 58.2, 59.1, + 68.123, 69.65, 70.89, 71.987]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/unpack_ex_3D_int_1.mod.py b/tests/nnapi/specs/Ex/unpack_ex_3D_int_1.mod.py new file mode 100644 index 000000000..2a668cf7e --- /dev/null +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_int_1.mod.py @@ -0,0 +1,25 @@ +# Sample UnPack model, axis = 0 +model = Model() +input = Input("input", "TENSOR_INT32", "{3, 2, 3, 4}") +axis = Int32Scalar("axis", 0) +num_splits = Int32Scalar("num_splits", 3) +out1 = Output("output1", "TENSOR_INT32", "{2, 3, 4}") +out2 = Output("output2", "TENSOR_INT32", "{2, 3, 4}") +out3 = Output("output3", "TENSOR_INT32", "{2, 3, 4}") +model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) + +input0 = {input: # input 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, + 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +output0 = {out1: # output 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], + out2: # output 1 + [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], + out3: # output 2 + [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/Ex/unpack_ex_3D_int_2.mod.py b/tests/nnapi/specs/Ex/unpack_ex_3D_int_2.mod.py new file mode 100644 index 000000000..115954972 --- /dev/null +++ b/tests/nnapi/specs/Ex/unpack_ex_3D_int_2.mod.py @@ -0,0 +1,25 @@ +# Sample UnPack model, axis = 2 +model = Model() +input = Input("input", "TENSOR_INT32", "{3, 2, 3, 4}") +axis = Int32Scalar("axis", 2) +num_splits = Int32Scalar("num_splits", 3) +out1 = Output("output1", "TENSOR_INT32", "{3, 2, 4}") +out2 = Output("output2", "TENSOR_INT32", "{3, 2, 4}") +out3 = Output("output3", "TENSOR_INT32", "{3, 2, 4}") +model = model.Operation("UNPACK_EX", input, num_splits, axis).To([out1, out2, out3]) + +input0 = {input: # input 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, + 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]} + +output0 = {out1: # output 0 + [0, 1, 2, 3, 12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51, 60, 61, 62, 63], + out2: # output 1 + [4, 5, 6, 7, 16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55, 64, 65, 66, 67], + out3: # output 2 + [8, 9,10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59, 68, 69, 70, 71]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/add.mod.py b/tests/nnapi/specs/V1_0/add.mod.py index 3374321f0..3374321f0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/add.mod.py +++ b/tests/nnapi/specs/V1_0/add.mod.py diff --git a/tests/nnapi/specs/V1_0/add_broadcast_4D_2D_after_nops_float_nnfw.mod.py b/tests/nnapi/specs/V1_0/add_broadcast_4D_2D_after_nops_float_nnfw.mod.py new file mode 100644 index 000000000..141682e47 --- /dev/null +++ b/tests/nnapi/specs/V1_0/add_broadcast_4D_2D_after_nops_float_nnfw.mod.py @@ -0,0 +1,31 @@ +# model +model = Model() + +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 1, 3, 4}") +axis0 = Int32Scalar("axis0", 0) + +i3 = Input("op3", "TENSOR_FLOAT32", "{1, 2, 1, 1}") +i4 = Parameter("op4", "TENSOR_INT32", "{4}", [1, 2, 1, 1]) + +i5 = Internal("op5", "TENSOR_FLOAT32", "{1, 1, 3, 4}") +i6 = Internal("op6", "TENSOR_FLOAT32", "{1, 2, 1, 1}") + +i7 = Output("op7", "TENSOR_FLOAT32", "{1, 2, 3, 4}") + +act = Int32Scalar("act", 0) + +model = model.Operation("CONCATENATION", i1, axis0).To(i5) # Actually NOP +model = model.Operation("RESHAPE", i3, i4).To(i6) # Actually NOP +model = model.Operation("ADD", i5, i6, act).To(i7) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + i3: # input 1 + [100, 200]} + +output0 = {i7: # output 0 + [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/add_broadcast_quant8.mod.py b/tests/nnapi/specs/V1_0/add_broadcast_quant8.mod.py index cf2dbb749..cf2dbb749 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/add_broadcast_quant8.mod.py +++ b/tests/nnapi/specs/V1_0/add_broadcast_quant8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/add_quant8.mod.py b/tests/nnapi/specs/V1_0/add_quant8.mod.py index bfbdab765..bfbdab765 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/add_quant8.mod.py +++ b/tests/nnapi/specs/V1_0/add_quant8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_1.mod.py b/tests/nnapi/specs/V1_0/avg_pool_float_1.mod.py index 457313ef3..457313ef3 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_1.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_2.mod.py b/tests/nnapi/specs/V1_0/avg_pool_float_2.mod.py index 35b73a40b..35b73a40b 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_2.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_float_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_3.mod.py b/tests/nnapi/specs/V1_0/avg_pool_float_3.mod.py index 1de8ad13d..1de8ad13d 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_3.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_float_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_4.mod.py b/tests/nnapi/specs/V1_0/avg_pool_float_4.mod.py index 480c70d1d..480c70d1d 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_4.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_float_4.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_5.mod.py b/tests/nnapi/specs/V1_0/avg_pool_float_5.mod.py index ef13bb749..ef13bb749 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_float_5.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_float_5.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_1.mod.py b/tests/nnapi/specs/V1_0/avg_pool_quant8_1.mod.py index 3c933ebbf..3c933ebbf 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_2.mod.py b/tests/nnapi/specs/V1_0/avg_pool_quant8_2.mod.py index 9d49c303b..9d49c303b 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_2.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_quant8_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_3.mod.py b/tests/nnapi/specs/V1_0/avg_pool_quant8_3.mod.py index 895ab5e49..895ab5e49 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_3.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_quant8_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_4.mod.py b/tests/nnapi/specs/V1_0/avg_pool_quant8_4.mod.py index 8215a882a..8215a882a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_4.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_quant8_4.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_5.mod.py b/tests/nnapi/specs/V1_0/avg_pool_quant8_5.mod.py index f6db46eb8..f6db46eb8 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/avg_pool_quant8_5.mod.py +++ b/tests/nnapi/specs/V1_0/avg_pool_quant8_5.mod.py diff --git 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a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_large.mod.py b/tests/nnapi/specs/V1_0/fully_connected_quant8_large.mod.py index f04d150d0..f04d150d0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_large.mod.py +++ b/tests/nnapi/specs/V1_0/fully_connected_quant8_large.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_large_weights_as_inputs.mod.py b/tests/nnapi/specs/V1_0/fully_connected_quant8_large_weights_as_inputs.mod.py index 70ea52556..70ea52556 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_large_weights_as_inputs.mod.py +++ b/tests/nnapi/specs/V1_0/fully_connected_quant8_large_weights_as_inputs.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_weights_as_inputs.mod.py b/tests/nnapi/specs/V1_0/fully_connected_quant8_weights_as_inputs.mod.py index 50e0220e8..50e0220e8 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/fully_connected_quant8_weights_as_inputs.mod.py +++ b/tests/nnapi/specs/V1_0/fully_connected_quant8_weights_as_inputs.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/hashtable_lookup_float.mod.py b/tests/nnapi/specs/V1_0/hashtable_lookup_float.mod.py index 2cdafa3f7..2cdafa3f7 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/hashtable_lookup_float.mod.py +++ b/tests/nnapi/specs/V1_0/hashtable_lookup_float.mod.py diff --git a/tests/nnapi/specs/V1_0/hashtable_lookup_float_4D_nnfw.mod.py b/tests/nnapi/specs/V1_0/hashtable_lookup_float_4D_nnfw.mod.py new file mode 100644 index 000000000..9db9f27f8 --- /dev/null +++ b/tests/nnapi/specs/V1_0/hashtable_lookup_float_4D_nnfw.mod.py @@ -0,0 +1,62 @@ +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +lookups = 4 +keys = 3 +batchs = 3 +cols = 4 +rows = 5 +features = 2 + +table = [x for x in range(batchs * cols * rows * features)] +for i in range(batchs): + for j in range(cols): + for k in range(rows): + for l in range(features): + table[i * cols * rows * features + j * rows * features + k * features + l] = i * cols * rows * features + j * rows * features + k * features + l + +model = Model() + +lookup = Input("lookup", "TENSOR_INT32", "{%d}" % lookups) +key = Input("key", "TENSOR_INT32", "{%d}" % (keys)) +value = Input("value", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batchs, cols, rows, features)) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (lookups, cols, rows, features)) +hits = Output("hits", "TENSOR_QUANT8_ASYMM", "{%d}, 1.f, 0" % (lookups)) +model = model.Operation("HASHTABLE_LOOKUP", lookup, key, value).To([output, hits]) + +input0 = {lookup: [1234, -292, -11, 0], + key: [-11, 0, 1234], + value: table} + +output0 = {output: + [ + # 2-rd item + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + # Not found + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + # 0-th item + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, + # 1-st item + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + ], + hits: + [ + 1, 0, 1, 1, + ] +} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/hashtable_lookup_quant8.mod.py b/tests/nnapi/specs/V1_0/hashtable_lookup_quant8.mod.py index ed735f286..ed735f286 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/hashtable_lookup_quant8.mod.py +++ b/tests/nnapi/specs/V1_0/hashtable_lookup_quant8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/l2_normalization.mod.py b/tests/nnapi/specs/V1_0/l2_normalization.mod.py index df44c688e..df44c688e 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/l2_normalization.mod.py +++ b/tests/nnapi/specs/V1_0/l2_normalization.mod.py diff --git 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a/runtimes/tests/neural_networks_test/specs/V1_0/mobilenet_quantized.mod.py +++ b/tests/nnapi/specs/V1_0/mobilenet_quantized.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/mul.mod.py b/tests/nnapi/specs/V1_0/mul.mod.py index d5ea1e036..d5ea1e036 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/mul.mod.py +++ b/tests/nnapi/specs/V1_0/mul.mod.py diff --git a/tests/nnapi/specs/V1_0/mul_4D_nnfw.mod.py b/tests/nnapi/specs/V1_0/mul_4D_nnfw.mod.py new file mode 100644 index 000000000..ae01d989f --- /dev/null +++ b/tests/nnapi/specs/V1_0/mul_4D_nnfw.mod.py @@ -0,0 +1,19 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +act = Int32Scalar("act", 0) # an int32_t scalar fuse_activation +i3 = Output("op3", "TENSOR_FLOAT32", "{2, 2, 2, 2}") +model = model.Operation("MUL", i1, i2, act).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, -3, -4, -15, 6, 23, 8, -1, -2, 3, 4, 10, -6, 7, -2], + i2: # input 1 + [-1, -2, 3, 4, -5, -6, 7, -8, 1, -2, -3, -4, -5, 6, 7, 8]} + +output0 = {i3: # output 0 + [-1, -4, -9, -16, 75, -36, 161, -64, -1, 4, -9, -16, -50, -36, 49, -16]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/mul_broadcast_3D_1D_1_nnfw.mod.py b/tests/nnapi/specs/V1_0/mul_broadcast_3D_1D_1_nnfw.mod.py index 49729eb8c..49729eb8c 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/mul_broadcast_3D_1D_1_nnfw.mod.py +++ b/tests/nnapi/specs/V1_0/mul_broadcast_3D_1D_1_nnfw.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/mul_broadcast_3D_1D_2_nnfw.mod.py b/tests/nnapi/specs/V1_0/mul_broadcast_3D_1D_2_nnfw.mod.py index a24ad5889..a24ad5889 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/mul_broadcast_3D_1D_2_nnfw.mod.py +++ 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b/tests/nnapi/specs/V1_0/softmax_quant8_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_1.mod.py b/tests/nnapi/specs/V1_0/space_to_depth_float_1.mod.py index d11d8de9c..d11d8de9c 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_1.mod.py +++ b/tests/nnapi/specs/V1_0/space_to_depth_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_2.mod.py b/tests/nnapi/specs/V1_0/space_to_depth_float_2.mod.py index df557f6dc..df557f6dc 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_2.mod.py +++ b/tests/nnapi/specs/V1_0/space_to_depth_float_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_3.mod.py b/tests/nnapi/specs/V1_0/space_to_depth_float_3.mod.py index e5298dff1..e5298dff1 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_float_3.mod.py +++ b/tests/nnapi/specs/V1_0/space_to_depth_float_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_quant8_1.mod.py b/tests/nnapi/specs/V1_0/space_to_depth_quant8_1.mod.py index 844a77f80..844a77f80 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_0/space_to_depth_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_quant8_2.mod.py b/tests/nnapi/specs/V1_0/space_to_depth_quant8_2.mod.py index b8f0d5f81..b8f0d5f81 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/space_to_depth_quant8_2.mod.py +++ b/tests/nnapi/specs/V1_0/space_to_depth_quant8_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py b/tests/nnapi/specs/V1_0/svdf.mod.py index 4f3e42c20..4f3e42c20 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/svdf.mod.py +++ b/tests/nnapi/specs/V1_0/svdf.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/svdf2.mod.py b/tests/nnapi/specs/V1_0/svdf2.mod.py index c34926bc3..c34926bc3 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/svdf2.mod.py +++ b/tests/nnapi/specs/V1_0/svdf2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/svdf_state.mod.py b/tests/nnapi/specs/V1_0/svdf_state.mod.py index dc3e4f420..dc3e4f420 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/svdf_state.mod.py +++ b/tests/nnapi/specs/V1_0/svdf_state.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_0/tanh_.mod.py b/tests/nnapi/specs/V1_0/tanh_.mod.py index daff31687..daff31687 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_0/tanh_.mod.py +++ b/tests/nnapi/specs/V1_0/tanh_.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space.mod.py b/tests/nnapi/specs/V1_1/batch_to_space.mod.py index bf8f56ac5..bf8f56ac5 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space.mod.py +++ b/tests/nnapi/specs/V1_1/batch_to_space.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space_float_1.mod.py b/tests/nnapi/specs/V1_1/batch_to_space_float_1.mod.py index 019242a68..019242a68 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/batch_to_space_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space_quant8_1.mod.py b/tests/nnapi/specs/V1_1/batch_to_space_quant8_1.mod.py index 8c6a72793..8c6a72793 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/batch_to_space_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/batch_to_space_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/div_.mod.py b/tests/nnapi/specs/V1_1/div_.mod.py index e012f79f0..e012f79f0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/div_.mod.py +++ b/tests/nnapi/specs/V1_1/div_.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/div_broadcast_float.mod.py b/tests/nnapi/specs/V1_1/div_broadcast_float.mod.py index d4e0ea91d..d4e0ea91d 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/div_broadcast_float.mod.py +++ b/tests/nnapi/specs/V1_1/div_broadcast_float.mod.py diff --git a/tests/nnapi/specs/V1_1/div_broadcast_float_4D_2D_nnfw.mod.py b/tests/nnapi/specs/V1_1/div_broadcast_float_4D_2D_nnfw.mod.py new file mode 100644 index 000000000..08d995ba4 --- /dev/null +++ b/tests/nnapi/specs/V1_1/div_broadcast_float_4D_2D_nnfw.mod.py @@ -0,0 +1,22 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") +act = Int32Scalar("act", 0) +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +model = model.Operation("DIV", i1, i2, act).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [16, 11, 23, 3, 9, 14, 9, 2, 0, 23, 13, 2, 13, 17, 16, 10, 15, 19, 12, 16, 15, 20, 9, 7], + i2: # input 1 + [16, 7, 23, 16]} + +output0 = {i3: # output 0 + [1.0, 1.57142857, 1.0, 0.1875, 0.5625, 2.0, 0.39130435, 0.125, + 0, 3.28571429, 0.56521739, 0.125, 0.8125, 2.42857143, 0.69565217, 0.625, + 0.9375, 2.71428571, 0.52173913, 1.0, 0.9375, 2.85714286, 0.39130435, 0.4375] + } + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/fully_connected_float_4d_simple.mod.py b/tests/nnapi/specs/V1_1/fully_connected_float_4d_simple.mod.py index b9a629025..b9a629025 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/fully_connected_float_4d_simple.mod.py +++ b/tests/nnapi/specs/V1_1/fully_connected_float_4d_simple.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean.mod.py b/tests/nnapi/specs/V1_1/mean.mod.py index 28bd6af03..28bd6af03 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean.mod.py +++ b/tests/nnapi/specs/V1_1/mean.mod.py diff --git a/tests/nnapi/specs/V1_1/mean_4D_float_reducing_C_nnfw.mod.py b/tests/nnapi/specs/V1_1/mean_4D_float_reducing_C_nnfw.mod.py new file mode 100644 index 000000000..37bc383d8 --- /dev/null +++ b/tests/nnapi/specs/V1_1/mean_4D_float_reducing_C_nnfw.mod.py @@ -0,0 +1,39 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + input_table[i * rows * cols * depth + j * cols * depth + k * depth + l] = i * rows * cols * depth + j * cols * depth + k * depth + l; + +output_table = [0 for x in range(batch * rows * cols)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + output_table[i * rows * cols + j * cols + k] += input_table[i * rows * cols * depth + j * cols * depth + k * depth + l]; + +for i in range(batch * rows * cols): + output_table[i] /= float(depth); + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +axis = Parameter("axis", "TENSOR_INT32", "{2}", [3, -1]) +keepDims = Int32Scalar("keepDims", 0) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (batch, rows, cols)) + +model = model.Operation("MEAN", i1, axis, keepDims).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/mean_4D_float_reducing_HW_nnfw.mod.py b/tests/nnapi/specs/V1_1/mean_4D_float_reducing_HW_nnfw.mod.py new file mode 100644 index 000000000..a778a657a --- /dev/null +++ b/tests/nnapi/specs/V1_1/mean_4D_float_reducing_HW_nnfw.mod.py @@ -0,0 +1,39 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 + +input_table = [x for x in range(batch * rows * cols * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + input_table[i * rows * cols * depth + j * cols * depth + k * depth + l] = i * rows * cols * depth + j * cols * depth + k * depth + l; + +output_table = [0 for x in range(batch * depth)] +for i in range(batch): + for j in range(rows): + for k in range(cols): + for l in range(depth): + output_table[i * depth + l] += input_table[i * rows * cols * depth + j * cols * depth + k * depth + l]; + +for i in range(batch * depth): + output_table[i] /= float(rows * cols); + +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 2, -3, -2]) +keepDims = Int32Scalar("keepDims", 0) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batch, depth)) + +model = model.Operation("MEAN", i1, axis, keepDims).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_axis01_1_nnfw.mod.py b/tests/nnapi/specs/V1_1/mean_axis01_1_nnfw.mod.py index 00b63a59c..00b63a59c 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_axis01_1_nnfw.mod.py +++ b/tests/nnapi/specs/V1_1/mean_axis01_1_nnfw.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_axis01_2_nnfw.mod.py b/tests/nnapi/specs/V1_1/mean_axis01_2_nnfw.mod.py index 1f0eb9c00..1f0eb9c00 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_axis01_2_nnfw.mod.py +++ b/tests/nnapi/specs/V1_1/mean_axis01_2_nnfw.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_float_1.mod.py b/tests/nnapi/specs/V1_1/mean_float_1.mod.py index 5fde65d40..5fde65d40 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/mean_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_float_2.mod.py b/tests/nnapi/specs/V1_1/mean_float_2.mod.py index 4b71d472a..4b71d472a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_float_2.mod.py +++ b/tests/nnapi/specs/V1_1/mean_float_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_quant8_1.mod.py b/tests/nnapi/specs/V1_1/mean_quant8_1.mod.py index 666b0c28f..666b0c28f 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/mean_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/mean_quant8_2.mod.py b/tests/nnapi/specs/V1_1/mean_quant8_2.mod.py index 23fd87c63..23fd87c63 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/mean_quant8_2.mod.py +++ b/tests/nnapi/specs/V1_1/mean_quant8_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/pad.mod.py b/tests/nnapi/specs/V1_1/pad.mod.py index 54a5a469d..54a5a469d 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/pad.mod.py +++ b/tests/nnapi/specs/V1_1/pad.mod.py diff --git a/tests/nnapi/specs/V1_1/pad_2D_HW_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_2D_HW_nnfw.mod.py new file mode 100644 index 000000000..d21a4f17f --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_2D_HW_nnfw.mod.py @@ -0,0 +1,21 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2}") +i2 = Parameter("op2", "TENSOR_INT32", "{2, 2}", [1, 2, 2, 1]) +i3 = Output("op3", "TENSOR_FLOAT32", "{5, 5}") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0,]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 1.0, 2.0, 0.0, + 0.0, 0.0, 3.0, 4.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/pad_3D_HWC_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_3D_HWC_nnfw.mod.py new file mode 100644 index 000000000..d98ec6c26 --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_3D_HWC_nnfw.mod.py @@ -0,0 +1,27 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{2, 2, 2}") +i2 = Parameter("op2", "TENSOR_INT32", "{3, 2}", [1, 0, 0, 1, 1, 1]) +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 3, 4}") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0, + 5.0, 6.0, + 7.0, 8.0]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 1.0, 2.0, 0.0, + 0.0, 3.0, 4.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 5.0, 6.0, 0.0, + 0.0, 7.0, 8.0, 0.0, + 0.0, 0.0, 0.0, 0.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/pad_BHWC_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_BHWC_nnfw.mod.py new file mode 100644 index 000000000..2495abbb5 --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_BHWC_nnfw.mod.py @@ -0,0 +1,76 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +i2 = Parameter("op2", "TENSOR_INT32", "{4, 2}", [1, 1, 1, 1, 2, 1, 1, 1]) +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 4, 5, 3}") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0,]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 1.0, 0.0, + 0.0, 2.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 3.0, 0.0, + 0.0, 4.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/pad_BHW_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_BHW_nnfw.mod.py new file mode 100644 index 000000000..91a21852a --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_BHW_nnfw.mod.py @@ -0,0 +1,28 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +i2 = Parameter("op2", "TENSOR_INT32", "{4, 2}", [1, 1, 1, 1, 1, 1, 0, 0]) +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 4, 4, 1}") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0,]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 1.0, 2.0, 0.0, + 0.0, 3.0, 4.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/pad_HWD_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_HWD_nnfw.mod.py new file mode 100644 index 000000000..a021b3b59 --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_HWD_nnfw.mod.py @@ -0,0 +1,32 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") +i2 = Parameter("op2", "TENSOR_INT32", "{4, 2}", [0, 0, 1, 1, 1, 1, 1, 2]) +i3 = Output("op3", "TENSOR_FLOAT32", "{1, 4, 4, 4}") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1.0, 2.0, + 3.0, 4.0,]} + +output0 = {i3: # output 0 + [0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 1.0, 0.0, 0.0, + 0.0, 2.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 3.0, 0.0, 0.0, + 0.0, 4.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/pad_float_1.mod.py b/tests/nnapi/specs/V1_1/pad_float_1.mod.py index 081712769..081712769 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/pad_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/pad_float_1.mod.py diff --git a/tests/nnapi/specs/V1_1/pad_quant8_nnfw.mod.py b/tests/nnapi/specs/V1_1/pad_quant8_nnfw.mod.py new file mode 100644 index 000000000..c23ab7de0 --- /dev/null +++ b/tests/nnapi/specs/V1_1/pad_quant8_nnfw.mod.py @@ -0,0 +1,20 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_QUANT8_ASYMM", "{1, 2, 2, 1}, 1.0, 2") +i2 = Parameter("op2", "TENSOR_INT32", "{4, 2}", [0, 0, 1, 1, 1, 1, 0, 0]) +i3 = Output("op3", "TENSOR_QUANT8_ASYMM", "{1, 4, 4, 1}, 1.0, 2") +model = model.Operation("PAD", i1, i2).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, + 3, 4,]} + +output0 = {i3: # output 0 + [2, 2, 2, 2, + 2, 1, 2, 2, + 2, 3, 4, 2, + 2, 2, 2, 2]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch.mod.py b/tests/nnapi/specs/V1_1/space_to_batch.mod.py index 8c10231f0..8c10231f0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_1.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_float_1.mod.py index 890ced869..890ced869 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_float_1.mod.py diff --git a/tests/nnapi/specs/V1_1/space_to_batch_float_1_nnfw.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_float_1_nnfw.mod.py new file mode 100644 index 000000000..db0a994eb --- /dev/null +++ b/tests/nnapi/specs/V1_1/space_to_batch_float_1_nnfw.mod.py @@ -0,0 +1,54 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 +block_size_height = 2 +block_size_width = 3 +padding_size_height_top = 1 +padding_size_height_bottom = 2 +padding_size_width_left = 3 +padding_size_width_right = 2 +out_batch = batch * block_size_height * block_size_width +out_rows = (int)((rows + padding_size_height_top + padding_size_height_bottom) / block_size_height) +out_cols = (int)((cols + padding_size_width_left + padding_size_width_right) / block_size_width) + +input_table = [x for x in range(batch * rows * cols * depth)] +stride_b_in = rows * cols * depth +stride_h_in = cols * depth +stride_w_in = depth + +output_table = [0 for x in range(out_batch * out_rows * out_cols * depth)] +stride_b_out = out_rows * out_cols * depth +stride_h_out = out_cols * depth +stride_w_out = depth + +for b in range(batch): + for h in range(rows + padding_size_height_top + padding_size_height_bottom): + for w in range(cols + padding_size_width_left + padding_size_width_right): + for d in range(depth): + out_d = d; + out_h = (int)(h / block_size_height); + out_w = (int)(w / block_size_width); + out_b = b + ((h % block_size_height) * block_size_width + w % block_size_width) * batch; + + if ((h >= padding_size_height_top) and (h < (rows + padding_size_height_top)) and (w >= padding_size_width_left) and (w < (cols + padding_size_width_left))): + output_table[out_b * stride_b_out + out_h * stride_h_out + out_w * stride_w_out + out_d] = input_table[b * stride_b_in + (h - padding_size_height_top) * stride_h_in + (w - padding_size_width_left) * stride_w_in + d]; + +i1 = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (batch, rows, cols, depth)) +block = Parameter("block_size", "TENSOR_INT32", "{2}", [block_size_height, block_size_width]) +paddings = Parameter("paddings", "TENSOR_INT32", "{2, 2}", [padding_size_height_top, padding_size_height_bottom, padding_size_width_left, padding_size_width_right]) +output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d, %d}" % (out_batch, out_rows, out_cols, depth)) + +model = Model() +model = model.Operation("SPACE_TO_BATCH_ND", i1, block, paddings).To(output) + + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_2.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_float_2.mod.py index c6259005a..c6259005a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_2.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_float_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_3.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_float_3.mod.py index 9d7c8b313..9d7c8b313 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_float_3.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_float_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_1.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_quant8_1.mod.py index 726250d3f..726250d3f 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_quant8_1.mod.py diff --git a/tests/nnapi/specs/V1_1/space_to_batch_quant8_1_nnfw.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_quant8_1_nnfw.mod.py new file mode 100644 index 000000000..b720c7a8c --- /dev/null +++ b/tests/nnapi/specs/V1_1/space_to_batch_quant8_1_nnfw.mod.py @@ -0,0 +1,19 @@ +model = Model() +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{1, 4, 2, 1}, 1.0, 9") +block = Parameter("block_size", "TENSOR_INT32", "{2}", [3, 2]) +paddings = Parameter("paddings", "TENSOR_INT32", "{2, 2}", [1, 1, 2, 4]) +output = Output("output", "TENSOR_QUANT8_ASYMM", "{6, 2, 4, 1}, 1.0, 9") + +model = model.Operation("SPACE_TO_BATCH_ND", i1, block, paddings).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [1, 2, 3, 4, 5, 6, 7, 8]} + +output0 = {output: # output 0 + [9, 9, 9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, + 9, 1, 9, 9, 9, 7, 9, 9, 9, 2, 9, 9, 9, 8, 9, 9, + 9, 3, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_2.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_quant8_2.mod.py index 8adc2623a..8adc2623a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_2.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_quant8_2.mod.py diff --git a/tests/nnapi/specs/V1_1/space_to_batch_quant8_2_nnfw.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_quant8_2_nnfw.mod.py new file mode 100644 index 000000000..47b721a6c --- /dev/null +++ b/tests/nnapi/specs/V1_1/space_to_batch_quant8_2_nnfw.mod.py @@ -0,0 +1,51 @@ +batch = 2 +rows = 3 +cols = 4 +depth = 5 +block_size_height = 2 +block_size_width = 3 +padding_size_height_top = 1 +padding_size_height_bottom = 2 +padding_size_width_left = 3 +padding_size_width_right = 2 +zero_value = 200 +scale = 1.0 +out_batch = batch * block_size_height * block_size_width +out_rows = (int)((rows + padding_size_height_top + padding_size_height_bottom) / block_size_height) +out_cols = (int)((cols + padding_size_width_left + padding_size_width_right) / block_size_width) + +input_table = [x for x in range(batch * rows * cols * depth)] + +output_table = [x for x in range(out_batch * out_rows * out_cols * depth)] +for b in range(batch): + for h in range(rows + padding_size_height_top + padding_size_height_bottom): + for w in range(cols + padding_size_width_left + padding_size_width_right): + for d in range(depth): + out_d = d; + out_h = (int)(h / block_size_height); + out_w = (int)(w / block_size_width); + out_b = b + ((h % block_size_height) * block_size_width + w % block_size_width) * batch; + + if (h < padding_size_height_top) or (h >= (rows + padding_size_height_top)) or (w < padding_size_width_left) or (w >= (cols + padding_size_width_left)): + output_table[out_b * out_rows * out_cols * depth + out_h * out_cols * depth + out_w * depth + out_d] = zero_value; + else: + output_table[out_b * out_rows * out_cols * depth + out_h * out_cols * depth + out_w * depth + out_d] = input_table[b * rows * cols * depth + (h - padding_size_height_top) * cols * depth + (w - padding_size_width_left) * depth + d]; + +i1 = Input("input", "TENSOR_QUANT8_ASYMM", "{%d, %d, %d, %d}, %d, %d" % (batch, rows, cols, depth, scale, zero_value)) +block = Parameter("block_size", "TENSOR_INT32", "{2}", [block_size_height, block_size_width]) +paddings = Parameter("paddings", "TENSOR_INT32", "{2, 2}", [padding_size_height_top, padding_size_height_bottom, padding_size_width_left, padding_size_width_right]) +output = Output("output", "TENSOR_QUANT8_ASYMM", "{%d, %d, %d, %d}, %d, %d" % (out_batch, out_rows, out_cols, depth, scale, zero_value)) + +model = Model() +model = model.Operation("SPACE_TO_BATCH_ND", i1, block, paddings).To(output) + + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + input_table} + +output0 = {output: # output 0 + output_table} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_3.mod.py b/tests/nnapi/specs/V1_1/space_to_batch_quant8_3.mod.py index e9e88bbd6..e9e88bbd6 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/space_to_batch_quant8_3.mod.py +++ b/tests/nnapi/specs/V1_1/space_to_batch_quant8_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze.mod.py b/tests/nnapi/specs/V1_1/squeeze.mod.py index 4bf3189fa..4bf3189fa 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze.mod.py +++ b/tests/nnapi/specs/V1_1/squeeze.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_2D_float_1_nnfw.mod.py b/tests/nnapi/specs/V1_1/squeeze_2D_float_1_nnfw.mod.py index 8397902e3..8397902e3 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_2D_float_1_nnfw.mod.py +++ b/tests/nnapi/specs/V1_1/squeeze_2D_float_1_nnfw.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_float_1.mod.py b/tests/nnapi/specs/V1_1/squeeze_float_1.mod.py index 1a54ae7a1..1a54ae7a1 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/squeeze_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_quant8_1.mod.py b/tests/nnapi/specs/V1_1/squeeze_quant8_1.mod.py index 5710c1d9a..5710c1d9a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/squeeze_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/squeeze_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice.mod.py b/tests/nnapi/specs/V1_1/strided_slice.mod.py index 9bc94d110..9bc94d110 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_1.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_1.mod.py index 0725cff30..0725cff30 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_10.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_10.mod.py index 178421f53..178421f53 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_10.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_10.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_11.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_11.mod.py index 444ae63b6..444ae63b6 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_11.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_11.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_2.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_2.mod.py index 7dd3d8399..7dd3d8399 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_2.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_3.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_3.mod.py index e476bca08..e476bca08 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_3.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_4.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_4.mod.py index 939cc1457..939cc1457 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_4.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_4.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_5.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_5.mod.py index db7372708..db7372708 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_5.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_5.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_6.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_6.mod.py index c8d42d95a..c8d42d95a 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_6.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_6.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_7.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_7.mod.py index 668748a91..668748a91 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_7.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_7.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_8.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_8.mod.py index 2c1cc9416..2c1cc9416 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_8.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_9.mod.py b/tests/nnapi/specs/V1_1/strided_slice_float_9.mod.py index 4bafd3da6..4bafd3da6 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_float_9.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_float_9.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_qaunt8_10.mod.py b/tests/nnapi/specs/V1_1/strided_slice_qaunt8_10.mod.py index fc29552ac..fc29552ac 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_qaunt8_10.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_qaunt8_10.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_qaunt8_11.mod.py b/tests/nnapi/specs/V1_1/strided_slice_qaunt8_11.mod.py index d7374ab29..d7374ab29 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_qaunt8_11.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_qaunt8_11.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_1.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_1.mod.py index 4b76de27b..4b76de27b 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_2.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_2.mod.py index d6cd6aa6f..d6cd6aa6f 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_2.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_2.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_3.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_3.mod.py index 411a6fa88..411a6fa88 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_3.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_3.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_4.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_4.mod.py index f8a54f29d..f8a54f29d 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_4.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_4.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_5.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_5.mod.py index 4fa42f5f0..4fa42f5f0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_5.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_5.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_6.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_6.mod.py index bcd8841f0..bcd8841f0 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_6.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_6.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_7.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_7.mod.py index e1ae9db6b..e1ae9db6b 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_7.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_7.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_8.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_8.mod.py index 6531dd3d4..6531dd3d4 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_8.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_8.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_9.mod.py b/tests/nnapi/specs/V1_1/strided_slice_quant8_9.mod.py index 7f066011e..7f066011e 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/strided_slice_quant8_9.mod.py +++ b/tests/nnapi/specs/V1_1/strided_slice_quant8_9.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/sub.mod.py b/tests/nnapi/specs/V1_1/sub.mod.py index 1e4afb205..1e4afb205 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/sub.mod.py +++ b/tests/nnapi/specs/V1_1/sub.mod.py diff --git a/tests/nnapi/specs/V1_1/sub_broadcast_4D_2D_float_nnfw.mod.py b/tests/nnapi/specs/V1_1/sub_broadcast_4D_2D_float_nnfw.mod.py new file mode 100644 index 000000000..ede48f083 --- /dev/null +++ b/tests/nnapi/specs/V1_1/sub_broadcast_4D_2D_float_nnfw.mod.py @@ -0,0 +1,24 @@ +# model +model = Model() +i1 = Input("op1", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +i2 = Input("op2", "TENSOR_FLOAT32", "{2, 2}") +act = Int32Scalar("act", 0) # an int32_t scalar fuse_activation +i3 = Output("op3", "TENSOR_FLOAT32", "{3, 2, 2, 2}") +model = model.Operation("SUB", i1, i2, act).To(i3) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0.02648412, 0.12737854, 0.92319058, 0.60023185, 0.35821535, 0.96402954, 0.64612486, 0.71984435, + 0.59125833, 0.18123407, 0.47523563, 0.35624883, 0.67594663, 0.69254679, 0.63816926, 0.82754998, + 0.4535841, 0.66132381, 0.40122975, 0.02461688, 0.58031493, 0.44518958, 0.26908303, 0.82039221], + i2: # input 1 + [0.52241209, 0.02719438, 0.071708, 0.6779468] + } +output0 = {i3: # output 0 + [-0.49592797, 0.10018416, 0.85148259, -0.07771495, -0.16419674, 0.93683516, 0.57441687, 0.04189755, + 0.06884624, 0.15403969, 0.40352763, -0.32169797, 0.15353454, 0.66535241, 0.56646126, 0.14960318, + -0.06882799, 0.63412943, 0.32952176, -0.65332992, 0.05790284, 0.4179952, 0.19737503, 0.14244541] + } + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/sub_broadcast_float.mod.py b/tests/nnapi/specs/V1_1/sub_broadcast_float.mod.py index 53bdf9e86..53bdf9e86 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/sub_broadcast_float.mod.py +++ b/tests/nnapi/specs/V1_1/sub_broadcast_float.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/transpose.mod.py b/tests/nnapi/specs/V1_1/transpose.mod.py index 49f15a7cb..49f15a7cb 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/transpose.mod.py +++ b/tests/nnapi/specs/V1_1/transpose.mod.py diff --git a/tests/nnapi/specs/V1_1/transpose_2D_nnfw.mod.py b/tests/nnapi/specs/V1_1/transpose_2D_nnfw.mod.py new file mode 100644 index 000000000..2397aa0d7 --- /dev/null +++ b/tests/nnapi/specs/V1_1/transpose_2D_nnfw.mod.py @@ -0,0 +1,16 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{3,4}") +perms = Parameter("perms", "TENSOR_INT32", "{2}", [1, 0]) +output = Output("output", "TENSOR_FLOAT32", "{4,3}") + +model = model.Operation("TRANSPOSE", i1, perms).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]} + +output0 = {output: # output 0 + [0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11]} + +# Instantiate an example +Example((input0, output0)) diff --git a/tests/nnapi/specs/V1_1/transpose_3D_nnfw.mod.py b/tests/nnapi/specs/V1_1/transpose_3D_nnfw.mod.py new file mode 100644 index 000000000..b2939dc41 --- /dev/null +++ b/tests/nnapi/specs/V1_1/transpose_3D_nnfw.mod.py @@ -0,0 +1,16 @@ +model = Model() +i1 = Input("input", "TENSOR_FLOAT32", "{3,4,2}") +perms = Parameter("perms", "TENSOR_INT32", "{3}", [1, 0, 2]) +output = Output("output", "TENSOR_FLOAT32", "{4,3,2}") + +model = model.Operation("TRANSPOSE", i1, perms).To(output) + +# Example 1. Input in operand 0, +input0 = {i1: # input 0 + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]} + +output0 = {output: # output 0 + [ 0, 1, 8, 9, 16, 17, 2, 3, 10, 11, 18, 19, 4, 5, 12, 13, 20, 21, 6, 7, 14, 15, 22, 23]} + +# Instantiate an example +Example((input0, output0)) diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/transpose_float_1.mod.py b/tests/nnapi/specs/V1_1/transpose_float_1.mod.py index e8f0ea84b..e8f0ea84b 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/transpose_float_1.mod.py +++ b/tests/nnapi/specs/V1_1/transpose_float_1.mod.py diff --git a/runtimes/tests/neural_networks_test/specs/V1_1/transpose_quant8_1.mod.py b/tests/nnapi/specs/V1_1/transpose_quant8_1.mod.py index 6893a62e6..6893a62e6 100644 --- a/runtimes/tests/neural_networks_test/specs/V1_1/transpose_quant8_1.mod.py +++ b/tests/nnapi/specs/V1_1/transpose_quant8_1.mod.py diff --git a/tests/nnapi/specs/generate_test.sh b/tests/nnapi/specs/generate_test.sh new file mode 100755 index 000000000..2d7e5ccf9 --- /dev/null +++ b/tests/nnapi/specs/generate_test.sh @@ -0,0 +1,125 @@ +#!/bin/bash + +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# Copyright (C) 2017 The Android Open Source Project +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +NNAPI_VERSION=" +V1_0 +V1_1 +Ex +" + +# Process one test spec, and optionally provide the log file argument +# for the slicing tool. The first argument is the test spec file; the +# second optional argument specifies the log file this test should dump +# results into. Only used by the test slicing tool to collect reference +# outputs from the CPU. Also, it outputs the right #includes in the +# test harness so the test would be invoked by TestGenerated.cpp +# +# This function shouldn't be directly called from other scripts. Use +# generate_wrapper below for generating models and examples and updating the +# test framework in one shot. + +spec_dir=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd ) +export NNAPI_BASE=$(readlink -f "$spec_dir/../../..") +[ -z "$TEST_DIR" ] && TEST_DIR="tests/nnapi" + +function generate_one_testcase { + # Generate one testcase + local LOGFILE=$2 + if [ -n "$2" ]; then + local LOGFILE=", \"$2\"" + fi + local BASENAME=`basename $1` + BASENAME=${BASENAME%".mod.py"}; + local EXAMPLE="-e $NNAPI_BASE/$TEST_DIR/src/generated/examples/$BASENAME.example.cpp" + + $NNAPI_BASE/externals/nnapi_test_generator/test_generator.py ./`basename $1`\ + -m $NNAPI_BASE/$TEST_DIR/src/generated/models/$BASENAME.model.cpp $EXAMPLE + ret=$? + # Paste these lines into TestGenerated.cpp + echo + echo namespace $BASENAME { + echo std::vector\<MixedTypedExample\> examples \= { + echo // Generated $BASENAME test + echo \#include \"generated/examples/$BASENAME.example.cpp\" + echo }\; + echo // Generated model constructor + echo \#include \"generated/models/$BASENAME.model.cpp\" + echo } // namespace $BASENAME + echo TEST_F\(GeneratedTests\, $BASENAME\) { + echo ' execute'\($BASENAME\:\:CreateModel\, + echo ' '$BASENAME\:\:is_ignored\, + echo ' '$BASENAME\:\:examples${LOGFILE}\)\; + echo } + return $ret +} + +# Driver for generate_one_testcase. Append the output of generate_one_testcase +# (which are C++ snippets that invokes the test harness) to the +# all_generated_tests.cpp +# Optionally, the "LOG" file ($2), only used by the slicing tool, would be +# passed to generate_one_testcase. +# +# This function should be called to process one test spec from other scripts. +function generate_wrapper { + local LOGFILE="" + if [ $1 = "log" ]; then + local LOGFILE=$2 + shift + shift + fi + + mkdir -vp $NNAPI_BASE/$TEST_DIR/src/generated/models + mkdir -vp $NNAPI_BASE/$TEST_DIR/src/generated/examples + + cd $NNAPI_BASE/$TEST_DIR/specs + + for ver in $NNAPI_VERSION; + do + OUTFILE=$NNAPI_BASE/$TEST_DIR/src/generated/all_generated_${ver}_cts_tests.cpp + echo "// DO NOT EDIT;" > $OUTFILE + echo "// Generated by ${TEST_DIR}/specs/generate_test.sh" >> $OUTFILE + + VER_DIR=$NNAPI_BASE/$TEST_DIR/specs/$ver + [ ! -d $VER_DIR ] && continue + cd $VER_DIR + for f in $@; + do + generate_one_testcase $f $LOGFILE >> $OUTFILE + if [ $? -ne 0 ]; then + echo "Failed processing $f" + return $? + fi + done + done + return $? +} + +# Only run the following when not sourced by another script +if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then + if [ $# -eq 0 ]; then + FILES=*.mod.py + else + FILES="$@" + fi + generate_wrapper $FILES + if [ $? -ne 0 ]; then + exit $? + fi + echo "Generated file in ${TEST_DIR}/src/generated/"`basename $OUTFILE` +fi # [[ "${BASH_SOURCE[0]}" == "${0}" ]] + diff --git a/runtimes/tests/neural_networks_test/specs/generate_vts_test.sh b/tests/nnapi/specs/generate_vts_test.sh index f2df2eba7..f2df2eba7 100755 --- a/runtimes/tests/neural_networks_test/specs/generate_vts_test.sh +++ b/tests/nnapi/specs/generate_vts_test.sh diff --git a/runtimes/tests/neural_networks_test/specs/slicing.sh b/tests/nnapi/specs/slicing.sh index 564a7d263..564a7d263 100755 --- a/runtimes/tests/neural_networks_test/specs/slicing.sh +++ b/tests/nnapi/specs/slicing.sh diff --git a/runtimes/tests/neural_networks_test/TestGenerated.cpp b/tests/nnapi/src/TestGenerated.cpp index ef35dcf7a..ef35dcf7a 100644 --- a/runtimes/tests/neural_networks_test/TestGenerated.cpp +++ b/tests/nnapi/src/TestGenerated.cpp diff --git a/tests/nnapi/src/TestGenerated_Ex.cpp b/tests/nnapi/src/TestGenerated_Ex.cpp new file mode 100644 index 000000000..2953c34f6 --- /dev/null +++ b/tests/nnapi/src/TestGenerated_Ex.cpp @@ -0,0 +1,24 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * generated/all_generated_tests.cpp that is included in the bottom line + * in file is also modified. + * + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TestGenerated_common.cpp" +using namespace generated_tests; +#include "generated/all_generated_Ex_cts_tests.cpp" diff --git a/tests/nnapi/src/TestGenerated_V1_0.cpp b/tests/nnapi/src/TestGenerated_V1_0.cpp new file mode 100644 index 000000000..7266e775b --- /dev/null +++ b/tests/nnapi/src/TestGenerated_V1_0.cpp @@ -0,0 +1,24 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * generated/all_generated_tests.cpp that is included in the bottom line + * in file is also modified. + * + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TestGenerated_common.cpp" +using namespace generated_tests; +#include "generated/all_generated_V1_0_cts_tests.cpp" diff --git a/tests/nnapi/src/TestGenerated_V1_1.cpp b/tests/nnapi/src/TestGenerated_V1_1.cpp new file mode 100644 index 000000000..e3251a05e --- /dev/null +++ b/tests/nnapi/src/TestGenerated_V1_1.cpp @@ -0,0 +1,24 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * generated/all_generated_tests.cpp that is included in the bottom line + * in file is also modified. + * + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TestGenerated_common.cpp" +using namespace generated_tests; +#include "generated/all_generated_V1_1_cts_tests.cpp" diff --git a/tests/nnapi/src/TestGenerated_common.cpp b/tests/nnapi/src/TestGenerated_common.cpp new file mode 100644 index 000000000..2d17efe51 --- /dev/null +++ b/tests/nnapi/src/TestGenerated_common.cpp @@ -0,0 +1,134 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * generated/all_generated_tests.cpp that is included in the bottom line + * in file is also modified. + * + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// Top level driver for models and examples generated by test_generator.py + +#include "NeuralNetworksWrapper.h" +#include "TestHarness.h" + +#include <gtest/gtest.h> +#include <cassert> +#include <cmath> +#include <fstream> +#include <iostream> +#include <map> + +// Uncomment the following line to generate DOT graphs. +// +// #define GRAPH GRAPH + +namespace generated_tests { +using namespace nnfw::rt::wrapper; + +template <typename T> +static void print(std::ostream& os, const MixedTyped& test) { + // dump T-typed inputs + for_each<T>(test, [&os](int idx, const std::vector<T>& f) { + os << " aliased_output" << idx << ": ["; + for (size_t i = 0; i < f.size(); ++i) { + os << (i == 0 ? "" : ", ") << +f[i]; + } + os << "],\n"; + }); +} + +static void printAll(std::ostream& os, const MixedTyped& test) { + print<float>(os, test); + print<int32_t>(os, test); + print<uint8_t>(os, test); +} + +// Test driver for those generated from ml/nn/runtime/test/spec +static void execute(std::function<void(Model*)> createModel, + std::function<bool(int)> isIgnored, + std::vector<MixedTypedExampleType>& examples, + std::string dumpFile = "") { + Model model; + createModel(&model); + model.finish(); + bool dumpToFile = !dumpFile.empty(); + + std::ofstream s; + if (dumpToFile) { + s.open(dumpFile, std::ofstream::trunc); + ASSERT_TRUE(s.is_open()); + } + + int exampleNo = 0; + Compilation compilation(&model); + compilation.finish(); + + const float fpRange = 1e-5f; + for (auto& example : examples) { + SCOPED_TRACE(exampleNo); + // TODO: We leave it as a copy here. + // Should verify if the input gets modified by the test later. + MixedTyped inputs = example.first; + const MixedTyped& golden = example.second; + + Execution execution(&compilation); + + // Set all inputs + for_all(inputs, [&execution](int idx, const void* p, size_t s) { + const void* buffer = s == 0 ? nullptr : p; + ASSERT_EQ(Result::NO_ERROR, execution.setInput(idx, buffer, s)); + }); + + MixedTyped test; + // Go through all typed outputs + resize_accordingly(golden, test); + for_all(test, [&execution](int idx, void* p, size_t s) { + void* buffer = s == 0 ? nullptr : p; + ASSERT_EQ(Result::NO_ERROR, execution.setOutput(idx, buffer, s)); + }); + + Result r = execution.compute(); + ASSERT_EQ(Result::NO_ERROR, r); + + // Dump all outputs for the slicing tool + if (dumpToFile) { + s << "output" << exampleNo << " = {\n"; + printAll(s, test); + // all outputs are done + s << "}\n"; + } + + // Filter out don't cares + MixedTyped filteredGolden = filter(golden, isIgnored); + MixedTyped filteredTest = filter(test, isIgnored); + // We want "close-enough" results for float + + compare(filteredGolden, filteredTest, fpRange); + exampleNo++; + } +} + +}; // namespace generated_tests + +using namespace nnfw::rt::wrapper; + +// Mixed-typed examples +typedef generated_tests::MixedTypedExampleType MixedTypedExample; + +class GeneratedTests : public ::testing::Test { + protected: + virtual void SetUp() {} +}; diff --git a/runtimes/tests/neural_networks_test/TestMain.cpp b/tests/nnapi/src/TestMain.cpp index 7408972e9..7408972e9 100644 --- a/runtimes/tests/neural_networks_test/TestMain.cpp +++ b/tests/nnapi/src/TestMain.cpp diff --git a/runtimes/tests/neural_networks_test/TestTrivialModel.cpp b/tests/nnapi/src/TestTrivialModel.cpp index 72ae912e9..72ae912e9 100644 --- a/runtimes/tests/neural_networks_test/TestTrivialModel.cpp +++ b/tests/nnapi/src/TestTrivialModel.cpp diff --git a/tests/nnapi/src/TestValidation.cpp b/tests/nnapi/src/TestValidation.cpp new file mode 100644 index 000000000..2d605bb7e --- /dev/null +++ b/tests/nnapi/src/TestValidation.cpp @@ -0,0 +1,612 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "NeuralNetworksWrapper.h" +#if 0 // TODO-NNRT : Enable if we support OEM +#include "NeuralNetworksOEM.h" +#endif +#include <gtest/gtest.h> +#include <string> +#include <sys/mman.h> +#include <stdio.h> +#include <stdlib.h> +// This file tests all the validations done by the Neural Networks API. +namespace { + +#define PATH_MAX 256 + +static int shmem_num = 0; +static int shmem_create_region(size_t size) +{ + char temp[PATH_MAX]; + snprintf(temp, sizeof(temp), "/tmp/nn-shmem-%d-%d-XXXXXXXXX", getpid(), shmem_num++); + + // Set umask and recover after generate temporary file to avoid security issue + mode_t umaskPrev = umask(S_IRUSR|S_IWUSR); + int fd = mkstemp(temp); + umask(umaskPrev); + + if (fd == -1) return -1; + + unlink(temp); + + if (TEMP_FAILURE_RETRY(ftruncate(fd, size)) == -1) { + close(fd); + return -1; + } + + return fd; +} + +class ValidationTest : public ::testing::Test { +protected: + virtual void SetUp() {} +}; + +class ValidationTestModel : public ValidationTest { +protected: + virtual void SetUp() { + ValidationTest::SetUp(); + ASSERT_EQ(ANeuralNetworksModel_create(&mModel), ANEURALNETWORKS_NO_ERROR); + } + virtual void TearDown() { + ANeuralNetworksModel_free(mModel); + ValidationTest::TearDown(); + } + ANeuralNetworksModel* mModel = nullptr; +}; + +class ValidationTestIdentify : public ValidationTestModel { + virtual void SetUp() { + ValidationTestModel::SetUp(); + + uint32_t dimensions[]{1}; + ANeuralNetworksOperandType tensorType{.type = ANEURALNETWORKS_TENSOR_FLOAT32, + .dimensionCount = 1, + .dimensions = dimensions}; + ANeuralNetworksOperandType scalarType{.type = ANEURALNETWORKS_INT32, + .dimensionCount = 0, + .dimensions = nullptr}; + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &scalarType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + uint32_t inList[3]{0, 1, 2}; + uint32_t outList[1]{3}; + ASSERT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_ADD, 3, inList, 1, + outList), + ANEURALNETWORKS_NO_ERROR); + } + virtual void TearDown() { + ValidationTestModel::TearDown(); + } +}; + +class ValidationTestCompilation : public ValidationTestModel { +protected: + virtual void SetUp() { + ValidationTestModel::SetUp(); + + uint32_t dimensions[]{1}; + ANeuralNetworksOperandType tensorType{.type = ANEURALNETWORKS_TENSOR_FLOAT32, + .dimensionCount = 1, + .dimensions = dimensions}; + ANeuralNetworksOperandType scalarType{.type = ANEURALNETWORKS_INT32, + .dimensionCount = 0, + .dimensions = nullptr}; + + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &scalarType), ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_addOperand(mModel, &tensorType), ANEURALNETWORKS_NO_ERROR); + uint32_t inList[3]{0, 1, 2}; + uint32_t outList[1]{3}; + ASSERT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_ADD, 3, inList, 1, + outList), + ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), + ANEURALNETWORKS_NO_ERROR); + ASSERT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); + + ASSERT_EQ(ANeuralNetworksCompilation_create(mModel, &mCompilation), + ANEURALNETWORKS_NO_ERROR); + } + virtual void TearDown() { + ANeuralNetworksCompilation_free(mCompilation); + ValidationTestModel::TearDown(); + } + ANeuralNetworksCompilation* mCompilation = nullptr; +}; + +class ValidationTestExecution : public ValidationTestCompilation { +protected: + virtual void SetUp() { + ValidationTestCompilation::SetUp(); + + ASSERT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_NO_ERROR); + + ASSERT_EQ(ANeuralNetworksExecution_create(mCompilation, &mExecution), + ANEURALNETWORKS_NO_ERROR); + } + virtual void TearDown() { + ANeuralNetworksExecution_free(mExecution); + ValidationTestCompilation::TearDown(); + } + ANeuralNetworksExecution* mExecution = nullptr; +}; + +TEST_F(ValidationTest, CreateModel) { + EXPECT_EQ(ANeuralNetworksModel_create(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); +} + +TEST_F(ValidationTestModel, AddOperand) { + ANeuralNetworksOperandType floatType{ + .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(nullptr, &floatType), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); + + ANeuralNetworksOperandType quant8TypeInvalidScale{ + .type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, + .dimensionCount = 0, + .dimensions = nullptr, + // Scale has to be non-negative + .scale = -1.0f, + .zeroPoint = 0, + }; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &quant8TypeInvalidScale), + ANEURALNETWORKS_BAD_DATA); + + ANeuralNetworksOperandType quant8TypeInvalidZeroPoint{ + .type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, + .dimensionCount = 0, + .dimensions = nullptr, + .scale = 1.0f, + // zeroPoint has to be in [0, 255] + .zeroPoint = -1, + }; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &quant8TypeInvalidZeroPoint), + ANEURALNETWORKS_BAD_DATA); + + uint32_t dim = 2; + ANeuralNetworksOperandType invalidScalarType{ + .type = ANEURALNETWORKS_INT32, + // scalar types can only 0 dimensions. + .dimensionCount = 1, + .dimensions = &dim, + }; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &invalidScalarType), + ANEURALNETWORKS_BAD_DATA); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), + ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestModel, SetOptionalOperand) { + ANeuralNetworksOperandType floatType{ + .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); + + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, nullptr, 0), + ANEURALNETWORKS_NO_ERROR); +} + +TEST_F(ValidationTestModel, SetOperandValue) { + ANeuralNetworksOperandType floatType{ + .type = ANEURALNETWORKS_FLOAT32, .dimensionCount = 0, .dimensions = nullptr}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); + + char buffer[20]; + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(nullptr, 0, buffer, sizeof(buffer)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, nullptr, sizeof(buffer)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since buffer is not the size of a float32. + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(buffer)), + ANEURALNETWORKS_BAD_DATA); + + // This should succeed. + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(float)), + ANEURALNETWORKS_NO_ERROR); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 1, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestModel, SetOperandValueFromMemory) { + uint32_t dimensions[]{1}; + ANeuralNetworksOperandType floatType{ + .type = ANEURALNETWORKS_TENSOR_FLOAT32, + .dimensionCount = 1, + .dimensions = dimensions}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &floatType), ANEURALNETWORKS_NO_ERROR); + + const size_t memorySize = 20; + int memoryFd = shmem_create_region(memorySize); + ASSERT_GT(memoryFd, 0); + + ANeuralNetworksMemory* memory; + EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, + memoryFd, 0, &memory), + ANEURALNETWORKS_NO_ERROR); + + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(nullptr, 0, + memory, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, + nullptr, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since the operand does not exist. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, -1, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since memory is not the size of a float32. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, + memory, 0, memorySize), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 1, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since offset is larger than memorySize. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, + memory, memorySize + 1, + sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since requested size is larger than the memory. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, + memory, memorySize - 3, + sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_setOperandValueFromMemory(mModel, 0, + memory, 0, + sizeof(float)), + ANEURALNETWORKS_BAD_STATE); +} + + +#if 0 // TODO-NNRT : Enable if we support OEM OP. +TEST_F(ValidationTestModel, AddOEMOperand) { + ANeuralNetworksOperandType OEMScalarType{ + .type = ANEURALNETWORKS_OEM_SCALAR, .dimensionCount = 0, .dimensions = nullptr}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMScalarType), ANEURALNETWORKS_NO_ERROR); + char buffer[20]; + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 0, buffer, sizeof(buffer)), + ANEURALNETWORKS_NO_ERROR); + + const size_t kByteSizeOfOEMTensor = 4; + uint32_t dimensions[]{kByteSizeOfOEMTensor}; + ANeuralNetworksOperandType OEMTensorType{ + .type = ANEURALNETWORKS_TENSOR_OEM_BYTE, + .dimensionCount = 1, + .dimensions = dimensions}; + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMTensorType), ANEURALNETWORKS_NO_ERROR); + EXPECT_EQ(ANeuralNetworksModel_setOperandValue(mModel, 1, buffer, kByteSizeOfOEMTensor), + ANEURALNETWORKS_NO_ERROR); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &OEMTensorType), ANEURALNETWORKS_BAD_STATE); +} +#endif // TODO-NNRT + +TEST_F(ValidationTestModel, AddOperation) { + uint32_t input = 0; + uint32_t output = 0; + EXPECT_EQ(ANeuralNetworksModel_addOperation(nullptr, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, + 1, &output), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 0, nullptr, + 1, &output), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, + 0, nullptr), + ANEURALNETWORKS_UNEXPECTED_NULL); + + ANeuralNetworksOperationType invalidOp = -1; + EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, invalidOp, 1, &input, + 1, &output), + ANEURALNETWORKS_BAD_DATA); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_addOperation(mModel, ANEURALNETWORKS_AVERAGE_POOL_2D, 1, &input, + 1, &output), + ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestModel, IdentifyInputsAndOutputs) { + uint32_t input = 0; + uint32_t output = 0; + EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(nullptr, 1, &input, 1, &output), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 0, nullptr, 1, &output), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 1, &input, 0, nullptr), + ANEURALNETWORKS_UNEXPECTED_NULL); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 1, &input, 1, &output), + ANEURALNETWORKS_BAD_STATE); +} + +#if 0 // TODO-NNRT : Enable if we support 'RelaxComputationFloat32toFloat16' +TEST_F(ValidationTestModel, RelaxComputationFloat32toFloat16) { + EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(nullptr, true), + ANEURALNETWORKS_UNEXPECTED_NULL); + + ANeuralNetworksModel_finish(mModel); + // This should fail, as the model is already finished. + EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(mModel, true), + ANEURALNETWORKS_BAD_STATE); + EXPECT_EQ(ANeuralNetworksModel_relaxComputationFloat32toFloat16(mModel, false), + ANEURALNETWORKS_BAD_STATE); +} +#endif + +TEST_F(ValidationTestModel, Finish) { + EXPECT_EQ(ANeuralNetworksModel_finish(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); + EXPECT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestModel, CreateCompilation) { + ANeuralNetworksCompilation* compilation = nullptr; + EXPECT_EQ(ANeuralNetworksCompilation_create(nullptr, &compilation), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksCompilation_create(mModel, nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksCompilation_create(mModel, &compilation), ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestIdentify, Ok) { + uint32_t inList[3]{0, 1, 2}; + uint32_t outList[1]{3}; + + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 1, outList), + ANEURALNETWORKS_NO_ERROR); + + ASSERT_EQ(ANeuralNetworksModel_finish(mModel), ANEURALNETWORKS_NO_ERROR); +} + +TEST_F(ValidationTestIdentify, InputIsOutput) { + uint32_t inList[3]{0, 1, 2}; + uint32_t outList[2]{3, 0}; + + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestIdentify, OutputIsInput) { + uint32_t inList[4]{0, 1, 2, 3}; + uint32_t outList[1]{3}; + + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestIdentify, DuplicateInputs) { + uint32_t inList[4]{0, 1, 2, 0}; + uint32_t outList[1]{3}; + + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 4, inList, 1, outList), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestIdentify, DuplicateOutputs) { + uint32_t inList[3]{0, 1, 2}; + uint32_t outList[2]{3, 3}; + + ASSERT_EQ(ANeuralNetworksModel_identifyInputsAndOutputs(mModel, 3, inList, 2, outList), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestCompilation, SetPreference) { + EXPECT_EQ(ANeuralNetworksCompilation_setPreference(nullptr, ANEURALNETWORKS_PREFER_LOW_POWER), + ANEURALNETWORKS_UNEXPECTED_NULL); + + EXPECT_EQ(ANeuralNetworksCompilation_setPreference(mCompilation, 40), ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestCompilation, CreateExecution) { + ANeuralNetworksExecution* execution = nullptr; + EXPECT_EQ(ANeuralNetworksExecution_create(nullptr, &execution), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, nullptr), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), + ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestCompilation, Finish) { + EXPECT_EQ(ANeuralNetworksCompilation_finish(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_NO_ERROR); + EXPECT_EQ(ANeuralNetworksCompilation_setPreference(mCompilation, + ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER), + ANEURALNETWORKS_BAD_STATE); + EXPECT_EQ(ANeuralNetworksCompilation_finish(mCompilation), ANEURALNETWORKS_BAD_STATE); +} + +TEST_F(ValidationTestExecution, SetInput) { + ANeuralNetworksExecution* execution; + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); + + char buffer[20]; + EXPECT_EQ(ANeuralNetworksExecution_setInput(nullptr, 0, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 0, nullptr, nullptr, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since memory is not the size of a float32. + EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 0, nullptr, buffer, 20), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, 999, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setInput(execution, -1, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestExecution, SetOutput) { + ANeuralNetworksExecution* execution; + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); + + char buffer[20]; + EXPECT_EQ(ANeuralNetworksExecution_setOutput(nullptr, 0, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 0, nullptr, nullptr, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since memory is not the size of a float32. + EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 0, nullptr, buffer, 20), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, 999, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, as this operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setOutput(execution, -1, nullptr, buffer, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestExecution, SetInputFromMemory) { + ANeuralNetworksExecution* execution; + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); + + const size_t memorySize = 20; + int memoryFd = shmem_create_region(memorySize); + ASSERT_GT(memoryFd, 0); + + ANeuralNetworksMemory* memory; + EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, + memoryFd, 0, &memory), + ANEURALNETWORKS_NO_ERROR); + + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(nullptr, 0, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, + nullptr, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since the operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 999, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since the operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, -1, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since memory is not the size of a float32. + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, + memory, 0, memorySize), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since offset is larger than memorySize. + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, + memory, memorySize + 1, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since requested size is larger than the memory. + EXPECT_EQ(ANeuralNetworksExecution_setInputFromMemory(execution, 0, nullptr, + memory, memorySize - 3, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestExecution, SetOutputFromMemory) { + ANeuralNetworksExecution* execution; + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); + + const size_t memorySize = 20; + int memoryFd = shmem_create_region(memorySize); + ASSERT_GT(memoryFd, 0); + + ANeuralNetworksMemory* memory; + EXPECT_EQ(ANeuralNetworksMemory_createFromFd(memorySize, PROT_READ | PROT_WRITE, + memoryFd, 0, &memory), + ANEURALNETWORKS_NO_ERROR); + + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(nullptr, 0, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, + nullptr, 0, sizeof(float)), + ANEURALNETWORKS_UNEXPECTED_NULL); + + // This should fail, since the operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 999, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since the operand does not exist. + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, -1, nullptr, + memory, 0, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since memory is not the size of a float32. + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, + memory, 0, memorySize), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since offset is larger than memorySize. + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, + memory, memorySize + 1, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); + + // This should fail, since requested size is larger than the memory. + EXPECT_EQ(ANeuralNetworksExecution_setOutputFromMemory(execution, 0, nullptr, + memory, memorySize - 3, sizeof(float)), + ANEURALNETWORKS_BAD_DATA); +} + +TEST_F(ValidationTestExecution, StartCompute) { + ANeuralNetworksExecution* execution; + EXPECT_EQ(ANeuralNetworksExecution_create(mCompilation, &execution), ANEURALNETWORKS_NO_ERROR); + + ANeuralNetworksEvent* event; + EXPECT_EQ(ANeuralNetworksExecution_startCompute(nullptr, &event), + ANEURALNETWORKS_UNEXPECTED_NULL); + EXPECT_EQ(ANeuralNetworksExecution_startCompute(execution, nullptr), + ANEURALNETWORKS_UNEXPECTED_NULL); +} + +TEST_F(ValidationTestExecution, EventWait) { + EXPECT_EQ(ANeuralNetworksEvent_wait(nullptr), ANEURALNETWORKS_UNEXPECTED_NULL); +} +} // namespace diff --git a/tests/scripts/README.md b/tests/scripts/README.md new file mode 100644 index 000000000..66a197d51 --- /dev/null +++ b/tests/scripts/README.md @@ -0,0 +1,63 @@ +# How the test driver works + +## Unittest +- There are two kinds of unittest: + - Kernel ACL + - Runtime +- Related file : `run_unittest.sh` +- Usage : +``` +$ ./tests/scripts/test_driver.sh \ + --artifactpath=. \ + --unittest +``` +- The `run_unittest.sh` usage : + +``` +$ LD_LIBRARY_PATH=Product/out/lib \ + ./tests/scripts/run_unittest.sh \ + --reportdir=report \ + --unittestdir=Product/out/unittest +``` + +### Kernel ACL Unittest +- Test whether the various operations are performed successfully and whether the output and the expected value are the same. +- TC location : `libs/kernel/acl/src/` + +### Runtime Unittest +- Test whether the expected value and the actual output value are the same when the model is configured, compiled and executed. +- TC location : `runtimes/tests/neural_networks_test/` + +## Framework test +- Execute the **tflite model** using the given **driver**. +- There is a TC directory for each model, and a `config.sh` file exists in each TC directory. +- When `run_test.sh`, refer to the **tflite model** information in `config.sh`, download the file, and run the **tflite model** with the given **driver**. +- Related files : `run_test.sh` and `run_frameworktest.sh` +- TC location : + - `tests/framework/tests/` : Config directory for TC + - `tests/framework/cache/` : TC (Downloaded tflite model files) + +### Run tflite_run with various tflite models +- Driver : `tflite_run` +- Driver source location : `tools/tflite_run/` +- Usage : +``` +$ ./tests/scripts/test_driver.sh \ + --artifactpath=. \ + --frameworktest +``` +- Related pages : [tflite_run](https://github.sec.samsung.net/STAR/nnfw/tree/master/tools/tflite_run) + +### Run nnapi_test with various tflite models +- `nnapi_test` runs tflite in two ways and compares the result: + 1. tflite interpreter + 2. `libneuralnetworks.so`, which could be PureACL or NNAPI depending on `--ldlibrarypath`(`LD_LIBRARY_PATH`) +- Driver : `nnapi_test` +- Driver source location : `tools/nnapi_test/` +- Usage : +``` +$ ./tests/scripts/test_driver.sh \ + --artifactpath=. \ + --verification . +``` + diff --git a/tools/test_driver/common.sh b/tests/scripts/common.sh index 12a35fd35..12a35fd35 100755 --- a/tools/test_driver/common.sh +++ b/tests/scripts/common.sh diff --git a/tests/scripts/neurun_frameworktest_list.txt b/tests/scripts/neurun_frameworktest_list.txt new file mode 100644 index 000000000..9c4e97dac --- /dev/null +++ b/tests/scripts/neurun_frameworktest_list.txt @@ -0,0 +1,9 @@ +average_pool_2d +concat/concat1 +concat/concat2 +conv_2d +fullyconnected/fc1 +max_pool_2d +softmax +reshape/reshape1 +MODELS/inception_module diff --git a/tools/test_driver/nnfw_kernel_env_list.txt b/tests/scripts/nnfw_kernel_env_list.txt index 9cb3fc46c..9cb3fc46c 100644 --- a/tools/test_driver/nnfw_kernel_env_list.txt +++ b/tests/scripts/nnfw_kernel_env_list.txt diff --git a/tools/test_driver/print_to_json.sh b/tests/scripts/print_to_json.sh index a5ea5689b..a5ea5689b 100755 --- a/tools/test_driver/print_to_json.sh +++ b/tests/scripts/print_to_json.sh diff --git a/tools/test_driver/py/common.py b/tests/scripts/py/common.py index 37b40e1ce..37b40e1ce 100755 --- a/tools/test_driver/py/common.py +++ b/tests/scripts/py/common.py diff --git a/tools/test_driver/py/run_frameworktest.py b/tests/scripts/py/run_frameworktest.py index a4fbd075a..a4fbd075a 100755 --- a/tools/test_driver/py/run_frameworktest.py +++ b/tests/scripts/py/run_frameworktest.py diff --git a/tools/test_driver/py/run_unittest.py b/tests/scripts/py/run_unittest.py index 2e2ad3434..2e2ad3434 100755 --- a/tools/test_driver/py/run_unittest.py +++ b/tests/scripts/py/run_unittest.py diff --git a/tests/scripts/py/test_driver.py b/tests/scripts/py/test_driver.py new file mode 100755 index 000000000..f9c48e11f --- /dev/null +++ b/tests/scripts/py/test_driver.py @@ -0,0 +1,398 @@ +#!/usr/bin/env python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +import argparse +import common +import subprocess +import sys + +mypath = os.path.abspath(os.path.dirname(__file__)) + + +def get_parsed_options(): + parser = argparse.ArgumentParser(prog='test_driver.py', usage='%(prog)s [options]') + + # artifactpath + parser.add_argument( + "--artifactpath", + action="store", + type=str, + dest="artifactpath", + default=".", + help="(should be passed) path that has tests/ and Product/") + + # test + parser.add_argument( + "--unittest", + action="store_true", + dest="unittest_on", + default=False, + help="(default=on) run unit test") + parser.add_argument( + "--unittestall", + action="store_true", + dest="unittestall_on", + default=False, + help="((default=off) run all unit test without skip, overrite --unittest option") + parser.add_argument( + "--verification", + action="store_true", + dest="verification_on", + default=False, + help="(default=on) run verification") + parser.add_argument( + "--frameworktest", + action="store_true", + dest="frameworktest_on", + default=False, + help="(default=off)run framework test") + + # benchmark + parser.add_argument( + "--benchmark", + action="store_true", + dest="benchmark_on", + default=False, + help="(default=off) run benchmark") + parser.add_argument( + "--benchmark_acl", + action="store_true", + dest="benchmarkacl_on", + default=False, + help="(default=off) run benchmark-acl") + parser.add_argument( + "--benchmark_op", + action="store_true", + dest="benchmarkop_on", + default=False, + help="(default=off) run benchmark per operation") + + # profile + parser.add_argument( + "--profile", + action="store_true", + dest="profile_on", + default=False, + help="(default=off) run profiling") + + # driverbin + parser.add_argument( + "--framework_driverbin", + action="store", + type=str, + dest="framework_driverbin", + help= + "(default=../../Product/out/bin/tflite_run) runner for runnning framework tests") + parser.add_argument( + "--verification_driverbin", + action="store", + type=str, + dest="verification_driverbin", + help= + "(default=../../Product/out/bin/nnapi_test) runner for runnning verification tests" + ) + parser.add_argument( + "--benchmark_driverbin", + action="store", + type=str, + dest="benchmark_driverbin", + help= + "(default=../../Product/out/bin/tflite_benchmark) runner for runnning benchmark") + + # etc. + parser.add_argument( + "--runtestsh", + action="store", + type=str, + dest="runtestsh", + help= + "(default=ARTIFACT_PATH/tests/framework/run_test.sh) run_test.sh with path where it is for framework test and verification" + ) + parser.add_argument( + "--unittestdir", + action="store", + type=str, + dest="unittestdir", + help= + "(default=ARTIFACT_PATH/Product/out/unittest) directory that has unittest binaries for unit test" + ) + parser.add_argument( + "--ldlibrarypath", + action="store", + type=str, + dest="ldlibrarypath", + help= + "(default=ARTIFACT_PATH/Product/out/lib) path that you want to include libraries") + parser.add_argument( + "--frameworktest_list_file", + action="store", + type=str, + dest="frameworktest_list_file", + help= + "(default=ARTIFACT_PATH/tests/scripts/pureacl_frameworktest_list.txt) filepath of model list for test" + ) + parser.add_argument( + "--reportdir", + action="store", + type=str, + dest="reportdir", + help="(default=ARTIFACT_PATH/report) directory to save report") + + # env + parser.add_argument( + "--usennapi", + action="store_true", + dest="usennapi_on", + default=True, + help="(default=on) declare USE_NNAPI=1") + parser.add_argument( + "--nousennapi", + action="store_false", + dest="usennapi_on", + help="(default=off) declare nothing about USE_NNAPI") + parser.add_argument( + "--acl_envon", + action="store_true", + dest="aclenv_on", + default=False, + help="(default=off) declare envs for ACL") + + options = parser.parse_args() + return options + + +def run_unittest(options): + cmd = "{artifactpath}/tests/scripts/run_unittest.sh \ + --reportdir={reportdir} \ + --unittestdir={unittestdir}".format( + artifactpath=options.artifactpath, + reportdir=options.reportdir, + unittestdir=options.unittestdir) + if options.unittestall_on: + cmd += " --runall" + os.system(cmd) + + +def run_frameworktest(options): + if type(options.framework_driverbin) is not str: + options.framework_driverbin = options.artifactpath + "/Product/out/bin/tflite_run" + if (os.path.exists(options.framework_driverbin) == False): + print("Cannot find {driverbin}".format(driverbin=options.framework_driverbin)) + sys.exit(1) + + cmd = "{artifactpath}/tests/scripts/run_frameworktest.sh \ + --runtestsh={runtestsh} \ + --driverbin={driverbin} \ + --reportdir={reportdir} \ + --tapname=framework_test.tap \ + --logname=framework_test.log \ + --testname='Frameworktest'".format( + runtestsh=options.runtestsh, + driverbin=options.framework_driverbin, + reportdir=options.reportdir, + artifactpath=options.artifactpath) + os.system(cmd) + + +def run_verification(options): + if type(options.verification_driverbin) is not str: + options.verification_driverbin = options.artifactpath + "/Product/out/bin/nnapi_test" + if (os.path.exists(options.verification_driverbin) == False): + print("Cannot find {driverbin}".format( + driverbin=options.verification_driverbin)) + sys.exit(1) + + cmd = "{artifactpath}/tests/scripts/run_frameworktest.sh \ + --runtestsh={runtestsh} \ + --driverbin={driverbin} \ + --reportdir={reportdir} \ + --tapname=verification_test.tap \ + --logname=verification_test.log \ + --testname='Verification'".format( + runtestsh=options.runtestsh, + driverbin=options.verification_driverbin, + reportdir=options.reportdir, + artifactpath=options.artifactpath) + os.system(cmd) + + +def run_benchmark(options): + if type(options.benchmark_driverbin) is not str: + options.benchmark_driverbin = options.artifactpath + "/Product/out/bin/tflite_benchmark" + if (os.path.exists(options.benchmark_driverbin) == False): + print("Cannot find {driverbin}".format(driverbin=options.benchmark_driverbin)) + sys.exit(1) + + cmd = "{artifactpath}/tests/scripts/run_benchmark.sh \ + --runtestsh={runtestsh} \ + --driverbin={driverbin} \ + --reportdir={reportdir}/benchmark".format( + runtestsh=options.runtestsh, + driverbin=options.benchmark_driverbin, + reportdir=options.reportdir, + artifactpath=options.artifactpath) + os.system(cmd) + + +def run_benchmarkop(options): + if type(options.benchmark_driverbin) is not str: + options.benchmark_driverbin = options.artifactpath + "/Product/out/bin/tflite_benchmark" + if (os.path.exists(options.benchmark_driverbin) == False): + print("Cannot find {driverbin}".format(driverbin=options.benchmark_driverbin)) + sys.exit(1) + + cmd = "{artifactpath}/tests/scripts/run_benchmark_op.sh \ + --runtestsh={runtestsh} \ + --driverbin={driverbin} \ + --reportdir={reportdir}/benchmark_op \ + --modelfilepath={artifactpath}/tests/framework \ + --frameworktest_list_file={frameworktest_list_file}".format( + runtestsh=options.runtestsh, + driverbin=options.benchmark_driverbin, + artifactpath=options.artifactpath, + reportdir=options.reportdir, + frameworktest_list_file=options.frameworktest_list_file) + os.system(cmd) + + +def run_benchmarkacl(options): + cmd = "{artifactpath}/tests/scripts/run_benchmark_acl.sh \ + --reportdir={reportdir}/benchmark \ + --bindir={artifactpath}/Product/out/bin".format( + reportdir=options.reportdir, artifactpath=options.artifactpath) + os.system(cmd) + + +def make_json_for_benchmark_result(options): + cmd = "source {artifactpath}/tests/scripts/print_to_json.sh && ".format( + artifactpath=options.artifactpath) + if options.benchmarkop_on: + cmd += "print_to_json {artifactpath}/report/benchmark_op \ + {reportdir} \"benchmark_op_result.json\"".format( + reportdir=options.reportdir, artifactpath=options.artifactpath) + else: + cmd += "print_to_json {artifactpath}/report/benchmark \ + {reportdir} \"benchmark_result.json\"".format( + reportdir=options.reportdir, artifactpath=options.artifactpath) + sp = subprocess.Popen(["/bin/bash", "-i", "-c", cmd]) + sp.communicate() + + +def run_profile(options): + # FIXME: These driver and tflite test are set temporarily. Fix these to support flexibility + driver_bin = options.artifactpath + "/Product/out/bin/tflite_run" + tflite_test = options.artifactpath + "/tests/framework/cache/inceptionv3/inception_module/inception_test.tflite" + + # TODO: Enable operf to set directory where sample data puts on + shutil.rmtree("oprofile_data", ignore_errors=True) + + print("") + print("============================================") + cmd = "operf -g {driver_bin} {tflite_test}".format( + driver_bin=driver_bin, tflite_test=tflite_test) + os.system(cmd) + print("============================================") + print("") + + +def main(): + options = get_parsed_options() + + alltest_on = True + if True in [ + options.unittest_on, options.frameworktest_on, options.verification_on, + options.benchmark_on, options.benchmarkacl_on, options.benchmarkop_on, + options.profile_on + ]: + alltest_on = False + + # artifactpath + if os.path.isdir(options.artifactpath) and os.path.isdir( + options.artifactpath + "/tests") and os.path.isdir(options.artifactpath + + "/Product"): + options.artifactpath = os.path.abspath(options.artifactpath) + else: + print("Pass on with proper arifactpath") + sys.exit(1) + + # run_test.sh + if type(options.runtestsh) is not str or options.runtestsh == "": + options.runtestsh = options.artifactpath + "/tests/framework/run_test.sh" + + if (os.path.exists(options.runtestsh) == False): + print("Cannot find {runtestsh}".format(runtestsh=options.runtestsh)) + sys.exit(1) + + # unittest dir + if type(options.unittestdir) is not str or options.unittestdir == "": + options.unittestdir = options.artifactpath + "/Product/out/unittest" + + # LD_LIBRARY_PATH + if type(options.ldlibrarypath) is not str or options.ldlibrarypath == "": + options.ldlibrarypath = options.artifactpath + "/Product/out/lib" + + # report dir + if type(options.reportdir) is not str or options.reportdir == "": + options.reportdir = options.artifactpath + "/report" + + # set LD_LIBRARY_PATH + os.environ["LD_LIBRARY_PATH"] = options.ldlibrarypath + + # set USE_NNAPI + if options.usennapi_on == True: + os.environ["USE_NNAPI"] = "1" + + # set acl + if options.aclenv_on: + common.switch_nnfw_kernel_env("acl") + + # unittest + if alltest_on or options.unittest_on: + run_unittest(options) + + # frameworktest + if options.frameworktest_on: + run_frameworktest(options) + + # verification + if alltest_on or options.verification_on: + run_verification(options) + + # benchmark + if options.benchmark_on: + run_benchmark(options) + + # benchmark_acl + if options.benchmarkacl_on: + run_benchmarkacl(options) + + # benchmark_op + if options.benchmarkop_on: + run_benchmarkop(options) + + # make json file for benchmark result on ci + if options.benchmark_on or options.benchmarkacl_on or options.benchmarkop_on: + make_json_for_benchmark_result(options) + + # run profile + if options.profile_on: + run_profile(options) + + +if __name__ == "__main__": + main() diff --git a/tests/scripts/run_benchmark.sh b/tests/scripts/run_benchmark.sh new file mode 100755 index 000000000..6980475b6 --- /dev/null +++ b/tests/scripts/run_benchmark.sh @@ -0,0 +1,146 @@ +#!/bin/bash +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +MY_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" + +source $MY_PATH/common.sh + +BENCHMARK_RUN_TEST_SH= +BENCHMARK_DRIVER_BIN= +BENCHMARK_REPORT_DIR= +BENCHMARK_MODELS_FILE= +BENCHMARK_MODEL_LIST="MODELS/inception_nonslim MODELS/inception_slim MODELS/mobilenet" + +function Usage() +{ + # TODO: Fill this + echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report" +} + +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --runtestsh=*) + BENCHMARK_RUN_TEST_SH=${i#*=} + ;; + --driverbin=*) + BENCHMARK_DRIVER_BIN=${i#*=} + ;; + --reportdir=*) + BENCHMARK_REPORT_DIR=${i#*=} + BENCHMARK_MODELS_FILE=$BENCHMARK_REPORT_DIR/benchmark_models.txt + ;; + esac + shift +done + +function get_result_of_benchmark_test() +{ + local RUN_TEST_SH=$1 + local DRIVER_BIN=$2 + local MODEL=$3 + local LOG_FILE=$4 + + local RET=0 + $RUN_TEST_SH --driverbin=$DRIVER_BIN $MODEL > $LOG_FILE 2>&1 + RET=$? + if [[ $RET -ne 0 ]]; then + echo "Testing $MODEL aborted... exit code: $RET" + exit $RET + fi + + local RESULT=`grep -E '^Mean:' $LOG_FILE | sed -e 's/ms//g' | awk '{print $2}'` + echo "$RESULT" +} + +function print_result_of_benchmark_test() +{ + local NAME=$1 + local RESULT=$2 + local RESULT_FILE=$3 + + echo "$NAME $RESULT" > $RESULT_FILE +} + +function run_benchmark_test() +{ + local DRIVER_BIN=$BENCHMARK_DRIVER_BIN + local LOG_FILE= + local RESULT_FILE= + local RESULT= + local REPORT_MODEL_DIR= + + export COUNT=5 + echo "============================================" + local i=0 + for MODEL in $BENCHMARK_MODEL_LIST; do + echo "Benchmark test with `basename $DRIVER_BIN` & `echo $MODEL`" + echo $MODEL >> $BENCHMARK_MODELS_FILE + + REPORT_MODEL_DIR=$BENCHMARK_REPORT_DIR/$MODEL + mkdir -p $REPORT_MODEL_DIR + + # TFLite+CPU + LOG_FILE=$REPORT_MODEL_DIR/tflite_cpu.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_cpu.result + echo -n "TFLite + CPU................... " + unset USE_NNAPI + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "TFLite_CPU" $RESULT $RESULT_FILE + + # TFLite+NNAPI(CPU fallback) + LOG_FILE=$REPORT_MODEL_DIR/tflite_nnapi_cpu.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnapi_cpu.result + echo -n "TFLite + NNAPI(CPU)............ " + export USE_NNAPI=1 + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "TFLite_NNAPI_CPU" $RESULT $RESULT_FILE + + # TFLite+NNAPI(ACL) + LOG_FILE=$REPORT_MODEL_DIR/tflite_nnapi_acl.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnapi_acl.result + echo -n "TFLite + NNAPI(ACL)............ " + switch_nnfw_kernel_env "ON" "acl" + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "TFLite_NNAPI_ACL" $RESULT $RESULT_FILE + unset USE_NNAPI + switch_nnfw_kernel_env "OFF" + + if [[ $i -ne $(echo $BENCHMARK_MODEL_LIST | wc -w)-1 ]]; then + echo "" + fi + i=$((i+1)) + done + echo "============================================" + unset COUNT +} + +if [ ! -e "$BENCHMARK_REPORT_DIR" ]; then + mkdir -p $BENCHMARK_REPORT_DIR +fi + +rm -rf $BENCHMARK_MODELS_FILE + +echo "" +run_benchmark_test +echo "" diff --git a/tools/test_driver/run_benchmark_acl.sh b/tests/scripts/run_benchmark_acl.sh index c6a643baa..c6a643baa 100755 --- a/tools/test_driver/run_benchmark_acl.sh +++ b/tests/scripts/run_benchmark_acl.sh diff --git a/tests/scripts/run_benchmark_op.sh b/tests/scripts/run_benchmark_op.sh new file mode 100755 index 000000000..6576d1287 --- /dev/null +++ b/tests/scripts/run_benchmark_op.sh @@ -0,0 +1,197 @@ +#!/bin/bash +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +MY_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +NNFW_HOME="$(dirname $(dirname ${MY_PATH}))" +source $MY_PATH/common.sh + +BENCHMARK_RUN_TEST_SH= +BENCHMARK_DRIVER_BIN= +BENCHMARK_REPORT_DIR= +BENCHMARK_MODELS_FILE= +BENCHMARK_MODEL_LIST= +MODEL_CACHE_ROOT_PATH= +MODEL_TEST_ROOT_PATH= +PURE_ACL_RT_LIB_PATH= +PURE_LD_LIBRARY_PATH= +ORIGIN_LD_LIBRARY_PATH= +PURE_ACL_RT_ENV_FILE=$MY_PATH/benchmark_op_list.txt + +function Usage() +{ + # TODO: Fill this + echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report" +} + +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --runtestsh=*) + BENCHMARK_RUN_TEST_SH=${i#*=} + ;; + --driverbin=*) + BENCHMARK_DRIVER_BIN=${i#*=} + ;; + --reportdir=*) + BENCHMARK_REPORT_DIR=${i#*=} + BENCHMARK_MODELS_FILE=$BENCHMARK_REPORT_DIR/benchmark_op_models.txt + ;; + --modelfilepath=*) + TEST_LIST_PATH=${i#*=} + MODEL_CACHE_ROOT_PATH=$TEST_LIST_PATH/cache + MODEL_TEST_ROOT_PATH=$TEST_LIST_PATH/tests + ;; + esac + shift +done + +function get_result_of_benchmark_test() +{ + local RUN_TEST_SH=$1 + local DRIVER_BIN=$2 + local MODEL=$3 + local LOG_FILE=$4 + local PUREACL_LD_LIBRARY_PATH=$5 + + local RET=0 + $RUN_TEST_SH --driverbin=$DRIVER_BIN --ldlibrarypath=$PUREACL_LD_LIBRARY_PATH $MODEL > $LOG_FILE 2>&1 + RET=$? + if [[ $RET -ne 0 ]]; then + echo "Testing $MODEL aborted... exit code: $RET" + exit $RET + fi + + local RESULT=`grep -E '^Mean:' $LOG_FILE | sed -e 's/ms//g' | awk '{print $2}'` + echo "$RESULT" +} + +function print_result_of_benchmark_test() +{ + local NAME=$1 + local RESULT=$2 + local RESULT_FILE=$3 + echo "$NAME $RESULT" > $RESULT_FILE +} + +function get_benchmark_op_list() +{ + local TEST_DIRS="$@" + local TESTS_TO_RUN="" + + if [[ $# -eq 0 ]]; then + TEST_DIRS="." + fi + + shift $# + + pushd $MODEL_TEST_ROOT_PATH > /dev/null + for DIR in $TEST_DIRS; do + if [ -d "$DIR" ]; then + TESTS_FOUND=$(find "$DIR" -type f -name 'config.sh' -exec dirname {} \;| sed 's|^./||' | grep -v '^MODELS/' | sort) + TESTS_TO_RUN="$TESTS_TO_RUN $TESTS_FOUND" + fi + done + popd > /dev/null + + BENCHMARK_MODEL_LIST=$(echo "${TESTS_TO_RUN}") +} + +function run_benchmark_test() +{ + local DRIVER_BIN=$BENCHMARK_DRIVER_BIN + local LOG_FILE= + local RESULT_FILE= + local RESULT= + local REPORT_MODEL_DIR= + local PAUSE_TIME_IN_SEC=15 + + export COUNT=5 + echo "============================================" + local i=0 + for MODEL in $BENCHMARK_MODEL_LIST; do + STATUS="enabled" + source $MODEL_TEST_ROOT_PATH/$MODEL/config.sh + + echo "Benchmark test with `basename $DRIVER_BIN` & `echo $MODEL`" + echo $MODEL >> $BENCHMARK_MODELS_FILE + + REPORT_MODEL_DIR=$BENCHMARK_REPORT_DIR/$MODEL + mkdir -p $REPORT_MODEL_DIR + + # TFLite Interpreter + LOG_FILE=$REPORT_MODEL_DIR/tflite_cpu_op.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_cpu_op.result + echo -n "TFLite Interpreter............. " + unset USE_NNAPI + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "TFLite_Interpreter" $RESULT $RESULT_FILE + sleep $PAUSE_TIME_IN_SEC + + # PureACL-Neon + LOG_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_neon_op.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_neon_op.result + echo -n "PureACL-Neon................... " + export USE_NNAPI=1 + export NEON=1 + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "PureACL_Neon" $RESULT $RESULT_FILE + sleep $PAUSE_TIME_IN_SEC + + # PureACL-CL + LOG_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_opencl_op.txt + RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_opencl_op.result + export USE_NNAPI=1 + unset NEON + echo -n "PureACL-CL..................... " + RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) + echo "$RESULT ms" + print_result_of_benchmark_test "PureACL-CL" $RESULT $RESULT_FILE + sleep $PAUSE_TIME_IN_SEC + + # Clean Up + unset USE_NNAPI NEON + + if [[ $i -ne $(echo $BENCHMARK_MODEL_LIST | wc -w)-1 ]]; then + echo "" + fi + i=$((i+1)) + done + unset USE_NNAPI + unset COUNT + echo "============================================" +} + +if [ ! -e "$BENCHMARK_REPORT_DIR" ]; then + mkdir -p $BENCHMARK_REPORT_DIR +fi + +if [ -z "$PURE_ACL_RT_LIB_PATH" ]; then + PURE_ACL_RT_LIB_PATH="$NNFW_HOME/Product/out/lib/pureacl" +fi + +get_benchmark_op_list + +rm -rf $BENCHMARK_MODELS_FILE + +echo "" +run_benchmark_test +echo "" diff --git a/tools/test_driver/run_benchmark_tflite_model.in b/tests/scripts/run_benchmark_tflite_model.in index 1003ecc13..1003ecc13 100644 --- a/tools/test_driver/run_benchmark_tflite_model.in +++ b/tests/scripts/run_benchmark_tflite_model.in diff --git a/tools/test_driver/run_benchmark_tflite_model.sh b/tests/scripts/run_benchmark_tflite_model.sh index 50a2a5fb3..50a2a5fb3 100755 --- a/tools/test_driver/run_benchmark_tflite_model.sh +++ b/tests/scripts/run_benchmark_tflite_model.sh diff --git a/tools/test_driver/run_frameworktest.sh b/tests/scripts/run_frameworktest.sh index 343fd0292..343fd0292 100755 --- a/tools/test_driver/run_frameworktest.sh +++ b/tests/scripts/run_frameworktest.sh diff --git a/tests/scripts/run_unittest.sh b/tests/scripts/run_unittest.sh new file mode 100755 index 000000000..5eddfb32c --- /dev/null +++ b/tests/scripts/run_unittest.sh @@ -0,0 +1,109 @@ +#!/bin/bash +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +UNITTEST_REPORT_DIR= +UNITTEST_TEST_DIR= +UNITTEST_RESULT=0 +UNITTEST_RUN_ALL="" + +function Usage() +{ + # TODO: Fill this + echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report --unittestdir=Product/out/bin" +} + +get_gtest_option() +{ + local output_option="--gtest_output=xml:$UNITTEST_REPORT_DIR/$TEST_BIN.xml" + local filter_option + if [ -r "$UNITTEST_TEST_DIR/$TEST_BIN.skip" ]; then + filter_option="--gtest_filter=-$(grep -v '#' "$UNITTEST_TEST_DIR/$TEST_BIN.skip" | tr '\n' ':')" + fi + echo "$output_option $filter_option" +} + +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --reportdir=*) + UNITTEST_REPORT_DIR=${i#*=} + ;; + --unittestdir=*) + UNITTEST_TEST_DIR=${i#*=} + ;; + --runall) + UNITTEST_RUN_ALL="true" + esac + shift +done + +# TODO: handle exceptions for params + +if [ ! -e "$UNITTEST_REPORT_DIR" ]; then + mkdir -p $UNITTEST_REPORT_DIR +fi + +echo "" +echo "============================================" +echo "Unittest start" +echo "============================================" + +num_unittest=0 +for TEST_BIN in `ls $UNITTEST_TEST_DIR --hide=*.skip*`; do + num_unittest=$((num_unittest+1)) + echo "============================================" + echo "Starting set $num_unittest: $TEST_BIN..." + echo "============================================" + TEMP_UNITTEST_RESULT=0 + + if [ "$UNITTEST_RUN_ALL" == "true" ]; then + for TEST_LIST_VERBOSE_LINE in $($UNITTEST_TEST_DIR/$TEST_BIN --gtest_list_tests); do + if [[ $TEST_LIST_VERBOSE_LINE == *\. ]]; then + TEST_LIST_CATEGORY=$TEST_LIST_VERBOSE_LINE + else + TEST_LIST_ITEM="$TEST_LIST_CATEGORY""$TEST_LIST_VERBOSE_LINE" + $UNITTEST_TEST_DIR/$TEST_BIN --gtest_filter=$TEST_LIST_ITEM --gtest_output="xml:$UNITTEST_REPORT_DIR/$TEST_LIST_ITEM.xml" + fi + done + else + $UNITTEST_TEST_DIR/$TEST_BIN $(get_gtest_option) + TEMP_UNITTEST_RESULT=$? + fi + + if [[ $TEMP_UNITTEST_RESULT -ne 0 ]]; then + UNITTEST_RESULT=$TEMP_UNITTEST_RESULT + echo "$TEST_BIN failed... return code: $TEMP_UNITTEST_RESULT" + fi + echo "============================================" + echo "Finishing set $num_unittest: $TEST_BIN..." + echo "============================================" +done + +if [[ $UNITTEST_RESULT -ne 0 ]]; then + echo "============================================" + echo "Failed unit test... exit code: $UNITTEST_RESULT" + echo "============================================" + exit $UNITTEST_RESULT +fi + +echo "============================================" +echo "Completed total $num_unittest set of unittest" +echo "Unittest end" +echo "============================================" diff --git a/tests/scripts/test_driver.sh b/tests/scripts/test_driver.sh new file mode 100755 index 000000000..97a3dab27 --- /dev/null +++ b/tests/scripts/test_driver.sh @@ -0,0 +1,328 @@ +#!/bin/bash +# +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e +# NOTE: Supposed that this script would be executed with an artifact path. +# The artifact path has tests/(test suite) and Product/ +# Reference this PR(https://github.sec.samsung.net/STAR/nnfw/pull/375). + +function Usage() +{ + echo "Usage: ./$0 --artifactpath=. # run all tests" + echo "Usage: ./$0 --artifactpath=/home/dragon/nnfw --frameworktest --verification --benchmark # run fw test & verfication and benchmark" + echo "" + echo "--artifactpath - (default={test_driver.sh's path}/../../) it should contain tests/ and Product/" + echo "" + echo "Following options are needed when you want to tests of specific types. If you don't pass any one, unittest and verification will be run" + echo "--unittest - (default=on) run unit test" + echo "--unittestall - (default=off) run all unit test without skip, overrite --unittest option" + echo "--frameworktest - (default=off) run framework test" + echo "--verification - (default=on) run verification" + echo "--frameworktest_list_file - filepath of model list for test" + echo "" + echo "Following option is only needed when you want to test benchmark." + echo "--benchmark_acl - (default=off) run benchmark-acl" + echo "--benchmark - (default=off) run benchmark" + echo "--benchmark_op - (default=off) run benchmark per operation" + echo "--benchmark_tflite_model - (default=off) run tflite_benchmark_model" + echo "" + echo "Following option is used for profiling." + echo "--profile - (default=off) run operf" + echo "" + echo "etc." + echo "--framework_driverbin - (default=../../Product/out/bin/tflite_run) runner for runnning framework tests" + echo "--verification_driverbin - (default=../../Product/out/bin/nnapi_test) runner for runnning verification tests" + echo "--benchmark_driverbin - (default=../../Product/out/bin/tflite_benchmark) runner for runnning benchmark" + echo "--runtestsh - (default=\$ARTIFACT_PATH/tests/framework/run_test.sh) run_test.sh with path where it is for framework test and verification" + echo "--unittestdir - (default=\$ARTIFACT_PATH/Product/out/unittest) directory that has unittest binaries for unit test" + echo "" + echo "--ldlibrarypath - (default=\$ARTIFACT_PATH/Product/out/lib) path that you want to include libraries" + echo "--usennapi - (default=on) declare USE_NNAPI=1" + echo "--nousennapi - (default=off) declare nothing about USE_NNAPI" + echo "--acl_envon - (default=off) declare envs for ACL" + echo "--reportdir - (default=\$ARTIFACT_PATH/report) directory to save report" + echo "" +} + +TEST_DRIVER_DIR="$( cd "$( dirname "${BASH_SOURCE}" )" && pwd )" +ARTIFACT_PATH="$TEST_DRIVER_DIR/../../" +FRAMEWORK_DRIVER_BIN="" +VERIFICATION_DRIVER_BIN="" +BENCHMARK_DRIVER_BIN="" +RUN_TEST_SH="" +UNIT_TEST_DIR="" +LD_LIBRARY_PATH_IN_SHELL="" +USE_NNAPI="USE_NNAPI=1" +ALLTEST_ON="true" +UNITTEST_ON="false" +UNITTESTALL_ON="false" +FRAMEWORKTEST_ON="false" +VERIFICATION_ON="false" +BENCHMARK_ON="false" +BENCHMARK_OP_ON="false" +BENCHMARK_TFLITE_MODEL_ON="false" +BENCHMARK_ACL_ON="false" +ACL_ENV_ON="false" +PROFILE_ON="false" +REPORT_DIR="" + +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --artifactpath=*) + ARTIFACT_PATH=${i#*=} + ;; + --framework_driverbin=*) + FRAMEWORK_DRIVER_BIN=${i#*=} + ;; + --verification_driverbin=*) + VERIFICATION_DRIVER_BIN=${i#*=} + ;; + --benchmark_driverbin=*) + BENCHMARK_DRIVER_BIN=${i#*=} + ;; + --runtestsh=*) + RUN_TEST_SH=${i#*=} + ;; + --unittestdir=*) + UNIT_TEST_DIR=${i#*=} + ;; + --ldlibrarypath=*) + LD_LIBRARY_PATH_IN_SHELL=${i#*=} + ;; + --usennapi) + USE_NNAPI="USE_NNAPI=1" + ;; + --nousennapi) + USE_NNAPI="" + ;; + --unittest) + ALLTEST_ON="false" + UNITTEST_ON="true" + ;; + --unittestall) + ALLTEST_ON="false" + UNITTEST_ON="true" + UNITTESTALL_ON="true" + ;; + --frameworktest) + ALLTEST_ON="false" + FRAMEWORKTEST_ON="true" + ;; + --frameworktest_list_file=*) + FRAMEWORKTEST_LIST_FILE=$PWD/${i#*=} + if [ ! -e "$FRAMEWORKTEST_LIST_FILE" ]; then + echo "Pass on with proper frameworktest_list_file" + exit 1 + fi + ;; + --verification) + ALLTEST_ON="false" + VERIFICATION_ON="true" + ;; + --benchmark) + ALLTEST_ON="false" + BENCHMARK_ON="true" + ;; + --benchmark_op) + ALLTEST_ON="false" + BENCHMARK_OP_ON="true" + ;; + --benchmark_tflite_model) + ALLTEST_ON="false" + BENCHMARK_TFLITE_MODEL_ON="true" + ;; + --benchmark_acl) + ALLTEST_ON="false" + BENCHMARK_ACL_ON="true" + ;; + --acl_envon) + ACL_ENV_ON="true" + ;; + --profile) + ALLTEST_ON="false" + PROFILE_ON="true" + ;; + --reportdir=*) + REPORT_DIR=${i#*=} + ;; + *) + # Be careful that others params are handled as $ARTIFACT_PATH + ARTIFACT_PATH="$i" + ;; + esac + shift +done + +ARTIFACT_PATH="$(readlink -f $ARTIFACT_PATH)" + +if [ -z "$RUN_TEST_SH" ]; then + RUN_TEST_SH=$ARTIFACT_PATH/tests/framework/run_test.sh +fi + +if [ ! -e "$RUN_TEST_SH" ]; then + echo "Cannot find $RUN_TEST_SH" + exit 1 +fi + +if [ -z "$UNIT_TEST_DIR" ]; then + UNIT_TEST_DIR=$ARTIFACT_PATH/Product/out/unittest +fi + +if [ -z "$REPORT_DIR" ]; then + REPORT_DIR=$ARTIFACT_PATH/report +fi + +if [ -z "$LD_LIBRARY_PATH_IN_SHELL" ]; then + LD_LIBRARY_PATH="$ARTIFACT_PATH/Product/out/lib:$LD_LIBRARY_PATH" +else + LD_LIBRARY_PATH="$LD_LIBRARY_PATH_IN_SHELL:$LD_LIBRARY_PATH" +fi + +# Set env +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH +if [ -n "$USE_NNAPI" ]; then + export "$USE_NNAPI" +fi + +source $TEST_DRIVER_DIR/common.sh + +if [ "$ACL_ENV_ON" == "true" ]; then + switch_nnfw_kernel_env "ON" "acl" +fi + +# Run unittest in each part such as Runtime, ACL +if [ "$ALLTEST_ON" == "true" ] || [ "$UNITTEST_ON" == "true" ]; then + if [ "$UNITTESTALL_ON" == "true" ]; then + $TEST_DRIVER_DIR/run_unittest.sh \ + --reportdir=$REPORT_DIR \ + --unittestdir=$UNIT_TEST_DIR \ + --runall + else + $TEST_DRIVER_DIR/run_unittest.sh \ + --reportdir=$REPORT_DIR \ + --unittestdir=$UNIT_TEST_DIR + fi +fi + +# Run tflite_run with various tflite models +if [ "$FRAMEWORKTEST_ON" == "true" ]; then + if [ -z "$FRAMEWORK_DRIVER_BIN" ]; then + FRAMEWORK_DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_run + fi + + $TEST_DRIVER_DIR/run_frameworktest.sh \ + --runtestsh=$RUN_TEST_SH \ + --driverbin=$FRAMEWORK_DRIVER_BIN \ + --reportdir=$REPORT_DIR \ + --tapname=framework_test.tap \ + --logname=framework_test.log \ + --testname="Frameworktest" \ + --frameworktest_list_file=${FRAMEWORKTEST_LIST_FILE:-} +fi + +# Run nnapi_test with various tflite models +if [ "$ALLTEST_ON" == "true" ] || [ "$VERIFICATION_ON" == "true" ]; then + if [ -z "$VERIFICATION_DRIVER_BIN" ]; then + VERIFICATION_DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/nnapi_test + fi + + # verification uses the same script as frameworktest does + $TEST_DRIVER_DIR/run_frameworktest.sh \ + --runtestsh=$RUN_TEST_SH \ + --driverbin=$VERIFICATION_DRIVER_BIN \ + --reportdir=$REPORT_DIR \ + --tapname=verification_test.tap \ + --logname=verification_test.log \ + --testname="Verification" \ + --frameworktest_list_file=${FRAMEWORKTEST_LIST_FILE:-} +fi + +# Run tflite_benchmark with tflite models +if [ "$BENCHMARK_ON" == "true" ]; then + if [ -z "$BENCHMARK_DRIVER_BIN" ]; then + DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_benchmark + else + DRIVER_BIN=$BENCHMARK_DRIVER_BIN + fi + + $TEST_DRIVER_DIR/run_benchmark.sh \ + --runtestsh=$RUN_TEST_SH \ + --driverbin=$DRIVER_BIN \ + --reportdir=$REPORT_DIR/benchmark +fi + +# Run tflite_benchmark from a list of tflite models. +# Each model has only one operator. +if [ "$BENCHMARK_OP_ON" == "true" ]; then + if [ -z "$BENCHMARK_DRIVER_BIN" ]; then + DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_benchmark + else + DRIVER_BIN=$BENCHMARK_DRIVER_BIN + fi + + $TEST_DRIVER_DIR/run_benchmark_op.sh \ + --runtestsh=$RUN_TEST_SH \ + --driverbin=$DRIVER_BIN \ + --reportdir=$REPORT_DIR/benchmark_op \ + --modelfilepath=$ARTIFACT_PATH/tests/framework +fi + +# Run benchmark/acl/benchmark_googlenet, mobilenet and inception_v3 +if [ "$BENCHMARK_ACL_ON" == "true" ]; then + $TEST_DRIVER_DIR/run_benchmark_acl.sh \ + --reportdir=$REPORT_DIR/benchmark \ + --bindir=$ARTIFACT_PATH/Product/out/bin +fi + +# Make json file. Actually, this process is only needed on CI. That's why it is in test_driver.sh. +if [ "$BENCHMARK_ON" == "true" ] || [ "$BENCHMARK_ACL_ON" == "true" ] || [ "$BENCHMARK_OP_ON" == "true" ]; then + # functions to fill json with benchmark results + source $ARTIFACT_PATH/tests/scripts/print_to_json.sh + if [ "$BENCHMARK_OP_ON" == "true" ]; then + print_to_json $REPORT_DIR/benchmark_op $REPORT_DIR "benchmark_op_result.json" + else + print_to_json $REPORT_DIR/benchmark $REPORT_DIR "benchmark_result.json" + fi +fi + +# Run tflite_benchmark_model (= per-operation profiling tool). +# Each model can contain arbitrary number of operators. +if [ "$BENCHMARK_TFLITE_MODEL_ON" == "true" ]; then + $TEST_DRIVER_DIR/run_benchmark_tflite_model.sh \ + --reportdir=$REPORT_DIR/benchmark_tflite_model \ + --modelroot=$ARTIFACT_PATH/tests/framework/tests +fi + +# Run profiling +if [ "$PROFILE_ON" == "true" ]; then + # FIXME: These driver and tflite test are set temporarily. Fix these to support flexibility + DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_run + TFLITE_TEST=$ARTIFACT_PATH/tests/framework/cache/inceptionv3/inception_module/inception_test.tflite + + # TODO: Enable operf to set directory where sample data puts on + rm -rf oprofile_data + + echo "" + echo "============================================" + operf -g $DRIVER_BIN $TFLITE_TEST + echo "============================================" + echo "" +fi diff --git a/tests/tools/CMakeLists.txt b/tests/tools/CMakeLists.txt new file mode 100644 index 000000000..b1eea12f9 --- /dev/null +++ b/tests/tools/CMakeLists.txt @@ -0,0 +1,8 @@ +set(EXCLUDE_DIR "") + +if(OBS_BUILD) + list(APPEND EXCLUDE_DIR tflite_benchmark_model) + list(APPEND EXCLUDE_DIR tflite_run) +endif(OBS_BUILD) + +add_subdirectories(EXCLUDES ${EXCLUDE_DIR}) diff --git a/tests/tools/nnapi_test/CMakeLists.txt b/tests/tools/nnapi_test/CMakeLists.txt new file mode 100644 index 000000000..b52f4f34b --- /dev/null +++ b/tests/tools/nnapi_test/CMakeLists.txt @@ -0,0 +1,5 @@ +list(APPEND SOURCES "src/nnapi_test.cc") + +add_executable(nnapi_test ${SOURCES}) +target_link_libraries(nnapi_test nnfw_lib_tflite) +install(TARGETS nnapi_test DESTINATION bin) diff --git a/tests/tools/nnapi_test/src/nnapi_test.cc b/tests/tools/nnapi_test/src/nnapi_test.cc new file mode 100644 index 000000000..73e80f01f --- /dev/null +++ b/tests/tools/nnapi_test/src/nnapi_test.cc @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#include "tflite/interp/FlatBufferBuilder.h" +#include "tflite/Diff.h" + +#include <iostream> +#include <stdexcept> + +using namespace tflite; +using namespace nnfw::tflite; + +int main(const int argc, char **argv) +{ + if (argc < 2) + { + std::cerr << "nnapi_test\n\n"; + std::cerr << "Usage: " << argv[0] << " <.tflite>\n\n"; + return 1; + } + + const auto filename = argv[1]; + + StderrReporter error_reporter; + + auto model = FlatBufferModel::BuildFromFile(filename, &error_reporter); + + const nnfw::tflite::FlatBufferBuilder builder(*model); + + try + { + return RandomTestRunner::make(0).run(builder); + } + catch (const std::exception &e) + { + std::cerr << e.what() << std::endl; + return 1; + } +} diff --git a/tests/tools/tflite_benchmark/CMakeLists.txt b/tests/tools/tflite_benchmark/CMakeLists.txt new file mode 100644 index 000000000..56421a294 --- /dev/null +++ b/tests/tools/tflite_benchmark/CMakeLists.txt @@ -0,0 +1,5 @@ +list(APPEND SOURCES "src/tflite_benchmark.cc") + +add_executable(tflite_benchmark ${SOURCES}) +target_link_libraries(tflite_benchmark nnfw_lib_tflite tensorflow-lite ${LIB_PTHREAD} dl nnfw_lib_misc) +install(TARGETS tflite_benchmark DESTINATION bin) diff --git a/tests/tools/tflite_benchmark/src/tflite_benchmark.cc b/tests/tools/tflite_benchmark/src/tflite_benchmark.cc new file mode 100644 index 000000000..b77afc189 --- /dev/null +++ b/tests/tools/tflite_benchmark/src/tflite_benchmark.cc @@ -0,0 +1,239 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#include "tflite/Assert.h" +#include "tflite/Session.h" +#include "tflite/InterpreterSession.h" +#include "tflite/NNAPISession.h" +#include "tflite/Diff.h" +#include "misc/tensor/IndexIterator.h" + +#include <boost/accumulators/accumulators.hpp> +#include <boost/accumulators/statistics/stats.hpp> +#include <boost/accumulators/statistics/min.hpp> +#include <boost/accumulators/statistics/max.hpp> +#include <boost/accumulators/statistics/mean.hpp> + +#include <iostream> + +#include "misc/environment.h" +#include "misc/benchmark.h" + +using namespace tflite; +using namespace nnfw::tflite; + +void help(std::ostream &out, const int argc, char **argv) +{ + std::string cmd = argv[0]; + auto pos = cmd.find_last_of("/"); + if (pos != std::string::npos) + cmd = cmd.substr(pos + 1); + + out << "use:" << std::endl << cmd << " <model file name>" << std::endl; +} + +bool checkParams(const int argc, char **argv) +{ + if (argc < 2) + { + help(std::cerr, argc, argv); + return false; + } + return true; +} + +int main(const int argc, char **argv) +{ + + if (!checkParams(argc, argv)) + { + return -1; + } + + const auto filename = argv[1]; + + const bool use_nnapi = nnfw::misc::get_env_bool("USE_NNAPI"); + const auto thread_count = nnfw::misc::get_env_int("THREAD", -1); + + std::cout << "Num threads: " << thread_count << std::endl; + if (use_nnapi) + { + std::cout << "Use NNAPI" << std::endl; + } + + StderrReporter error_reporter; + + auto model = FlatBufferModel::BuildFromFile(filename, &error_reporter); + if (model == nullptr) + { + std::cerr << "Cannot create model" << std::endl; + return -1; + } + + BuiltinOpResolver resolver; + + InterpreterBuilder builder(*model, resolver); + + std::unique_ptr<Interpreter> interpreter; + + try + { + TFLITE_ENSURE(builder(&interpreter)); + } + catch (const std::exception &e) + { + std::cerr << e.what() << std::endl; + return 1; + } + + // Show inputs + for (uint32_t n = 0; n < interpreter->inputs().size(); ++n) + { + // TODO Print shape + auto tensor_id = interpreter->inputs().at(n); + auto tensor_ptr = interpreter->tensor(tensor_id); + + std::cout << "Input #" << n << ":" << std::endl; + std::cout << " Name: " << tensor_ptr->name << std::endl; + } + + // Show outputs + for (uint32_t n = 0; n < interpreter->outputs().size(); ++n) + { + // TODO Print shape + auto tensor_id = interpreter->outputs().at(n); + auto tensor_ptr = interpreter->tensor(tensor_id); + + std::cout << "Output #" << n << ":" << std::endl; + std::cout << " Name: " << tensor_ptr->name << std::endl; + } + + interpreter->SetNumThreads(thread_count); + + std::shared_ptr<nnfw::tflite::Session> sess; + + if (use_nnapi) + { + sess = std::make_shared<nnfw::tflite::NNAPISession>(interpreter.get()); + } + else + { + sess = std::make_shared<nnfw::tflite::InterpreterSession>(interpreter.get()); + } + + // + // Warming-up + // + for (uint32_t n = 0; n < 3; ++n) + { + std::chrono::milliseconds elapsed(0); + + sess->prepare(); + + for (const auto &id : interpreter->inputs()) + { + TfLiteTensor *tensor = interpreter->tensor(id); + if (tensor->type == kTfLiteInt32) + { + // Generate singed 32-bit integer (s32) input + auto tensor_view = nnfw::tflite::TensorView<int32_t>::make(*interpreter, id); + + int32_t value = 0; + + nnfw::misc::tensor::iterate(tensor_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + // Gather operation: index should be within input coverage. + tensor_view.at(ind) = value; + value++; + }; + } + else if (tensor->type == kTfLiteUInt8) + { + // Generate unsigned 8-bit integer input + auto tensor_view = nnfw::tflite::TensorView<uint8_t>::make(*interpreter, id); + + uint8_t value = 0; + + nnfw::misc::tensor::iterate(tensor_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + tensor_view.at(ind) = value; + value = (value + 1) & 0xFF; + }; + } + else + { + assert(tensor->type == kTfLiteFloat32); + + const int seed = 1; /* TODO Add an option for seed value */ + RandomGenerator randgen{seed, 0.0f, 0.2f}; + const float *end = reinterpret_cast<const float *>(tensor->data.raw_const + tensor->bytes); + for (float *ptr = tensor->data.f; ptr < end; ptr++) + { + *ptr = randgen.generate<float>(); + } + } + } + + nnfw::misc::benchmark::measure(elapsed) << [&](void) { + if (!sess->run()) + { + assert(0 && "run failed"); + } + }; + sess->teardown(); + + std::cout << "Warming-up " << n << ": " << elapsed.count() << "ms" << std::endl; + } + + // + // Measure + // + const auto cnt = nnfw::misc::get_env_int("COUNT", 1); + + using namespace boost::accumulators; + + accumulator_set<double, stats<tag::mean, tag::min, tag::max>> acc; + + for (int n = 0; n < cnt; ++n) + { + std::chrono::milliseconds elapsed(0); + + sess->prepare(); + nnfw::misc::benchmark::measure(elapsed) << [&](void) { + if (!sess->run()) + { + assert(0 && "run failed"); + } + }; + sess->teardown(); + + acc(elapsed.count()); + + std::cout << "Iteration " << n << ": " << elapsed.count() << "ms" << std::endl; + } + + std::cout << "--------" << std::endl; + std::cout << "Min: " << min(acc) << "ms" << std::endl; + std::cout << "Max: " << max(acc) << "ms" << std::endl; + std::cout << "Mean: " << mean(acc) << "ms" << std::endl; + + return 0; +} diff --git a/tests/tools/tflite_benchmark_model/.FORMATDENY b/tests/tools/tflite_benchmark_model/.FORMATDENY new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/tests/tools/tflite_benchmark_model/.FORMATDENY diff --git a/tests/tools/tflite_benchmark_model/CMakeLists.txt b/tests/tools/tflite_benchmark_model/CMakeLists.txt new file mode 100644 index 000000000..c48f658c1 --- /dev/null +++ b/tests/tools/tflite_benchmark_model/CMakeLists.txt @@ -0,0 +1,18 @@ +if (NOT BUILD_TFLITE_BENCHMARK_MODEL) + return() +endif(NOT BUILD_TFLITE_BENCHMARK_MODEL) + +file(GLOB_RECURSE SOURCES "*.cc") + +nnfw_find_package(TensorFlowSource REQUIRED) +set(TENSORFLOW_LITE_BASE "${TensorFlowSource_DIR}/tensorflow/contrib/lite") +list(APPEND SOURCES "${TENSORFLOW_LITE_BASE}/tools/benchmark/benchmark_main.cc" + "${TENSORFLOW_LITE_BASE}/tools/benchmark/benchmark_model.cc" + "${TENSORFLOW_LITE_BASE}/tools/benchmark/benchmark_params.cc" + "${TENSORFLOW_LITE_BASE}/tools/benchmark/command_line_flags.cc") + +add_executable(tflite_benchmark_model ${SOURCES}) +target_compile_definitions(tflite_benchmark_model PUBLIC "TFLITE_PROFILING_ENABLED") +target_link_libraries(tflite_benchmark_model nnfw_lib_misc nnfw_lib_tflite nnfw_lib_profiling) +target_link_libraries(tflite_benchmark_model tensorflow-lite ${LIB_PTHREAD} dl) +install(TARGETS tflite_benchmark_model DESTINATION bin) diff --git a/tests/tools/tflite_benchmark_model/README.md b/tests/tools/tflite_benchmark_model/README.md new file mode 100644 index 000000000..8d997639f --- /dev/null +++ b/tests/tools/tflite_benchmark_model/README.md @@ -0,0 +1,197 @@ +# TFLite Model Benchmark Tool + +## Description + +A simple C++ binary to benchmark a TFLite model and its individual operators, +both on desktop machines and on Android. The binary takes a TFLite model, +generates random inputs and then repeatedly runs the model for specified number +of runs. Aggregrate latency statistics are reported after running the benchmark. + +The instructions below are for running the binary on Desktop and Android, +for iOS please use the +[iOS benchmark app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios). + +## Parameters + +The binary takes the following required parameters: + +* `graph`: `string` \ + The path to the TFLite model file. + +and the following optional parameters: + +* `num_threads`: `int` (default=1) \ + The number of threads to use for running TFLite interpreter. +* `warmup_runs`: `int` (default=1) \ + The number of warmup runs to do before starting the benchmark. +* `num_runs`: `int` (default=50) \ + The number of runs. Increase this to reduce variance. +* `run_delay`: `float` (default=-1.0) \ + The delay in seconds between subsequent benchmark runs. Non-positive values + mean use no delay. +* `use_nnapi`: `bool` (default=false) \ + Whether to use [Android NNAPI](https://developer.android.com/ndk/guides/neuralnetworks/). + This API is available on recent Android devices. + +## To build/install/run + +### On Android: + +(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the `WORKSPACE` to configure the android NDK/SDK. + +(1) Build for your specific platform, e.g.: + +``` +bazel build -c opt \ + --config=android_arm \ + --cxxopt='--std=c++11' \ + tensorflow/contrib/lite/tools/benchmark:benchmark_model +``` + +(2) Connect your phone. Push the binary to your phone with adb push + (make the directory if required): + +``` +adb push bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model /data/local/tmp +``` + +(3) Make the binary executable. + +``` +adb shell chmod +x /data/local/tmp/benchmark_model +``` + +(4) Push the compute graph that you need to test. For example: + +``` +adb push mobilenet_quant_v1_224.tflite /data/local/tmp +``` + +(5) Run the benchmark. For example: + +``` +adb shell /data/local/tmp/benchmark_model \ + --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ + --num_threads=4 +``` + +### On desktop: +(1) build the binary + +``` +bazel build -c opt tensorflow/contrib/lite/tools/benchmark:benchmark_model +``` + +(2) Run on your compute graph, similar to the Android case but without the need of adb shell. +For example: + +``` +bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model \ + --graph=mobilenet_quant_v1_224.tflite \ + --num_threads=4 +``` + +The MobileNet graph used as an example here may be downloaded from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip). + + +## Reducing variance between runs on Android. + +Most modern Android phones use [ARM big.LITTLE](https://en.wikipedia.org/wiki/ARM_big.LITTLE) +architecture where some cores are more power hungry but faster than other cores. +When running benchmarks on these phones there can be significant variance +between different runs of the benchmark. One way to reduce variance between runs +is to set the [CPU affinity](https://en.wikipedia.org/wiki/Processor_affinity) +before running the benchmark. On Android this can be done using the `taskset` +command. +E.g. for running the benchmark on big cores on Pixel 2 with a single thread one +can use the following command: + +``` +adb shell taskset f0 /data/local/tmp/benchmark_model \ + --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ + --num_threads=1 +``` + +where `f0` is the affinity mask for big cores on Pixel 2. +Note: The affinity mask varies with the device. + +## Profiling model operators +The benchmark model binary also allows you to profile operators and give execution times of each operator. To do this, +compile the binary with a compiler flag that enables profiling to be compiled in. Pass **--copt=-DTFLITE_PROFILING_ENABLED** +to compile benchmark with profiling support. +For example, to compile with profiling support on Android, add this flag to the previous command: + +``` +bazel build -c opt \ + --config=android_arm \ + --cxxopt='--std=c++11' \ + --copt=-DTFLITE_PROFILING_ENABLED \ + tensorflow/contrib/lite/tools/benchmark:benchmark_model +``` +This compiles TFLite with profiling enabled, now you can run the benchmark binary like before. The binary will produce detailed statistics for each operation similar to those shown below: + +``` + +============================== Run Order ============================== + [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] + CONV_2D 0.000 4.269 4.269 0.107% 0.107% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] + DEPTHWISE_CONV_2D 4.270 2.150 2.150 0.054% 0.161% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6] + CONV_2D 6.421 6.107 6.107 0.153% 0.314% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] + DEPTHWISE_CONV_2D 12.528 1.366 1.366 0.034% 0.348% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6] + CONV_2D 13.895 4.195 4.195 0.105% 0.454% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6] + DEPTHWISE_CONV_2D 18.091 1.260 1.260 0.032% 0.485% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6] + CONV_2D 19.352 6.652 6.652 0.167% 0.652% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] + DEPTHWISE_CONV_2D 26.005 0.698 0.698 0.018% 0.670% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6] + CONV_2D 26.703 3.344 3.344 0.084% 0.754% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6] + DEPTHWISE_CONV_2D 30.047 0.646 0.646 0.016% 0.770% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6] + CONV_2D 30.694 5.800 5.800 0.145% 0.915% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] + DEPTHWISE_CONV_2D 36.495 0.331 0.331 0.008% 0.924% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] + CONV_2D 36.826 2.838 2.838 0.071% 0.995% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] + DEPTHWISE_CONV_2D 39.665 0.439 0.439 0.011% 1.006% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] + CONV_2D 40.105 5.293 5.293 0.133% 1.139% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] + DEPTHWISE_CONV_2D 45.399 0.352 0.352 0.009% 1.147% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] + CONV_2D 45.752 5.322 5.322 0.133% 1.281% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] + DEPTHWISE_CONV_2D 51.075 0.357 0.357 0.009% 1.290% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] + CONV_2D 51.432 5.693 5.693 0.143% 1.433% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] + DEPTHWISE_CONV_2D 57.126 0.366 0.366 0.009% 1.442% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6] + CONV_2D 57.493 5.472 5.472 0.137% 1.579% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] + DEPTHWISE_CONV_2D 62.966 0.364 0.364 0.009% 1.588% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6] + CONV_2D 63.330 5.404 5.404 0.136% 1.724% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] + DEPTHWISE_CONV_2D 68.735 0.155 0.155 0.004% 1.728% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6] + CONV_2D 68.891 2.970 2.970 0.074% 1.802% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6] + DEPTHWISE_CONV_2D 71.862 0.206 0.206 0.005% 1.807% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6] + CONV_2D 72.069 5.888 5.888 0.148% 1.955% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] + AVERAGE_POOL_2D 77.958 0.036 0.036 0.001% 1.956% 0.000 0 [MobilenetV1/Logits/AvgPool_1a/AvgPool] + CONV_2D 77.994 1.445 1.445 0.036% 1.992% 0.000 0 [MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd] + RESHAPE 79.440 0.002 0.002 0.000% 1.992% 0.000 0 [MobilenetV1/Predictions/Reshape] + SOFTMAX 79.443 0.029 0.029 0.001% 1.993% 0.000 0 [MobilenetV1/Predictions/Softmax] + +============================== Top by Computation Time ============================== + [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] + CONV_2D 19.352 6.652 6.652 0.167% 0.167% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] + CONV_2D 6.421 6.107 6.107 0.153% 0.320% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] + CONV_2D 72.069 5.888 5.888 0.148% 0.468% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] + CONV_2D 30.694 5.800 5.800 0.145% 0.613% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] + CONV_2D 51.432 5.693 5.693 0.143% 0.756% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] + CONV_2D 57.493 5.472 5.472 0.137% 0.893% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] + CONV_2D 63.330 5.404 5.404 0.136% 1.029% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] + CONV_2D 45.752 5.322 5.322 0.133% 1.162% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] + CONV_2D 40.105 5.293 5.293 0.133% 1.295% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] + CONV_2D 0.000 4.269 4.269 0.107% 1.402% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] + +Number of nodes executed: 31 +============================== Summary by node type ============================== + [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called] + CONV_2D 15 1.406 89.270% 89.270% 0.000 0 + DEPTHWISE_CONV_2D 13 0.169 10.730% 100.000% 0.000 0 + SOFTMAX 1 0.000 0.000% 100.000% 0.000 0 + RESHAPE 1 0.000 0.000% 100.000% 0.000 0 + AVERAGE_POOL_2D 1 0.000 0.000% 100.000% 0.000 0 + +Timings (microseconds): count=50 first=79449 curr=81350 min=77385 max=88213 avg=79732 std=1929 +Memory (bytes): count=0 +31 nodes observed + + +Average inference timings in us: Warmup: 83235, Init: 38467, no stats: 79760.9 +``` diff --git a/tests/tools/tflite_benchmark_model/benchmark_tflite_model.cc b/tests/tools/tflite_benchmark_model/benchmark_tflite_model.cc new file mode 100644 index 000000000..efc8bae52 --- /dev/null +++ b/tests/tools/tflite_benchmark_model/benchmark_tflite_model.cc @@ -0,0 +1,401 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h" + +#include <cstdarg> +#include <cstdlib> +#include <iostream> +#include <memory> +#include <string> +#include <unordered_set> +#include <vector> + +#ifdef TFLITE_FLEX +#include "tensorflow/contrib/lite/delegates/flex/delegate.h" +#endif // TFLITE_FLEX +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/contrib/lite/op_resolver.h" +#include "tensorflow/contrib/lite/string_util.h" +#include "tensorflow/contrib/lite/tools/benchmark/logging.h" + +// For profiling nnapi_delegate +#include "profiling/profiling.h" +#include "tflite/ext/nnapi_delegate.h" + +namespace { + nnfw::tflite::NNAPIDelegate nnfw_delegate_; +} + +#ifdef TFLITE_CUSTOM_OPS_HEADER +void RegisterSelectedOps(::tflite::MutableOpResolver* resolver); +#endif + +namespace tflite { +namespace benchmark { + +void ProfilingListener::SetInterpreter(tflite::Interpreter* interpreter) { + TFLITE_BENCHMARK_CHECK(interpreter); + interpreter_ = interpreter; + interpreter_->SetProfiler(&profiler_); +} + +void ProfilingListener::OnSingleRunStart(RunType run_type) { + if (run_type == REGULAR) { + profiler_.Reset(); + profiler_.StartProfiling(); + } +} + +void ProfilingListener::OnBenchmarkEnd(const BenchmarkResults& results) { + if (has_profiles_) { + TFLITE_LOG(INFO) << summarizer_.GetOutputString(); + } +} + +void ProfilingListener::OnSingleRunEnd() { + profiler_.StopProfiling(); + auto profile_events = profiler_.GetProfileEvents(); + has_profiles_ = !profile_events.empty(); + summarizer_.ProcessProfiles(profile_events, *interpreter_); +} + +namespace { + +std::vector<std::string> Split(const std::string& str, const char delim) { + std::istringstream input(str); + std::vector<std::string> results; + std::string item; + while (std::getline(input, item, delim)) { + results.push_back(item); + } + return results; +} + +template <typename T> +bool SplitAndParse(const std::string& str, char delim, std::vector<T>* values) { + std::istringstream input(str); + bool first = true; + while (!input.eof()) { + if (!first) { + char c; + input >> c; + if (c != delim) { + return false; + } + } else { + first = false; + } + T val; + input >> val; + if (!input.eof() && !input.good()) { + return false; + } + values->push_back(val); + } + return true; +} + +template <typename T> +void FillRandomValue(T* ptr, const std::vector<int>& sizes, + const std::function<T()>& random_func) { + int num_elements = 1; + for (int dim : sizes) { + num_elements *= dim; + } + for (int i = 0; i < num_elements; ++i) { + *ptr++ = random_func(); + } +} + +void FillRandomString(tflite::DynamicBuffer* buffer, + const std::vector<int>& sizes, + const std::function<string()>& random_func) { + int num_elements = 1; + for (int dim : sizes) { + num_elements *= dim; + } + for (int i = 0; i < num_elements; ++i) { + auto str = random_func(); + buffer->AddString(str.data(), str.length()); + } +} + +bool PopulateInputLayerInfo( + const string& names_string, const string& shapes_string, + std::vector<BenchmarkTfLiteModel::InputLayerInfo>* info) { + std::vector<std::string> names = Split(names_string, ','); + std::vector<std::string> shapes = Split(shapes_string, ':'); + + if (names.size() != shapes.size()) { + TFLITE_LOG(ERROR) << "The number of items in" + << " --input_layer_shape (" << shapes_string << ", with " + << shapes.size() << " items)" + << " must match the number of items in" + << " --input_layer (" << names_string << ", with " + << names.size() << " items)." + << " For example --input_layer=input1,input2" + << " --input_layer_shape=1,224,224,4:1,20"; + return false; + } + + for (int i = 0; i < names.size(); ++i) { + info->push_back(BenchmarkTfLiteModel::InputLayerInfo()); + BenchmarkTfLiteModel::InputLayerInfo& input = info->back(); + + input.name = names[i]; + + TFLITE_BENCHMARK_CHECK(SplitAndParse(shapes[i], ',', &input.shape)) + << "Incorrect size string specified: " << shapes[i]; + for (int dim : input.shape) { + if (dim == -1) { + TFLITE_LOG(ERROR) + << "Any unknown sizes in the shapes (-1's) must be replaced" + << " with the size you want to benchmark with."; + return false; + } + } + } + + return true; +} + +BenchmarkParams GetDefaultParams() { + BenchmarkParams default_params = BenchmarkModel::DefaultParams(); + default_params.AddParam("graph", BenchmarkParam::Create<std::string>("")); + default_params.AddParam("input_layer", + BenchmarkParam::Create<std::string>("")); + default_params.AddParam("input_layer_shape", + BenchmarkParam::Create<std::string>("")); + default_params.AddParam("use_nnapi", BenchmarkParam::Create<bool>(false)); + return default_params; +} + +} // namespace + +BenchmarkTfLiteModel::BenchmarkTfLiteModel() + : BenchmarkModel(GetDefaultParams()) { + AddListener(&profiling_listener_); +} + +BenchmarkTfLiteModel::BenchmarkTfLiteModel(BenchmarkParams params) + : BenchmarkModel(std::move(params)) { + AddListener(&profiling_listener_); +} + +std::vector<Flag> BenchmarkTfLiteModel::GetFlags() { + std::vector<Flag> flags = BenchmarkTfLiteModel::BenchmarkModel::GetFlags(); + std::vector<Flag> specific_flags = { + CreateFlag<std::string>("graph", ¶ms_, "graph file name"), + CreateFlag<std::string>("input_layer", ¶ms_, "input layer names"), + CreateFlag<std::string>("input_layer_shape", ¶ms_, + "input layer shape"), + CreateFlag<bool>("use_nnapi", ¶ms_, "use nnapi api")}; + + flags.insert(flags.end(), specific_flags.begin(), specific_flags.end()); + return flags; +} + +void BenchmarkTfLiteModel::LogParams() { + BenchmarkModel::LogParams(); + TFLITE_LOG(INFO) << "Graph: [" << params_.Get<std::string>("graph") << "]"; + TFLITE_LOG(INFO) << "Input layers: [" + << params_.Get<std::string>("input_layer") << "]"; + TFLITE_LOG(INFO) << "Input shapes: [" + << params_.Get<std::string>("input_layer_shape") << "]"; + TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get<bool>("use_nnapi") << "]"; +} + +bool BenchmarkTfLiteModel::ValidateParams() { + if (params_.Get<std::string>("graph").empty()) { + TFLITE_LOG(ERROR) + << "Please specify the name of your TF Lite input file with --graph"; + return false; + } + return PopulateInputLayerInfo(params_.Get<std::string>("input_layer"), + params_.Get<std::string>("input_layer_shape"), + &inputs); +} + +uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { + TFLITE_BENCHMARK_CHECK(interpreter); + uint64_t total_input_bytes = 0; + for (int input : interpreter->inputs()) { + auto* t = interpreter->tensor(input); + total_input_bytes += t->bytes; + } + return total_input_bytes; +} + +void BenchmarkTfLiteModel::PrepareInputsAndOutputs() { + auto interpreter_inputs = interpreter->inputs(); + // Set the values of the input tensors. + for (int j = 0; j < inputs.size(); ++j) { + const InputLayerInfo& input = inputs[j]; + int i = interpreter_inputs[j]; + TfLiteTensor* t = interpreter->tensor(i); + std::vector<int> sizes = input.shape; + + // TODO(ahentz): below we ignore the O-th dimension (number of batches). + if (t->type == kTfLiteFloat32) { + FillRandomValue<float>( + interpreter->typed_tensor<float>(i), + std::vector<int>(sizes.begin() + 1, sizes.end()), + []() { return static_cast<float>(rand()) / RAND_MAX - 0.5f; }); + } else if (t->type == kTfLiteInt32) { + // TODO(yunluli): This is currently only used for handling embedding input + // for speech models. Generalize if necessary. + FillRandomValue<int32_t>( + interpreter->typed_tensor<int32_t>(i), + std::vector<int32_t>(sizes.begin() + 1, sizes.end()), + []() { return static_cast<int32_t>(rand()) % 100; }); + } else if (t->type == kTfLiteUInt8) { + FillRandomValue<uint8_t>( + interpreter->typed_tensor<uint8_t>(i), + std::vector<int>(sizes.begin() + 1, sizes.end()), + []() { return static_cast<uint8_t>(rand()) % 255; }); + } else if (t->type == kTfLiteString) { + tflite::DynamicBuffer buffer; + FillRandomString(&buffer, sizes, []() { + return "we're have some friends over saturday to hang out in the yard"; + }); + buffer.WriteToTensor(interpreter->tensor(i)); + } else { + TFLITE_LOG(FATAL) << "Don't know how to populate tensor " << t->name + << " of type " << t->type; + } + } +} + +void BenchmarkTfLiteModel::Init() { + std::string graph = params_.Get<std::string>("graph"); + model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); + if (!model) { + TFLITE_LOG(FATAL) << "Failed to mmap model " << graph; + } + TFLITE_LOG(INFO) << "Loaded model " << graph; + model->error_reporter(); + TFLITE_LOG(INFO) << "resolved reporter"; + +#ifdef TFLITE_CUSTOM_OPS_HEADER + tflite::MutableOpResolver resolver; + RegisterSelectedOps(&resolver); +#else + nnfw::tflite::BuiltinOpResolver resolver; +#endif + + tflite::InterpreterBuilder(*model, resolver)(&interpreter); + if (!interpreter) { + TFLITE_LOG(FATAL) << "Failed to construct interpreter"; + } + profiling_listener_.SetInterpreter(interpreter.get()); + ::profiling::Context::get().setProfiler(interpreter->GetProfiler()); + + auto enable_sync = std::getenv("PROFILING_OP_SYNC"); + if (enable_sync && std::atoi(enable_sync) != 0) + { + ::profiling::Context::get().setSync(); + } + + const int32_t num_threads = params_.Get<int32_t>("num_threads"); + + if (num_threads != -1) { + interpreter->SetNumThreads(num_threads); + } + + bool use_nnapi = params_.Get<bool>("use_nnapi"); + + interpreter->UseNNAPI(use_nnapi); + + if (use_nnapi) { + if (nnfw_delegate_.BuildGraph(interpreter.get()) != kTfLiteOk) { + TFLITE_LOG(FATAL) << "Failed to BuildGraph!"; + } + } + +#ifdef TFLITE_FLEX + TFLITE_LOG(INFO) << "Instantiating Flex Delegate"; + delegate_ = FlexDelegate::Create(); + if (delegate_) { + interpreter->ModifyGraphWithDelegate(delegate_.get(), + /*allow_dynamic_tensors=*/true); + } +#endif // TFLITE_FLEX + + auto interpreter_inputs = interpreter->inputs(); + + if (!inputs.empty()) { + TFLITE_BENCHMARK_CHECK_EQ(inputs.size(), interpreter_inputs.size()) + << "Inputs mismatch: Model inputs #:" << interpreter_inputs.size() + << " expected: " << inputs.size(); + } + + // TFLITE_BENCHMARK_CHECK that all names and types match + for (int j = 0; j < inputs.size(); ++j) { + const InputLayerInfo& input = inputs[j]; + int i = interpreter_inputs[j]; + TfLiteTensor* t = interpreter->tensor(i); + TFLITE_BENCHMARK_CHECK_EQ(t->name, input.name) + << "Tensor # " << i << " is named " << t->name << " but flags call it " + << input.name; + } + + // Resize all non-string tensors. + for (int j = 0; j < inputs.size(); ++j) { + const InputLayerInfo& input = inputs[j]; + int i = interpreter_inputs[j]; + TfLiteTensor* t = interpreter->tensor(i); + if (t->type != kTfLiteString) { + interpreter->ResizeInputTensor(i, input.shape); + } + } + + if (interpreter->AllocateTensors() != kTfLiteOk) { + TFLITE_LOG(FATAL) << "Failed to allocate tensors!"; + } +} + +void BenchmarkTfLiteModel::RunImpl() { + bool use_nnapi = params_.Get<bool>("use_nnapi"); + if (use_nnapi) { + if (nnfw_delegate_.Invoke(interpreter.get()) != kTfLiteOk) { + TFLITE_LOG(FATAL) << "Failed to invoke!"; + } + } else { + if (interpreter->Invoke() != kTfLiteOk) { + TFLITE_LOG(FATAL) << "Failed to invoke!"; + } + } +} + +} // namespace benchmark +} // namespace tflite diff --git a/tests/tools/tflite_benchmark_model/profile_summarizer.cc b/tests/tools/tflite_benchmark_model/profile_summarizer.cc new file mode 100644 index 000000000..ce19b0c98 --- /dev/null +++ b/tests/tools/tflite_benchmark_model/profile_summarizer.cc @@ -0,0 +1,161 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/profiling/profile_summarizer.h" + +#include <sstream> + +#include "tensorflow/contrib/lite/schema/schema_generated.h" + +namespace tflite { +namespace profiling { +namespace { + +struct OperatorDetails { + std::string name; + std::vector<std::string> inputs; + std::vector<std::string> outputs; +}; + +std::string GetTensorName(const tflite::Interpreter& interpreter, + int tensor_index) { + const auto tensor = interpreter.tensor(tensor_index); + if (tensor == nullptr || tensor->name == nullptr) { + return "Unknown"; + } + return tensor->name; +} +std::vector<std::string> GetTensorNames(const tflite::Interpreter& interpreter, + const TfLiteIntArray* tensor_indices) { + std::vector<std::string> tensors; + tensors.reserve(tensor_indices->size); + for (int i = 0; i < tensor_indices->size; i++) { + tensors.push_back(GetTensorName(interpreter, tensor_indices->data[i])); + } + return tensors; +} + +std::string ToString(const std::vector<std::string>& str_vector) { + std::stringstream stream; + stream << "["; + bool first = true; + for (const auto& s : str_vector) { + if (!first) { + stream << ", "; + } else { + first = false; + } + stream << s; + } + stream << "]"; + return stream.str(); +} + +OperatorDetails GetOperatorDetails(const tflite::Interpreter& interpreter, + int node_index) { + auto node_reg = interpreter.node_and_registration(node_index); + auto inputs = node_reg->first.inputs; + auto outputs = node_reg->first.outputs; + int code = node_reg->second.builtin_code; + const char* op_name = nullptr; + if (code == tflite::BuiltinOperator_CUSTOM) { + const char* custom_name = node_reg->second.custom_name; + op_name = custom_name ? custom_name : "UnknownCustomOp"; + } else { + op_name = tflite::EnumNamesBuiltinOperator()[code]; + } + const char* profiling_string = + interpreter.OpProfilingString(node_reg->second, &node_reg->first); + OperatorDetails details; + details.name = op_name; + if (profiling_string) { + details.name += ":" + std::string(profiling_string); + } + details.inputs = GetTensorNames(interpreter, inputs); + details.outputs = GetTensorNames(interpreter, outputs); + return details; +} + +tensorflow::StatSummarizerOptions GetProfileSummarizerOptions() { + auto options = tensorflow::StatSummarizerOptions(); + options.show_summary = true; + options.show_memory = false; + return options; +} + +} // namespace + +ProfileSummarizer::ProfileSummarizer() + : stats_calculator_( + new ::tensorflow::StatsCalculator(GetProfileSummarizerOptions())) {} + +void ProfileSummarizer::ProcessProfiles( + const std::vector<const ProfileEvent*>& profile_stats, + const tflite::Interpreter& interpreter) { + std::vector<const ProfileEvent*> events; + std::copy_if(profile_stats.begin(), profile_stats.end(), + std::back_inserter(events), [](const ProfileEvent* e) { + return e->event_type == + ProfileEvent::EventType::OPERATOR_INVOKE_EVENT && + e->end_timestamp_us >= e->begin_timestamp_us; + }); + // Sort with begin_time. + std::sort(events.begin(), events.end(), + [](const ProfileEvent* const& a, const ProfileEvent* const& b) { + return a->begin_timestamp_us < b->begin_timestamp_us; + }); + if (events.empty()) { + return; + } + + int64_t base_start_us = events[0]->begin_timestamp_us; + int node_num = 0; + int64_t curr_total_us = 0; + int prev_op_idx = -1; + int child_op_no = 1; + for (auto event : events) { + auto op_details = GetOperatorDetails(interpreter, event->event_metadata); + bool from_same_op = (prev_op_idx == event->event_metadata); + child_op_no = from_same_op ? child_op_no + 1 : 1; + auto node_name = ToString(op_details.outputs) + "#" + std::to_string(child_op_no); + int64_t start_us = event->begin_timestamp_us - base_start_us; + int64_t node_exec_time = + event->end_timestamp_us - event->begin_timestamp_us; + stats_calculator_->AddNodeStats(node_name, op_details.name, node_num, + start_us, node_exec_time, 0 /*memory */); + curr_total_us += node_exec_time; + ++node_num; + prev_op_idx = event->event_metadata; + } + stats_calculator_->UpdateRunTotalUs(curr_total_us); +} +} // namespace profiling +} // namespace tflite diff --git a/tests/tools/tflite_benchmark_model/stats_calculator.cc b/tests/tools/tflite_benchmark_model/stats_calculator.cc new file mode 100644 index 000000000..578650701 --- /dev/null +++ b/tests/tools/tflite_benchmark_model/stats_calculator.cc @@ -0,0 +1,317 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/util/stats_calculator.h" + +#include <iomanip> +#include <map> +#include <queue> +#include <sstream> +#include <string> +#include <algorithm> + +namespace tensorflow { + +StatsCalculator::StatsCalculator(const StatSummarizerOptions& options) + : options_(options) {} + +std::string StatsCalculator::GetShortSummary() const { + std::stringstream stream; + stream << "Timings (microseconds): "; + run_total_us_.OutputToStream(&stream); + stream << std::endl; + + stream << "Memory (bytes): "; + memory_.OutputToStream(&stream); + stream << std::endl; + + stream << details_.size() << " nodes observed" << std::endl; + return stream.str(); +} + +std::ostream& InitField(std::ostream& stream, int width) { + stream << "\t" << std::right << std::setw(width) << std::fixed + << std::setprecision(3); + return stream; +} + +std::string StatsCalculator::HeaderString(const std::string& title) const { + std::stringstream stream; + + stream << "============================== " << title + << " ==============================" << std::endl; + + InitField(stream, 24) << "[node type]"; + InitField(stream, 9) << "[start]"; + InitField(stream, 9) << "[first]"; + InitField(stream, 9) << "[avg ms]"; + InitField(stream, 8) << "[%]"; + InitField(stream, 8) << "[cdf%]"; + InitField(stream, 10) << "[mem KB]"; + InitField(stream, 9) << "[times called]"; + stream << "\t" + << "[Name]"; + return stream.str(); +} + +std::string StatsCalculator::ColumnString(const Detail& detail, + const int64_t cumulative_stat_on_node, + const Stat<int64_t>& stat) const { + const double start_ms = detail.start_us.avg() / 1000.0; + const double first_time_ms = detail.rel_end_us.first() / 1000.0; + const double avg_time_ms = detail.rel_end_us.avg() / 1000.0; + const double percentage = detail.rel_end_us.sum() * 100.0 / stat.sum(); + const double cdf_percentage = (cumulative_stat_on_node * 100.0f) / stat.sum(); + const int64_t times_called = detail.times_called / num_runs(); + + std::stringstream stream; + InitField(stream, 24) << detail.type; + InitField(stream, 9) << start_ms; + InitField(stream, 9) << first_time_ms; + InitField(stream, 9) << avg_time_ms; + InitField(stream, 7) << percentage << "%"; + InitField(stream, 7) << cdf_percentage << "%"; + InitField(stream, 10) << detail.mem_used.newest() / 1000.0; + InitField(stream, 9) << times_called; + stream << "\t" << detail.name; + + return stream.str(); +} + +void StatsCalculator::OrderNodesByMetric( + SortingMetric metric, std::vector<const Detail*>* details) const { + std::priority_queue<std::pair<std::string, const Detail*>> sorted_list; + const int num_nodes = details_.size(); + + for (const auto& det : details_) { + const Detail* detail = &(det.second); + std::stringstream stream; + stream << std::setw(20) << std::right << std::setprecision(10) + << std::fixed; + + switch (metric) { + case BY_NAME: + stream << detail->name; + break; + case BY_RUN_ORDER: + stream << num_nodes - detail->run_order; + break; + case BY_TIME: + stream << detail->rel_end_us.avg(); + break; + case BY_MEMORY: + stream << detail->mem_used.avg(); + break; + case BY_TYPE: + stream << detail->type; + break; + default: + stream << ""; + break; + } + + sorted_list.emplace(stream.str(), detail); + } + + while (!sorted_list.empty()) { + auto entry = sorted_list.top(); + sorted_list.pop(); + details->push_back(entry.second); + } +} + +void StatsCalculator::ComputeStatsByType( + std::map<std::string, int64_t>* node_type_map_count, + std::map<std::string, int64_t>* node_type_map_time, + std::map<std::string, int64_t>* node_type_map_memory, + std::map<std::string, int64_t>* node_type_map_times_called, + int64_t* accumulated_us) const { + int64_t run_count = run_total_us_.count(); + + for (const auto& det : details_) { + const std::string node_name = det.first; + const Detail& detail = det.second; + + int64_t curr_time_val = + static_cast<int64_t>(detail.rel_end_us.sum() / run_count); + *accumulated_us += curr_time_val; + + int64_t curr_memory_val = detail.mem_used.newest(); + + const std::string& node_type = detail.type; + + const std::string sharp1("#1"); + bool first = std::mismatch(sharp1.rbegin(), sharp1.rend(), node_name.rbegin()).first == sharp1.rend(); + + if (first) { + (*node_type_map_count)[node_type] += 1; + (*node_type_map_times_called)[node_type] += detail.times_called / run_count; + } + (*node_type_map_time)[node_type] += curr_time_val; + (*node_type_map_memory)[node_type] += curr_memory_val; + } +} + +std::string StatsCalculator::GetStatsByNodeType() const { + std::stringstream stream; + + stream << "Number of nodes executed: " << details_.size() << std::endl; + + stream << "============================== Summary by node type " + "==============================" + << std::endl; + + std::map<std::string, int64_t> node_type_map_count; + std::map<std::string, int64_t> node_type_map_time; + std::map<std::string, int64_t> node_type_map_memory; + std::map<std::string, int64_t> node_type_map_times_called; + int64_t accumulated_us = 0; + + ComputeStatsByType(&node_type_map_count, &node_type_map_time, + &node_type_map_memory, &node_type_map_times_called, + &accumulated_us); + + // Sort them. + std::priority_queue<std::pair<int64_t, std::pair<std::string, int64_t>>> + timings; + for (const auto& node_type : node_type_map_time) { + const int64_t mem_used = node_type_map_memory[node_type.first]; + timings.emplace(node_type.second, + std::pair<std::string, int64_t>(node_type.first, mem_used)); + } + + InitField(stream, 24) << "[Node type]"; + InitField(stream, 9) << "[count]"; + InitField(stream, 10) << "[avg ms]"; + InitField(stream, 11) << "[avg %]"; + InitField(stream, 11) << "[cdf %]"; + InitField(stream, 10) << "[mem KB]"; + InitField(stream, 10) << "[times called]"; + stream << std::endl; + + float cdf = 0.0f; + while (!timings.empty()) { + auto entry = timings.top(); + timings.pop(); + + const std::string node_type = entry.second.first; + const float memory = entry.second.second / 1000.0f; + + const int64_t node_type_total_us = entry.first; + const float time_per_run_ms = node_type_total_us / 1000.0f; + + const float percentage = + ((entry.first / static_cast<float>(accumulated_us)) * 100.0f); + cdf += percentage; + + InitField(stream, 24) << node_type; + InitField(stream, 9) << node_type_map_count[node_type]; + InitField(stream, 10) << time_per_run_ms; + InitField(stream, 10) << percentage << "%"; + InitField(stream, 10) << cdf << "%"; + InitField(stream, 10) << memory; + InitField(stream, 9) << node_type_map_times_called[node_type]; + stream << std::endl; + } + stream << std::endl; + return stream.str(); +} + +std::string StatsCalculator::GetStatsByMetric(const std::string& title, + SortingMetric sorting_metric, + int num_stats) const { + std::vector<const Detail*> details; + OrderNodesByMetric(sorting_metric, &details); + + double cumulative_stat_on_node = 0; + + std::stringstream stream; + stream << HeaderString(title) << std::endl; + int stat_num = 0; + for (auto detail : details) { + ++stat_num; + if (num_stats > 0 && stat_num > num_stats) { + break; + } + + // TODO(andrewharp): Make this keep track of the particular metric for cdf. + cumulative_stat_on_node += detail->rel_end_us.sum(); + stream << ColumnString(*detail, cumulative_stat_on_node, run_total_us_) + << std::endl; + } + stream << std::endl; + return stream.str(); +} + +std::string StatsCalculator::GetOutputString() const { + std::stringstream stream; + if (options_.show_run_order) { + stream << GetStatsByMetric("Run Order", BY_RUN_ORDER, + options_.run_order_limit); + } + if (options_.show_time) { + stream << GetStatsByMetric("Top by Computation Time", BY_TIME, + options_.time_limit); + } + if (options_.show_memory) { + stream << GetStatsByMetric("Top by Memory Use", BY_MEMORY, + options_.memory_limit); + } + if (options_.show_type) { + stream << GetStatsByNodeType(); + } + if (options_.show_summary) { + stream << GetShortSummary() << std::endl; + } + return stream.str(); +} + +void StatsCalculator::AddNodeStats(const std::string& name, + const std::string& type, int64_t run_order, + int64_t start_us, int64_t rel_end_us, + int64_t mem_used) { + Detail* detail = nullptr; + if (details_.find(name) == details_.end()) { + details_.insert({name, {}}); + detail = &details_.at(name); + detail->type = type; + detail->name = name; + detail->run_order = run_order; + } else { + detail = &details_.at(name); + } + detail->start_us.UpdateStat(start_us); + detail->rel_end_us.UpdateStat(rel_end_us); + detail->mem_used.UpdateStat(mem_used); + detail->times_called++; +} + +} // namespace tensorflow diff --git a/tests/tools/tflite_run/CMakeLists.txt b/tests/tools/tflite_run/CMakeLists.txt new file mode 100644 index 000000000..49d87318f --- /dev/null +++ b/tests/tools/tflite_run/CMakeLists.txt @@ -0,0 +1,26 @@ +list(APPEND TFLITE_RUN_SRCS "src/tflite_run.cc") +list(APPEND TFLITE_RUN_SRCS "src/bin_image.cc") +list(APPEND TFLITE_RUN_SRCS "src/args.cc") +list(APPEND TFLITE_RUN_SRCS "src/tensor_dumper.cc") +list(APPEND TFLITE_RUN_SRCS "src/tensor_loader.cc") + +add_executable(tflite_run ${TFLITE_RUN_SRCS}) +target_include_directories(tflite_run PRIVATE src) +target_link_libraries(tflite_run tensorflow-lite ${LIB_PTHREAD} dl nnfw_lib_tflite) +target_link_libraries(tflite_run boost_program_options boost_system boost_filesystem) + +install(TARGETS tflite_run DESTINATION bin) + +# TEST BUILD +nnfw_find_package(GTest) + +if(NOT GTest_FOUND) + return() +endif(NOT GTest_FOUND) + +## Add test cpp file +add_executable(tflite_test src/tflite_test.cc) +## Link test executable against gtest & gtest_main +target_link_libraries(tflite_test gtest gtest_main ${LIB_PTHREAD}) +## install test binary for packaging +install(TARGETS tflite_test DESTINATION unittest) diff --git a/tools/tflite_run/README.md b/tests/tools/tflite_run/README.md index 35d2b6497..35d2b6497 100644 --- a/tools/tflite_run/README.md +++ b/tests/tools/tflite_run/README.md diff --git a/tests/tools/tflite_run/src/args.cc b/tests/tools/tflite_run/src/args.cc new file mode 100644 index 000000000..713a0a9d2 --- /dev/null +++ b/tests/tools/tflite_run/src/args.cc @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "args.h" + +#include <iostream> + +#include <boost/filesystem.hpp> + +namespace TFLiteRun +{ + +Args::Args(const int argc, char **argv) +{ + Initialize(); + Parse(argc, argv); +} + +void Args::Initialize(void) +{ + + // General options + po::options_description general("General options"); + + // clang-format off + general.add_options() + ("help,h", "Display available options") + ("dump,d", po::value<std::string>()->default_value(""), "Output filename") + ("compare,c", po::value<std::string>()->default_value(""), "filename to be compared with") + ("tflite", po::value<std::string>()->required()); + // clang-format on + + _options.add(general); + _positional.add("tflite", 1); +} + +void Args::Parse(const int argc, char **argv) +{ + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(_options).positional(_positional).run(), + vm); + po::notify(vm); + +#if 0 // Enable this when we have mutually conflicting options + { + auto conflicting_options = [&](const std::string &o1, const std::string &o2) { + if ((vm.count(o1) && !vm[o1].defaulted()) && (vm.count(o2) && !vm[o2].defaulted())) + { + throw boost::program_options::error(std::string("Two options '") + o1 + "' and '" + o2 + + "' cannot be given at once."); + } + }; + + conflicting_options("input", "compare"); + } +#endif + + if (vm.count("help")) + { + std::cout << "tflite_run\n\n"; + std::cout << "Usage: " << argv[0] << " <.tflite> [<options>]\n\n"; + std::cout << _options; + std::cout << "\n"; + + exit(0); + } + + if (vm.count("dump")) + { + _dump_filename = vm["dump"].as<std::string>(); + } + + if (vm.count("compare")) + { + _compare_filename = vm["compare"].as<std::string>(); + } + + if (vm.count("tflite")) + { + _tflite_filename = vm["tflite"].as<std::string>(); + + if (_tflite_filename.empty()) + { + // TODO Print usage instead of the below message + std::cerr << "Please specify tflite file. Run with `--help` for usage." + << "\n"; + + exit(1); + } + else + { + if (!boost::filesystem::exists(_tflite_filename)) + { + std::cerr << "tflite file not found: " << _tflite_filename << "\n"; + } + } + } +} + +} // end of namespace TFLiteRun diff --git a/tests/tools/tflite_run/src/args.h b/tests/tools/tflite_run/src/args.h new file mode 100644 index 000000000..5561544eb --- /dev/null +++ b/tests/tools/tflite_run/src/args.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_RUN_ARGS_H__ +#define __TFLITE_RUN_ARGS_H__ + +#include <string> +#include <boost/program_options.hpp> + +namespace po = boost::program_options; + +namespace TFLiteRun +{ + +class Args +{ +public: + Args(const int argc, char **argv); + void print(void); + + const std::string &getTFLiteFilename(void) const { return _tflite_filename; } + const std::string &getDumpFilename(void) const { return _dump_filename; } + const std::string &getCompareFilename(void) const { return _compare_filename; } + +private: + void Initialize(); + void Parse(const int argc, char **argv); + +private: + po::positional_options_description _positional; + po::options_description _options; + + std::string _tflite_filename; + std::string _dump_filename; + std::string _compare_filename; +}; + +} // end of namespace TFLiteRun + +#endif // __TFLITE_RUN_ARGS_H__ diff --git a/tools/tflite_run/src/bin_image.cc b/tests/tools/tflite_run/src/bin_image.cc index 16d4c94f7..16d4c94f7 100644 --- a/tools/tflite_run/src/bin_image.cc +++ b/tests/tools/tflite_run/src/bin_image.cc diff --git a/tools/tflite_run/src/bin_image.h b/tests/tools/tflite_run/src/bin_image.h index 845011be6..845011be6 100644 --- a/tools/tflite_run/src/bin_image.h +++ b/tests/tools/tflite_run/src/bin_image.h diff --git a/tools/tflite_run/src/tensor_dumper.cc b/tests/tools/tflite_run/src/tensor_dumper.cc index 8568c9b67..8568c9b67 100644 --- a/tools/tflite_run/src/tensor_dumper.cc +++ b/tests/tools/tflite_run/src/tensor_dumper.cc diff --git a/tools/tflite_run/src/tensor_dumper.h b/tests/tools/tflite_run/src/tensor_dumper.h index 2805f1076..2805f1076 100644 --- a/tools/tflite_run/src/tensor_dumper.h +++ b/tests/tools/tflite_run/src/tensor_dumper.h diff --git a/tests/tools/tflite_run/src/tensor_loader.cc b/tests/tools/tflite_run/src/tensor_loader.cc new file mode 100644 index 000000000..934b78f40 --- /dev/null +++ b/tests/tools/tflite_run/src/tensor_loader.cc @@ -0,0 +1,67 @@ +#include "tensor_loader.h" + +#include <assert.h> + +#include <fstream> + +#include "misc/tensor/Shape.h" + +namespace TFLiteRun +{ + +TensorLoader::TensorLoader(tflite::Interpreter &interpreter) + : _interpreter(interpreter), _raw_data(nullptr) +{ +} + +void TensorLoader::load(const std::string &filename) +{ + // TODO Handle file open/read error + std::ifstream file(filename, std::ios::ate | std::ios::binary); + size_t file_size = file.tellg(); + file.seekg(0, std::ios::beg); + + uint32_t num_tensors = 0; + file.read(reinterpret_cast<char *>(&num_tensors), sizeof(num_tensors)); + + int tensor_indices_raw[num_tensors]; + file.read(reinterpret_cast<char *>(tensor_indices_raw), sizeof(tensor_indices_raw)); + std::vector<int> tensor_indices(tensor_indices_raw, tensor_indices_raw + num_tensors); + + _raw_data = std::unique_ptr<float>(new float[file_size]); + file.read(reinterpret_cast<char *>(_raw_data.get()), file_size); + + size_t offset = 0; + for (const auto &o : tensor_indices) + { + const TfLiteTensor *tensor = _interpreter.tensor(o); + + // Convert tensor shape to `Shape` from `tensor->dims` + nnfw::misc::tensor::Shape shape(static_cast<size_t>(tensor->dims->size)); + for (int d = 0; d < tensor->dims->size; d++) + { + shape.dim(d) = tensor->dims->data[d]; + } + + float *base = _raw_data.get() + offset; + + assert(tensor->bytes % sizeof(float) == 0); + offset += (tensor->bytes / sizeof(float)); + + _tensor_map.insert(std::make_pair(o, nnfw::tflite::TensorView<float>(shape, base))); + } + + // The file size and total output tensor size must match + assert(file_size == sizeof(num_tensors) + sizeof(tensor_indices_raw) + offset * sizeof(float)); + + file.close(); +} + +const nnfw::tflite::TensorView<float> &TensorLoader::get(int tensor_idx) const +{ + auto found = _tensor_map.find(tensor_idx); + assert(found != _tensor_map.end()); + return found->second; +} + +} // end of namespace TFLiteRun diff --git a/tests/tools/tflite_run/src/tensor_loader.h b/tests/tools/tflite_run/src/tensor_loader.h new file mode 100644 index 000000000..fc4a37a08 --- /dev/null +++ b/tests/tools/tflite_run/src/tensor_loader.h @@ -0,0 +1,35 @@ +#ifndef __TFLITE_RUN_TENSOR_LOADER_H__ +#define __TFLITE_RUN_TENSOR_LOADER_H__ + +#include <sys/mman.h> + +#include <string> +#include <unordered_map> + +#include "tflite/TensorView.h" + +namespace tflite +{ +class Interpreter; +} + +namespace TFLiteRun +{ + +class TensorLoader +{ +public: + TensorLoader(tflite::Interpreter &interpreter); + void load(const std::string &filename); + const nnfw::tflite::TensorView<float> &get(int tensor_idx) const; + size_t getNums() const { return _tensor_map.size(); } + +private: + tflite::Interpreter &_interpreter; + std::unique_ptr<float> _raw_data; + std::unordered_map<int, nnfw::tflite::TensorView<float>> _tensor_map; +}; + +} // end of namespace TFLiteRun + +#endif // __TFLITE_RUN_TENSOR_LOADER_H__ diff --git a/tests/tools/tflite_run/src/tflite_run.cc b/tests/tools/tflite_run/src/tflite_run.cc new file mode 100644 index 000000000..5be6909e5 --- /dev/null +++ b/tests/tools/tflite_run/src/tflite_run.cc @@ -0,0 +1,262 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite/ext/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#include "bin_image.h" +#include "args.h" +#include "tensor_dumper.h" +#include "tensor_loader.h" +#include "misc/benchmark.h" +#include "misc/environment.h" +#include "misc/fp32.h" +#include "tflite/Diff.h" +#include "tflite/Assert.h" +#include "tflite/Session.h" +#include "tflite/InterpreterSession.h" +#include "tflite/NNAPISession.h" +#include "misc/tensor/IndexIterator.h" +#include "misc/tensor/Object.h" + +#include <iostream> +#include <chrono> +#include <algorithm> + +using namespace tflite; +using namespace nnfw::tflite; +using namespace std::placeholders; // for _1, _2 ... + +void print_max_idx(float *f, int size) +{ + float *p = std::max_element(f, f + size); + std::cout << "max:" << p - f; +} + +int main(const int argc, char **argv) +{ + bool use_nnapi = false; + + if (std::getenv("USE_NNAPI") != nullptr) + { + use_nnapi = true; + } + + StderrReporter error_reporter; + + TFLiteRun::Args args(argc, argv); + + auto model = FlatBufferModel::BuildFromFile(args.getTFLiteFilename().c_str(), &error_reporter); + std::unique_ptr<Interpreter> interpreter; + + std::chrono::milliseconds t_prepare(0); + std::chrono::milliseconds t_invoke(0); + + nnfw::misc::benchmark::measure(t_prepare) << [&](void) { + BuiltinOpResolver resolver; + + InterpreterBuilder builder(*model, resolver); + + TFLITE_ENSURE(builder(&interpreter)) + + interpreter->SetNumThreads(1); + }; + + std::shared_ptr<nnfw::tflite::Session> sess; + + if (use_nnapi) + { + sess = std::make_shared<nnfw::tflite::NNAPISession>(interpreter.get()); + } + else + { + sess = std::make_shared<nnfw::tflite::InterpreterSession>(interpreter.get()); + } + + sess->prepare(); + + TFLiteRun::TensorLoader tensor_loader(*interpreter); + + // Load input from dumped tensor file. + if (!args.getCompareFilename().empty()) + { + tensor_loader.load(args.getCompareFilename()); + + for (const auto &o : interpreter->inputs()) + { + const auto &tensor_view = tensor_loader.get(o); + TfLiteTensor *tensor = interpreter->tensor(o); + + memcpy(reinterpret_cast<void *>(tensor->data.f), + reinterpret_cast<const void *>(tensor_view._base), tensor->bytes); + } + } + else + { + const int seed = 1; /* TODO Add an option for seed value */ + RandomGenerator randgen{seed, 0.0f, 2.0f}; + + // No input specified. So we fill the input tensors with random values. + for (const auto &o : interpreter->inputs()) + { + TfLiteTensor *tensor = interpreter->tensor(o); + if (tensor->type == kTfLiteInt32) + { + // Generate singed 32-bit integer (s32) input + auto tensor_view = nnfw::tflite::TensorView<int32_t>::make(*interpreter, o); + + int32_t value = 0; + + nnfw::misc::tensor::iterate(tensor_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + // Gather operation: index should be within input coverage. + tensor_view.at(ind) = value; + value++; + }; + } + else if (tensor->type == kTfLiteUInt8) + { + // Generate unsigned 8-bit integer input + auto tensor_view = nnfw::tflite::TensorView<uint8_t>::make(*interpreter, o); + + uint8_t value = 0; + + nnfw::misc::tensor::iterate(tensor_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + // TODO Generate random values + tensor_view.at(ind) = value; + value = (value + 1) & 0xFF; + }; + } + else if (tensor->type == kTfLiteBool) + { + // Generate bool input + auto tensor_view = nnfw::tflite::TensorView<bool>::make(*interpreter, o); + + auto fp = static_cast<bool (RandomGenerator::*)(const ::nnfw::misc::tensor::Shape &, + const ::nnfw::misc::tensor::Index &)>( + &RandomGenerator::generate<bool>); + const nnfw::misc::tensor::Object<bool> data(tensor_view.shape(), + std::bind(fp, randgen, _1, _2)); + + nnfw::misc::tensor::iterate(tensor_view.shape()) + << [&](const nnfw::misc::tensor::Index &ind) { + const auto value = data.at(ind); + tensor_view.at(ind) = value; + }; + } + else + { + assert(tensor->type == kTfLiteFloat32); + + const float *end = reinterpret_cast<const float *>(tensor->data.raw_const + tensor->bytes); + for (float *ptr = tensor->data.f; ptr < end; ptr++) + { + *ptr = randgen.generate<float>(); + } + } + } + } + + TFLiteRun::TensorDumper tensor_dumper; + // Must be called before `interpreter->Invoke()` + tensor_dumper.addTensors(*interpreter, interpreter->inputs()); + + std::cout << "input tensor indices = ["; + for (const auto &o : interpreter->inputs()) + { + std::cout << o << ","; + } + std::cout << "]" << std::endl; + + nnfw::misc::benchmark::measure(t_invoke) << [&sess](void) { + if (!sess->run()) + { + assert(0 && "run failed!"); + } + }; + + sess->teardown(); + + // Must be called after `interpreter->Invoke()` + tensor_dumper.addTensors(*interpreter, interpreter->outputs()); + + std::cout << "output tensor indices = ["; + for (const auto &o : interpreter->outputs()) + { + std::cout << o << "("; + + print_max_idx(interpreter->tensor(o)->data.f, interpreter->tensor(o)->bytes / sizeof(float)); + + std::cout << "),"; + } + std::cout << "]" << std::endl; + + std::cout << "Prepare takes " << t_prepare.count() / 1000.0 << " seconds" << std::endl; + std::cout << "Invoke takes " << t_invoke.count() / 1000.0 << " seconds" << std::endl; + + if (!args.getDumpFilename().empty()) + { + const std::string &dump_filename = args.getDumpFilename(); + tensor_dumper.dump(dump_filename); + std::cout << "Input/output tensors have been dumped to file \"" << dump_filename << "\"." + << std::endl; + } + + if (!args.getCompareFilename().empty()) + { + const std::string &compare_filename = args.getCompareFilename(); + std::cout << "========================================" << std::endl; + std::cout << "Comparing the results with \"" << compare_filename << "\"." << std::endl; + std::cout << "========================================" << std::endl; + + // TODO Code duplication (copied from RandomTestRunner) + + int tolerance = 1; + nnfw::misc::env::IntAccessor("TOLERANCE").access(tolerance); + + auto equals = [tolerance](float lhs, float rhs) { + // NOTE Hybrid approach + // TODO Allow users to set tolerance for absolute_epsilon_equal + if (nnfw::misc::fp32::absolute_epsilon_equal(lhs, rhs)) + { + return true; + } + + return nnfw::misc::fp32::epsilon_equal(lhs, rhs, tolerance); + }; + + nnfw::misc::tensor::Comparator comparator(equals); + TfLiteInterpMatchApp app(comparator); + bool res = true; + + for (const auto &o : interpreter->outputs()) + { + auto expected = tensor_loader.get(o); + auto obtained = nnfw::tflite::TensorView<float>::make(*interpreter, o); + + res = res && app.compareSingleTensorView(expected, obtained, o); + } + + if (!res) + { + return 255; + } + } + + return 0; +} diff --git a/tools/tflite_run/src/tflite_test.cc b/tests/tools/tflite_run/src/tflite_test.cc index d0d36c229..d0d36c229 100644 --- a/tools/tflite_run/src/tflite_test.cc +++ b/tests/tools/tflite_run/src/tflite_test.cc diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index f57262f29..7181629f3 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -1,9 +1,6 @@ -add_subdirectory(tflite_run) -add_subdirectory(tflite_benchmark) -if (BUILD_TFLITE_BENCHMARK_MODEL) - add_subdirectory(tflite_benchmark_model) -endif() -add_subdirectory(tflite_examples) -add_subdirectory(nnapi_test) +if(OBS_BUILD) + return() +endif(OBS_BUILD) + add_subdirectory(nnapi_quickcheck) add_subdirectory(opencl_tool) diff --git a/tools/cross/arm/sources.list.bionic b/tools/cross/arm/sources.list.bionic new file mode 100644 index 000000000..210955740 --- /dev/null +++ b/tools/cross/arm/sources.list.bionic @@ -0,0 +1,11 @@ +deb http://ports.ubuntu.com/ubuntu-ports/ bionic main restricted universe +deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic main restricted universe + +deb http://ports.ubuntu.com/ubuntu-ports/ bionic-updates main restricted universe +deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-updates main restricted universe + +deb http://ports.ubuntu.com/ubuntu-ports/ bionic-backports main restricted +deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-backports main restricted + +deb http://ports.ubuntu.com/ubuntu-ports/ bionic-security main restricted universe multiverse +deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-security main restricted universe multiverse diff --git a/tools/cross/build_rootfs.sh b/tools/cross/build_rootfs.sh index 11e5eff76..3d0837bb6 100755 --- a/tools/cross/build_rootfs.sh +++ b/tools/cross/build_rootfs.sh @@ -73,6 +73,9 @@ for i in "$@" ; do xenial) __LinuxCodeName=xenial ;; + bionic) + __LinuxCodeName=bionic + ;; --setproxy*) proxyip="${i#*=}" __Apt="Acquire::http::proxy \"http://$proxyip/\";\n" diff --git a/tools/modelgen/CONV_2D.template.json b/tools/modelgen/CONV_2D.template.json deleted file mode 100644 index 34ce09f35..000000000 --- a/tools/modelgen/CONV_2D.template.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "version":3, - "operator_codes": [ - { - "builtin_code": "CONV_2D" - } - ], - "subgraphs": [ - { - "tensors": [ - { - "shape": [ - 1, - 3, - 3, - 3 - ], - "buffer": 0, - "name": "input", - "quantization": { - "min": [ - 0.0 - ], - "max": [ - 255.0 - ] - } - }, - { - "shape": [ - 1, - 3, - 3, - 3 - ], - "buffer": 1, - "name": "weights", - "quantization": { - } - }, - { - "shape": [ - 1 - ], - "buffer": 2, - "name": "convolution_bias", - "quantization": { - } - }, - { - "shape": [ - 1, - 3, - 3, - 1 - ], - "buffer": 3, - "name": "output", - "quantization": { - } - } - ], - "inputs": [ - 0 - ], - "outputs": [ - 3 - ], - "operators": [ - { - "opcode_index": 0, - "inputs": [ - 0, - 1, - 2 - ], - "outputs": [ - 3 - ], - "builtin_options_type": "Conv2DOptions", - "builtin_options": { - "padding": "SAME", - "stride_w": 1, - "stride_h": 1, - "fused_activation_function": "RELU" - } - } - ] - } - ], - "description": "TOCO Converted.", - "buffers": [ - {}, - { - "data": [] - }, - { - "data": [] - }, - {} - ] -} diff --git a/tools/modelgen/modelgen.py b/tools/modelgen/modelgen.py deleted file mode 100755 index 112a1e8a1..000000000 --- a/tools/modelgen/modelgen.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/python - -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import numpy as np -import os -import struct - -dir_path = os.path.dirname(os.path.realpath(__file__)) - -builtin_ops = [ - "CONV_2D", -] - -# N,H,W,C -input_shape = [1, 6, 6, 3] -kernel_shape = [1, 3, 3, 3] - -# load template file. -with open(dir_path + "/CONV_2D.template.json", 'r') as f: - graph = json.loads(f.read()) - f.close() - -tensors = graph['subgraphs'][0]['tensors'] -buffers = graph['buffers'] - -buffer_map = {} - -# shape setup -for t in tensors: - if t['name'] == 'input': - t['shape'] = input_shape - elif t['name'] == 'weights': - t['shape'] = kernel_shape - elif t['name'] == 'convolution_bias': - # bias size = N of weight - t['shape'] = [kernel_shape[0]] - elif t['name'] == 'output': - # just for now, the same padding algorithm. - # stride = 1 - t['shape'][0] = 1 # N - t['shape'][1] = input_shape[1] # H - t['shape'][2] = input_shape[2] # W - t['shape'][3] = kernel_shape[0] # C - - buffer_map[t['buffer']] = {'name': t['name'], 'shape': t['shape']} - -# buffer setup -for i in range(len(buffers)): - if buffer_map[i]['name'] == 'weights': - shape = buffer_map[i]['shape'] - - weight = np.ones(shape) - n = shape[0] - h = shape[1] - w = shape[2] - c = shape[3] - for nn in range(n): - for hh in range(h): - for ww in range(w): - for cc in range(c): - if cc == 0: - weight[nn][hh][ww][cc] = 1.0 - else: - weight[nn][hh][ww][cc] = 0.0 - - weight_list = weight.flatten() - weight_bytes = struct.pack('%sf' % (len(weight_list)), *weight_list) - weight_uints = struct.unpack('%sB' % (len(weight_list) * 4), weight_bytes) - - buffers[i]['data'] = list(weight_uints) - - elif buffer_map[i]['name'] == 'convolution_bias': - # weight of N - shape = buffer_map[i]['shape'] - - bias = np.zeros(shape) - bias_list = bias.flatten() - bias_bytes = struct.pack('%sf' % (len(bias_list)), *bias_list) - bias_uints = struct.unpack('%sB' % (len(bias_list) * 4), bias_bytes) - - buffers[i]['data'] = list(bias_uints) - -with open('model.json', 'w') as f: - f.write(json.dumps(graph, indent=2)) diff --git a/tools/modelgen/modelgen.sh b/tools/modelgen/modelgen.sh deleted file mode 100755 index 563240da9..000000000 --- a/tools/modelgen/modelgen.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -SCRIPT_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" -ROOT_PATH=$SCRIPT_PATH/../.. -FLATC=$ROOT_PATH/Product/out/bin/flatc - -if [ ! -e "$1" ]; then - echo "file not exists: $1" - exit 1 -fi - -JSON_FILE=$1 -JSON_FILENAME=${TFLITE_FILE##*\/} -TFLITE_FILENAME=${TFLITE_FILENAME%\.json}.tflite - -$FLATC -b $ROOT_PATH/externals/tensorflow/tensorflow/contrib/lite/schema/schema.fbs $JSON_FILE - diff --git a/tools/nnapi_quickcheck/CMakeLists.txt b/tools/nnapi_quickcheck/CMakeLists.txt index 9dd7f5b3b..c88155aa1 100644 --- a/tools/nnapi_quickcheck/CMakeLists.txt +++ b/tools/nnapi_quickcheck/CMakeLists.txt @@ -8,8 +8,8 @@ list(REMOVE_ITEM NNAPI_QUICKCHECK_LIB_SOURCES ${NNAPI_QUICKCHECK_LIB_TESTS}) add_library(nnapi_quickcheck_common ${NNAPI_QUICKCHECK_LIB_SOURCES}) target_include_directories(nnapi_quickcheck_common PUBLIC "inc") -target_link_libraries(nnapi_quickcheck_common nnfw_util) -target_link_libraries(nnapi_quickcheck_common nnfw_support_tflite) +target_link_libraries(nnapi_quickcheck_common nnfw_lib_misc) +target_link_libraries(nnapi_quickcheck_common nnfw_lib_tflite) add_executable(nnapi_quickcheck_lib_env_test "lib/env.test.cpp") target_link_libraries(nnapi_quickcheck_lib_env_test nnapi_quickcheck_common) diff --git a/tools/nnapi_quickcheck/tests/add_1.cpp b/tools/nnapi_quickcheck/tests/add_1.cpp index 52aa2afa0..098b8591b 100644 --- a/tools/nnapi_quickcheck/tests/add_1.cpp +++ b/tools/nnapi_quickcheck/tests/add_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_2.cpp b/tools/nnapi_quickcheck/tests/add_2.cpp index 9b5b19c06..12851f1b0 100644 --- a/tools/nnapi_quickcheck/tests/add_2.cpp +++ b/tools/nnapi_quickcheck/tests/add_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_3.cpp b/tools/nnapi_quickcheck/tests/add_3.cpp index e692fe314..a7b2f436e 100644 --- a/tools/nnapi_quickcheck/tests/add_3.cpp +++ b/tools/nnapi_quickcheck/tests/add_3.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/TensorShapeUtils.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/TensorShapeUtils.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_4.cpp b/tools/nnapi_quickcheck/tests/add_4.cpp index e519f1731..217438fb1 100644 --- a/tools/nnapi_quickcheck/tests/add_4.cpp +++ b/tools/nnapi_quickcheck/tests/add_4.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_5.cpp b/tools/nnapi_quickcheck/tests/add_5.cpp index cacb5e42d..2f02c8292 100644 --- a/tools/nnapi_quickcheck/tests/add_5.cpp +++ b/tools/nnapi_quickcheck/tests/add_5.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_6.cpp b/tools/nnapi_quickcheck/tests/add_6.cpp index 245b7ad39..3af2c4af3 100644 --- a/tools/nnapi_quickcheck/tests/add_6.cpp +++ b/tools/nnapi_quickcheck/tests/add_6.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_7.cpp b/tools/nnapi_quickcheck/tests/add_7.cpp index 43d285c72..1ebf18e32 100644 --- a/tools/nnapi_quickcheck/tests/add_7.cpp +++ b/tools/nnapi_quickcheck/tests/add_7.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_8.cpp b/tools/nnapi_quickcheck/tests/add_8.cpp index ec11c3969..d4a5e4838 100644 --- a/tools/nnapi_quickcheck/tests/add_8.cpp +++ b/tools/nnapi_quickcheck/tests/add_8.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_9.cpp b/tools/nnapi_quickcheck/tests/add_9.cpp index f3cf02875..6c0cc2240 100644 --- a/tools/nnapi_quickcheck/tests/add_9.cpp +++ b/tools/nnapi_quickcheck/tests/add_9.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/add_quan_1.cpp b/tools/nnapi_quickcheck/tests/add_quan_1.cpp index 45f0ba681..daf15e091 100644 --- a/tools/nnapi_quickcheck/tests/add_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/add_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/avg_pool_1.cpp b/tools/nnapi_quickcheck/tests/avg_pool_1.cpp index c938ed690..e68cfc5b3 100644 --- a/tools/nnapi_quickcheck/tests/avg_pool_1.cpp +++ b/tools/nnapi_quickcheck/tests/avg_pool_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/avg_pool_quan_1.cpp b/tools/nnapi_quickcheck/tests/avg_pool_quan_1.cpp index ba41c030c..b24733ab0 100644 --- a/tools/nnapi_quickcheck/tests/avg_pool_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/avg_pool_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/cast_1.cpp b/tools/nnapi_quickcheck/tests/cast_1.cpp index 01d49cd59..bf493d091 100644 --- a/tools/nnapi_quickcheck/tests/cast_1.cpp +++ b/tools/nnapi_quickcheck/tests/cast_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/cast_2.cpp b/tools/nnapi_quickcheck/tests/cast_2.cpp index b0032210d..d85781d0c 100644 --- a/tools/nnapi_quickcheck/tests/cast_2.cpp +++ b/tools/nnapi_quickcheck/tests/cast_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/cast_q_to_f_1.cpp b/tools/nnapi_quickcheck/tests/cast_q_to_f_1.cpp index 763ca940c..ede763701 100644 --- a/tools/nnapi_quickcheck/tests/cast_q_to_f_1.cpp +++ b/tools/nnapi_quickcheck/tests/cast_q_to_f_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/concat_1.cpp b/tools/nnapi_quickcheck/tests/concat_1.cpp index 77d670fed..cc14d083a 100644 --- a/tools/nnapi_quickcheck/tests/concat_1.cpp +++ b/tools/nnapi_quickcheck/tests/concat_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/concat_quan_1.cpp b/tools/nnapi_quickcheck/tests/concat_quan_1.cpp index cd522b049..f804b819b 100644 --- a/tools/nnapi_quickcheck/tests/concat_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/concat_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/conv_1.cpp b/tools/nnapi_quickcheck/tests/conv_1.cpp index 10046d1ce..57b6abdfd 100644 --- a/tools/nnapi_quickcheck/tests/conv_1.cpp +++ b/tools/nnapi_quickcheck/tests/conv_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/conv_quan_1.cpp b/tools/nnapi_quickcheck/tests/conv_quan_1.cpp index aebf2333a..025cccadd 100644 --- a/tools/nnapi_quickcheck/tests/conv_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/conv_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/dconv_1.cpp b/tools/nnapi_quickcheck/tests/dconv_1.cpp index bd0cacfd0..21d648331 100644 --- a/tools/nnapi_quickcheck/tests/dconv_1.cpp +++ b/tools/nnapi_quickcheck/tests/dconv_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/dconv_quan_1.cpp b/tools/nnapi_quickcheck/tests/dconv_quan_1.cpp index 43f305f06..7cb9383e4 100644 --- a/tools/nnapi_quickcheck/tests/dconv_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/dconv_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/dequantize_1.cpp b/tools/nnapi_quickcheck/tests/dequantize_1.cpp index fe310a11d..467392dc3 100644 --- a/tools/nnapi_quickcheck/tests/dequantize_1.cpp +++ b/tools/nnapi_quickcheck/tests/dequantize_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/div_1.cpp b/tools/nnapi_quickcheck/tests/div_1.cpp index ffa0d6cac..1fe0f7a28 100644 --- a/tools/nnapi_quickcheck/tests/div_1.cpp +++ b/tools/nnapi_quickcheck/tests/div_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/div_2.cpp b/tools/nnapi_quickcheck/tests/div_2.cpp index c836c259a..883167e95 100644 --- a/tools/nnapi_quickcheck/tests/div_2.cpp +++ b/tools/nnapi_quickcheck/tests/div_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/fully_connected_1.cpp b/tools/nnapi_quickcheck/tests/fully_connected_1.cpp index 57af253f5..ea5df23d2 100644 --- a/tools/nnapi_quickcheck/tests/fully_connected_1.cpp +++ b/tools/nnapi_quickcheck/tests/fully_connected_1.cpp @@ -16,16 +16,16 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" #include "env.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp b/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp index 1cb75fea0..c434d75f8 100644 --- a/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp @@ -16,16 +16,16 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" #include "env.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/gather_1.cpp b/tools/nnapi_quickcheck/tests/gather_1.cpp index 0d5b30eb6..b916676c7 100644 --- a/tools/nnapi_quickcheck/tests/gather_1.cpp +++ b/tools/nnapi_quickcheck/tests/gather_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/gather_2.cpp b/tools/nnapi_quickcheck/tests/gather_2.cpp index b3cb3c6ef..4b115685d 100644 --- a/tools/nnapi_quickcheck/tests/gather_2.cpp +++ b/tools/nnapi_quickcheck/tests/gather_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/logistic_quan_1.cpp b/tools/nnapi_quickcheck/tests/logistic_quan_1.cpp index dc6902d66..e9e3bc1f7 100644 --- a/tools/nnapi_quickcheck/tests/logistic_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/logistic_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/max_pool_1.cpp b/tools/nnapi_quickcheck/tests/max_pool_1.cpp index bb538141d..07fa9ac5b 100644 --- a/tools/nnapi_quickcheck/tests/max_pool_1.cpp +++ b/tools/nnapi_quickcheck/tests/max_pool_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp b/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp index 5768ddde8..d241c43ba 100644 --- a/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/mul_1.cpp b/tools/nnapi_quickcheck/tests/mul_1.cpp index 3a4ae5c8e..cf7282bc7 100644 --- a/tools/nnapi_quickcheck/tests/mul_1.cpp +++ b/tools/nnapi_quickcheck/tests/mul_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/mul_2.cpp b/tools/nnapi_quickcheck/tests/mul_2.cpp index b117cd602..734be8e3f 100644 --- a/tools/nnapi_quickcheck/tests/mul_2.cpp +++ b/tools/nnapi_quickcheck/tests/mul_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/mul_quan_1.cpp b/tools/nnapi_quickcheck/tests/mul_quan_1.cpp index 7207a90fb..72903fe42 100644 --- a/tools/nnapi_quickcheck/tests/mul_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/mul_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,8 +24,8 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/relu1_1.cpp b/tools/nnapi_quickcheck/tests/relu1_1.cpp index aeefe2f06..b729c50c3 100644 --- a/tools/nnapi_quickcheck/tests/relu1_1.cpp +++ b/tools/nnapi_quickcheck/tests/relu1_1.cpp @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -23,9 +23,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu6_1.cpp b/tools/nnapi_quickcheck/tests/relu6_1.cpp index 76bbe954c..cafbe8377 100644 --- a/tools/nnapi_quickcheck/tests/relu6_1.cpp +++ b/tools/nnapi_quickcheck/tests/relu6_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu6_quan_1.cpp b/tools/nnapi_quickcheck/tests/relu6_quan_1.cpp index fe849f955..3d8ce4f7c 100644 --- a/tools/nnapi_quickcheck/tests/relu6_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/relu6_quan_1.cpp @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -23,9 +23,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu_1.cpp b/tools/nnapi_quickcheck/tests/relu_1.cpp index f754c3d8e..c7141ca6a 100644 --- a/tools/nnapi_quickcheck/tests/relu_1.cpp +++ b/tools/nnapi_quickcheck/tests/relu_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu_2.cpp b/tools/nnapi_quickcheck/tests/relu_2.cpp index c08764520..74ab113c2 100644 --- a/tools/nnapi_quickcheck/tests/relu_2.cpp +++ b/tools/nnapi_quickcheck/tests/relu_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu_3.cpp b/tools/nnapi_quickcheck/tests/relu_3.cpp index 6c41bc12a..f876e18ee 100644 --- a/tools/nnapi_quickcheck/tests/relu_3.cpp +++ b/tools/nnapi_quickcheck/tests/relu_3.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/relu_quan_1.cpp b/tools/nnapi_quickcheck/tests/relu_quan_1.cpp index 59fe5d254..7bc6181ad 100644 --- a/tools/nnapi_quickcheck/tests/relu_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/relu_quan_1.cpp @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -23,9 +23,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/reshape_1.cpp b/tools/nnapi_quickcheck/tests/reshape_1.cpp index 21d35a5ca..a35623ced 100644 --- a/tools/nnapi_quickcheck/tests/reshape_1.cpp +++ b/tools/nnapi_quickcheck/tests/reshape_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/reshape_quan_1.cpp b/tools/nnapi_quickcheck/tests/reshape_quan_1.cpp index 7f852fd80..fb1db77e7 100644 --- a/tools/nnapi_quickcheck/tests/reshape_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/reshape_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/resize_bilinear_1.cpp b/tools/nnapi_quickcheck/tests/resize_bilinear_1.cpp index 37d8ab525..3c7ce28b1 100644 --- a/tools/nnapi_quickcheck/tests/resize_bilinear_1.cpp +++ b/tools/nnapi_quickcheck/tests/resize_bilinear_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_quickcheck/tests/softmax_1.cpp b/tools/nnapi_quickcheck/tests/softmax_1.cpp index 5e15b6169..b9cc6bf0f 100644 --- a/tools/nnapi_quickcheck/tests/softmax_1.cpp +++ b/tools/nnapi_quickcheck/tests/softmax_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/softmax_2.cpp b/tools/nnapi_quickcheck/tests/softmax_2.cpp index 489016af5..402435718 100644 --- a/tools/nnapi_quickcheck/tests/softmax_2.cpp +++ b/tools/nnapi_quickcheck/tests/softmax_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/softmax_quan_1.cpp b/tools/nnapi_quickcheck/tests/softmax_quan_1.cpp index 347262fa6..2c12e1104 100644 --- a/tools/nnapi_quickcheck/tests/softmax_quan_1.cpp +++ b/tools/nnapi_quickcheck/tests/softmax_quan_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/split_1.cpp b/tools/nnapi_quickcheck/tests/split_1.cpp index 742c5dbed..735a0777f 100644 --- a/tools/nnapi_quickcheck/tests/split_1.cpp +++ b/tools/nnapi_quickcheck/tests/split_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/split_2.cpp b/tools/nnapi_quickcheck/tests/split_2.cpp index d70e35ca7..0d4bf3e13 100644 --- a/tools/nnapi_quickcheck/tests/split_2.cpp +++ b/tools/nnapi_quickcheck/tests/split_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/split_3.cpp b/tools/nnapi_quickcheck/tests/split_3.cpp index 47359642d..eaa65bdc2 100644 --- a/tools/nnapi_quickcheck/tests/split_3.cpp +++ b/tools/nnapi_quickcheck/tests/split_3.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/split_4.cpp b/tools/nnapi_quickcheck/tests/split_4.cpp index d16e75d5c..394c392b6 100644 --- a/tools/nnapi_quickcheck/tests/split_4.cpp +++ b/tools/nnapi_quickcheck/tests/split_4.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -25,9 +25,9 @@ #include "util/environment.h" #include "util/feature/Shape.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <random> diff --git a/tools/nnapi_quickcheck/tests/sub_1.cpp b/tools/nnapi_quickcheck/tests/sub_1.cpp index 2734f525e..9e4ff4ce7 100644 --- a/tools/nnapi_quickcheck/tests/sub_1.cpp +++ b/tools/nnapi_quickcheck/tests/sub_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/sub_2.cpp b/tools/nnapi_quickcheck/tests/sub_2.cpp index 88e060847..2702edbf1 100644 --- a/tools/nnapi_quickcheck/tests/sub_2.cpp +++ b/tools/nnapi_quickcheck/tests/sub_2.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/sub_3.cpp b/tools/nnapi_quickcheck/tests/sub_3.cpp index fd2d4aaea..6122e2486 100644 --- a/tools/nnapi_quickcheck/tests/sub_3.cpp +++ b/tools/nnapi_quickcheck/tests/sub_3.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/sub_4.cpp b/tools/nnapi_quickcheck/tests/sub_4.cpp index 993acddce..28922fe4b 100644 --- a/tools/nnapi_quickcheck/tests/sub_4.cpp +++ b/tools/nnapi_quickcheck/tests/sub_4.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/sub_5.cpp b/tools/nnapi_quickcheck/tests/sub_5.cpp index 610be5754..e421a12c2 100644 --- a/tools/nnapi_quickcheck/tests/sub_5.cpp +++ b/tools/nnapi_quickcheck/tests/sub_5.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/sub_6.cpp b/tools/nnapi_quickcheck/tests/sub_6.cpp index b9e37c8d7..00bfc5423 100644 --- a/tools/nnapi_quickcheck/tests/sub_6.cpp +++ b/tools/nnapi_quickcheck/tests/sub_6.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/tanh_1.cpp b/tools/nnapi_quickcheck/tests/tanh_1.cpp index 67847eceb..6a9a29933 100644 --- a/tools/nnapi_quickcheck/tests/tanh_1.cpp +++ b/tools/nnapi_quickcheck/tests/tanh_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <iostream> #include <cassert> diff --git a/tools/nnapi_quickcheck/tests/topk_v2_1.cpp b/tools/nnapi_quickcheck/tests/topk_v2_1.cpp index bb9d8535e..4b145e5a7 100644 --- a/tools/nnapi_quickcheck/tests/topk_v2_1.cpp +++ b/tools/nnapi_quickcheck/tests/topk_v2_1.cpp @@ -16,7 +16,7 @@ #include "gtest/gtest.h" -#include "support/tflite/kernels/register.h" +#include "tflite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -24,9 +24,9 @@ #include "memory.h" #include "util/environment.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Quantization.h" -#include "support/tflite/interp/FunctionBuilder.h" +#include "tflite/Diff.h" +#include "tflite/Quantization.h" +#include "tflite/interp/FunctionBuilder.h" #include <chrono> #include <iostream> diff --git a/tools/nnapi_test/CMakeLists.txt b/tools/nnapi_test/CMakeLists.txt deleted file mode 100644 index cd9f2a524..000000000 --- a/tools/nnapi_test/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -list(APPEND SOURCES "src/nnapi_test.cc") - -add_executable(nnapi_test ${SOURCES}) -target_link_libraries(nnapi_test nnfw_support_tflite) -install(TARGETS nnapi_test DESTINATION bin) diff --git a/tools/nnapi_test/src/nnapi_test.cc b/tools/nnapi_test/src/nnapi_test.cc deleted file mode 100644 index 1f98bb07d..000000000 --- a/tools/nnapi_test/src/nnapi_test.cc +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" - -#include "support/tflite/interp/FlatBufferBuilder.h" -#include "support/tflite/Diff.h" - -#include <iostream> -#include <stdexcept> - -using namespace tflite; -using namespace tflite::ops::builtin; - -int main(const int argc, char **argv) -{ - if (argc < 2) - { - std::cerr << "nnapi_test\n\n"; - std::cerr << "Usage: " << argv[0] << " <.tflite>\n\n"; - return 1; - } - - const auto filename = argv[1]; - - StderrReporter error_reporter; - - auto model = FlatBufferModel::BuildFromFile(filename, &error_reporter); - - const nnfw::support::tflite::interp::FlatBufferBuilder builder(*model); - - try - { - return RandomTestRunner::make(0).run(builder); - } - catch (const std::exception &e) - { - std::cerr << e.what() << std::endl; - return 1; - } -} diff --git a/tools/opencl_tool/src/opencl_info.cc b/tools/opencl_tool/src/opencl_info.cc index 49673d1de..f8b635d43 100644 --- a/tools/opencl_tool/src/opencl_info.cc +++ b/tools/opencl_tool/src/opencl_info.cc @@ -42,11 +42,9 @@ #include <iostream> #include <vector> -void printDeviceInfo(int n, cl::Device &device, cl::Device &default_device) +void printDeviceInfo(int n, cl::Device &device) { - bool is_default = (device() == default_device()); - std::cout << "\t\t\t#" << n << " Device: (id: " << device() << ") " - << (is_default ? " -> default" : "") << "\n"; + std::cout << "\t\t\t#" << n << " Device: (id: " << device() << ")\n"; const auto name = device.getInfo<CL_DEVICE_NAME>(); std::cout << "\t\t\t\tName: " << name << "\n"; @@ -67,7 +65,7 @@ void printDeviceInfo(int n, cl::Device &device, cl::Device &default_device) std::cout << "\t\t\t\tMax Clock Frequency: " << max_clock_frequency << "\n"; } -void printContext(int n, cl::Platform &plat, int device_type, cl::Context &default_context) +void printContext(int n, cl::Platform &plat, int device_type) { if (device_type == CL_DEVICE_TYPE_DEFAULT) std::cout << "\t #" << n << " context when CL_DEVICE_TYPE_DEFAULT"; @@ -98,9 +96,7 @@ void printContext(int n, cl::Platform &plat, int device_type, cl::Context &defau return; } - bool is_default = (context() == default_context()); - - std::cout << " (id: " << context() << ") " << (is_default ? " -> default" : "") << "\n"; + std::cout << " (id: " << context() << ")\n"; const auto device_num = context.getInfo<CL_CONTEXT_NUM_DEVICES>(); std::cout << "\t\t\tDevice num: " << device_num << "\n"; @@ -108,46 +104,63 @@ void printContext(int n, cl::Platform &plat, int device_type, cl::Context &defau return; auto devices = context.getInfo<CL_CONTEXT_DEVICES>(); - auto default_device = cl::Device::getDefault(); int d = 0; for (auto device : devices) - printDeviceInfo(++d, device, default_device); + printDeviceInfo(++d, device); } -void printPlatform(int n, cl::Platform &plat, cl::Platform &default_platform) +void printPlatform(int n, cl::Platform &plat) { - bool is_default = (plat() == default_platform()); - - std::cout << "#" << n << ". Platform: (id: " << plat() << ") " - << (is_default ? " -> default" : "") << "\n"; + std::cout << "#" << n << ". Platform: (id: " << plat() << ")\n"; - cl::Context default_context = cl::Context::getDefault(); - std::cout << "\t" - << "default context: " << default_context() << "\n"; + cl::Context default_context; int x = 0; - printContext(++x, plat, CL_DEVICE_TYPE_DEFAULT, default_context); - printContext(++x, plat, CL_DEVICE_TYPE_GPU, default_context); - printContext(++x, plat, CL_DEVICE_TYPE_CPU, default_context); - printContext(++x, plat, CL_DEVICE_TYPE_ACCELERATOR, default_context); - printContext(++x, plat, CL_DEVICE_TYPE_CUSTOM, default_context); - printContext(++x, plat, CL_DEVICE_TYPE_ALL, default_context); + printContext(++x, plat, CL_DEVICE_TYPE_DEFAULT); + printContext(++x, plat, CL_DEVICE_TYPE_GPU); + printContext(++x, plat, CL_DEVICE_TYPE_CPU); + printContext(++x, plat, CL_DEVICE_TYPE_ACCELERATOR); + printContext(++x, plat, CL_DEVICE_TYPE_CUSTOM); + printContext(++x, plat, CL_DEVICE_TYPE_ALL); } int main(const int argc, char **argv) { - std::cout << "\nOpenCL Platform, Context, Device Info are as follows:\n\n"; - - std::vector<cl::Platform> platforms; - cl::Platform::get(&platforms); - - cl::Platform defaultPlatform = cl::Platform::getDefault(); - - int n = 0; - for (auto &p : platforms) + try + { + std::cout << "\nOpenCL Platform, Context, Device Info are as follows:\n\n"; + { + std::vector<cl::Platform> platforms; + cl::Platform::get(&platforms); + + int n = 0; + for (auto &p : platforms) + { + printPlatform(++n, p); + } + } + + std::cout << "\n\nShowing default platfom, context, and device:\n" + << "(Note: this may throw an error depending on system or compiler)\n"; + { + auto platform = cl::Platform::getDefault(); + std::cout << "* default platform (id: " << platform() << ")\n"; + auto context = cl::Context::getDefault(); + std::cout << "* default context (id: " << context() << ")\n"; + auto device = cl::Device::getDefault(); + printDeviceInfo(0, device); + } + } + catch (cl::Error &err) + { + std::cerr << "cl::Error was thrown: Please refer to the info below:\n" + << "\terror code: " << err.err() << ", meaning: " << err.what() << std::endl; + } + catch (std::system_error &err) { - printPlatform(++n, p, defaultPlatform); + std::cerr << "std::system_error was thrown: Please refer to the info below:\n" + << "\terror code: " << err.code() << ", meaning: " << err.what() << std::endl; } return 0; diff --git a/tools/tensorflow_model_freezer/base_freezer.py b/tools/tensorflow_model_freezer/base_freezer.py index ccfd811cf..96d011718 100644 --- a/tools/tensorflow_model_freezer/base_freezer.py +++ b/tools/tensorflow_model_freezer/base_freezer.py @@ -129,8 +129,9 @@ class BaseFreezer(object): util.importGraphIntoSession(sess, frozen_pb_path, "") try: - tflite_model = tf.contrib.lite.toco_convert(sess.graph_def, input_node_list, - output_node_list) + converter = tf.contrib.lite.TFLiteConverter.from_session( + sess, input_node_list, output_node_list) + tflite_model = converter.convert() open(tflite_path, "wb").write(tflite_model) print("# 3. TOCO : Created TFLITE file :\n\t-{}\n".format(tflite_path)) except Exception: diff --git a/tools/tensorflow_model_freezer/readme.md b/tools/tensorflow_model_freezer/readme.md index f627f11a5..0aabf1085 100644 --- a/tools/tensorflow_model_freezer/readme.md +++ b/tools/tensorflow_model_freezer/readme.md @@ -1,3 +1,13 @@ +## Prerequisites + +The scripts here use TensorFlow's tools, so you need an environment to run TensorFlow. Running the scripts within this tutorial requires: + +* Install [TensorFlow](https://www.tensorflow.org/install/) v1.12 or later + * Use pip + ``` + $ pip install tensorflow==1.12 + ``` + ## What this tool is about This tool generaes the following files: @@ -16,5 +26,13 @@ By define `Test Cases`, you can easily and quickly generate files for various ra - Run `~/nnfw$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ python tools/tensorflow_model_freezer/sample/MUL_gen.py ~/temp` - Files will be generated under `~/temp` +## How to run +``` +$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py +$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \ + tools/tensorflow_model_freezer/sample/name_of_this_file.py \ + ~/temp # directory where model files are saved +``` + ## Note - This tool is tested with Python 2.7 and 3 diff --git a/tools/tensorflow_model_freezer/sample/ARGMAX_gen.py b/tools/tensorflow_model_freezer/sample/ARGMAX_gen.py new file mode 100755 index 000000000..68e2262c0 --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/ARGMAX_gen.py @@ -0,0 +1,100 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'argmax') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"argmax_4d": [base.Tensor([1, 2, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + + output_node = tf.arg_max(x_tensor, 0, output_type=tf.int32, name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/ARGMIN_gen.py b/tools/tensorflow_model_freezer/sample/ARGMIN_gen.py new file mode 100755 index 000000000..68b399234 --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/ARGMIN_gen.py @@ -0,0 +1,100 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'argmin') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"argmin_4d": [base.Tensor([1, 2, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + + output_node = tf.arg_min(x_tensor, 0, name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/DIV_gen.py b/tools/tensorflow_model_freezer/sample/DIV_gen.py index c4e9cde07..d1b794cd7 100755 --- a/tools/tensorflow_model_freezer/sample/DIV_gen.py +++ b/tools/tensorflow_model_freezer/sample/DIV_gen.py @@ -124,13 +124,6 @@ class Gen(base.BaseFreezer): return (input_list, [output_node]) -''' -How to run -$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py -$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \ - tools/tensorflow_model_freezer/sample/name_of_this_file.py \ - ~/temp # directory where model files are saved -''' # -------- if __name__ == "__main__": diff --git a/tools/tensorflow_model_freezer/sample/LOGICAL_AND_gen.py b/tools/tensorflow_model_freezer/sample/LOGICAL_AND_gen.py new file mode 100755 index 000000000..912af65b2 --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/LOGICAL_AND_gen.py @@ -0,0 +1,105 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'logical_and') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"logical_and_4d": [base.Tensor([1, 2, 4, 3]), base.Tensor([1, 2, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + y_tensor = self.createTFInput(test_case_tensor[1], input_list) + + output_node = tf.logical_and( + tf.greater(x_tensor, tf.constant(0.0)), + tf.less(y_tensor, tf.constant(1.0)), + name=tc_name) + + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/LOGICAL_NOT_gen.py b/tools/tensorflow_model_freezer/sample/LOGICAL_NOT_gen.py new file mode 100755 index 000000000..34c0994ea --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/LOGICAL_NOT_gen.py @@ -0,0 +1,100 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'logical_not') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"logical_not_4d": [base.Tensor([1, 2, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + + output_node = tf.logical_not(tf.greater(x_tensor, tf.constant(0.0)), name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/LOGICAL_OR_gen.py b/tools/tensorflow_model_freezer/sample/LOGICAL_OR_gen.py new file mode 100755 index 000000000..714a52e8d --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/LOGICAL_OR_gen.py @@ -0,0 +1,104 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'logical_or') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"logical_or_4d": [base.Tensor([1, 2, 4, 3]), base.Tensor([1, 2, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + y_tensor = self.createTFInput(test_case_tensor[1], input_list) + + output_node = tf.logical_or( + tf.greater(x_tensor, tf.constant(0.0)), + tf.less(y_tensor, tf.constant(1.0)), + name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/MUL_gen.py b/tools/tensorflow_model_freezer/sample/MUL_gen.py index f2a92547b..596898dbb 100755 --- a/tools/tensorflow_model_freezer/sample/MUL_gen.py +++ b/tools/tensorflow_model_freezer/sample/MUL_gen.py @@ -59,8 +59,8 @@ class Gen(base.BaseFreezer): "mul_1d_scalarConst": [base.Tensor([5]), base.Tensor([], const_val=1.1)], # mul by scalar "mul_2d_scalarConst": [base.Tensor([5, 3]), - base.Tensor([], const_val=1.1)], - "mul_1d_scalar": [base.Tensor([5, 3]), base.Tensor([])] + base.Tensor([], const_val=1.1)] + # "mul_2d_scalar": [base.Tensor([5, 3]), base.Tensor([])] # not support scalar input } def buildModel(self, sess, test_case_tensor, tc_name): @@ -104,13 +104,6 @@ class Gen(base.BaseFreezer): return (input_list, [output_node]) -''' -How to run -$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py -$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \ - tools/tensorflow_model_freezer/sample/name_of_this_file.py \ - ~/temp # directory where model files are saved -''' # -------- if __name__ == "__main__": diff --git a/tools/tensorflow_model_freezer/sample/SQUEEZE_gen.py b/tools/tensorflow_model_freezer/sample/SQUEEZE_gen.py index 88b3dfcb2..12fb5122e 100755 --- a/tools/tensorflow_model_freezer/sample/SQUEEZE_gen.py +++ b/tools/tensorflow_model_freezer/sample/SQUEEZE_gen.py @@ -103,13 +103,6 @@ class Gen(base.BaseFreezer): return (input_list, [output_node]) -''' -How to run -$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py -$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \ - tools/tensorflow_model_freezer/sample/name_of_this_file.py \ - ~/temp # directory where model files are saved -''' # -------- if __name__ == "__main__": diff --git a/tools/tensorflow_model_freezer/sample/STACK_gen.py b/tools/tensorflow_model_freezer/sample/STACK_gen.py new file mode 100755 index 000000000..2bea40698 --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/STACK_gen.py @@ -0,0 +1,101 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'stack') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"stack_4d": [base.Tensor([1, 4, 3]), base.Tensor([1, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + y_tensor = self.createTFInput(test_case_tensor[1], input_list) + + output_node = tf.stack([x_tensor, y_tensor], 0, name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/tensorflow_model_freezer/sample/TOPK_gen.py b/tools/tensorflow_model_freezer/sample/TOPK_gen.py index 0c16d5b75..8f1882bd1 100755 --- a/tools/tensorflow_model_freezer/sample/TOPK_gen.py +++ b/tools/tensorflow_model_freezer/sample/TOPK_gen.py @@ -63,6 +63,7 @@ class Gen(base.BaseFreezer): ''' input_list = [] + output_list = [] # ------ modify below for your model FROM here -------# @@ -70,11 +71,13 @@ class Gen(base.BaseFreezer): y_tensor = self.createTFInput(test_case_tensor[1], input_list) # defining output node and input list - output_node = tf.nn.top_k( + values_op, indices_op = tf.nn.top_k( x_tensor, y_tensor, # add your input here name=tc_name) # do not modify name + output_list.append(values_op) + output_list.append(indices_op) # ------ modify UNTIL here for your model -------# # Note if don't have any CONST value, creating checkpoint file fails. @@ -92,16 +95,9 @@ class Gen(base.BaseFreezer): # ------ modify appropriate return value -------# # returning (input_node_list, output_node_list) - return (input_list, [output_node]) + return (input_list, output_list) -''' -How to run -$ chmod +x tools/tensorflow_model_freezer/sample/name_of_this_file.py -$ PYTHONPATH=$PYTHONPATH:./tools/tensorflow_model_freezer/ \ - tools/tensorflow_model_freezer/sample/name_of_this_file.py \ - ~/temp # directory where model files are saved -''' # -------- if __name__ == "__main__": diff --git a/tools/tensorflow_model_freezer/sample/UNSTACK_gen.py b/tools/tensorflow_model_freezer/sample/UNSTACK_gen.py new file mode 100644 index 000000000..c5bce0d18 --- /dev/null +++ b/tools/tensorflow_model_freezer/sample/UNSTACK_gen.py @@ -0,0 +1,100 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +import platform +import tensorflow as tf +import argparse + +import base_freezer as base +import model_freezer_util as util + + +class Gen(base.BaseFreezer): + ''' + class to generate tflite files for MUL + ''' + + def __init__(self, path): + super(self.__class__, self).__init__(path) + + def getOutputDirectory(self): + return os.path.join(self.root_output_path, + 'unstack') # the root path of generated files + + def getTestCases(self): + ''' + this returns a a hash containg test cases. + key of return hash is test case name and + value of return hash is test is a list of input tensor metadata. + test name (key of hash) is used as + - prefix of file name to be generated (don't use white space or special characters) + - output node name pf graph + ''' + return {"unstack_4d": [base.Tensor([4, 4, 3])]} + + def buildModel(self, sess, test_case_tensor, tc_name): + ''' + This method is called per test case (defined by getTestCases()). + + keyword argument: + test_case_tensor -- test case tensor metadata + For example, if a test case is { "mul_1d_1d": [base.Tensor([5]), base.Tensor([5])] } + test_case_tensor is [base.Tensor([5]), base.Tensor([5])] + ''' + + input_list = [] + + # ------ modify below for your model FROM here -------# + x_tensor = self.createTFInput(test_case_tensor[0], input_list) + + output_node = tf.unstack([x_tensor], 4, 1, name=tc_name) + # ------ modify UNTIL here for your model -------# + + # Note if don't have any CONST value, creating checkpoint file fails. + # The next lines insert such (CONST) to prevent such error. + # So, Graph.pb/pbtxt contains this garbage info, + # but this garbage info will be removed in Graph_frozen.pb/pbtxt + garbage = tf.get_variable( + "garbage", [1], dtype=tf.float32, initializer=tf.zeros_initializer()) + init_op = tf.global_variables_initializer() + garbage_value = [0] + sess.run(tf.assign(garbage, garbage_value)) + + sess.run(init_op) + + # ------ modify appropriate return value -------# + + # returning (input_node_list, output_node_list) + return (input_list, [output_node]) + + +# -------- +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description='Converted Tensorflow model in python to frozen model.') + parser.add_argument( + "out_dir", + help= + "directory where generated pb, pbtxt, checkpoint and Tensorboard log files are stored." + ) + + args = parser.parse_args() + root_output_path = args.out_dir + + Gen(root_output_path).createSaveFreezeModel() diff --git a/tools/test_driver/README.md b/tools/test_driver/README.md deleted file mode 100644 index 2981267a5..000000000 --- a/tools/test_driver/README.md +++ /dev/null @@ -1,63 +0,0 @@ -# How the test driver works - -## Unittest -- There are two kinds of unittest: - - Kernel ACL - - Runtime -- Related file : `run_unittest.sh` -- Usage : -``` -$ ./tools/test_driver/test_driver.sh \ - --artifactpath=. \ - --unittest -``` -- The `run_unittest.sh` usage : - -``` -$ LD_LIBRARY_PATH=Product/out/lib \ - ./tools/test_driver/run_unittest.sh \ - --reportdir=report \ - --unittestdir=Product/out/unittest -``` - -### Kernel ACL Unittest -- Test whether the various operations are performed successfully and whether the output and the expected value are the same. -- TC location : `libs/kernel/acl/src/` - -### Runtime Unittest -- Test whether the expected value and the actual output value are the same when the model is configured, compiled and executed. -- TC location : `runtimes/tests/neural_networks_test/` - -## Framework test -- Execute the **tflite model** using the given **driver**. -- There is a TC directory for each model, and a `config.sh` file exists in each TC directory. -- When `run_test.sh`, refer to the **tflite model** information in `config.sh`, download the file, and run the **tflite model** with the given **driver**. -- Related files : `run_test.sh` and `run_frameworktest.sh` -- TC location : - - `tests/framework/tests/` : Config directory for TC - - `tests/framework/cache/` : TC (Downloaded tflite model files) - -### Run tflite_run with various tflite models -- Driver : `tflite_run` -- Driver source location : `tools/tflite_run/` -- Usage : -``` -$ ./tools/test_driver/test_driver.sh \ - --artifactpath=. \ - --frameworktest -``` -- Related pages : [tflite_run](https://github.sec.samsung.net/STAR/nnfw/tree/master/tools/tflite_run) - -### Run nnapi_test with various tflite models -- `nnapi_test` runs tflite in two ways and compares the result: - 1. tflite interpreter - 2. `libneuralnetworks.so`, which could be PureACL or NNAPI depending on `--ldlibrarypath`(`LD_LIBRARY_PATH`) -- Driver : `nnapi_test` -- Driver source location : `tools/nnapi_test/` -- Usage : -``` -$ ./tools/test_driver/test_driver.sh \ - --artifactpath=. \ - --verification . -``` - diff --git a/tools/test_driver/benchmark_op_list.txt b/tools/test_driver/benchmark_op_list.txt deleted file mode 100644 index 166ddb7d7..000000000 --- a/tools/test_driver/benchmark_op_list.txt +++ /dev/null @@ -1,11 +0,0 @@ -add/4D -average_pool_2d/avgpool1 -average_pool_2d/avgpool1 -concat/concat1 -conv_2d/convolution1 -conv_2d/convolution2 -div/broadcast -max_pool_2d/maxpool1 -max_pool_2d/maxpool2 -resize_bilinear -softmax diff --git a/tools/test_driver/neurun_frameworktest_list.txt b/tools/test_driver/neurun_frameworktest_list.txt deleted file mode 100644 index b97558b0c..000000000 --- a/tools/test_driver/neurun_frameworktest_list.txt +++ /dev/null @@ -1,10 +0,0 @@ -average_pool_2d/avgpool1 -average_pool_2d/avgpool1 -conv_2d/convolution1 -conv_2d/convolution2 -max_pool_2d/maxpool1 -max_pool_2d/maxpool2 -softmax -reshape/reshape1 -MODELS/inception_nonslim -MODELS/inception_slim diff --git a/tools/test_driver/py/test_driver.py b/tools/test_driver/py/test_driver.py deleted file mode 100755 index 9ed97d202..000000000 --- a/tools/test_driver/py/test_driver.py +++ /dev/null @@ -1,398 +0,0 @@ -#!/usr/bin/env python - -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import shutil -import argparse -import common -import subprocess -import sys - -mypath = os.path.abspath(os.path.dirname(__file__)) - - -def get_parsed_options(): - parser = argparse.ArgumentParser(prog='test_driver.py', usage='%(prog)s [options]') - - # artifactpath - parser.add_argument( - "--artifactpath", - action="store", - type=str, - dest="artifactpath", - default=".", - help="(should be passed) path that has tests/ and Product/") - - # test - parser.add_argument( - "--unittest", - action="store_true", - dest="unittest_on", - default=False, - help="(default=on) run unit test") - parser.add_argument( - "--unittestall", - action="store_true", - dest="unittestall_on", - default=False, - help="((default=off) run all unit test without skip, overrite --unittest option") - parser.add_argument( - "--verification", - action="store_true", - dest="verification_on", - default=False, - help="(default=on) run verification") - parser.add_argument( - "--frameworktest", - action="store_true", - dest="frameworktest_on", - default=False, - help="(default=off)run framework test") - - # benchmark - parser.add_argument( - "--benchmark", - action="store_true", - dest="benchmark_on", - default=False, - help="(default=off) run benchmark") - parser.add_argument( - "--benchmark_acl", - action="store_true", - dest="benchmarkacl_on", - default=False, - help="(default=off) run benchmark-acl") - parser.add_argument( - "--benchmark_op", - action="store_true", - dest="benchmarkop_on", - default=False, - help="(default=off) run benchmark per operation") - - # profile - parser.add_argument( - "--profile", - action="store_true", - dest="profile_on", - default=False, - help="(default=off) run profiling") - - # driverbin - parser.add_argument( - "--framework_driverbin", - action="store", - type=str, - dest="framework_driverbin", - help= - "(default=../../Product/out/bin/tflite_run) runner for runnning framework tests") - parser.add_argument( - "--verification_driverbin", - action="store", - type=str, - dest="verification_driverbin", - help= - "(default=../../Product/out/bin/nnapi_test) runner for runnning verification tests" - ) - parser.add_argument( - "--benchmark_driverbin", - action="store", - type=str, - dest="benchmark_driverbin", - help= - "(default=../../Product/out/bin/tflite_benchmark) runner for runnning benchmark") - - # etc. - parser.add_argument( - "--runtestsh", - action="store", - type=str, - dest="runtestsh", - help= - "(default=ARTIFACT_PATH/tests/framework/run_test.sh) run_test.sh with path where it is for framework test and verification" - ) - parser.add_argument( - "--unittestdir", - action="store", - type=str, - dest="unittestdir", - help= - "(default=ARTIFACT_PATH/Product/out/unittest) directory that has unittest binaries for unit test" - ) - parser.add_argument( - "--ldlibrarypath", - action="store", - type=str, - dest="ldlibrarypath", - help= - "(default=ARTIFACT_PATH/Product/out/lib) path that you want to include libraries") - parser.add_argument( - "--frameworktest_list_file", - action="store", - type=str, - dest="frameworktest_list_file", - help= - "(default=ARTIFACT_PATH/tools/test_driver/pureacl_frameworktest_list.txt) filepath of model list for test" - ) - parser.add_argument( - "--reportdir", - action="store", - type=str, - dest="reportdir", - help="(default=ARTIFACT_PATH/report) directory to save report") - - # env - parser.add_argument( - "--usennapi", - action="store_true", - dest="usennapi_on", - default=True, - help="(default=on) declare USE_NNAPI=1") - parser.add_argument( - "--nousennapi", - action="store_false", - dest="usennapi_on", - help="(default=off) declare nothing about USE_NNAPI") - parser.add_argument( - "--acl_envon", - action="store_true", - dest="aclenv_on", - default=False, - help="(default=off) declare envs for ACL") - - options = parser.parse_args() - return options - - -def run_unittest(options): - cmd = "{artifactpath}/tools/test_driver/run_unittest.sh \ - --reportdir={reportdir} \ - --unittestdir={unittestdir}".format( - artifactpath=options.artifactpath, - reportdir=options.reportdir, - unittestdir=options.unittestdir) - if options.unittestall_on: - cmd += " --runall" - os.system(cmd) - - -def run_frameworktest(options): - if type(options.framework_driverbin) is not str: - options.framework_driverbin = options.artifactpath + "/Product/out/bin/tflite_run" - if (os.path.exists(options.framework_driverbin) == False): - print("Cannot find {driverbin}".format(driverbin=options.framework_driverbin)) - sys.exit(1) - - cmd = "{artifactpath}/tools/test_driver/run_frameworktest.sh \ - --runtestsh={runtestsh} \ - --driverbin={driverbin} \ - --reportdir={reportdir} \ - --tapname=framework_test.tap \ - --logname=framework_test.log \ - --testname='Frameworktest'".format( - runtestsh=options.runtestsh, - driverbin=options.framework_driverbin, - reportdir=options.reportdir, - artifactpath=options.artifactpath) - os.system(cmd) - - -def run_verification(options): - if type(options.verification_driverbin) is not str: - options.verification_driverbin = options.artifactpath + "/Product/out/bin/nnapi_test" - if (os.path.exists(options.verification_driverbin) == False): - print("Cannot find {driverbin}".format( - driverbin=options.verification_driverbin)) - sys.exit(1) - - cmd = "{artifactpath}/tools/test_driver/run_frameworktest.sh \ - --runtestsh={runtestsh} \ - --driverbin={driverbin} \ - --reportdir={reportdir} \ - --tapname=verification_test.tap \ - --logname=verification_test.log \ - --testname='Verification'".format( - runtestsh=options.runtestsh, - driverbin=options.verification_driverbin, - reportdir=options.reportdir, - artifactpath=options.artifactpath) - os.system(cmd) - - -def run_benchmark(options): - if type(options.benchmark_driverbin) is not str: - options.benchmark_driverbin = options.artifactpath + "/Product/out/bin/tflite_benchmark" - if (os.path.exists(options.benchmark_driverbin) == False): - print("Cannot find {driverbin}".format(driverbin=options.benchmark_driverbin)) - sys.exit(1) - - cmd = "{artifactpath}/tools/test_driver/run_benchmark.sh \ - --runtestsh={runtestsh} \ - --driverbin={driverbin} \ - --reportdir={reportdir}/benchmark".format( - runtestsh=options.runtestsh, - driverbin=options.benchmark_driverbin, - reportdir=options.reportdir, - artifactpath=options.artifactpath) - os.system(cmd) - - -def run_benchmarkop(options): - if type(options.benchmark_driverbin) is not str: - options.benchmark_driverbin = options.artifactpath + "/Product/out/bin/tflite_benchmark" - if (os.path.exists(options.benchmark_driverbin) == False): - print("Cannot find {driverbin}".format(driverbin=options.benchmark_driverbin)) - sys.exit(1) - - cmd = "{artifactpath}/tools/test_driver/run_benchmark_op.sh \ - --runtestsh={runtestsh} \ - --driverbin={driverbin} \ - --reportdir={reportdir}/benchmark_op \ - --modelfilepath={artifactpath}/tests/framework \ - --frameworktest_list_file={frameworktest_list_file}".format( - runtestsh=options.runtestsh, - driverbin=options.benchmark_driverbin, - artifactpath=options.artifactpath, - reportdir=options.reportdir, - frameworktest_list_file=options.frameworktest_list_file) - os.system(cmd) - - -def run_benchmarkacl(options): - cmd = "{artifactpath}/tools/test_driver/run_benchmark_acl.sh \ - --reportdir={reportdir}/benchmark \ - --bindir={artifactpath}/Product/out/bin".format( - reportdir=options.reportdir, artifactpath=options.artifactpath) - os.system(cmd) - - -def make_json_for_benchmark_result(options): - cmd = "source {artifactpath}/tools/test_driver/print_to_json.sh && ".format( - artifactpath=options.artifactpath) - if options.benchmarkop_on: - cmd += "print_to_json {artifactpath}/report/benchmark_op \ - {reportdir} \"benchmark_op_result.json\"".format( - reportdir=options.reportdir, artifactpath=options.artifactpath) - else: - cmd += "print_to_json {artifactpath}/report/benchmark \ - {reportdir} \"benchmark_result.json\"".format( - reportdir=options.reportdir, artifactpath=options.artifactpath) - sp = subprocess.Popen(["/bin/bash", "-i", "-c", cmd]) - sp.communicate() - - -def run_profile(options): - # FIXME: These driver and tflite test are set temporarily. Fix these to support flexibility - driver_bin = options.artifactpath + "/Product/out/bin/tflite_run" - tflite_test = options.artifactpath + "/tests/framework/cache/inceptionv3/inception_module/inception_test.tflite" - - # TODO: Enable operf to set directory where sample data puts on - shutil.rmtree("oprofile_data", ignore_errors=True) - - print("") - print("============================================") - cmd = "operf -g {driver_bin} {tflite_test}".format( - driver_bin=driver_bin, tflite_test=tflite_test) - os.system(cmd) - print("============================================") - print("") - - -def main(): - options = get_parsed_options() - - alltest_on = True - if True in [ - options.unittest_on, options.frameworktest_on, options.verification_on, - options.benchmark_on, options.benchmarkacl_on, options.benchmarkop_on, - options.profile_on - ]: - alltest_on = False - - # artifactpath - if os.path.isdir(options.artifactpath) and os.path.isdir( - options.artifactpath + "/tests") and os.path.isdir(options.artifactpath + - "/Product"): - options.artifactpath = os.path.abspath(options.artifactpath) - else: - print("Pass on with proper arifactpath") - sys.exit(1) - - # run_test.sh - if type(options.runtestsh) is not str or options.runtestsh == "": - options.runtestsh = options.artifactpath + "/tests/framework/run_test.sh" - - if (os.path.exists(options.runtestsh) == False): - print("Cannot find {runtestsh}".format(runtestsh=options.runtestsh)) - sys.exit(1) - - # unittest dir - if type(options.unittestdir) is not str or options.unittestdir == "": - options.unittestdir = options.artifactpath + "/Product/out/unittest" - - # LD_LIBRARY_PATH - if type(options.ldlibrarypath) is not str or options.ldlibrarypath == "": - options.ldlibrarypath = options.artifactpath + "/Product/out/lib" - - # report dir - if type(options.reportdir) is not str or options.reportdir == "": - options.reportdir = options.artifactpath + "/report" - - # set LD_LIBRARY_PATH - os.environ["LD_LIBRARY_PATH"] = options.ldlibrarypath - - # set USE_NNAPI - if options.usennapi_on == True: - os.environ["USE_NNAPI"] = "1" - - # set acl - if options.aclenv_on: - common.switch_nnfw_kernel_env("acl") - - # unittest - if alltest_on or options.unittest_on: - run_unittest(options) - - # frameworktest - if options.frameworktest_on: - run_frameworktest(options) - - # verification - if alltest_on or options.verification_on: - run_verification(options) - - # benchmark - if options.benchmark_on: - run_benchmark(options) - - # benchmark_acl - if options.benchmarkacl_on: - run_benchmarkacl(options) - - # benchmark_op - if options.benchmarkop_on: - run_benchmarkop(options) - - # make json file for benchmark result on ci - if options.benchmark_on or options.benchmarkacl_on or options.benchmarkop_on: - make_json_for_benchmark_result(options) - - # run profile - if options.profile_on: - run_profile(options) - - -if __name__ == "__main__": - main() diff --git a/tools/test_driver/run_benchmark.sh b/tools/test_driver/run_benchmark.sh deleted file mode 100755 index a1a0c2fa2..000000000 --- a/tools/test_driver/run_benchmark.sh +++ /dev/null @@ -1,146 +0,0 @@ -#!/bin/bash -# -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -MY_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" - -source $MY_PATH/common.sh - -BENCHMARK_RUN_TEST_SH= -BENCHMARK_DRIVER_BIN= -BENCHMARK_REPORT_DIR= -BENCHMARK_MODELS_FILE= -BENCHMARK_MODEL_LIST="inceptionv3/inception_nonslim inceptionv3/inception_slim mobilenet" - -function Usage() -{ - # TODO: Fill this - echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report" -} - -for i in "$@" -do - case $i in - -h|--help|help) - Usage - exit 1 - ;; - --runtestsh=*) - BENCHMARK_RUN_TEST_SH=${i#*=} - ;; - --driverbin=*) - BENCHMARK_DRIVER_BIN=${i#*=} - ;; - --reportdir=*) - BENCHMARK_REPORT_DIR=${i#*=} - BENCHMARK_MODELS_FILE=$BENCHMARK_REPORT_DIR/benchmark_models.txt - ;; - esac - shift -done - -function get_result_of_benchmark_test() -{ - local RUN_TEST_SH=$1 - local DRIVER_BIN=$2 - local MODEL=$3 - local LOG_FILE=$4 - - local RET=0 - $RUN_TEST_SH --driverbin=$DRIVER_BIN $MODEL > $LOG_FILE 2>&1 - RET=$? - if [[ $RET -ne 0 ]]; then - echo "Testing $MODEL aborted... exit code: $RET" - exit $RET - fi - - local RESULT=`grep -E '^Mean:' $LOG_FILE | sed -e 's/ms//g' | awk '{print $2}'` - echo "$RESULT" -} - -function print_result_of_benchmark_test() -{ - local NAME=$1 - local RESULT=$2 - local RESULT_FILE=$3 - - echo "$NAME $RESULT" > $RESULT_FILE -} - -function run_benchmark_test() -{ - local DRIVER_BIN=$BENCHMARK_DRIVER_BIN - local LOG_FILE= - local RESULT_FILE= - local RESULT= - local REPORT_MODEL_DIR= - - export COUNT=5 - echo "============================================" - local i=0 - for MODEL in $BENCHMARK_MODEL_LIST; do - echo "Benchmark test with `basename $DRIVER_BIN` & `echo $MODEL`" - echo $MODEL >> $BENCHMARK_MODELS_FILE - - REPORT_MODEL_DIR=$BENCHMARK_REPORT_DIR/$MODEL - mkdir -p $REPORT_MODEL_DIR - - # TFLite+CPU - LOG_FILE=$REPORT_MODEL_DIR/tflite_cpu.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_cpu.result - echo -n "TFLite + CPU................... " - unset USE_NNAPI - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_CPU" $RESULT $RESULT_FILE - - # TFLite+NNAPI(CPU fallback) - LOG_FILE=$REPORT_MODEL_DIR/tflite_nnapi_cpu.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnapi_cpu.result - echo -n "TFLite + NNAPI(CPU)............ " - export USE_NNAPI=1 - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_NNAPI_CPU" $RESULT $RESULT_FILE - - # TFLite+NNAPI(ACL) - LOG_FILE=$REPORT_MODEL_DIR/tflite_nnapi_acl.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnapi_acl.result - echo -n "TFLite + NNAPI(ACL)............ " - switch_nnfw_kernel_env "ON" "acl" - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_NNAPI_ACL" $RESULT $RESULT_FILE - unset USE_NNAPI - switch_nnfw_kernel_env "OFF" - - if [[ $i -ne $(echo $BENCHMARK_MODEL_LIST | wc -w)-1 ]]; then - echo "" - fi - i=$((i+1)) - done - echo "============================================" - unset COUNT -} - -if [ ! -e "$BENCHMARK_REPORT_DIR" ]; then - mkdir -p $BENCHMARK_REPORT_DIR -fi - -rm -rf $BENCHMARK_MODELS_FILE - -echo "" -run_benchmark_test -echo "" diff --git a/tools/test_driver/run_benchmark_op.sh b/tools/test_driver/run_benchmark_op.sh deleted file mode 100755 index 8ff80a45b..000000000 --- a/tools/test_driver/run_benchmark_op.sh +++ /dev/null @@ -1,209 +0,0 @@ -#!/bin/bash -# -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -MY_PATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" -NNFW_HOME="$(dirname $(dirname ${MY_PATH}))" -source $MY_PATH/common.sh - -BENCHMARK_RUN_TEST_SH= -BENCHMARK_DRIVER_BIN= -BENCHMARK_REPORT_DIR= -BENCHMARK_MODELS_FILE= -BENCHMARK_MODEL_LIST= -MODEL_CACHE_ROOT_PATH= -MODEL_TEST_ROOT_PATH= -PURE_ACL_RT_LIB_PATH= -PURE_LD_LIBRARY_PATH= -ORIGIN_LD_LIBRARY_PATH= -PURE_ACL_RT_ENV_FILE=$MY_PATH/benchmark_op_list.txt - -function Usage() -{ - # TODO: Fill this - echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report" -} - -for i in "$@" -do - case $i in - -h|--help|help) - Usage - exit 1 - ;; - --runtestsh=*) - BENCHMARK_RUN_TEST_SH=${i#*=} - ;; - --driverbin=*) - BENCHMARK_DRIVER_BIN=${i#*=} - ;; - --reportdir=*) - BENCHMARK_REPORT_DIR=${i#*=} - BENCHMARK_MODELS_FILE=$BENCHMARK_REPORT_DIR/benchmark_op_models.txt - ;; - --modelfilepath=*) - TEST_LIST_PATH=${i#*=} - MODEL_CACHE_ROOT_PATH=$TEST_LIST_PATH/cache - MODEL_TEST_ROOT_PATH=$TEST_LIST_PATH/tests - ;; - --frameworktest_list_file=*) - FRAMEWORKTEST_LIST_FILE=${i#*=} - ;; - esac - shift -done - -function get_result_of_benchmark_test() -{ - local RUN_TEST_SH=$1 - local DRIVER_BIN=$2 - local MODEL=$3 - local LOG_FILE=$4 - local PUREACL_LD_LIBRARY_PATH=$5 - - local RET=0 - $RUN_TEST_SH --driverbin=$DRIVER_BIN --ldlibrarypath=$PUREACL_LD_LIBRARY_PATH $MODEL > $LOG_FILE 2>&1 - RET=$? - if [[ $RET -ne 0 ]]; then - echo "Testing $MODEL aborted... exit code: $RET" - exit $RET - fi - - local RESULT=`grep -E '^Mean:' $LOG_FILE | sed -e 's/ms//g' | awk '{print $2}'` - echo "$RESULT" -} - -function print_result_of_benchmark_test() -{ - local NAME=$1 - local RESULT=$2 - local RESULT_FILE=$3 - echo "$NAME $RESULT" > $RESULT_FILE -} - -function get_benchmark_op_list() -{ - if [ ! -z "$FRAMEWORKTEST_LIST_FILE" ]; then - BENCHMARK_MODEL_LIST=$(cat "${FRAMEWORKTEST_LIST_FILE}") - else - BENCHMARK_MODEL_LIST=$(cat "${PURE_ACL_RT_ENV_FILE}") - fi - echo "BENCHMARK_MODEL_LIST=> $BENCHMARK_MODEL_LIST" -} - -function run_benchmark_test() -{ - local DRIVER_BIN=$BENCHMARK_DRIVER_BIN - local LOG_FILE= - local RESULT_FILE= - local RESULT= - local REPORT_MODEL_DIR= - - export COUNT=5 - echo "============================================" - local i=0 - for MODEL in $BENCHMARK_MODEL_LIST; do - STATUS="enabled" - source $MODEL_TEST_ROOT_PATH/$MODEL/config.sh - - LOWER_STATUS="$(echo $STATUS | awk '{print tolower($0)}')" - if [ "$LOWER_STATUS" == "disabled" ]; then - echo "" - echo "Skip $MODEL" - continue - fi - - echo "Benchmark test with `basename $DRIVER_BIN` & `echo $MODEL`" - echo $MODEL >> $BENCHMARK_MODELS_FILE - - REPORT_MODEL_DIR=$BENCHMARK_REPORT_DIR/$MODEL - mkdir -p $REPORT_MODEL_DIR - - # TFLite(CPU fallback) - LOG_FILE=$REPORT_MODEL_DIR/tflite_cpu_op.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_cpu_op.result - echo -n "TFLite(CPU fallback)................... " - unset USE_NNAPI - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_CPU" $RESULT $RESULT_FILE - - # TFLite+NNRuntime(CPU fallback) - LOG_FILE=$REPORT_MODEL_DIR/tflite_nnrt_cpu_op.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnrt_cpu_op.result - echo -n "TFLite + NNRuntime(CPU fallback)............ " - export USE_NNAPI=1 - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_NNRT_CPU" $RESULT $RESULT_FILE - - # TFLite+NNRuntime+ACL-Neon - LOG_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_neon_op.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_neon_op.result - echo -n "TFLite + NNRuntime + ACL-Neon............ " - switch_nnfw_kernel_env "ON" "neon" - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_NNRT_ACL-NEON" $RESULT $RESULT_FILE - switch_nnfw_kernel_env "OFF" - - # TFLite+NNRuntime+ACL-OpenCL - LOG_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_opencl_op.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_nnrt_acl_opencl_op.result - echo -n "TFLite + NNRuntime + ACL-OpenCL............ " - switch_nnfw_kernel_env "ON" "acl" - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_NNRT_ACL-OPENCL" $RESULT $RESULT_FILE - switch_nnfw_kernel_env "OFF" - - # TFLite+PureACLRuntime+ACL-OpenCL - if [ ! -d "$PURE_ACL_RT_LIB_PATH" ]; then - echo "Skip $MODEL in Pure ACL Runtime " - continue - fi - LOG_FILE=$REPORT_MODEL_DIR/tflite_pureaclrt_acl_opencl_op.txt - RESULT_FILE=$REPORT_MODEL_DIR/tflite_pureaclrt_acl_opencl_op.result - echo -n "TFLite + PureACLRuntime + ACL-OpenCL............ " - RESULT=$(get_result_of_benchmark_test $BENCHMARK_RUN_TEST_SH $DRIVER_BIN $MODEL $LOG_FILE $PURE_ACL_RT_LIB_PATH) - echo "$RESULT ms" - print_result_of_benchmark_test "TFLite_PUREACLRT_ACL-OPENCL" $RESULT $RESULT_FILE - unset USE_NNAPI - - if [[ $i -ne $(echo $BENCHMARK_MODEL_LIST | wc -w)-1 ]]; then - echo "" - fi - i=$((i+1)) - done - unset USE_NNAPI - unset COUNT - echo "============================================" -} - -if [ ! -e "$BENCHMARK_REPORT_DIR" ]; then - mkdir -p $BENCHMARK_REPORT_DIR -fi - -if [ -z "$PURE_ACL_RT_LIB_PATH" ]; then - PURE_ACL_RT_LIB_PATH="$NNFW_HOME/Product/out/lib/pureacl" -fi - -get_benchmark_op_list - -rm -rf $BENCHMARK_MODELS_FILE - -echo "" -run_benchmark_test -echo "" diff --git a/tools/test_driver/run_unittest.sh b/tools/test_driver/run_unittest.sh deleted file mode 100755 index abf3194ad..000000000 --- a/tools/test_driver/run_unittest.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/bash -# -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -UNITTEST_REPORT_DIR= -UNITTEST_TEST_DIR= -UNITTEST_RESULT=0 -UNITTEST_RUN_ALL="" - -function Usage() -{ - # TODO: Fill this - echo "Usage: LD_LIBRARY_PATH=Product/out/lib ./$0 --reportdir=report --unittestdir=Product/out/bin" -} - -get_gtest_option() -{ - local output_option="--gtest_output=xml:$UNITTEST_REPORT_DIR/$TEST_BIN.xml" - local filter_option - if [ -r "$UNITTEST_TEST_DIR/$TEST_BIN.skip" ]; then - filter_option="--gtest_filter=-$(grep -v '#' "$UNITTEST_TEST_DIR/$TEST_BIN.skip" | tr '\n' ':')" - fi - echo "$output_option $filter_option" -} - -for i in "$@" -do - case $i in - -h|--help|help) - Usage - exit 1 - ;; - --reportdir=*) - UNITTEST_REPORT_DIR=${i#*=} - ;; - --unittestdir=*) - UNITTEST_TEST_DIR=${i#*=} - ;; - --runall) - UNITTEST_RUN_ALL="true" - esac - shift -done - -# TODO: handle exceptions for params - -if [ ! -e "$UNITTEST_REPORT_DIR" ]; then - mkdir -p $UNITTEST_REPORT_DIR -fi - -echo "" -echo "============================================" -echo "Unittest start" -echo "============================================" - -num_unittest=0 -for TEST_BIN in `ls $UNITTEST_TEST_DIR --hide=*.skip`; do - num_unittest=$((num_unittest+1)) - echo "============================================" - echo "Starting set $num_unittest: $TEST_BIN..." - echo "============================================" - TEMP_UNITTEST_RESULT=0 - - if [ "$UNITTEST_RUN_ALL" == "true" ]; then - for TEST_LIST_VERBOSE_LINE in $($UNITTEST_TEST_DIR/$TEST_BIN --gtest_list_tests); do - if [[ $TEST_LIST_VERBOSE_LINE == *\. ]]; then - TEST_LIST_CATEGORY=$TEST_LIST_VERBOSE_LINE - else - TEST_LIST_ITEM="$TEST_LIST_CATEGORY""$TEST_LIST_VERBOSE_LINE" - $UNITTEST_TEST_DIR/$TEST_BIN --gtest_filter=$TEST_LIST_ITEM --gtest_output="xml:$UNITTEST_REPORT_DIR/$TEST_LIST_ITEM.xml" - fi - done - else - $UNITTEST_TEST_DIR/$TEST_BIN $(get_gtest_option) - TEMP_UNITTEST_RESULT=$? - fi - - if [[ $TEMP_UNITTEST_RESULT -ne 0 ]]; then - UNITTEST_RESULT=$TEMP_UNITTEST_RESULT - echo "$TEST_BIN failed... return code: $TEMP_UNITTEST_RESULT" - fi - echo "============================================" - echo "Finishing set $num_unittest: $TEST_BIN..." - echo "============================================" -done - -if [[ $UNITTEST_RESULT -ne 0 ]]; then - echo "============================================" - echo "Failed unit test... exit code: $UNITTEST_RESULT" - echo "============================================" - exit $UNITTEST_RESULT -fi - -echo "============================================" -echo "Completed total $num_unittest set of unittest" -echo "Unittest end" -echo "============================================" diff --git a/tools/test_driver/test_driver.sh b/tools/test_driver/test_driver.sh deleted file mode 100755 index 72be65dc1..000000000 --- a/tools/test_driver/test_driver.sh +++ /dev/null @@ -1,329 +0,0 @@ -#!/bin/bash -# -# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -e -# NOTE: Supposed that this script would be executed with an artifact path. -# The artifact path has tests/(test suite) and Product/ -# Reference this PR(https://github.sec.samsung.net/STAR/nnfw/pull/375). - -function Usage() -{ - echo "Usage: ./$0 --artifactpath=. # run all tests" - echo "Usage: ./$0 --artifactpath=/home/dragon/nnfw --frameworktest --verification --benchmark # run fw test & verfication and benchmark" - echo "" - echo "--artifactpath - (default={test_driver.sh's path}/../../) it should contain tests/ and Product/" - echo "" - echo "Following options are needed when you want to tests of specific types. If you don't pass any one, unittest and verification will be run" - echo "--unittest - (default=on) run unit test" - echo "--unittestall - (default=off) run all unit test without skip, overrite --unittest option" - echo "--frameworktest - (default=off) run framework test" - echo "--verification - (default=on) run verification" - echo "--frameworktest_list_file - filepath of model list for test" - echo "" - echo "Following option is only needed when you want to test benchmark." - echo "--benchmark_acl - (default=off) run benchmark-acl" - echo "--benchmark - (default=off) run benchmark" - echo "--benchmark_op - (default=off) run benchmark per operation" - echo "--benchmark_tflite_model - (default=off) run tflite_benchmark_model" - echo "" - echo "Following option is used for profiling." - echo "--profile - (default=off) run operf" - echo "" - echo "etc." - echo "--framework_driverbin - (default=../../Product/out/bin/tflite_run) runner for runnning framework tests" - echo "--verification_driverbin - (default=../../Product/out/bin/nnapi_test) runner for runnning verification tests" - echo "--benchmark_driverbin - (default=../../Product/out/bin/tflite_benchmark) runner for runnning benchmark" - echo "--runtestsh - (default=\$ARTIFACT_PATH/tests/framework/run_test.sh) run_test.sh with path where it is for framework test and verification" - echo "--unittestdir - (default=\$ARTIFACT_PATH/Product/out/unittest) directory that has unittest binaries for unit test" - echo "" - echo "--ldlibrarypath - (default=\$ARTIFACT_PATH/Product/out/lib) path that you want to include libraries" - echo "--usennapi - (default=on) declare USE_NNAPI=1" - echo "--nousennapi - (default=off) declare nothing about USE_NNAPI" - echo "--acl_envon - (default=off) declare envs for ACL" - echo "--reportdir - (default=\$ARTIFACT_PATH/report) directory to save report" - echo "" -} - -TEST_DRIVER_DIR="$( cd "$( dirname "${BASH_SOURCE}" )" && pwd )" -ARTIFACT_PATH="$TEST_DRIVER_DIR/../../" -FRAMEWORK_DRIVER_BIN="" -VERIFICATION_DRIVER_BIN="" -BENCHMARK_DRIVER_BIN="" -RUN_TEST_SH="" -UNIT_TEST_DIR="" -LD_LIBRARY_PATH_IN_SHELL="" -USE_NNAPI="USE_NNAPI=1" -ALLTEST_ON="true" -UNITTEST_ON="false" -UNITTESTALL_ON="false" -FRAMEWORKTEST_ON="false" -VERIFICATION_ON="false" -BENCHMARK_ON="false" -BENCHMARK_OP_ON="false" -BENCHMARK_TFLITE_MODEL_ON="false" -BENCHMARK_ACL_ON="false" -ACL_ENV_ON="false" -PROFILE_ON="false" -REPORT_DIR="" - -for i in "$@" -do - case $i in - -h|--help|help) - Usage - exit 1 - ;; - --artifactpath=*) - ARTIFACT_PATH=${i#*=} - ;; - --framework_driverbin=*) - FRAMEWORK_DRIVER_BIN=${i#*=} - ;; - --verification_driverbin=*) - VERIFICATION_DRIVER_BIN=${i#*=} - ;; - --benchmark_driverbin=*) - BENCHMARK_DRIVER_BIN=${i#*=} - ;; - --runtestsh=*) - RUN_TEST_SH=${i#*=} - ;; - --unittestdir=*) - UNIT_TEST_DIR=${i#*=} - ;; - --ldlibrarypath=*) - LD_LIBRARY_PATH_IN_SHELL=${i#*=} - ;; - --usennapi) - USE_NNAPI="USE_NNAPI=1" - ;; - --nousennapi) - USE_NNAPI="" - ;; - --unittest) - ALLTEST_ON="false" - UNITTEST_ON="true" - ;; - --unittestall) - ALLTEST_ON="false" - UNITTEST_ON="true" - UNITTESTALL_ON="true" - ;; - --frameworktest) - ALLTEST_ON="false" - FRAMEWORKTEST_ON="true" - ;; - --frameworktest_list_file=*) - FRAMEWORKTEST_LIST_FILE=$PWD/${i#*=} - if [ ! -e "$FRAMEWORKTEST_LIST_FILE" ]; then - echo "Pass on with proper frameworktest_list_file" - exit 1 - fi - ;; - --verification) - ALLTEST_ON="false" - VERIFICATION_ON="true" - ;; - --benchmark) - ALLTEST_ON="false" - BENCHMARK_ON="true" - ;; - --benchmark_op) - ALLTEST_ON="false" - BENCHMARK_OP_ON="true" - ;; - --benchmark_tflite_model) - ALLTEST_ON="false" - BENCHMARK_TFLITE_MODEL_ON="true" - ;; - --benchmark_acl) - ALLTEST_ON="false" - BENCHMARK_ACL_ON="true" - ;; - --acl_envon) - ACL_ENV_ON="true" - ;; - --profile) - ALLTEST_ON="false" - PROFILE_ON="true" - ;; - --reportdir=*) - REPORT_DIR=${i#*=} - ;; - *) - # Be careful that others params are handled as $ARTIFACT_PATH - ARTIFACT_PATH="$i" - ;; - esac - shift -done - -ARTIFACT_PATH="$(readlink -f $ARTIFACT_PATH)" - -if [ -z "$RUN_TEST_SH" ]; then - RUN_TEST_SH=$ARTIFACT_PATH/tests/framework/run_test.sh -fi - -if [ ! -e "$RUN_TEST_SH" ]; then - echo "Cannot find $RUN_TEST_SH" - exit 1 -fi - -if [ -z "$UNIT_TEST_DIR" ]; then - UNIT_TEST_DIR=$ARTIFACT_PATH/Product/out/unittest -fi - -if [ -z "$REPORT_DIR" ]; then - REPORT_DIR=$ARTIFACT_PATH/report -fi - -if [ -z "$LD_LIBRARY_PATH_IN_SHELL" ]; then - LD_LIBRARY_PATH="$ARTIFACT_PATH/Product/out/lib:$LD_LIBRARY_PATH" -else - LD_LIBRARY_PATH="$LD_LIBRARY_PATH_IN_SHELL:$LD_LIBRARY_PATH" -fi - -# Set env -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH -if [ -n "$USE_NNAPI" ]; then - export "$USE_NNAPI" -fi - -source $TEST_DRIVER_DIR/common.sh - -if [ "$ACL_ENV_ON" == "true" ]; then - switch_nnfw_kernel_env "ON" "acl" -fi - -# Run unittest in each part such as Runtime, ACL -if [ "$ALLTEST_ON" == "true" ] || [ "$UNITTEST_ON" == "true" ]; then - if [ "$UNITTESTALL_ON" == "true" ]; then - $TEST_DRIVER_DIR/run_unittest.sh \ - --reportdir=$REPORT_DIR \ - --unittestdir=$UNIT_TEST_DIR \ - --runall - else - $TEST_DRIVER_DIR/run_unittest.sh \ - --reportdir=$REPORT_DIR \ - --unittestdir=$UNIT_TEST_DIR - fi -fi - -# Run tflite_run with various tflite models -if [ "$FRAMEWORKTEST_ON" == "true" ]; then - if [ -z "$FRAMEWORK_DRIVER_BIN" ]; then - FRAMEWORK_DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_run - fi - - $TEST_DRIVER_DIR/run_frameworktest.sh \ - --runtestsh=$RUN_TEST_SH \ - --driverbin=$FRAMEWORK_DRIVER_BIN \ - --reportdir=$REPORT_DIR \ - --tapname=framework_test.tap \ - --logname=framework_test.log \ - --testname="Frameworktest" \ - --frameworktest_list_file=${FRAMEWORKTEST_LIST_FILE:-} -fi - -# Run nnapi_test with various tflite models -if [ "$ALLTEST_ON" == "true" ] || [ "$VERIFICATION_ON" == "true" ]; then - if [ -z "$VERIFICATION_DRIVER_BIN" ]; then - VERIFICATION_DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/nnapi_test - fi - - # verification uses the same script as frameworktest does - $TEST_DRIVER_DIR/run_frameworktest.sh \ - --runtestsh=$RUN_TEST_SH \ - --driverbin=$VERIFICATION_DRIVER_BIN \ - --reportdir=$REPORT_DIR \ - --tapname=verification_test.tap \ - --logname=verification_test.log \ - --testname="Verification" \ - --frameworktest_list_file=${FRAMEWORKTEST_LIST_FILE:-} -fi - -# Run tflite_benchmark with tflite models -if [ "$BENCHMARK_ON" == "true" ]; then - if [ -z "$BENCHMARK_DRIVER_BIN" ]; then - DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_benchmark - else - DRIVER_BIN=$BENCHMARK_DRIVER_BIN - fi - - $TEST_DRIVER_DIR/run_benchmark.sh \ - --runtestsh=$RUN_TEST_SH \ - --driverbin=$DRIVER_BIN \ - --reportdir=$REPORT_DIR/benchmark -fi - -# Run tflite_benchmark from a list of tflite models. -# Each model has only one operator. -if [ "$BENCHMARK_OP_ON" == "true" ]; then - if [ -z "$BENCHMARK_DRIVER_BIN" ]; then - DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_benchmark - else - DRIVER_BIN=$BENCHMARK_DRIVER_BIN - fi - - $TEST_DRIVER_DIR/run_benchmark_op.sh \ - --runtestsh=$RUN_TEST_SH \ - --driverbin=$DRIVER_BIN \ - --reportdir=$REPORT_DIR/benchmark_op \ - --modelfilepath=$ARTIFACT_PATH/tests/framework \ - --frameworktest_list_file=${FRAMEWORKTEST_LIST_FILE:-} -fi - -# Run benchmark/acl/benchmark_googlenet, mobilenet and inception_v3 -if [ "$BENCHMARK_ACL_ON" == "true" ]; then - $TEST_DRIVER_DIR/run_benchmark_acl.sh \ - --reportdir=$AREPORT_DIR/benchmark \ - --bindir=$ARTIFACT_PATH/Product/out/bin -fi - -# Make json file. Actually, this process is only needed on CI. That's why it is in test_driver.sh. -if [ "$BENCHMARK_ON" == "true" ] || [ "$BENCHMARK_ACL_ON" == "true" ] || [ "$BENCHMARK_OP_ON" == "true" ]; then - # functions to fill json with benchmark results - source $ARTIFACT_PATH/tools/test_driver/print_to_json.sh - if [ "$BENCHMARK_OP_ON" == "true" ]; then - print_to_json $REPORT_DIR/benchmark_op $REPORT_DIR "benchmark_op_result.json" - else - print_to_json $REPORT_DIR/benchmark $REPORT_DIR "benchmark_result.json" - fi -fi - -# Run tflite_benchmark_model (= per-operation profiling tool). -# Each model can contain arbitrary number of operators. -if [ "$BENCHMARK_TFLITE_MODEL_ON" == "true" ]; then - $TEST_DRIVER_DIR/run_benchmark_tflite_model.sh \ - --reportdir=$REPORT_DIR/benchmark_tflite_model \ - --modelroot=$ARTIFACT_PATH/tests/framework/tests -fi - -# Run profiling -if [ "$PROFILE_ON" == "true" ]; then - # FIXME: These driver and tflite test are set temporarily. Fix these to support flexibility - DRIVER_BIN=$ARTIFACT_PATH/Product/out/bin/tflite_run - TFLITE_TEST=$ARTIFACT_PATH/tests/framework/cache/inceptionv3/inception_module/inception_test.tflite - - # TODO: Enable operf to set directory where sample data puts on - rm -rf oprofile_data - - echo "" - echo "============================================" - operf -g $DRIVER_BIN $TFLITE_TEST - echo "============================================" - echo "" -fi diff --git a/tools/tflite_benchmark/CMakeLists.txt b/tools/tflite_benchmark/CMakeLists.txt deleted file mode 100644 index 72ee5d3f9..000000000 --- a/tools/tflite_benchmark/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -list(APPEND SOURCES "src/tflite_benchmark.cc") - -add_executable(tflite_benchmark ${SOURCES}) -target_link_libraries(tflite_benchmark tensorflow-lite ${LIB_PTHREAD} dl nnfw_util nnfw_support_tflite) -install(TARGETS tflite_benchmark DESTINATION bin) diff --git a/tools/tflite_benchmark/src/tflite_benchmark.cc b/tools/tflite_benchmark/src/tflite_benchmark.cc deleted file mode 100644 index ba2e62883..000000000 --- a/tools/tflite_benchmark/src/tflite_benchmark.cc +++ /dev/null @@ -1,231 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" - -#include "support/tflite/Assert.h" -#include "support/tflite/Session.h" -#include "support/tflite/InterpreterSession.h" -#include "support/tflite/NNAPISession.h" -#include "support/tflite/Diff.h" -#include "util/tensor/IndexIterator.h" - -#include <boost/accumulators/accumulators.hpp> -#include <boost/accumulators/statistics/stats.hpp> -#include <boost/accumulators/statistics/min.hpp> -#include <boost/accumulators/statistics/max.hpp> -#include <boost/accumulators/statistics/mean.hpp> - -#include <iostream> - -#include "util/environment.h" -#include "util/benchmark.h" - -using namespace tflite; -using namespace tflite::ops::builtin; - -void help(std::ostream &out, const int argc, char **argv) -{ - std::string cmd = argv[0]; - auto pos = cmd.find_last_of("/"); - if (pos != std::string::npos) - cmd = cmd.substr(pos + 1); - - out << "use:" << std::endl << cmd << " <model file name>" << std::endl; -} - -bool checkParams(const int argc, char **argv) -{ - if (argc < 2) - { - help(std::cerr, argc, argv); - return false; - } - return true; -} - -int main(const int argc, char **argv) -{ - - if (!checkParams(argc, argv)) - { - return -1; - } - - const auto filename = argv[1]; - - const bool use_nnapi = nnfw::util::get_env_bool("USE_NNAPI"); - const auto thread_count = nnfw::util::get_env_int("THREAD", -1); - - std::cout << "Num threads: " << thread_count << std::endl; - if (use_nnapi) - { - std::cout << "Use NNAPI" << std::endl; - } - - StderrReporter error_reporter; - - auto model = FlatBufferModel::BuildFromFile(filename, &error_reporter); - if (model == nullptr) - { - std::cerr << "Cannot create model" << std::endl; - return -1; - } - - BuiltinOpResolver resolver; - - InterpreterBuilder builder(*model, resolver); - - std::unique_ptr<Interpreter> interpreter; - - TFLITE_ENSURE(builder(&interpreter)); - - // Show inputs - for (uint32_t n = 0; n < interpreter->inputs().size(); ++n) - { - // TODO Print shape - auto tensor_id = interpreter->inputs().at(n); - auto tensor_ptr = interpreter->tensor(tensor_id); - - std::cout << "Input #" << n << ":" << std::endl; - std::cout << " Name: " << tensor_ptr->name << std::endl; - } - - // Show outputs - for (uint32_t n = 0; n < interpreter->outputs().size(); ++n) - { - // TODO Print shape - auto tensor_id = interpreter->outputs().at(n); - auto tensor_ptr = interpreter->tensor(tensor_id); - - std::cout << "Output #" << n << ":" << std::endl; - std::cout << " Name: " << tensor_ptr->name << std::endl; - } - - interpreter->SetNumThreads(thread_count); - - std::shared_ptr<nnfw::support::tflite::Session> sess; - - if (use_nnapi) - { - sess = std::make_shared<nnfw::support::tflite::NNAPISession>(interpreter.get()); - } - else - { - sess = std::make_shared<nnfw::support::tflite::InterpreterSession>(interpreter.get()); - } - - // - // Warming-up - // - for (uint32_t n = 0; n < 3; ++n) - { - std::chrono::milliseconds elapsed(0); - - sess->prepare(); - - for (const auto &id : interpreter->inputs()) - { - TfLiteTensor *tensor = interpreter->tensor(id); - if (tensor->type == kTfLiteInt32) - { - // Generate singed 32-bit integer (s32) input - auto tensor_view = nnfw::support::tflite::TensorView<int32_t>::make(*interpreter, id); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tensor_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - // Gather operation: index should be within input coverage. - tensor_view.at(ind) = value; - value++; - }; - } - else if (tensor->type == kTfLiteUInt8) - { - // Generate unsigned 8-bit integer input - auto tensor_view = nnfw::support::tflite::TensorView<uint8_t>::make(*interpreter, id); - - uint8_t value = 0; - - nnfw::util::tensor::iterate(tensor_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tensor_view.at(ind) = value; - value = (value + 1) & 0xFF; - }; - } - else - { - assert(tensor->type == kTfLiteFloat32); - - const int seed = 1; /* TODO Add an option for seed value */ - RandomGenerator randgen{seed, 0.0f, 0.2f}; - const float *end = reinterpret_cast<const float *>(tensor->data.raw_const + tensor->bytes); - for (float *ptr = tensor->data.f; ptr < end; ptr++) - { - *ptr = randgen.generate<float>(); - } - } - } - - nnfw::util::benchmark::measure(elapsed) << [&](void) { - if (!sess->run()) - { - assert(0 && "run failed"); - } - }; - sess->teardown(); - - std::cout << "Warming-up " << n << ": " << elapsed.count() << "ms" << std::endl; - } - - // - // Measure - // - const auto cnt = nnfw::util::get_env_int("COUNT", 1); - - using namespace boost::accumulators; - - accumulator_set<double, stats<tag::mean, tag::min, tag::max>> acc; - - for (int n = 0; n < cnt; ++n) - { - std::chrono::milliseconds elapsed(0); - - sess->prepare(); - nnfw::util::benchmark::measure(elapsed) << [&](void) { - if (!sess->run()) - { - assert(0 && "run failed"); - } - }; - sess->teardown(); - - acc(elapsed.count()); - - std::cout << "Iteration " << n << ": " << elapsed.count() << "ms" << std::endl; - } - - std::cout << "--------" << std::endl; - std::cout << "Min: " << min(acc) << "ms" << std::endl; - std::cout << "Max: " << max(acc) << "ms" << std::endl; - std::cout << "Mean: " << mean(acc) << "ms" << std::endl; - - return 0; -} diff --git a/tools/tflite_benchmark_model/CMakeLists.txt b/tools/tflite_benchmark_model/CMakeLists.txt deleted file mode 100644 index d52690460..000000000 --- a/tools/tflite_benchmark_model/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -file(GLOB_RECURSE SOURCES "*.cc") - -add_executable(tflite_benchmark_model ${SOURCES}) -target_compile_definitions(tflite_benchmark_model PUBLIC "TFLITE_PROFILING_ENABLED") -target_link_libraries(tflite_benchmark_model tensorflow-lite ${LIB_PTHREAD} dl nnfw_util nnfw_support_tflite) -install(TARGETS tflite_benchmark_model DESTINATION bin) diff --git a/tools/tflite_benchmark_model/README.md b/tools/tflite_benchmark_model/README.md deleted file mode 100644 index 93769305b..000000000 --- a/tools/tflite_benchmark_model/README.md +++ /dev/null @@ -1,209 +0,0 @@ -# TFLite Model Benchmark Tool - -## Description - -A simple C++ binary to benchmark a TFLite model and its individual operators, -both on desktop machines and on Android. The binary takes a TFLite model, -generates random inputs and then repeatedly runs the model for specified number -of runs. Aggregrate latency statistics are reported after running the benchmark. - -The instructions below are for running the binary on Desktop and Android, -for iOS please use the -[iOS benchmark app] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios). - -## Parameters - -The binary takes the following required parameters: - -* `graph`: `string` \ - The path to the TFLite model file. -* `input_layer`: `string` \ - The name of the input layer, this is typically the first layer of the model. -* `input_layer_shape`: `string` \ - The shape of the input layer. This is a comma separated string of the shape - of tensor of input layer. - -and the following optional parameters: - -* `num_threads`: `int` (default=1) \ - The number of threads to use for running TFLite interpreter. -* `warmup_runs`: `int` (default=1) \ - The number of warmup runs to do before starting the benchmark. -* `run_delay`: `float` (default=-1.0) \ - The delay in seconds between subsequent benchmark runs. Non-positive values - mean use no delay. -* `use_nnapi`: `bool` (default=false) \ - Whether to use [Android NNAPI] (https://developer.android.com/ndk/guides/neuralnetworks/). - This API is available on recent Android devices. - -## To build/install/run - -### On Android: - -(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the `WORKSPACE` to configure the android NDK/SDK. - -(1) Build for your specific platform, e.g.: - -``` -bazel build -c opt \ - --config=android_arm \ - --cxxopt='--std=c++11' \ - tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` - -(2) Connect your phone. Push the binary to your phone with adb push - (make the directory if required): - -``` -adb push bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model /data/local/tmp -``` - -(3) Make the binary executable. - -``` -adb shell chmod +x /data/local/tmp/benchmark_model -``` - -(4) Push the compute graph that you need to test. For example: - -``` -adb push mobilenet_quant_v1_224.tflite /data/local/tmp -``` - -(5) Run the benchmark. For example: - -``` -adb shell /data/local/tmp/benchmark_model \ - --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ - --input_layer="input" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=4 -``` - -### On desktop: -(1) build the binary - -``` -bazel build -c opt tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` - -(2) Run on your compute graph, similar to the Android case but without the need of adb shell. -For example: - -``` -bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model \ - --graph=mobilenet_quant_v1_224.tflite \ - --input_layer="Placeholder" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=4 -``` - -The MobileNet graph used as an example here may be downloaded from -https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip - - -## Reducing variance between runs on Android. - -Most modern Android phones use [ARM big.LITTLE](https://en.wikipedia.org/wiki/ARM_big.LITTLE) -architecture where some cores are more power hungry but faster than other cores. -When running benchmarks on these phones there can be significant variance -between different runs of the benchmark. One way to reduce variance between runs -is to set the [CPU affinity](https://en.wikipedia.org/wiki/Processor_affinity) -before running the benchmark. On Android this can be done using the `taskset` -command. -E.g. for running the benchmark on big cores on Pixel 2 with a single thread one -can use the following command: - -``` -adb shell tasket f0 /data/local/tmp/benchmark_model \ - --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ - --input_layer="input" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=1 -``` - -where `f0` is the affinity mask for big cores on Pixel 2. -Note: The affinity mask varies with the device. - -## Profiling model operators -The benchmark model binary also allows you to profile operators and give execution times of each operator. To do this, -compile the binary with a compiler flag that enables profiling to be compiled in. Pass **--copt=-DTFLITE_PROFILING_ENABLED** -to compile benchmark with profiling support. -For example, to compile with profiling support on Android, add this flag to the previous command: - -``` -bazel build -c opt \ - --config=android_arm \ - --cxxopt='--std=c++11' \ - --copt=-DTFLITE_PROFILING_ENABLED \ - tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` -This compiles TFLite with profiling enabled, now you can run the benchmark binary like before. The binary will produce detailed statistics for each operation similar to those shown below: - -``` - -============================== Run Order ============================== - [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 0.000 4.269 4.269 0.107% 0.107% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - DEPTHWISE_CONV_2D 4.270 2.150 2.150 0.054% 0.161% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6] - CONV_2D 6.421 6.107 6.107 0.153% 0.314% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - DEPTHWISE_CONV_2D 12.528 1.366 1.366 0.034% 0.348% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6] - CONV_2D 13.895 4.195 4.195 0.105% 0.454% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6] - DEPTHWISE_CONV_2D 18.091 1.260 1.260 0.032% 0.485% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6] - CONV_2D 19.352 6.652 6.652 0.167% 0.652% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - DEPTHWISE_CONV_2D 26.005 0.698 0.698 0.018% 0.670% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6] - CONV_2D 26.703 3.344 3.344 0.084% 0.754% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6] - DEPTHWISE_CONV_2D 30.047 0.646 0.646 0.016% 0.770% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6] - CONV_2D 30.694 5.800 5.800 0.145% 0.915% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - DEPTHWISE_CONV_2D 36.495 0.331 0.331 0.008% 0.924% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] - CONV_2D 36.826 2.838 2.838 0.071% 0.995% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] - DEPTHWISE_CONV_2D 39.665 0.439 0.439 0.011% 1.006% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] - CONV_2D 40.105 5.293 5.293 0.133% 1.139% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - DEPTHWISE_CONV_2D 45.399 0.352 0.352 0.009% 1.147% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] - CONV_2D 45.752 5.322 5.322 0.133% 1.281% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - DEPTHWISE_CONV_2D 51.075 0.357 0.357 0.009% 1.290% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] - CONV_2D 51.432 5.693 5.693 0.143% 1.433% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - DEPTHWISE_CONV_2D 57.126 0.366 0.366 0.009% 1.442% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6] - CONV_2D 57.493 5.472 5.472 0.137% 1.579% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - DEPTHWISE_CONV_2D 62.966 0.364 0.364 0.009% 1.588% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6] - CONV_2D 63.330 5.404 5.404 0.136% 1.724% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] - DEPTHWISE_CONV_2D 68.735 0.155 0.155 0.004% 1.728% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6] - CONV_2D 68.891 2.970 2.970 0.074% 1.802% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6] - DEPTHWISE_CONV_2D 71.862 0.206 0.206 0.005% 1.807% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6] - CONV_2D 72.069 5.888 5.888 0.148% 1.955% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - AVERAGE_POOL_2D 77.958 0.036 0.036 0.001% 1.956% 0.000 0 [MobilenetV1/Logits/AvgPool_1a/AvgPool] - CONV_2D 77.994 1.445 1.445 0.036% 1.992% 0.000 0 [MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd] - RESHAPE 79.440 0.002 0.002 0.000% 1.992% 0.000 0 [MobilenetV1/Predictions/Reshape] - SOFTMAX 79.443 0.029 0.029 0.001% 1.993% 0.000 0 [MobilenetV1/Predictions/Softmax] - -============================== Top by Computation Time ============================== - [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 19.352 6.652 6.652 0.167% 0.167% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - CONV_2D 6.421 6.107 6.107 0.153% 0.320% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - CONV_2D 72.069 5.888 5.888 0.148% 0.468% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - CONV_2D 30.694 5.800 5.800 0.145% 0.613% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - CONV_2D 51.432 5.693 5.693 0.143% 0.756% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - CONV_2D 57.493 5.472 5.472 0.137% 0.893% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - CONV_2D 63.330 5.404 5.404 0.136% 1.029% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] - CONV_2D 45.752 5.322 5.322 0.133% 1.162% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - CONV_2D 40.105 5.293 5.293 0.133% 1.295% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - CONV_2D 0.000 4.269 4.269 0.107% 1.402% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - -Number of nodes executed: 31 -============================== Summary by node type ============================== - [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called] - CONV_2D 15 1.406 89.270% 89.270% 0.000 0 - DEPTHWISE_CONV_2D 13 0.169 10.730% 100.000% 0.000 0 - SOFTMAX 1 0.000 0.000% 100.000% 0.000 0 - RESHAPE 1 0.000 0.000% 100.000% 0.000 0 - AVERAGE_POOL_2D 1 0.000 0.000% 100.000% 0.000 0 - -Timings (microseconds): count=50 first=79449 curr=81350 min=77385 max=88213 avg=79732 std=1929 -Memory (bytes): count=0 -31 nodes observed - - -Average inference timings in us: Warmup: 83235, Init: 38467, no stats: 79760.9 -``` - - diff --git a/tools/tflite_benchmark_model/benchmark_main.cc b/tools/tflite_benchmark_model/benchmark_main.cc deleted file mode 100644 index 7e4231c48..000000000 --- a/tools/tflite_benchmark_model/benchmark_main.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "benchmark_tflite_model.h" -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -int Main(int argc, char** argv) { -#ifdef TFLITE_CUSTOM_OPS_HEADER - TFLITE_LOG(INFO) << "STARTING with custom ops!"; -#else - TFLITE_LOG(INFO) << "STARTING!"; -#endif - BenchmarkTfLiteModel benchmark; - BenchmarkLoggingListener listener; - benchmark.AddListener(&listener); - benchmark.Run(argc, argv); - return 0; -} -} // namespace benchmark -} // namespace nnfw - -int main(int argc, char** argv) { return nnfw::benchmark::Main(argc, argv); } diff --git a/tools/tflite_benchmark_model/benchmark_model.cc b/tools/tflite_benchmark_model/benchmark_model.cc deleted file mode 100644 index 7869180bf..000000000 --- a/tools/tflite_benchmark_model/benchmark_model.cc +++ /dev/null @@ -1,175 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "benchmark_model.h" - -#include <time.h> - -#include <iostream> -#include <sstream> - -#include "tensorflow/contrib/lite/profiling/time.h" -#include "logging.h" - -namespace { -void SleepForSeconds(double sleep_seconds) { - if (sleep_seconds <= 0.0) { - return; - } - // Convert the run_delay string into a timespec. - timespec req; - req.tv_sec = static_cast<time_t>(sleep_seconds); - req.tv_nsec = (sleep_seconds - req.tv_sec) * 1000000000; - // If requested, sleep between runs for an arbitrary amount of time. - // This can be helpful to determine the effect of mobile processor - // scaling and thermal throttling. -#ifdef PLATFORM_WINDOWS - Sleep(sleep_seconds * 1000); -#else - nanosleep(&req, nullptr); -#endif -} - -} // namespace - -namespace nnfw { -namespace benchmark { -using tensorflow::Stat; - -BenchmarkParams BenchmarkModel::DefaultParams() { - BenchmarkParams params; - params.AddParam("num_runs", BenchmarkParam::Create<int32_t>(50)); - params.AddParam("run_delay", BenchmarkParam::Create<float>(-1.0f)); - params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1)); - params.AddParam("benchmark_name", BenchmarkParam::Create<std::string>("")); - params.AddParam("output_prefix", BenchmarkParam::Create<std::string>("")); - params.AddParam("warmup_runs", BenchmarkParam::Create<int32_t>(1)); - return params; -} - -BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {} - -void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { - auto inference_us = results.inference_time_us(); - auto init_us = results.startup_latency_us(); - auto warmup_us = results.warmup_time_us(); - TFLITE_LOG(INFO) << "Average inference timings in us: " - << "Warmup: " << warmup_us.avg() << ", " - << "Init: " << init_us << ", " - << "no stats: " << inference_us.avg(); -} - -std::vector<Flag> BenchmarkModel::GetFlags() { - return { - CreateFlag<int32_t>("num_runs", ¶ms_, "number of runs"), - CreateFlag<float>("run_delay", ¶ms_, "delay between runs in seconds"), - CreateFlag<int32_t>("num_threads", ¶ms_, "number of threads"), - CreateFlag<std::string>("benchmark_name", ¶ms_, "benchmark name"), - CreateFlag<std::string>("output_prefix", ¶ms_, - "benchmark output prefix"), - CreateFlag<int32_t>("warmup_runs", ¶ms_, - "how many runs to initialize model"), - }; -} - -void BenchmarkModel::LogFlags() { - TFLITE_LOG(INFO) << "Num runs: [" << params_.Get<int32_t>("num_runs") << "]"; - TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" - << params_.Get<float>("run_delay") << "]"; - TFLITE_LOG(INFO) << "Num threads: [" << params_.Get<int32_t>("num_threads") - << "]"; - TFLITE_LOG(INFO) << "Benchmark name: [" - << params_.Get<std::string>("benchmark_name") << "]"; - TFLITE_LOG(INFO) << "Output prefix: [" - << params_.Get<std::string>("output_prefix") << "]"; - TFLITE_LOG(INFO) << "Warmup runs: [" << params_.Get<int32_t>("warmup_runs") - << "]"; -} - -Stat<int64_t> BenchmarkModel::Run(int num_times, RunType run_type) { - Stat<int64_t> run_stats; - TFLITE_LOG(INFO) << "Running benchmark for " << num_times << " iterations "; - for (int run = 0; run < num_times; run++) { - listeners_.OnSingleRunStart(run_type); - int64_t start_us = tflite::profiling::time::NowMicros(); - RunImpl(); - int64_t end_us = tflite::profiling::time::NowMicros(); - listeners_.OnSingleRunEnd(); - - run_stats.UpdateStat(end_us - start_us); - SleepForSeconds(params_.Get<float>("run_delay")); - } - - std::stringstream stream; - run_stats.OutputToStream(&stream); - TFLITE_LOG(INFO) << stream.str() << std::endl; - - return run_stats; -} - -void BenchmarkModel::Run(int argc, char **argv) { - if (!ParseFlags(argc, argv)) { - return; - } - - LogFlags(); - - listeners_.OnBenchmarkStart(params_); - int64_t initialization_start_us = tflite::profiling::time::NowMicros(); - Init(); - int64_t initialization_end_us = tflite::profiling::time::NowMicros(); - int64_t startup_latency_us = initialization_end_us - initialization_start_us; - TFLITE_LOG(INFO) << "Initialized session in " << startup_latency_us / 1e3 - << "ms"; - - uint64_t input_bytes = ComputeInputBytes(); - Stat<int64_t> warmup_time_us = - Run(params_.Get<int32_t>("warmup_runs"), WARMUP); - Stat<int64_t> inference_time_us = - Run(params_.Get<int32_t>("num_runs"), REGULAR); - listeners_.OnBenchmarkEnd( - {startup_latency_us, input_bytes, warmup_time_us, inference_time_us}); -} - -bool BenchmarkModel::ParseFlags(int argc, char **argv) { - auto flag_list = GetFlags(); - const bool parse_result = - Flags::Parse(&argc, const_cast<const char **>(argv), flag_list); - if (!parse_result) { - std::string usage = Flags::Usage(argv[0], flag_list); - TFLITE_LOG(ERROR) << usage; - return false; - } - return ValidateFlags(); -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_model.h b/tools/tflite_benchmark_model/benchmark_model.h deleted file mode 100644 index 5645e2910..000000000 --- a/tools/tflite_benchmark_model/benchmark_model.h +++ /dev/null @@ -1,177 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ - -#include <cmath> -#include <limits> -#include <ostream> -#include <string> -#include <unordered_set> -#include <vector> - -#include "benchmark_params.h" -#include "command_line_flags.h" -#include "tensorflow/core/util/stats_calculator.h" - -namespace nnfw { -namespace benchmark { - -enum RunType { - WARMUP, - REGULAR, -}; - -class BenchmarkResults { - public: - BenchmarkResults(int64_t startup_latency_us, uint64_t input_bytes, - tensorflow::Stat<int64_t> warmup_time_us, - tensorflow::Stat<int64_t> inference_time_us) - : startup_latency_us_(startup_latency_us), - input_bytes_(input_bytes), - warmup_time_us_(warmup_time_us), - inference_time_us_(inference_time_us) {} - - tensorflow::Stat<int64_t> inference_time_us() const { - return inference_time_us_; - } - tensorflow::Stat<int64_t> warmup_time_us() const { return warmup_time_us_; } - int64_t startup_latency_us() const { return startup_latency_us_; } - uint64_t input_bytes() const { return input_bytes_; } - double throughput_MB_per_second() const { - double bytes_per_sec = (input_bytes_ * inference_time_us_.count() * 1e6) / - inference_time_us_.sum(); - return bytes_per_sec / (1024.0 * 1024.0); - } - - private: - int64_t startup_latency_us_; - uint64_t input_bytes_; - tensorflow::Stat<int64_t> warmup_time_us_; - tensorflow::Stat<int64_t> inference_time_us_; -}; - -class BenchmarkListener { - public: - virtual void OnBenchmarkStart(const BenchmarkParams& params) {} - virtual void OnSingleRunStart(RunType runType) {} - virtual void OnSingleRunEnd() {} - virtual void OnBenchmarkEnd(const BenchmarkResults& results) {} - virtual ~BenchmarkListener() {} -}; - -// A listener that forwards its method calls to a collection of listeners. -class BenchmarkListeners : public BenchmarkListener { - public: - // Added a listener to the listener collection. - // |listener| is not owned by the instance of |BenchmarkListeners|. - // |listener| should not be null and should outlast the instance of - // |BenchmarkListeners|. - void AddListener(BenchmarkListener* listener) { - listeners_.push_back(listener); - } - - void OnBenchmarkStart(const BenchmarkParams& params) override { - for (auto listener : listeners_) { - listener->OnBenchmarkStart(params); - } - } - - void OnSingleRunStart(RunType runType) override { - for (auto listener : listeners_) { - listener->OnSingleRunStart(runType); - } - } - - void OnSingleRunEnd() override { - for (auto listener : listeners_) { - listener->OnSingleRunEnd(); - } - } - - void OnBenchmarkEnd(const BenchmarkResults& results) override { - for (auto listener : listeners_) { - listener->OnBenchmarkEnd(results); - } - } - - ~BenchmarkListeners() {} - - private: - // Use vector so listeners are invoked in the order they are added. - std::vector<BenchmarkListener*> listeners_; -}; - -// Benchmark listener that just logs the results of benchmark run. -class BenchmarkLoggingListener : public BenchmarkListener { - void OnBenchmarkEnd(const BenchmarkResults& results) override; -}; - -template <typename T> -Flag CreateFlag(const char* name, BenchmarkParams* params, - const std::string& usage) { - return Flag(name, [params, name](const T& val) { params->Set<T>(name, val); }, - params->Get<T>(name), usage); -} - -// Benchmarks a model. -// -// Subclasses need to implement initialization and running of the model. -// The results can be collected by adding BenchmarkListener(s). -class BenchmarkModel { - public: - static BenchmarkParams DefaultParams(); - BenchmarkModel(); - BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} - virtual ~BenchmarkModel() {} - bool ParseFlags(int argc, char** argv); - virtual void Init() = 0; - void Run(int argc, char** argv); - void AddListener(BenchmarkListener* listener) { - listeners_.AddListener(listener); - } - - protected: - virtual void LogFlags(); - virtual bool ValidateFlags() { return true; } - virtual std::vector<Flag> GetFlags(); - virtual uint64_t ComputeInputBytes() = 0; - virtual tensorflow::Stat<int64_t> Run(int num_times, RunType run_type); - virtual void RunImpl() = 0; - BenchmarkParams params_; - BenchmarkListeners listeners_; -}; - -} // namespace benchmark -} // namespace nnfw - -#endif //__TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ diff --git a/tools/tflite_benchmark_model/benchmark_params.cc b/tools/tflite_benchmark_model/benchmark_params.cc deleted file mode 100644 index 7b667a442..000000000 --- a/tools/tflite_benchmark_model/benchmark_params.cc +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "benchmark_params.h" - -#include <string> -#include <unordered_map> -#include <vector> - -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -void BenchmarkParam::AssertHasSameType(BenchmarkParam::ParamType a, - BenchmarkParam::ParamType b) { - TFLITE_BENCHMARK_CHECK(a == b) << "Type mismatch while accessing parameter."; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<int32_t>() { - return BenchmarkParam::ParamType::TYPE_INT32; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<bool>() { - return BenchmarkParam::ParamType::TYPE_BOOL; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<float>() { - return BenchmarkParam::ParamType::TYPE_FLOAT; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<std::string>() { - return BenchmarkParam::ParamType::TYPE_STRING; -} - -void BenchmarkParams::AssertParamExists(const std::string& name) const { - TFLITE_BENCHMARK_CHECK(HasParam(name)) << name << " was not found."; -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_params.h b/tools/tflite_benchmark_model/benchmark_params.h deleted file mode 100644 index 1ac3f4af6..000000000 --- a/tools/tflite_benchmark_model/benchmark_params.h +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ -#include <memory> -#include <string> -#include <unordered_map> -#include <vector> - -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -template <typename T> -class TypedBenchmarkParam; - -class BenchmarkParam { - protected: - enum class ParamType { TYPE_INT32, TYPE_FLOAT, TYPE_BOOL, TYPE_STRING }; - - public: - template <typename T> - static std::unique_ptr<BenchmarkParam> Create(const T& default_value) { - return std::unique_ptr<BenchmarkParam>( - new TypedBenchmarkParam<T>(default_value)); - } - - template <typename T> - TypedBenchmarkParam<T>* AsTyped() { - AssertHasSameType(GetValueType<T>(), type_); - return static_cast<TypedBenchmarkParam<T>*>(this); - } - virtual ~BenchmarkParam() {} - BenchmarkParam(ParamType type) : type_(type) {} - - private: - static void AssertHasSameType(ParamType a, ParamType b); - protected: - template <typename T> - static ParamType GetValueType(); - - const ParamType type_; -}; - -template <typename T> -class TypedBenchmarkParam : public BenchmarkParam { - public: - TypedBenchmarkParam(const T& value) - : BenchmarkParam(GetValueType<T>()), value_(value) {} - void Set(const T& value) { value_ = value; } - - T Get() { return value_; } - - private: - T value_; -}; - -class BenchmarkParams { - public: - void AddParam(const std::string& name, - std::unique_ptr<BenchmarkParam> value) { - params_[name] = std::move(value); - } - - bool HasParam(const std::string& name) const { - return params_.find(name) != params_.end(); - } - - template <typename T> - void Set(const std::string& name, const T& value) { - AssertParamExists(name); - params_.at(name)->AsTyped<T>()->Set(value); - } - - template <typename T> - T Get(const std::string& name) const { - AssertParamExists(name); - return params_.at(name)->AsTyped<T>()->Get(); - } - - private: - void AssertParamExists(const std::string& name) const; - std::unordered_map<std::string, std::unique_ptr<BenchmarkParam>> params_; -}; - -} // namespace benchmark -} // namespace nnfw -#endif // __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ diff --git a/tools/tflite_benchmark_model/benchmark_tflite_model.cc b/tools/tflite_benchmark_model/benchmark_tflite_model.cc deleted file mode 100644 index d277795a3..000000000 --- a/tools/tflite_benchmark_model/benchmark_tflite_model.cc +++ /dev/null @@ -1,360 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "benchmark_tflite_model.h" - -#include <cstdarg> -#include <cstdlib> -#include <iostream> -#include <memory> -#include <string> -#include <unordered_set> -#include <vector> - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/op_resolver.h" -#include "tensorflow/contrib/lite/string_util.h" -#include "logging.h" -#include "util/profiling/profiling.h" -#include "support/tflite/nnapi_delegate.h" - -#ifdef TFLITE_CUSTOM_OPS_HEADER -void RegisterSelectedOps(::tflite::MutableOpResolver* resolver); -#endif - -namespace nnfw { -namespace benchmark { - -void ProfilingListener::SetInterpreter(tflite::Interpreter* interpreter) { - TFLITE_BENCHMARK_CHECK(interpreter); - interpreter_ = interpreter; - interpreter_->SetProfiler(&profiler_); -} - -void ProfilingListener::OnSingleRunStart(RunType run_type) { - if (run_type == REGULAR) { - profiler_.Reset(); - profiler_.StartProfiling(); - } -} - -void ProfilingListener::OnBenchmarkEnd(const BenchmarkResults& results) { - if (has_profiles_) { - TFLITE_LOG(INFO) << summarizer_.GetOutputString(); - } -} - -void ProfilingListener::OnSingleRunEnd() { - profiler_.StopProfiling(); - auto profile_events = profiler_.GetProfileEvents(); - has_profiles_ = !profile_events.empty(); - summarizer_.ProcessProfiles(profile_events, *interpreter_); -} - -namespace { - -std::vector<std::string> Split(const std::string& str, const char delim) { - std::istringstream input(str); - std::vector<std::string> results; - std::string item; - while (std::getline(input, item, delim)) { - results.push_back(item); - } - return results; -} - -template <typename T> -bool SplitAndParse(const std::string& str, char delim, std::vector<T>* values) { - std::istringstream input(str); - bool first = true; - while (!input.eof()) { - if (!first) { - char c; - input >> c; - if (c != delim) { - return false; - } - } else { - first = false; - } - T val; - input >> val; - if (!input.eof() && !input.good()) { - return false; - } - values->push_back(val); - } - return true; -} - -template <typename T> -void FillRandomValue(T* ptr, const std::vector<int>& sizes, - const std::function<T()>& random_func) { - int num_elements = 1; - for (int dim : sizes) { - num_elements *= dim; - } - for (int i = 0; i < num_elements; ++i) { - *ptr++ = random_func(); - } -} - -void FillRandomString(tflite::DynamicBuffer* buffer, - const std::vector<int>& sizes, - const std::function<std::string()>& random_func) { - int num_elements = 1; - for (int dim : sizes) { - num_elements *= dim; - } - for (int i = 0; i < num_elements; ++i) { - auto str = random_func(); - buffer->AddString(str.data(), str.length()); - } -} - -bool PopulateInputLayerInfo( - const std::string& names_string, const std::string& shapes_string, - std::vector<BenchmarkTfLiteModel::InputLayerInfo>* info) { - std::vector<std::string> names = Split(names_string, ','); - std::vector<std::string> shapes = Split(shapes_string, ':'); - - if (names.size() != shapes.size()) { - TFLITE_LOG(ERROR) << "The number of items in" - << " --input_layer_shape (" << shapes_string << ", with " - << shapes.size() << " items)" - << " must match the number of items in" - << " --input_layer (" << names_string << ", with " - << names.size() << " items)." - << " For example --input_layer=input1,input2" - << " --input_layer_shape=1,224,224,4:1,20"; - return false; - } - - for (int i = 0; i < names.size(); ++i) { - info->push_back(BenchmarkTfLiteModel::InputLayerInfo()); - BenchmarkTfLiteModel::InputLayerInfo& input = info->back(); - - input.name = names[i]; - - TFLITE_BENCHMARK_CHECK(SplitAndParse(shapes[i], ',', &input.shape)) - << "Incorrect size string specified: " << shapes[i]; - for (int dim : input.shape) { - if (dim == -1) { - TFLITE_LOG(ERROR) - << "Any unknown sizes in the shapes (-1's) must be replaced" - << " with the size you want to benchmark with."; - return false; - } - } - } - - return true; -} - -BenchmarkParams GetDefaultParams() { - BenchmarkParams default_params = BenchmarkModel::DefaultParams(); - default_params.AddParam("graph", BenchmarkParam::Create<std::string>("")); - default_params.AddParam("input_layer", - BenchmarkParam::Create<std::string>("")); - default_params.AddParam("input_layer_shape", - BenchmarkParam::Create<std::string>("")); - default_params.AddParam("use_nnapi", BenchmarkParam::Create<bool>(false)); - return default_params; -} - -} // namespace - -BenchmarkTfLiteModel::BenchmarkTfLiteModel() - : BenchmarkModel(GetDefaultParams()) { - AddListener(&profiling_listener_); -} - -BenchmarkTfLiteModel::BenchmarkTfLiteModel(BenchmarkParams params) - : BenchmarkModel(std::move(params)) { - AddListener(&profiling_listener_); -} - -std::vector<Flag> BenchmarkTfLiteModel::GetFlags() { - std::vector<Flag> flags = BenchmarkTfLiteModel::BenchmarkModel::GetFlags(); - std::vector<Flag> specific_flags = { - CreateFlag<std::string>("graph", ¶ms_, "graph file name"), - CreateFlag<std::string>("input_layer", ¶ms_, "input layer names"), - CreateFlag<std::string>("input_layer_shape", ¶ms_, - "input layer shape"), - CreateFlag<bool>("use_nnapi", ¶ms_, "use nnapi api")}; - - flags.insert(flags.end(), specific_flags.begin(), specific_flags.end()); - return flags; -} - -void BenchmarkTfLiteModel::LogFlags() { - BenchmarkModel::LogFlags(); - TFLITE_LOG(INFO) << "Graph: [" << params_.Get<std::string>("graph") << "]"; - TFLITE_LOG(INFO) << "Input layers: [" - << params_.Get<std::string>("input_layer") << "]"; - TFLITE_LOG(INFO) << "Input shapes: [" - << params_.Get<std::string>("input_layer_shape") << "]"; - TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get<bool>("use_nnapi") << "]"; -} - -bool BenchmarkTfLiteModel::ValidateFlags() { - if (params_.Get<std::string>("graph").empty()) { - TFLITE_LOG(ERROR) - << "Please specify the name of your TF Lite input file with --graph"; - return false; - } - return PopulateInputLayerInfo(params_.Get<std::string>("input_layer"), - params_.Get<std::string>("input_layer_shape"), - &inputs); -} - -uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { - TFLITE_BENCHMARK_CHECK(interpreter); - uint64_t total_input_bytes = 0; - for (int input : interpreter->inputs()) { - auto* t = interpreter->tensor(input); - total_input_bytes += t->bytes; - } - return total_input_bytes; -} - -void BenchmarkTfLiteModel::Init() { - std::string graph = params_.Get<std::string>("graph"); - model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); - if (!model) { - TFLITE_LOG(FATAL) << "Failed to mmap model " << graph; - } - TFLITE_LOG(INFO) << "Loaded model " << graph; - model->error_reporter(); - TFLITE_LOG(INFO) << "resolved reporter"; - -#ifdef TFLITE_CUSTOM_OPS_HEADER - tflite::MutableOpResolver resolver; - RegisterSelectedOps(&resolver); -#else - tflite::ops::builtin::BuiltinOpResolver resolver; -#endif - - tflite::InterpreterBuilder(*model, resolver)(&interpreter); - if (!interpreter) { - TFLITE_LOG(FATAL) << "Failed to construct interpreter"; - } - profiling_listener_.SetInterpreter(interpreter.get()); - profiling::Context::get().setProfiler(interpreter->GetProfiler()); - - const int32_t num_threads = params_.Get<int32_t>("num_threads"); - - if (num_threads != -1) { - interpreter->SetNumThreads(num_threads); - } - - bool use_nnapi = params_.Get<bool>("use_nnapi"); - - interpreter->UseNNAPI(use_nnapi); - auto interpreter_inputs = interpreter->inputs(); - - if (!inputs.empty()) { - TFLITE_BENCHMARK_CHECK_EQ(inputs.size(), interpreter_inputs.size()) - << "Inputs mismatch: Model inputs #:" << interpreter_inputs.size() - << " expected: " << inputs.size(); - } - - // TFLITE_BENCHMARK_CHECK that all names and types match - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - TFLITE_BENCHMARK_CHECK_EQ(t->name, input.name) - << "Tensor # " << i << " is named " << t->name << " but flags call it " - << input.name; - } - - // Resize all non-string tensors. - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - if (t->type != kTfLiteString) { - interpreter->ResizeInputTensor(i, input.shape); - } - } - - if (interpreter->AllocateTensors() != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to allocate tensors!"; - } - - // Set the values of the input tensors. - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - std::vector<int> sizes = input.shape; - - // TODO(ahentz): below we ignore the O-th dimension (number of batches). - if (t->type == kTfLiteFloat32) { - FillRandomValue<float>( - interpreter->typed_tensor<float>(i), - std::vector<int>(sizes.begin() + 1, sizes.end()), - []() { return static_cast<float>(rand()) / RAND_MAX - 0.5f; }); - } else if (t->type == kTfLiteUInt8) { - FillRandomValue<uint8_t>( - interpreter->typed_tensor<uint8_t>(i), - std::vector<int>(sizes.begin() + 1, sizes.end()), - []() { return static_cast<uint8_t>(rand()) % 255; }); - } else if (t->type == kTfLiteString) { - tflite::DynamicBuffer buffer; - FillRandomString(&buffer, sizes, []() { - return "we're have some friends over saturday to hang out in the yard"; - }); - buffer.WriteToTensor(interpreter->tensor(i)); - } else { - TFLITE_LOG(FATAL) << "Don't know how to populate tensor " << t->name - << " of type " << t->type; - } - } -} - -void BenchmarkTfLiteModel::RunImpl() { - bool use_nnapi = params_.Get<bool>("use_nnapi"); - if (use_nnapi) { - if (nnfw::NNAPIDelegate().Invoke(interpreter.get()) != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to invoke!"; - } - } else { - if (interpreter->Invoke() != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to invoke!"; - } - } -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_tflite_model.h b/tools/tflite_benchmark_model/benchmark_tflite_model.h deleted file mode 100644 index 7892de1f7..000000000 --- a/tools/tflite_benchmark_model/benchmark_tflite_model.h +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ - -#include <memory> -#include <string> -#include <vector> - -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/profiling/profile_summarizer.h" -#include "benchmark_model.h" - -namespace nnfw { -namespace benchmark { - -// Dumps profiling events if profiling is enabled -class ProfilingListener : public BenchmarkListener { - public: - explicit ProfilingListener() : interpreter_(nullptr), has_profiles_(false) {} - - void SetInterpreter(tflite::Interpreter* interpreter); - - void OnSingleRunStart(RunType run_type) override; - - void OnSingleRunEnd() override; - - void OnBenchmarkEnd(const BenchmarkResults& results) override; - - private: - tflite::Interpreter* interpreter_; - tflite::profiling::Profiler profiler_; - tflite::profiling::ProfileSummarizer summarizer_; - bool has_profiles_; -}; - -// Benchmarks a TFLite model by running tflite interpreter. -class BenchmarkTfLiteModel : public BenchmarkModel { - public: - BenchmarkTfLiteModel(); - BenchmarkTfLiteModel(BenchmarkParams params); - - std::vector<Flag> GetFlags() override; - void LogFlags() override; - bool ValidateFlags() override; - uint64_t ComputeInputBytes() override; - void Init() override; - void RunImpl() override; - virtual ~BenchmarkTfLiteModel() {} - - struct InputLayerInfo { - std::string name; - std::vector<int> shape; - }; - - private: - std::unique_ptr<tflite::FlatBufferModel> model; - std::unique_ptr<tflite::Interpreter> interpreter; - std::vector<InputLayerInfo> inputs; - ProfilingListener profiling_listener_; -}; - -} // namespace benchmark -} // namespace nnfw - -#endif //__TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ diff --git a/tools/tflite_benchmark_model/command_line_flags.cc b/tools/tflite_benchmark_model/command_line_flags.cc deleted file mode 100644 index eacca9f73..000000000 --- a/tools/tflite_benchmark_model/command_line_flags.cc +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "command_line_flags.h" - -#include <cstring> -#include <sstream> -#include <string> -#include <utility> -#include <vector> - -namespace nnfw { -namespace { - -template <typename T> -std::string ToString(T val) { - std::ostringstream stream; - stream << val; - return stream.str(); -} - -bool ParseFlag(const std::string& arg, const std::string& flag, - const std::function<bool(const std::string&)>& parse_func, - bool* value_parsing_ok) { - *value_parsing_ok = true; - std::string flag_prefix = "--" + flag + "="; - if (arg.find(flag_prefix) != 0) { - return false; - } - bool has_value = arg.size() >= flag_prefix.size(); - *value_parsing_ok = has_value; - if (has_value) { - *value_parsing_ok = parse_func(arg.substr(flag_prefix.size())); - } - return true; -} - -template <typename T> -bool ParseFlag(const std::string& flag_value, - const std::function<void(const T&)>& hook) { - std::istringstream stream(flag_value); - T read_value; - stream >> read_value; - if (!stream.eof() && !stream.good()) { - return false; - } - hook(read_value); - return true; -} - -bool ParseBoolFlag(const std::string& flag_value, - const std::function<void(const bool&)>& hook) { - if (flag_value != "true" && flag_value != "false") { - return false; - } - - hook(flag_value == "true"); - return true; -} -} // namespace - -Flag::Flag(const char* name, const std::function<void(const int32_t&)>& hook, - int32_t default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_INT32), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<int32_t>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const int64_t&)>& hook, - int64_t default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_INT64), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<int64_t>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const float&)>& hook, - float default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_FLOAT), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<float>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const bool&)>& hook, - bool default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_BOOL), - value_hook_([hook](const std::string& flag_value) { - return ParseBoolFlag(flag_value, hook); - }), - default_for_display_(default_value ? "true" : "false"), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, - const std::function<void(const std::string&)>& hook, - const std::string& default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_STRING), - value_hook_([hook](const std::string& flag_value) { - hook(flag_value); - return true; - }), - default_for_display_(default_value), - usage_text_(usage_text) {} - -bool Flag::Parse(const std::string& arg, bool* value_parsing_ok) const { - return ParseFlag(arg, name_, value_hook_, value_parsing_ok); -} - -std::string Flag::GetTypeName() const { - switch (type_) { - case TYPE_INT32: - return "int32"; - case TYPE_INT64: - return "int64"; - case TYPE_FLOAT: - return "float"; - case TYPE_BOOL: - return "bool"; - case TYPE_STRING: - return "string"; - } - - return "unknown"; -} - -/*static*/ bool Flags::Parse(int* argc, const char** argv, - const std::vector<Flag>& flag_list) { - bool result = true; - std::vector<const char*> unknown_flags; - for (int i = 1; i < *argc; ++i) { - if (std::string(argv[i]) == "--") { - while (i < *argc) { - unknown_flags.push_back(argv[i]); - ++i; - } - break; - } - - bool was_found = false; - for (const Flag& flag : flag_list) { - bool value_parsing_ok; - was_found = flag.Parse(argv[i], &value_parsing_ok); - if (!value_parsing_ok) { - result = false; - } - if (was_found) { - break; - } - } - if (!was_found) { - unknown_flags.push_back(argv[i]); - } - } - int dst = 1; // Skip argv[0] - for (auto f : unknown_flags) { - argv[dst++] = f; - } - argv[dst++] = nullptr; - *argc = unknown_flags.size() + 1; - return result && (*argc < 2 || std::strcmp(argv[1], "--help") != 0); -} - -/*static*/ std::string Flags::Usage(const std::string& cmdline, - const std::vector<Flag>& flag_list) { - std::ostringstream usage_text; - usage_text << "usage: " << cmdline << "\n"; - if (!flag_list.empty()) { - usage_text << "Flags:\n"; - } - - for (const Flag& flag : flag_list) { - auto type_name = flag.GetTypeName(); - usage_text << "\t"; - usage_text << "--" << flag.name_ << "=" << flag.default_for_display_; - usage_text << "\t" << type_name << "\t" << flag.usage_text_ << "\n"; - } - return usage_text.str(); -} - -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/command_line_flags.h b/tools/tflite_benchmark_model/command_line_flags.h deleted file mode 100644 index 766417d87..000000000 --- a/tools/tflite_benchmark_model/command_line_flags.h +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ -#define __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ - -#include <functional> -#include <string> -#include <vector> - -namespace nnfw { -// A simple command-line argument parsing module. -// Dependency free simplified port of core/util/command_line_flags. -// This class is written for benchmarks and uses inefficient string -// concatenation. This was written to avoid dependency on tensorflow/core/util -// which transitively brings in a lot of other dependencies that are not -// necessary for tflite benchmarking code. -// The recommended way of using it is with local variables and an initializer -// list of Flag objects, for example: -// -// int some_int = 10; -// bool some_switch = false; -// std::string some_name = "something"; -// -// std::vector<tensorFlow::Flag> flag_list = { -// Flag::CreateFlag("some_int", &some_int, "an integer that affects X"), -// Flag::CreateFlag("some_switch", &some_switch, "a bool that affects Y"), -// Flag::CreateFlag("some_name", &some_name, "a string that affects Z") -// }; -// // Get usage message before ParseFlags() to capture default values. -// std::string usage = Flag::Usage(argv[0], flag_list); -// bool parsed_values_ok = Flags::Parse(&argc, argv, flag_list); -// -// tensorflow::port::InitMain(usage.c_str(), &argc, &argv); -// if (argc != 1 || !parsed_values_ok) { -// ...output usage and error message... -// } -// -// The argc and argv values are adjusted by the Parse function so all that -// remains is the program name (at argv[0]) and any unknown arguments fill the -// rest of the array. This means you can check for flags that weren't understood -// by seeing if argv is greater than 1. -// The result indicates if there were any errors parsing the values that were -// passed to the command-line switches. For example, --some_int=foo would return -// false because the argument is expected to be an integer. -// -// NOTE: Unlike gflags-style libraries, this library is intended to be -// used in the `main()` function of your binary. It does not handle -// flag definitions that are scattered around the source code. - -// A description of a single command line flag, holding its name, type, usage -// text, and a pointer to the corresponding variable. -class Flag { - public: - template <typename T> - static Flag CreateFlag(const char* name, T* val, const char* usage) { - return Flag(name, [val](const T& v) { *val = v; }, *val, usage); - } - - Flag(const char* name, const std::function<void(const int32_t&)>& hook, - int32_t default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const int64_t&)>& hook, - int64_t default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const float&)>& hook, - float default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const bool&)>& hook, - bool default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const std::string&)>& hook, - const std::string& default_value, const std::string& usage_text); - - private: - friend class Flags; - - bool Parse(const std::string& arg, bool* value_parsing_ok) const; - - std::string name_; - enum { - TYPE_INT32, - TYPE_INT64, - TYPE_BOOL, - TYPE_STRING, - TYPE_FLOAT, - } type_; - - std::string GetTypeName() const; - - std::function<bool(const std::string&)> value_hook_; - std::string default_for_display_; - - std::string usage_text_; -}; - -class Flags { - public: - // Parse the command line represented by argv[0, ..., (*argc)-1] to find flag - // instances matching flags in flaglist[]. Update the variables associated - // with matching flags, and remove the matching arguments from (*argc, argv). - // Return true iff all recognized flag values were parsed correctly, and the - // first remaining argument is not "--help". - static bool Parse(int* argc, const char** argv, - const std::vector<Flag>& flag_list); - - // Return a usage message with command line cmdline, and the - // usage_text strings in flag_list[]. - static std::string Usage(const std::string& cmdline, - const std::vector<Flag>& flag_list); -}; - -} // namespace nnfw - -#endif // __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ - - diff --git a/tools/tflite_benchmark_model/logging.h b/tools/tflite_benchmark_model/logging.h deleted file mode 100644 index e694a0926..000000000 --- a/tools/tflite_benchmark_model/logging.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_LOGGING_H_ -#define __TFLITE_BENCHMARK_MODEL_LOGGING_H_ - -// LOG and CHECK macros for benchmarks. - -#include <cstdlib> -#include <iostream> -#include <sstream> - -namespace nnfw { -namespace logging { -// A wrapper that logs to stderr. -// -// Used for TFLITE_LOG and TFLITE_BENCHMARK_CHECK macros. -class LoggingWrapper { - public: - enum class LogSeverity : int { - INFO = 0, - WARN = 1, - ERROR = 2, - FATAL = 3, - }; - LoggingWrapper(LogSeverity severity) - : severity_(severity), should_log_(true) {} - LoggingWrapper(LogSeverity severity, bool log) - : severity_(severity), should_log_(log) {} - std::stringstream& Stream() { return stream_; } - ~LoggingWrapper() { - if (should_log_) { - std::cerr << stream_.str() << std::endl; - if (severity_ == LogSeverity::FATAL) { - std::flush(std::cerr); - std::abort(); - } - } - } - - private: - std::stringstream stream_; - LogSeverity severity_; - bool should_log_; -}; - -} // namespace logging - -} // namespace nnfw - -#define TFLITE_LOG(severity) \ - nnfw::logging::LoggingWrapper( \ - nnfw::logging::LoggingWrapper::LogSeverity::severity) \ - .Stream() - -#define TFLITE_BENCHMARK_CHECK(condition) \ - nnfw::logging::LoggingWrapper( \ - nnfw::logging::LoggingWrapper::LogSeverity::FATAL, \ - (condition) ? false : true) \ - .Stream() - -#define TFLITE_BENCHMARK_CHECK_EQ(a, b) TFLITE_BENCHMARK_CHECK(a == b) - -#endif // __TFLITE_BENCHMARK_MODEL_BENCHMARK_LOGGING_H_ diff --git a/tools/tflite_benchmark_model/profile_summarizer.cc b/tools/tflite_benchmark_model/profile_summarizer.cc deleted file mode 100644 index 4d12b50af..000000000 --- a/tools/tflite_benchmark_model/profile_summarizer.cc +++ /dev/null @@ -1,164 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/contrib/lite/profiling/profile_summarizer.h" - -#include <sstream> - -#include "tensorflow/contrib/lite/schema/schema_generated.h" - -namespace tflite { -namespace profiling { -namespace { - -using Detail = tensorflow::StatsCalculator::Detail; - -struct OperatorDetails { - std::string name; - std::vector<std::string> inputs; - std::vector<std::string> outputs; -}; - -std::string GetTensorName(const tflite::Interpreter& interpreter, - int tensor_index) { - const auto tensor = interpreter.tensor(tensor_index); - if (tensor == nullptr || tensor->name == nullptr) { - return "Unknown"; - } - return tensor->name; -} -std::vector<std::string> GetTensorNames(const tflite::Interpreter& interpreter, - const TfLiteIntArray* tensor_indices) { - std::vector<std::string> tensors; - tensors.reserve(tensor_indices->size); - for (int i = 0; i < tensor_indices->size; i++) { - tensors.push_back(GetTensorName(interpreter, tensor_indices->data[i])); - } - return tensors; -} - -std::string ToString(const std::vector<std::string>& str_vector) { - std::stringstream stream; - stream << "["; - bool first = true; - for (const auto& s : str_vector) { - if (!first) { - stream << ", "; - } else { - first = false; - } - stream << s; - } - stream << "]"; - return stream.str(); -} - -OperatorDetails GetOperatorDetails(const tflite::Interpreter& interpreter, - int node_index) { - auto node_reg = interpreter.node_and_registration(node_index); - auto inputs = node_reg->first.inputs; - auto outputs = node_reg->first.outputs; - int code = node_reg->second.builtin_code; - const char* op_name = nullptr; - if (code == tflite::BuiltinOperator_CUSTOM) { - const char* custom_name = node_reg->second.custom_name; - op_name = custom_name ? custom_name : "UnknownCustomOp"; - } else { - op_name = tflite::EnumNamesBuiltinOperator()[code]; - } - OperatorDetails details; - details.name = op_name; - details.inputs = GetTensorNames(interpreter, inputs); - details.outputs = GetTensorNames(interpreter, outputs); - return details; -} - -} // namespace - -ProfileSummarizer::ProfileSummarizer() - : stats_calculator_(new ::tensorflow::StatsCalculator( - tensorflow::StatSummarizerOptions())) {} - -void ProfileSummarizer::ProcessProfiles( - const std::vector<const ProfileEvent*>& profile_stats, - const tflite::Interpreter& interpreter) { - std::vector<const ProfileEvent*> events; - std::copy_if(profile_stats.begin(), profile_stats.end(), - std::back_inserter(events), [](const ProfileEvent* e) { - return e->event_type == - ProfileEvent::EventType::OPERATOR_INVOKE_EVENT && - e->end_timestamp_us >= e->begin_timestamp_us; - }); - // Sort with begin_time. - std::sort(events.begin(), events.end(), - [](const ProfileEvent* const& a, const ProfileEvent* const& b) { - return a->begin_timestamp_us < b->begin_timestamp_us; - }); - if (events.empty()) { - return; - } - - int64_t base_start_us = events[0]->begin_timestamp_us; - int node_num = 0; - int64_t curr_total_us = 0; - std::map<std::string, Detail> details; - int prev_op_idx = -1; - int seq_no = 1; - for (auto event : events) { - auto op_details = GetOperatorDetails(interpreter, event->event_metadata); - bool is_continued = (prev_op_idx == event->event_metadata); - seq_no = is_continued ? seq_no + 1 : 1; - auto node_name = ToString(op_details.outputs) + "#" + std::to_string(seq_no); - auto result = details.emplace(node_name, Detail()); - Detail* detail = &(result.first->second); - detail->start_us.UpdateStat(event->begin_timestamp_us - base_start_us); - int64_t node_exec_time = - event->end_timestamp_us - event->begin_timestamp_us; - detail->rel_end_us.UpdateStat(node_exec_time); - curr_total_us += node_exec_time; - ++node_num; - - if (result.second) { - detail->name = node_name; - detail->type = op_details.name; - detail->run_order = node_num; - detail->times_called = 0; - } - if (!is_continued) { - ++detail->times_called; - } - prev_op_idx = event->event_metadata; - } - stats_calculator_->UpdateDetails(details); - stats_calculator_->UpdateRunTotalUs(curr_total_us); -} -} // namespace profiling -} // namespace tflite diff --git a/tools/tflite_benchmark_model/profile_summarizer.h b/tools/tflite_benchmark_model/profile_summarizer.h deleted file mode 100644 index a529ff874..000000000 --- a/tools/tflite_benchmark_model/profile_summarizer.h +++ /dev/null @@ -1,55 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ -#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ - -#include <vector> - -#include "tensorflow/contrib/lite/interpreter.h" -#include "tensorflow/contrib/lite/profiling/profiler.h" -#include "tensorflow/core/util/stats_calculator.h" - -namespace tflite { -namespace profiling { - -// Creates a summary of operator invocations in the interpreter. -class ProfileSummarizer { - public: - ProfileSummarizer(); - virtual ~ProfileSummarizer() {} - - // Process profile events to update statistics for operator invocations. - void ProcessProfiles(const std::vector<const ProfileEvent*>& profile_stats, - const tflite::Interpreter& interpreter); - - // Returns a string detailing the accumulated runtime stats in a tab-separated - // format which can be pasted into a spreadsheet for further analysis. - std::string GetOutputString() const { - return stats_calculator_->GetOutputString(); - } - - std::string GetShortSummary() const { - return stats_calculator_->GetShortSummary(); - } - - private: - std::unique_ptr<tensorflow::StatsCalculator> stats_calculator_; -}; - -} // namespace profiling -} // namespace tflite - -#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ diff --git a/tools/tflite_examples/CMakeLists.txt b/tools/tflite_examples/CMakeLists.txt deleted file mode 100644 index 5fe33302a..000000000 --- a/tools/tflite_examples/CMakeLists.txt +++ /dev/null @@ -1,2 +0,0 @@ -add_executable(tflite_conv_example "src/conv.cpp") -target_link_libraries(tflite_conv_example tensorflow-lite ${LIB_PTHREAD} dl nnfw_support_tflite) diff --git a/tools/tflite_examples/src/conv.cpp b/tools/tflite_examples/src/conv.cpp deleted file mode 100644 index a647346ee..000000000 --- a/tools/tflite_examples/src/conv.cpp +++ /dev/null @@ -1,330 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/builtin_op_data.h" - -#include <iostream> - -using namespace tflite; -using namespace tflite::ops::builtin; - -namespace vector -{ - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual int32_t size(void) const = 0; - virtual T at(uint32_t off) const = 0; -}; -} - -namespace feature -{ - -struct Shape -{ - int32_t C; - int32_t H; - int32_t W; -}; - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual const Shape &shape(void) const = 0; - virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; -} - -namespace kernel -{ - -struct Shape -{ - int32_t N; - int32_t C; - int32_t H; - int32_t W; -}; - -template <typename T> struct View -{ - virtual ~View() = default; - - virtual const Shape &shape(void) const = 0; - virtual T at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; -} - -const int32_t N = 1; -const int32_t C = 2; - -class SampleBiasObject final : public vector::View<float> -{ -public: - SampleBiasObject() : _size(N) - { - // DO NOTHING - } - -public: - int32_t size(void) const override { return _size; } - - float at(uint32_t off) const override { return 0.0f; } - -private: - int32_t _size; -}; - -class SampleFeatureObject final : public feature::View<float> -{ -public: - SampleFeatureObject() - { - _shape.C = C; - _shape.H = 3; - _shape.W = 4; - - const uint32_t size = _shape.C * _shape.H * _shape.W; - - for (uint32_t off = 0; off < size; ++off) - { - _value.emplace_back(off); - } - - assert(_value.size() == size); - } - -public: - const feature::Shape &shape(void) const override { return _shape; }; - - float at(uint32_t ch, uint32_t row, uint32_t col) const override - { - return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); - } - -public: - float &at(uint32_t ch, uint32_t row, uint32_t col) - { - return _value.at(ch * _shape.H * _shape.W + row * _shape.W + col); - } - -private: - feature::Shape _shape; - std::vector<float> _value; -}; - -class SampleKernelObject final : public kernel::View<float> -{ -public: - SampleKernelObject() - { - _shape.N = N; - _shape.C = C; - _shape.H = 3; - _shape.W = 4; - - const uint32_t size = _shape.N * _shape.C * _shape.H * _shape.W; - - for (uint32_t off = 0; off < size; ++off) - { - _value.emplace_back(off); - } - - assert(_value.size() == size); - } - -public: - const kernel::Shape &shape(void) const override { return _shape; }; - - float at(uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) const override - { - return _value.at(nth * _shape.C * _shape.H * _shape.W + ch * _shape.H * _shape.W + - row * _shape.W + col); - } - -private: - kernel::Shape _shape; - std::vector<float> _value; -}; - -int main(int argc, char **argv) -{ - const SampleFeatureObject ifm; - const SampleKernelObject kernel; - const SampleBiasObject bias; - - const int32_t IFM_C = ifm.shape().C; - const int32_t IFM_H = ifm.shape().H; - const int32_t IFM_W = ifm.shape().W; - - const int32_t KER_N = kernel.shape().N; - const int32_t KER_C = kernel.shape().C; - const int32_t KER_H = kernel.shape().H; - const int32_t KER_W = kernel.shape().W; - - const int32_t OFM_C = kernel.shape().N; - const int32_t OFM_H = (IFM_H - KER_H) + 1; - const int32_t OFM_W = (IFM_W - KER_W) + 1; - - // Assumption on this example - assert(IFM_C == KER_C); - assert(KER_N == bias.size()); - - // Comment from 'context.h' - // - // Parameters for asymmetric quantization. Quantized values can be converted - // back to float using: - // real_value = scale * (quantized_value - zero_point); - // - // Q: Is this necessary? - TfLiteQuantizationParams quantization; - - quantization.scale = 1; - quantization.zero_point = 0; - - Interpreter interp; - - // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) - interp.AddTensors(5); - - // Configure OFM - interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */, - {1 /*N*/, OFM_H, OFM_W, OFM_C} /* dims */, quantization); - - // Configure IFM - interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */, - {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization); - - // Configure Filter - const uint32_t kernel_size = KER_N * KER_C * KER_H * KER_W; - float kernel_data[kernel_size] = { - 0.0f, - }; - - // Fill kernel data in NHWC order - { - uint32_t off = 0; - - for (uint32_t nth = 0; nth < KER_N; ++nth) - { - for (uint32_t row = 0; row < KER_H; ++row) - { - for (uint32_t col = 0; col < KER_W; ++col) - { - for (uint32_t ch = 0; ch < KER_C; ++ch) - { - const auto value = kernel.at(nth, ch, row, col); - kernel_data[off++] = value; - } - } - } - } - - assert(kernel_size == off); - } - - interp.SetTensorParametersReadOnly( - 2, kTfLiteFloat32 /* type */, "filter" /* name */, {KER_N, KER_H, KER_W, KER_C} /* dims */, - quantization, reinterpret_cast<const char *>(kernel_data), sizeof(kernel_data)); - - // Configure Bias - const uint32_t bias_size = bias.size(); - float bias_data[bias_size] = { - 0.0f, - }; - - // Fill bias data - for (uint32_t off = 0; off < bias.size(); ++off) - { - bias_data[off] = bias.at(off); - } - - interp.SetTensorParametersReadOnly(3, kTfLiteFloat32 /* type */, "bias" /* name */, - {bias.size()} /* dims */, quantization, - reinterpret_cast<const char *>(bias_data), sizeof(bias_data)); - - // Add Convolution Node - // - // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free - // So, param should be allocated with malloc - TfLiteConvParams *param = reinterpret_cast<TfLiteConvParams *>(malloc(sizeof(TfLiteConvParams))); - - param->padding = kTfLitePaddingValid; - param->stride_width = 1; - param->stride_height = 1; - param->activation = kTfLiteActRelu; - - // Run Convolution and store its result into Tensor #0 - // - Read IFM from Tensor #1 - // - Read Filter from Tensor #2, - // - Read Bias from Tensor #3 - interp.AddNodeWithParameters({1, 2, 3}, {0}, nullptr, 0, reinterpret_cast<void *>(param), - BuiltinOpResolver().FindOp(BuiltinOperator_CONV_2D, 1)); - - // Set Tensor #1 as Input #0, and Tensor #0 as Output #0 - interp.SetInputs({1}); - interp.SetOutputs({0}); - - // Let's use NNAPI (if possible) - interp.UseNNAPI(true); - - // Allocate Tensor - interp.AllocateTensors(); - - // Fill IFM data in HWC order - { - uint32_t off = 0; - - for (uint32_t row = 0; row < ifm.shape().H; ++row) - { - for (uint32_t col = 0; col < ifm.shape().W; ++col) - { - for (uint32_t ch = 0; ch < ifm.shape().C; ++ch) - { - const auto value = ifm.at(ch, row, col); - interp.typed_input_tensor<float>(0)[off++] = value; - } - } - } - } - - // Let's Rock-n-Roll! - interp.Invoke(); - - // Print OFM - { - uint32_t off = 0; - - for (uint32_t row = 0; row < OFM_H; ++row) - { - for (uint32_t col = 0; col < OFM_W; ++col) - { - for (uint32_t ch = 0; ch < kernel.shape().N; ++ch) - { - std::cout << interp.typed_output_tensor<float>(0)[off++] << std::endl; - } - } - } - } - - return 0; -} diff --git a/tools/tflite_run/CMakeLists.txt b/tools/tflite_run/CMakeLists.txt deleted file mode 100644 index c9f72acee..000000000 --- a/tools/tflite_run/CMakeLists.txt +++ /dev/null @@ -1,26 +0,0 @@ -list(APPEND TFLITE_RUN_SRCS "src/tflite_run.cc") -list(APPEND TFLITE_RUN_SRCS "src/bin_image.cc") -list(APPEND TFLITE_RUN_SRCS "src/args.cc") -list(APPEND TFLITE_RUN_SRCS "src/tensor_dumper.cc") -list(APPEND TFLITE_RUN_SRCS "src/tensor_loader.cc") - -add_executable(tflite_run ${TFLITE_RUN_SRCS}) -target_include_directories(tflite_run PRIVATE src) -target_link_libraries(tflite_run tensorflow-lite ${LIB_PTHREAD} dl nnfw_support_tflite) -target_link_libraries(tflite_run boost_program_options boost_system boost_filesystem) - -install(TARGETS tflite_run DESTINATION bin) - -# TEST BUILD -nnfw_find_package(GTest) - -if(NOT GTest_FOUND) - return() -endif(NOT GTest_FOUND) - -## Add test cpp file -add_executable(tflite_test src/tflite_test.cc) -## Link test executable against gtest & gtest_main -target_link_libraries(tflite_test gtest gtest_main ${LIB_PTHREAD}) -## install test binary for packaging -install(TARGETS tflite_test DESTINATION unittest) diff --git a/tools/tflite_run/src/args.cc b/tools/tflite_run/src/args.cc deleted file mode 100644 index 6aebbbbd7..000000000 --- a/tools/tflite_run/src/args.cc +++ /dev/null @@ -1,125 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "args.h" - -#include <iostream> - -#include <boost/filesystem.hpp> - -namespace TFLiteRun -{ - -Args::Args(const int argc, char **argv) -{ - Initialize(); - Parse(argc, argv); -} - -void Args::Initialize(void) -{ - - // General options - po::options_description general("General options"); - - // clang-format off - general.add_options() - ("help,h", "Display available options") - ("input,i", po::value<std::string>(&_input_filename)->default_value(""), "Input filename") - ("dump,d", po::value<std::string>()->default_value(""), "Output filename") - ("compare,c", po::value<std::string>()->default_value(""), "filename to be compared with") - ("tflite", po::value<std::string>()->required()); - // clang-format on - - _options.add(general); - _positional.add("tflite", 1); -} - -void Args::Parse(const int argc, char **argv) -{ - po::variables_map vm; - po::store(po::command_line_parser(argc, argv).options(_options).positional(_positional).run(), - vm); - po::notify(vm); - - { - auto conflicting_options = [&](const std::string &o1, const std::string &o2) { - if ((vm.count(o1) && !vm[o1].defaulted()) && (vm.count(o2) && !vm[o2].defaulted())) - { - throw boost::program_options::error(std::string("Two options '") + o1 + "' and '" + o2 + - "' cannot be given at once."); - } - }; - - conflicting_options("input", "compare"); - } - - if (vm.count("help")) - { - std::cout << "tflite_run\n\n"; - std::cout << "Usage: " << argv[0] << " <.tflite> [<options>]\n\n"; - std::cout << _options; - std::cout << "\n"; - - exit(0); - } - - if (vm.count("input")) - { - _input_filename = vm["input"].as<std::string>(); - - if (!_input_filename.empty()) - { - if (!boost::filesystem::exists(_input_filename)) - { - std::cerr << "input image file not found: " << _input_filename << "\n"; - } - } - } - - if (vm.count("dump")) - { - _dump_filename = vm["dump"].as<std::string>(); - } - - if (vm.count("compare")) - { - _compare_filename = vm["compare"].as<std::string>(); - } - - if (vm.count("tflite")) - { - _tflite_filename = vm["tflite"].as<std::string>(); - - if (_tflite_filename.empty()) - { - // TODO Print usage instead of the below message - std::cerr << "Please specify tflite file. Run with `--help` for usage." - << "\n"; - - exit(1); - } - else - { - if (!boost::filesystem::exists(_tflite_filename)) - { - std::cerr << "tflite file not found: " << _tflite_filename << "\n"; - } - } - } -} - -} // end of namespace TFLiteRun diff --git a/tools/tflite_run/src/args.h b/tools/tflite_run/src/args.h deleted file mode 100644 index 7b270d4ee..000000000 --- a/tools/tflite_run/src/args.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __TFLITE_RUN_ARGS_H__ -#define __TFLITE_RUN_ARGS_H__ - -#include <string> -#include <boost/program_options.hpp> - -namespace po = boost::program_options; - -namespace TFLiteRun -{ - -class Args -{ -public: - Args(const int argc, char **argv); - void print(void); - - const std::string &getTFLiteFilename(void) const { return _tflite_filename; } - const std::string &getInputFilename(void) const { return _input_filename; } - const std::string &getDumpFilename(void) const { return _dump_filename; } - const std::string &getCompareFilename(void) const { return _compare_filename; } - -private: - void Initialize(); - void Parse(const int argc, char **argv); - -private: - po::positional_options_description _positional; - po::options_description _options; - - std::string _tflite_filename; - std::string _input_filename; - std::string _dump_filename; - std::string _compare_filename; -}; - -} // end of namespace TFLiteRun - -#endif // __TFLITE_RUN_ARGS_H__ diff --git a/tools/tflite_run/src/tensor_loader.cc b/tools/tflite_run/src/tensor_loader.cc deleted file mode 100644 index 678ff083e..000000000 --- a/tools/tflite_run/src/tensor_loader.cc +++ /dev/null @@ -1,67 +0,0 @@ -#include "tensor_loader.h" - -#include <assert.h> - -#include <fstream> - -#include "util/tensor/Shape.h" - -namespace TFLiteRun -{ - -TensorLoader::TensorLoader(tflite::Interpreter &interpreter) - : _interpreter(interpreter), _raw_data(nullptr) -{ -} - -void TensorLoader::load(const std::string &filename) -{ - // TODO Handle file open/read error - std::ifstream file(filename, std::ios::ate | std::ios::binary); - size_t file_size = file.tellg(); - file.seekg(0, std::ios::beg); - - uint32_t num_tensors = 0; - file.read(reinterpret_cast<char *>(&num_tensors), sizeof(num_tensors)); - - int tensor_indices_raw[num_tensors]; - file.read(reinterpret_cast<char *>(tensor_indices_raw), sizeof(tensor_indices_raw)); - std::vector<int> tensor_indices(tensor_indices_raw, tensor_indices_raw + num_tensors); - - _raw_data = std::unique_ptr<float>(new float[file_size]); - file.read(reinterpret_cast<char *>(_raw_data.get()), file_size); - - size_t offset = 0; - for (const auto &o : tensor_indices) - { - const TfLiteTensor *tensor = _interpreter.tensor(o); - - // Convert tensor shape to `Shape` from `tensor->dims` - nnfw::util::tensor::Shape shape(static_cast<size_t>(tensor->dims->size)); - for (int d = 0; d < tensor->dims->size; d++) - { - shape.dim(d) = tensor->dims->data[d]; - } - - float *base = _raw_data.get() + offset; - - assert(tensor->bytes % sizeof(float) == 0); - offset += (tensor->bytes / sizeof(float)); - - _tensor_map.insert(std::make_pair(o, nnfw::support::tflite::TensorView<float>(shape, base))); - } - - // The file size and total output tensor size must match - assert(file_size == sizeof(num_tensors) + sizeof(tensor_indices_raw) + offset * sizeof(float)); - - file.close(); -} - -const nnfw::support::tflite::TensorView<float> &TensorLoader::get(int tensor_idx) const -{ - auto found = _tensor_map.find(tensor_idx); - assert(found != _tensor_map.end()); - return found->second; -} - -} // end of namespace TFLiteRun diff --git a/tools/tflite_run/src/tensor_loader.h b/tools/tflite_run/src/tensor_loader.h deleted file mode 100644 index f2a699185..000000000 --- a/tools/tflite_run/src/tensor_loader.h +++ /dev/null @@ -1,35 +0,0 @@ -#ifndef __TFLITE_RUN_TENSOR_LOADER_H__ -#define __TFLITE_RUN_TENSOR_LOADER_H__ - -#include <sys/mman.h> - -#include <string> -#include <unordered_map> - -#include "support/tflite/TensorView.h" - -namespace tflite -{ -class Interpreter; -} - -namespace TFLiteRun -{ - -class TensorLoader -{ -public: - TensorLoader(tflite::Interpreter &interpreter); - void load(const std::string &filename); - const nnfw::support::tflite::TensorView<float> &get(int tensor_idx) const; - size_t getNums() const { return _tensor_map.size(); } - -private: - tflite::Interpreter &_interpreter; - std::unique_ptr<float> _raw_data; - std::unordered_map<int, nnfw::support::tflite::TensorView<float>> _tensor_map; -}; - -} // end of namespace TFLiteRun - -#endif // __TFLITE_RUN_TENSOR_LOADER_H__ diff --git a/tools/tflite_run/src/tflite_run.cc b/tools/tflite_run/src/tflite_run.cc deleted file mode 100644 index 23a23809b..000000000 --- a/tools/tflite_run/src/tflite_run.cc +++ /dev/null @@ -1,253 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" - -#include "bin_image.h" -#include "args.h" -#include "tensor_dumper.h" -#include "tensor_loader.h" -#include "util/benchmark.h" -#include "util/environment.h" -#include "util/fp32.h" -#include "support/tflite/Diff.h" -#include "support/tflite/Assert.h" -#include "support/tflite/Session.h" -#include "support/tflite/InterpreterSession.h" -#include "support/tflite/NNAPISession.h" -#include "util/tensor/IndexIterator.h" - -#include <iostream> -#include <chrono> -#include <algorithm> - -using namespace tflite; -using namespace tflite::ops::builtin; - -void print_max_idx(float *f, int size) -{ - float *p = std::max_element(f, f + size); - std::cout << "max:" << p - f; -} - -int main(const int argc, char **argv) -{ - bool use_nnapi = false; - - if (std::getenv("USE_NNAPI") != nullptr) - { - use_nnapi = true; - } - - StderrReporter error_reporter; - - TFLiteRun::Args args(argc, argv); - - auto model = FlatBufferModel::BuildFromFile(args.getTFLiteFilename().c_str(), &error_reporter); - std::unique_ptr<Interpreter> interpreter; - - std::chrono::milliseconds t_prepare(0); - std::chrono::milliseconds t_invoke(0); - - nnfw::util::benchmark::measure(t_prepare) << [&](void) { - BuiltinOpResolver resolver; - - InterpreterBuilder builder(*model, resolver); - - TFLITE_ENSURE(builder(&interpreter)) - - interpreter->SetNumThreads(1); - }; - - std::shared_ptr<nnfw::support::tflite::Session> sess; - - if (use_nnapi) - { - sess = std::make_shared<nnfw::support::tflite::NNAPISession>(interpreter.get()); - } - else - { - sess = std::make_shared<nnfw::support::tflite::InterpreterSession>(interpreter.get()); - } - - sess->prepare(); - - TFLiteRun::TensorLoader tensor_loader(*interpreter); - - // Load input from image or dumped tensor file. Two options are exclusive and will be checked - // from Args. - if (args.getInputFilename().size() > 0) - { - BinImage image(299, 299, 3); - image.loadImage(args.getInputFilename()); - - for (const auto &o : interpreter->inputs()) - { - image.AssignTensor(interpreter->tensor(o)); - } - } - else if (!args.getCompareFilename().empty()) - { - tensor_loader.load(args.getCompareFilename()); - - for (const auto &o : interpreter->inputs()) - { - const auto &tensor_view = tensor_loader.get(o); - TfLiteTensor *tensor = interpreter->tensor(o); - - memcpy(reinterpret_cast<void *>(tensor->data.f), - reinterpret_cast<const void *>(tensor_view._base), tensor->bytes); - } - } - else - { - // No input specified. So we fill the input tensors with random values. - for (const auto &o : interpreter->inputs()) - { - TfLiteTensor *tensor = interpreter->tensor(o); - if (tensor->type == kTfLiteInt32) - { - // Generate singed 32-bit integer (s32) input - auto tensor_view = nnfw::support::tflite::TensorView<int32_t>::make(*interpreter, o); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tensor_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - // Gather operation: index should be within input coverage. - tensor_view.at(ind) = value; - value++; - }; - } - else if (tensor->type == kTfLiteUInt8) - { - // Generate unsigned 8-bit integer input - auto tensor_view = nnfw::support::tflite::TensorView<uint8_t>::make(*interpreter, o); - - uint8_t value = 0; - - nnfw::util::tensor::iterate(tensor_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tensor_view.at(ind) = value; - value = (value + 1) & 0xFF; - }; - } - else - { - assert(tensor->type == kTfLiteFloat32); - - const int seed = 1; /* TODO Add an option for seed value */ - RandomGenerator randgen{seed, 0.0f, 0.2f}; - const float *end = reinterpret_cast<const float *>(tensor->data.raw_const + tensor->bytes); - for (float *ptr = tensor->data.f; ptr < end; ptr++) - { - *ptr = randgen.generate<float>(); - } - } - } - } - - TFLiteRun::TensorDumper tensor_dumper; - // Must be called before `interpreter->Invoke()` - tensor_dumper.addTensors(*interpreter, interpreter->inputs()); - - std::cout << "input tensor indices = ["; - for (const auto &o : interpreter->inputs()) - { - std::cout << o << ","; - } - std::cout << "]" << std::endl; - - nnfw::util::benchmark::measure(t_invoke) << [&sess](void) { - if (!sess->run()) - { - assert(0 && "run failed!"); - } - }; - - sess->teardown(); - - // Must be called after `interpreter->Invoke()` - tensor_dumper.addTensors(*interpreter, interpreter->outputs()); - - std::cout << "output tensor indices = ["; - for (const auto &o : interpreter->outputs()) - { - std::cout << o << "("; - - print_max_idx(interpreter->tensor(o)->data.f, interpreter->tensor(o)->bytes / sizeof(float)); - - std::cout << "),"; - } - std::cout << "]" << std::endl; - - std::cout << "Prepare takes " << t_prepare.count() / 1000.0 << " seconds" << std::endl; - std::cout << "Invoke takes " << t_invoke.count() / 1000.0 << " seconds" << std::endl; - - if (!args.getDumpFilename().empty()) - { - const std::string &dump_filename = args.getDumpFilename(); - tensor_dumper.dump(dump_filename); - std::cout << "Input/output tensors have been dumped to file \"" << dump_filename << "\"." - << std::endl; - } - - if (!args.getCompareFilename().empty()) - { - const std::string &compare_filename = args.getCompareFilename(); - std::cout << "========================================" << std::endl; - std::cout << "Comparing the results with \"" << compare_filename << "\"." << std::endl; - std::cout << "========================================" << std::endl; - - // TODO Code duplication (copied from RandomTestRunner) - - int tolerance = 1; - nnfw::util::env::IntAccessor("TOLERANCE").access(tolerance); - - auto equals = [tolerance](float lhs, float rhs) { - // NOTE Hybrid approach - // TODO Allow users to set tolerance for absolute_epsilon_equal - if (nnfw::util::fp32::absolute_epsilon_equal(lhs, rhs)) - { - return true; - } - - return nnfw::util::fp32::epsilon_equal(lhs, rhs, tolerance); - }; - - nnfw::util::tensor::Comparator comparator(equals); - TfLiteInterpMatchApp app(comparator); - bool res = true; - - for (const auto &o : interpreter->outputs()) - { - auto expected = tensor_loader.get(o); - auto obtained = nnfw::support::tflite::TensorView<float>::make(*interpreter, o); - - res = res && app.compareSingleTensorView(expected, obtained, o); - } - - if (!res) - { - return 255; - } - } - - return 0; -} diff --git a/tools/tflitefile_tool/model_parser.py b/tools/tflitefile_tool/model_parser.py index b8967d33f..0edabbba1 100755 --- a/tools/tflitefile_tool/model_parser.py +++ b/tools/tflitefile_tool/model_parser.py @@ -1,4 +1,19 @@ #!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import sys import numpy @@ -13,6 +28,7 @@ import tflite.Model import tflite.SubGraph import argparse from operator_parser import OperatorParser +from model_printer import ModelPrinter from perf_predictor import PerfPredictor @@ -22,7 +38,6 @@ class TFLiteModelFileParser(object): self.tflite_file = args.input_file # Set print level (0 ~ 2) - # TODO: print information based on level self.print_level = args.verbose if (args.verbose > 2): self.print_level = 2 @@ -30,33 +45,34 @@ class TFLiteModelFileParser(object): self.print_level = 0 # Set tensor index list to print information - # TODO: - # Print tensors in list only - # Print all tensors if argument used and not specified index number + self.print_all_tensor = True if (args.tensor != None): - if (len(args.tensor) == 0): - self.print_all_tensor = True - else: + if (len(args.tensor) != 0): self.print_all_tensor = False self.print_tensor_index = [] - for tensor_index in args.tensor: self.print_tensor_index.append(int(tensor_index)) # Set operator index list to print information - # TODO: - # Print operators in list only - # Print all operators if argument used and not specified index number + self.print_all_operator = True if (args.operator != None): - if (len(args.operator) == 0): - self.print_all_oeprator = True - else: - self.print_all_oeprator = False + if (len(args.operator) != 0): + self.print_all_operator = False self.print_operator_index = [] - for operator_index in args.operator: self.print_operator_index.append(int(operator_index)) + def PrintModel(self, model_name, op_parser): + printer = ModelPrinter(self.print_level, op_parser, model_name) + + if self.print_all_tensor == False: + printer.SetPrintSpecificTensors(self.print_tensor_index) + + if self.print_all_operator == False: + printer.SetPrintSpecificOperators(self.print_operator_index) + + printer.PrintInfo() + def main(self): # Generate Model: top structure of tflite model file buf = self.tflite_file.read() @@ -71,19 +87,12 @@ class TFLiteModelFileParser(object): if (subgraph_index != 0): model_name = "Model #" + str(subgraph_index) - print("[" + model_name + "]\n") - - # Model inputs & outputs - model_inputs = tf_subgraph.InputsAsNumpy() - model_outputs = tf_subgraph.OutputsAsNumpy() - - print(model_name + " input tensors: " + str(model_inputs)) - print(model_name + " output tensors: " + str(model_outputs)) - - # Parse Operators and print all of operators + # Parse Operators op_parser = OperatorParser(tf_model, tf_subgraph, PerfPredictor()) op_parser.Parse() - op_parser.PrintAll() + + # print all of operators or requested objects + self.PrintModel(model_name, op_parser) if __name__ == '__main__': @@ -92,11 +101,7 @@ if __name__ == '__main__': arg_parser.add_argument( "input_file", type=argparse.FileType('rb'), help="tflite file to read") arg_parser.add_argument( - '-v', - '--verbose', - action='count', - default=0, - help="set print level (0~2, default: 0)") + '-v', '--verbose', type=int, default=1, help="set print level (0~2, default: 1)") arg_parser.add_argument( '-t', '--tensor', nargs='*', help="tensor ID to print information (default: all)") arg_parser.add_argument( diff --git a/tools/tflitefile_tool/model_printer.py b/tools/tflitefile_tool/model_printer.py new file mode 100644 index 000000000..ad06fa7a7 --- /dev/null +++ b/tools/tflitefile_tool/model_printer.py @@ -0,0 +1,142 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from operator_printer import OperatorPrinter +from tensor_printer import TensorPrinter + + +class ModelPrinter(object): + def __init__(self, verbose, op_parser, model_name): + self.verbose = verbose + self.op_parser = op_parser + self.model_name = model_name + self.print_all_tensor = True + self.print_tensor_index_list = None + self.print_all_operator = True + self.print_operator_index_list = None + + def SetPrintSpecificTensors(self, tensor_indices): + if len(tensor_indices) != 0: + self.print_all_tensor = False + self.print_tensor_index_list = tensor_indices + + def SetPrintSpecificOperators(self, operator_indices): + if len(operator_indices) != 0: + self.print_all_operator = False + self.print_operator_index_list = operator_indices + + def PrintInfo(self): + if self.print_all_tensor == True and self.print_all_operator == True: + self.PrintModelInfo() + self.PrintAllOperatorsInList() + self.PrintAllTypesInfo() + self.PrintTotalMemory() + + if self.print_all_tensor == False: + print('') + self.PrintSpecificTensors() + + if self.print_all_operator == False: + print('') + self.PrintSpecificOperators() + + def PrintModelInfo(self): + print("[" + self.model_name + "]\n") + if self.verbose > 0: + model_inputs = self.op_parser.tf_subgraph.InputsAsNumpy() + model_outputs = self.op_parser.tf_subgraph.OutputsAsNumpy() + print(self.model_name + " input tensors: " + str(model_inputs)) + print(self.model_name + " output tensors: " + str(model_outputs)) + print('') + + def PrintAllOperatorsInList(self): + if (self.verbose < 1): + return + + for operator in self.op_parser.operators_in_list: + printer = OperatorPrinter(self.verbose, operator) + printer.PrintInfo(self.op_parser.perf_predictor) + print('') + + print('') + + def PrintAllTypesInfo(self): + print("Number of all operator types: {0}".format( + len(self.op_parser.operators_per_type))) + + # number of instructions of all operator types to print if verbose level is 2 + total_instrs = 0 + + # (a string of the operator type, a list of operators which are the same operator type) + for type_str, oper_list in self.op_parser.operators_per_type.items(): + # number of occurrence of this operator type + occur = len(oper_list) + + optype_info_str = "\t{type_str:38}: {occur:4}".format( + type_str=type_str, occur=occur) + + if self.verbose == 2: + # this operator type can be computed? + can_compute = oper_list[0].operation.can_compute + + # total number of instructions of the same operator types + if can_compute: + instrs = sum( + operator.operation.TotalInstrNum() for operator in oper_list) + total_instrs = total_instrs + instrs + instrs = "{:,}".format(instrs) + else: + instrs = "???" + + optype_info_str = optype_info_str + " \t (instrs: {instrs})".format( + instrs=instrs) + + print(optype_info_str) + + summary_str = "{0:46}: {1:4}".format("Number of all operators", + len(self.op_parser.operators_in_list)) + if self.verbose == 2: + total_instrs = "{:,}".format(total_instrs) + summary_str = summary_str + " \t (total instrs: {0})".format(total_instrs) + + print(summary_str) + print('') + + def PrintSpecificTensors(self): + for tensor in self.op_parser.GetAllTensors(): + if tensor.tensor_idx in self.print_tensor_index_list: + printer = TensorPrinter(self.verbose, tensor) + printer.PrintInfo() + print('') + print('') + + def PrintSpecificOperators(self): + for operator in self.op_parser.operators_in_list: + if operator.operator_idx in self.print_operator_index_list: + printer = OperatorPrinter(self.verbose, operator) + printer.PrintInfo(self.op_parser.perf_predictor) + print('') + + print('') + + def PrintTotalMemory(self): + total_memory = 0 + for tensor in self.op_parser.GetAllTensors(): + total_memory += tensor.memory_size + + from tensor_printer import ConvertBytesToHuman + print("Expected total memory for allocating all tensors: {0}".format( + ConvertBytesToHuman(total_memory))) diff --git a/tools/tflitefile_tool/operation.py b/tools/tflitefile_tool/operation.py index 77fc5db9a..127d6c566 100755 --- a/tools/tflitefile_tool/operation.py +++ b/tools/tflitefile_tool/operation.py @@ -1,5 +1,19 @@ #!/usr/bin/python +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tflite.Conv2DOptions import tflite.Pool2DOptions import tflite.BuiltinOptions diff --git a/tools/tflitefile_tool/operator_parser.py b/tools/tflitefile_tool/operator_parser.py index 9728d53b7..71b1a6d93 100755 --- a/tools/tflitefile_tool/operator_parser.py +++ b/tools/tflitefile_tool/operator_parser.py @@ -1,5 +1,19 @@ #!/usr/bin/python +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tflite.Model import tflite.SubGraph import tflite.Operator @@ -62,6 +76,18 @@ class OperatorParser(object): return_list.append(Tensor(tensor_idx, tf_tensor, tf_buffer)) return return_list + def GetAllTensors(self): + return_list = list() + for tensor_idx in range(self.tf_subgraph.TensorsLength()): + if (tensor_idx < 0): + return_list.append(Tensor(tensor_idx, 0, 0)) + continue + tf_tensor = self.tf_subgraph.Tensors(tensor_idx) + buffer_idx = tf_tensor.Buffer() + tf_buffer = self.tf_model.Buffers(buffer_idx) + return_list.append(Tensor(tensor_idx, tf_tensor, tf_buffer)) + return return_list + def AppendOperator(self, operator): self.operators_in_list.append(operator) @@ -69,45 +95,3 @@ class OperatorParser(object): if opcode_str not in self.operators_per_type: self.operators_per_type[opcode_str] = list() self.operators_per_type[opcode_str].append(operator) - - def PrintAll(self): - print('') - self.PrintAllOperatorsInList() - print('') - self.PrintAllTypesInfo() - print('') - - def PrintAllOperatorsInList(self): - for operator in self.operators_in_list: - operator.PrintInfo(self.perf_predictor) - print('') - - def PrintAllTypesInfo(self): - print("Number of all operator types: {0}".format(len(self.operators_per_type))) - - # number of instructions of all operator types - total_instrs = 0 - - # (a string of the operator type, a list of operators which are the same operator type) - for type_str, oper_list in self.operators_per_type.items(): - # this operator type can be computed? - can_compute = oper_list[0].operation.can_compute - - # number of occurrence of this operator type - occur = len(oper_list) - - # total number of instructions of the same operator types - if can_compute: - instrs = sum(operator.operation.TotalInstrNum() for operator in oper_list) - total_instrs = total_instrs + instrs - instrs = "{:,}".format(instrs) - else: - instrs = "???" - - print("\t{type_str:38}: {occur:4} \t (instrs: {instrs})".format( - type_str=type_str, occur=occur, instrs=instrs)) - - total_instrs = "{:,}".format(total_instrs) - print("{0:46}: {1:4} \t (total instrs: {2})".format("Number of all operators", - len(self.operators_in_list), - total_instrs)) diff --git a/tools/tflitefile_tool/operator_printer.py b/tools/tflitefile_tool/operator_printer.py new file mode 100644 index 000000000..9b6f97d24 --- /dev/null +++ b/tools/tflitefile_tool/operator_printer.py @@ -0,0 +1,72 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from operator_wrapping import Operator +from tensor_printer import TensorPrinter +from option_printer import OptionPrinter +from perf_predictor import PerfPredictor + + +def GetStrTensorIndex(tensors): + return_string = "[" + for idx in range(len(tensors)): + if idx != 0: + return_string += ", " + return_string += str(tensors[idx].tensor_idx) + return_string += "]" + return return_string + + +class OperatorPrinter(object): + def __init__(self, verbose, operator): + self.verbose = verbose + self.operator = operator + + def PrintInfo(self, perf_predictor=None): + if (self.verbose < 1): + return + + op_str = "Operator {0}: {1}".format(self.operator.operator_idx, + self.operator.opcode_str) + + if self.verbose == 2: + # total instruction num + instrs = "{:,}".format(self.operator.operation.TotalInstrNum() + ) if self.operator.operation.can_compute else "???" + + # total operation cycles + cycles = "{:,}".format( + (perf_predictor.PredictCycles(self.operator.operation)) + ) if self.operator.operation.can_compute and perf_predictor != None else "???" + + op_str = op_str + "(instrs: {0}, cycls: {1})".format(instrs, cycles) + + print(op_str) + print("\tFused Activation: " + self.operator.fused_activation) + self.PrintTensors() + + def PrintTensors(self): + print("\tInput Tensors" + GetStrTensorIndex(self.operator.inputs)) + for tensor in self.operator.inputs: + TensorPrinter(self.verbose, tensor).PrintInfo("\t\t") + print("\tOutput Tensors" + GetStrTensorIndex(self.operator.outputs)) + for tensor in self.operator.outputs: + TensorPrinter(self.verbose, tensor).PrintInfo("\t\t") + + # operator option + # Some operations does not have option. In such case no option is printed + OptionPrinter(self.verbose, self.operator.opcode_str, + self.operator.options).PrintInfo("\t") diff --git a/tools/tflitefile_tool/operator_wrapping.py b/tools/tflitefile_tool/operator_wrapping.py index 1b7f55a4c..64bad1f08 100755 --- a/tools/tflitefile_tool/operator_wrapping.py +++ b/tools/tflitefile_tool/operator_wrapping.py @@ -1,12 +1,24 @@ #!/usr/bin/python +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tflite.Operator import tflite.OperatorCode import tflite.BuiltinOperator import tflite.ActivationFunctionType -from tensor_wrapping import Tensor from operation import Operation -from perf_predictor import PerfPredictor # Match enum value integer to name string @@ -30,16 +42,6 @@ class EnumStrMaps(): BuiltinOptions = BuildEnumClassStrMap(tflite.BuiltinOptions.BuiltinOptions()) -def GetStrTensorIndex(tensors): - return_string = "[" - for idx in range(len(tensors)): - if idx != 0: - return_string += ", " - return_string += str(tensors[idx].tensor_idx) - return_string += "]" - return return_string - - def GetAttribute(o, *args): import functools return functools.reduce(getattr, args, o) @@ -64,6 +66,11 @@ class OptionLoader: @staticmethod def GetBuiltinOptions(options_type, options_table): + if (options_table == None) and (options_type != 0): + print( + "Bad flatbuffer file: undefined builtin option table with defined option type" + ) + exit(1) options = OptionLoader.builtinOptionGen[options_type]() options.Init(options_table.Bytes, options_table.Pos) return options @@ -79,30 +86,19 @@ class Operator(object): self.opcode_str = opcode_str self.operation = Operation(self.tf_operator, self.opcode_str, self.inputs, self.outputs) + self.fused_activation = "NONE" + self.SetupBuiltinOption() + self.SetupFusedActivation() - def PrintInfo(self, perf_predictor=None): - # total instruction num - instrs = "{:,}".format( - self.operation.TotalInstrNum()) if self.operation.can_compute else "???" - - # total operation cycles - cycles = "{:,}".format( - (perf_predictor.PredictCycles(self.operation) - )) if self.operation.can_compute and perf_predictor != None else "???" - - print("Operator {0}: {1} (instrs: {2}, cycls: {3})".format( - self.operator_idx, self.opcode_str, instrs, cycles)) - - self.PrintOptionInfo() - - print("\tInput Tensors" + GetStrTensorIndex(self.inputs)) - for tensor in self.inputs: - tensor.PrintInfo("\t\t") - print("\tOutput Tensors" + GetStrTensorIndex(self.outputs)) - for tensor in self.outputs: - tensor.PrintInfo("\t\t") + def SetupBuiltinOption(self): + try: + self.options = OptionLoader.GetBuiltinOptions( + self.tf_operator.BuiltinOptionsType(), self.tf_operator.BuiltinOptions()) + except KeyError: + self.options = 0 + return - def PrintOptionInfo(self): + def SetupFusedActivation(self): # FIXME: workaround for ops such as custom try: options = OptionLoader.GetBuiltinOptions( @@ -113,8 +109,7 @@ class Operator(object): # fused activation function try: activation_code = options.FusedActivationFunction() - fused_activation = EnumStrMaps.ActivationFunctionType[activation_code] - print("\tFused Activation: " + fused_activation) + self.fused_activation = EnumStrMaps.ActivationFunctionType[activation_code] except AttributeError: # This operator does not support FusedActivationFunction pass diff --git a/tools/tflitefile_tool/option_printer.py b/tools/tflitefile_tool/option_printer.py new file mode 100644 index 000000000..08754f1ce --- /dev/null +++ b/tools/tflitefile_tool/option_printer.py @@ -0,0 +1,69 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class OptionPrinter(object): + def __init__(self, verbose, op_name, options): + self.verbose = verbose + self.op_name = op_name + self.options = options + + def GetPadding(self): + if self.options.Padding() == 0: + return "SAME" + elif self.options.Padding() == 1: + return "VALID" + else: + return "** wrong padding value **" + + def PrintInfo(self, tab=""): + if (self.verbose < 1): + pass + if (self.options == 0): + return + + if (self.op_name == "AVERAGE_POOL_2D" or self.op_name == "MAX_POOL_2D"): + print("{}Options".format(tab)) + + print("{}\t{}, {}, {}".format( + tab, "Filter W:H = {}:{}".format(self.options.FilterWidth(), + self.options.FilterHeight()), + "Stride W:H = {}:{}".format(self.options.StrideW(), + self.options.StrideH()), + "Padding = {}".format(self.GetPadding()))) + + elif (self.op_name == "CONV_2D"): + print("{}Options".format(tab)) + + print("{}\t{}, {}, {}".format( + tab, "Stride W:H = {}:{}".format(self.options.StrideW(), + self.options.StrideH()), + "Dilation W:H = {}:{}".format(self.options.DilationWFactor(), + self.options.DilationHFactor()), + "Padding = {}".format(self.GetPadding()))) + + elif (self.op_name == "DEPTHWISE_CONV_2D"): + print("{}Options".format(tab)) + + # yapf: disable + print("{}\t{}, {}, {}, {}".format( + tab, "Stride W:H = {}:{}".format(self.options.StrideW(), + self.options.StrideH()), + "Dilation W:H = {}:{}".format(self.options.DilationWFactor(), + self.options.DilationHFactor()), + "Padding = {}".format(self.GetPadding()), + "DepthMultiplier = {}".format(self.options.DepthMultiplier()))) + # yapf: enable diff --git a/tools/tflitefile_tool/perf_predictor.py b/tools/tflitefile_tool/perf_predictor.py index 8880c8e71..ea5c15a33 100755 --- a/tools/tflitefile_tool/perf_predictor.py +++ b/tools/tflitefile_tool/perf_predictor.py @@ -1,5 +1,18 @@ #!/usr/bin/python +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. from operation import Operation diff --git a/tools/tflitefile_tool/select_operator.py b/tools/tflitefile_tool/select_operator.py index 55ca1acd9..c5d311d59 100755..100644 --- a/tools/tflitefile_tool/select_operator.py +++ b/tools/tflitefile_tool/select_operator.py @@ -1,4 +1,19 @@ #!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import sys import numpy @@ -61,8 +76,8 @@ def GenerateOperatorCodes(new_builder, sample_model, used_operators_dic): if operator_code_idx in used_operators_dic: operator_code = sample_model.OperatorCodes(operator_code_idx) operator_code_string = operator_code.CustomCode() - if (operator_code_string != - "") and (not operator_code_string in new_operator_code_string_list): + if operator_code_string and (operator_code_string != "") and ( + not operator_code_string in new_operator_code_string_list): new_operator_code_string_list[ operator_code_string] = new_builder.CreateString(operator_code_string) @@ -209,26 +224,10 @@ def GenerateTensors(new_builder, selected_subgraph, used_tensors_dic, used_buffe return new_builder.EndVector(new_tensor_num) -import tflite.Conv2DOptions -import tflite.DepthwiseConv2DOptions -import tflite.Pool2DOptions -import tflite.FullyConnectedOptions -import tflite.SoftmaxOptions -import tflite.ConcatenationOptions -import tflite.ReshapeOptions -import tflite.AddOptions -import tflite.SubOptions -import tflite.MulOptions -import tflite.DivOptions -import tflite.ResizeBilinearOptions -import tflite.StridedSliceOptions -import tflite.CastOptions -import tflite.TopKV2Options -import tflite.GatherOptions - - def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_type): + # Conv2D option + import tflite.Conv2DOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().Conv2DOptions: conv2d_options = tflite.Conv2DOptions.Conv2DOptions() @@ -245,6 +244,8 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, conv2d_options.FusedActivationFunction()) return tflite.Conv2DOptions.Conv2DOptionsEnd(new_builder) + # DepthwiseConv2D option + import tflite.DepthwiseConv2DOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( ).DepthwiseConv2DOptions: @@ -263,8 +264,17 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, depthconv2d_option.DepthMultiplier()) tflite.DepthwiseConv2DOptions.DepthwiseConv2DOptionsAddFusedActivationFunction( new_builder, depthconv2d_option.FusedActivationFunction()) + tflite.DepthwiseConv2DOptions.DepthwiseConv2DOptionsAddDilationWFactor( + new_builder, depthconv2d_option.DilationWFactor()) + tflite.DepthwiseConv2DOptions.DepthwiseConv2DOptionsAddDilationHFactor( + new_builder, depthconv2d_option.DilationHFactor()) return tflite.DepthwiseConv2DOptions.DepthwiseConv2DOptionsEnd(new_builder) + # ConcatEmbeddingsOptions: not supported + # LSHProjectionOptions: not supported + + # Pool2DPOption + import tflite.Pool2DOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().Pool2DOptions: pool2d_option = tflite.Pool2DOptions.Pool2DOptions() @@ -282,6 +292,22 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, pool2d_option.FusedActivationFunction()) return tflite.Pool2DOptions.Pool2DOptionsEnd(new_builder) + # SVDFOptions: not supported + + # RNNOptions + import tflite.RNNOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().RNNOptions: + + rnn_option = tflite.RNNOptions.RNNOptions() + rnn_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.RNNOptions.RNNOptionsStart(new_builder) + tflite.RNNOptions.RNNOptionsAddFusedActivationFunction( + new_builder, rnn_option.FusedActivationFunction()) + return tflite.RNNOptions.RNNOptionsEnd(new_builder) + + # FullyConnectedOptions + import tflite.FullyConnectedOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( ).FullyConnectedOptions: @@ -293,6 +319,8 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, fc_option.FusedActivationFunction()) return tflite.FullyConnectedOptions.FullyConnectedOptionsEnd(new_builder) + # SoftmaxOptions + import tflite.SoftmaxOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SoftmaxOptions: softmax_option = tflite.SoftmaxOptions.SoftmaxOptions() @@ -302,6 +330,8 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t tflite.SoftmaxOptions.SoftmaxOptionsAddBeta(new_builder, softmax_option.Beta()) return tflite.SoftmaxOptions.SoftmaxOptionsEnd(new_builder) + # ConcatenationOptions + import tflite.ConcatenationOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().ConcatenationOptions: concat_option = tflite.ConcatenationOptions.ConcatenationOptions() @@ -314,6 +344,72 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, concat_option.FusedActivationFunction()) return tflite.ConcatenationOptions.ConcatenationOptionsEnd(new_builder) + # AddOptions + import tflite.AddOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().AddOptions: + + add_option = tflite.AddOptions.AddOptions() + add_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.AddOptions.AddOptionsStart(new_builder) + tflite.AddOptions.AddOptionsAddFusedActivationFunction( + new_builder, add_option.FusedActivationFunction()) + return tflite.AddOptions.AddOptionsEnd(new_builder) + + # L2NormOptions + import tflite.L2NormOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().L2NormOptions: + + l2norm_option = tflite.L2NormOptions.L2NormOptions() + l2norm_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.L2NormOptions.L2NormOptionsStart(new_builder) + tflite.L2NormOptions.L2NormOptionsAddFusedActivationFunction( + new_builder, l2norm_option.FusedActivationFunction()) + return tflite.L2NormOptions.L2NormOptionsEnd(new_builder) + + # LocalResponseNormalizationOptions + import tflite.LocalResponseNormalizationOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( + ).LocalResponseNormalizationOptions: + + lrn_option = tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptions( + ) + lrn_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsStart( + new_builder) + tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsAddRadius( + new_builder, lrn_option.Radius()) + tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsAddBias( + new_builder, lrn_option.Bias()) + tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsAddAlpha( + new_builder, lrn_option.Alpha()) + tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsAddBeta( + new_builder, lrn_option.Beta()) + return tflite.LocalResponseNormalizationOptions.LocalResponseNormalizationOptionsEnd( + new_builder) + + # LSTMOptions: not supported + + # ResizeBilinearOptions + import tflite.ResizeBilinearOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( + ).ResizeBilinearOptions: + + resize_bilinear_option = tflite.ResizeBilinearOptions.ResizeBilinearOptions() + resize_bilinear_option.Init(selected_builtin_option.Bytes, + selected_builtin_option.Pos) + + tflite.ResizeBilinearOptions.ResizeBilinearOptionsStart(new_builder) + tflite.ResizeBilinearOptions.ResizeBilinearOptionsAddAlignCorners( + new_builder, resize_bilinear_option.AlignCorners()) + return tflite.ResizeBilinearOptions.ResizeBilinearOptionsEnd(new_builder) + + # CallOptions: not supported + + # ReshapeOptions + import tflite.ReshapeOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().ReshapeOptions: reshape_option = tflite.ReshapeOptions.ReshapeOptions() @@ -333,26 +429,25 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t tflite.ReshapeOptions.ReshapeOptionsAddNewShape(new_builder, new_shape) return tflite.ReshapeOptions.ReshapeOptionsEnd(new_builder) - if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().AddOptions: - - add_option = tflite.AddOptions.AddOptions() - add_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + # SkipGramOptions: not supported - tflite.AddOptions.AddOptionsStart(new_builder) - tflite.AddOptions.AddOptionsAddFusedActivationFunction( - new_builder, add_option.FusedActivationFunction()) - return tflite.AddOptions.AddOptionsEnd(new_builder) + # SpaceToDepthOptions + import tflite.SpaceToDepthOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SpaceToDepthOptions: - if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SubOptions: + space_to_depth_option = tflite.SpaceToDepthOptions.SpaceToDepthOptions() + space_to_depth_option.Init(selected_builtin_option.Bytes, + selected_builtin_option.Pos) - sub_option = tflite.SubOptions.SubOptions() - sub_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + tflite.SpaceToDepthOptions.SpaceToDepthOptionsStart(new_builder) + tflite.SpaceToDepthOptions.SpaceToDepthOptionsAddBlockSize( + new_builder, space_to_depth_option.BlockSize()) + return tflite.SpaceToDepthOptions.SpaceToDepthOptionsEnd(new_builder) - tflite.SubOptions.SubOptionsStart(new_builder) - tflite.SubOptions.SubOptionsAddFusedActivationFunction( - new_builder, sub_option.FusedActivationFunction()) - return tflite.SubOptions.SubOptionsEnd(new_builder) + # EmbeddingLookupSparseOptions: not supported + # MulOptions + import tflite.MulOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().MulOptions: mul_option = tflite.MulOptions.MulOptions() @@ -363,6 +458,85 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, mul_option.FusedActivationFunction()) return tflite.MulOptions.MulOptionsEnd(new_builder) + # PadOptions + import tflite.PadOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().PadOptions: + + pad_option = tflite.PadOptions.PadOptions() + pad_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.PadOptions.PadOptionsStart(new_builder) + return tflite.PadOptions.PadOptionsEnd(new_builder) + + # GatherOptions + import tflite.GatherOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().GatherOptions: + + gather_option = tflite.GatherOptions.GatherOptions() + gather_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.GatherOptions.GatherOptionsStart(new_builder) + tflite.GatherOptions.GatherOptionsAddAxis(new_builder, gather_option.Axis()) + return tflite.GatherOptions.GatherOptionsEnd(new_builder) + + # BatchToSpaceNDOptions + import tflite.BatchToSpaceNDOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( + ).BatchToSpaceNDOptions: + + btsnd_option = tflite.BatchToSpaceNDOptions.BatchToSpaceNDOptions() + btsnd_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.BatchToSpaceNDOptions.BatchToSpaceNDOptionsStart(new_builder) + return tflite.BatchToSpaceNDOptions.BatchToSpaceNDOptionsEnd(new_builder) + + # SpaceToBatchNDOptions + import tflite.SpaceToBatchNDOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( + ).SpaceToBatchNDOptions: + + stbnd_option = tflite.SpaceToBatchNDOptions.SpaceToBatchNDOptions() + stbnd_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.SpaceToBatchNDOptions.SpaceToBatchNDOptionsStart(new_builder) + return tflite.SpaceToBatchNDOptions.SpaceToBatchNDOptionsEnd(new_builder) + + # TransposeOptions: + import tflite.TransposeOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().TransposeOptions: + + transpose_option = tflite.TransposeOptions.TransposeOptions() + transpose_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.TransposeOptions.TransposeOptionsStart(new_builder) + return tflite.TransposeOptions.TransposeOptionsEnd(new_builder) + + # ReducerOptions + import tflite.ReducerOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().ReducerOptions: + + reducer_option = tflite.ReducerOptions.ReducerOptions() + reducer_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.ReducerOptions.ReducerOptionsStart(new_builder) + tflite.ReducerOptions.ReducerOptionsAddKeepDims(new_builder, + reducer_option.KeepDims()) + return tflite.ReducerOptions.ReducerOptionsEnd(new_builder) + + # SubOptions + import tflite.SubOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SubOptions: + + sub_option = tflite.SubOptions.SubOptions() + sub_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.SubOptions.SubOptionsStart(new_builder) + tflite.SubOptions.SubOptionsAddFusedActivationFunction( + new_builder, sub_option.FusedActivationFunction()) + return tflite.SubOptions.SubOptionsEnd(new_builder) + + # DivOptions + import tflite.DivOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().DivOptions: div_option = tflite.DivOptions.DivOptions() @@ -373,18 +547,32 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t new_builder, div_option.FusedActivationFunction()) return tflite.DivOptions.DivOptionsEnd(new_builder) - if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions( - ).ResizeBilinearOptions: + # SqueezeOptions + import tflite.SqueezeOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SqueezeOptions: - resize_bilinear_option = tflite.ResizeBilinearOptions.ResizeBilinearOptions() - resize_bilinear_option.Init(selected_builtin_option.Bytes, - selected_builtin_option.Pos) + squeeze_option = tflite.SqueezeOptions.SqueezeOptions() + squeeze_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) - tflite.ResizeBilinearOptions.ResizeBilinearOptionsStart(new_builder) - tflite.ResizeBilinearOptions.ResizeBilinearOptionsAddAlignCorners( - new_builder, resize_bilinear_option.AlignCorners()) - return tflite.ResizeBilinearOptions.ResizeBilinearOptionsEnd(new_builder) + squeeze_dims_num = squeeze_option.SqueezeDimsLength() + if squeeze_dims_num != 0: + tflite.SqueezeOptions.SqueezeOptionsStartSqueezeDimsVector( + new_builder, squeeze_dims_num) + for squeeze_dims_idx in reversed(range(squeeze_dims_num)): + squeeze_dims_val = squeeze_option.SqueezeDims(squeeze_dims_idx) + new_builder.PrependInt32(squeeze_dims_val) + new_squeeze_dims = new_builder.EndVector(squeeze_dims_num) + + tflite.SqueezeOptions.SqueezeOptionsStart(new_builder) + if squeeze_dims_num != 0: + tflite.SqueezeOptions.SqueezeOptionsAddSqueezeDims(new_builder, + new_squeeze_dims) + return tflite.SqueezeOptions.SqueezeOptionsEnd(new_builder) + + # SequenceRNNOptions: not supported + # StridedSliceOptions + import tflite.StridedSliceOptions if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().StridedSliceOptions: stride_slice_option = tflite.StridedSliceOptions.StridedSliceOptions() @@ -405,14 +593,18 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t return tflite.StridedSliceOptions.StridedSliceOptionsEnd(new_builder) - if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().CastOptions: + # ExpOptions + import tflite.ExpOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().ExpOptions: - cast_option = tflite.CastOptions.CastOptions() - cast_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + exp_option = tflite.ExpOptions.ExpOptions() + exp_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) - tflite.CastOptions.CastOptionsStart(new_builder) - return tflite.CastOptions.CastOptionsEnd(new_builder) + tflite.ExpOptions.ExpOptionsStart(new_builder) + return tflite.ExpOptions.ExpOptionsEnd(new_builder) + # TopKV2Options + import tflite.TopKV2Options if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().TopKV2Options: topkv2_option = tflite.TopKV2Options.TopKV2Options() @@ -421,17 +613,185 @@ def GenerateBuiltinOption(new_builder, selected_builtin_option, builtin_option_t tflite.TopKV2Options.TopKV2OptionsStart(new_builder) return tflite.TopKV2Options.TopKV2OptionsEnd(new_builder) - if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().GatherOptions: + # SplitOptions + import tflite.SplitOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().SplitOptions: - gather_option = tflite.GatherOptions.GatherOptions() - gather_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + split_option = tflite.SplitOptions.SplitOptions() + split_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) - tflite.GatherOptions.GatherOptionsStart(new_builder) - tflite.GatherOptions.GatherOptionsAddAxis(new_builder, gather_option.Axis()) - return tflite.GatherOptions.GatherOptionsEnd(new_builder) + tflite.SplitOptions.SplitOptionsStart(new_builder) + tflite.SplitOptions.SplitOptionsAddNumSplits(new_builder, + split_option.NumSplits()) + return tflite.SplitOptions.SplitOptionsEnd(new_builder) + + # LogSoftmaxOptions: not supported + + # CastOptions: not supported + import tflite.CastOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().CastOptions: + + cast_option = tflite.CastOptions.CastOptions() + cast_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.CastOptions.CastOptionsStart(new_builder) + return tflite.CastOptions.CastOptionsEnd(new_builder) + + # DequantizeOptions: + import tflite.DequantizeOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().DequantizeOptions: + + dequantize_option = tflite.DequantizeOptions.DequantizeOptions() + dequantize_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.EqualOptions.DequantizeOptionsStart(new_builder) + return tflite.DequantizeOptions.DequantizeOptionsEnd(new_builder) + + # MaximumMinimumOptions: not supported + + # ArgMaxOptions + import tflite.ArgMaxOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().ArgMaxOptions: + + arg_max_option = tflite.ArgMaxOptions.ArgMaxOptions() + arg_max_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.ArgMaxOptions.ArgMaxOptionsStart(new_builder) + tflite.ArgMaxOptions.ArgMaxOptionsAddOutputType(new_builder, + arg_max_option.OutputType()) + return tflite.ArgMaxOptions.ArgMaxOptionsEnd(new_builder) + + # LessOptions: not supported + + # NegOptions + import tflite.NegOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().NegOptions: + + neg_option = tflite.NegOptions.NegOptions() + neg_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.NegOptions.NegOptionsStart(new_builder) + return tflite.NegOptions.NegOptionsEnd(new_builder) + + # EqualOptions + import tflite.EqualOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().EqualOptions: + + equal_option = tflite.EqualOptions.EqualOptions() + equal_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.EqualOptions.EqualOptionsStart(new_builder) + return tflite.EqualOptions.EqualOptionsEnd(new_builder) + + # PadV2Options: not supported + # GreaterOptions: not supported + # GreaterEqualOptions: not supported + # LessEqualOptions: not supported + # SelectOptions: not supported + # SliceOptions: not supported + + # TransposeConvOptions + import tflite.TransposeConvOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().TransposeConvOptions: + + transposeconv_option = tflite.TransposeConvOptions.TransposeConvOptions() + transposeconv_option.Init(selected_builtin_option.Bytes, + selected_builtin_option.Pos) + + tflite.TransposeConvOptions.TransposeConvOptionsStart(new_builder) + tflite.TransposeConvOptions.TransposeConvOptionsAddPadding( + new_builder, transposeconv_option.Padding()) + tflite.TransposeConvOptions.TransposeConvOptionsAddStrideW( + new_builder, transposeconv_option.StrideW()) + tflite.TransposeConvOptions.TransposeConvOptionsAddStrideH( + new_builder, transposeconv_option.StrideH()) + return tflite.TransposeConvOptions.TransposeConvOptionsEnd(new_builder) + + # SparseToDenseOptions: not supported + # TileOptions: not supported + # ExpandDimsOptions: not supported + + # NotEqualOptions: + import tflite.NotEqualOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().NotEqualOptions: + + notequal_option = tflite.NotEqualOptions.NotEqualOptions() + notequal_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.NotEqualOptions.NotEqualOptionsStart(new_builder) + return tflite.NotEqualOptions.NotEqualOptionsEnd(new_builder) + + # ShapeOptions: not supported + # PowOptions: not supported + # ArgMinOptions: not supported + # FakeQuantOptions: not supported + + # PackOptions: + import tflite.PackOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().PackOptions: + + pack_option = tflite.PackOptions.PackOptions() + pack_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.PackOptions.PackOptionsStart(new_builder) + tflite.PackOptions.PackOptionsAddValuesCount(new_builder, + pack_option.ValuesCount()) + tflite.PackOptions.PackOptionsAddAxis(new_builder, pack_option.Axis()) + return tflite.PackOptions.PackOptionsEnd(new_builder) + + # LogicalOrOptions: + import tflite.LogicalOrOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().LogicalOrOptions: + + logical_or_option = tflite.LogicalAndOptions.LogicalOrOptions() + logical_or_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.LogicalOrOptions.LogicalOrOptionsStart(new_builder) + return tflite.LogicalOrOptions.LogicalOrOptionsEnd(new_builder) + + # OneHotOptions: not supported + + # LogicalNotOptions + import tflite.LogicalNotOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().LogicalNotOptions: + + equal_option = tflite.LogicalNotOptions.LogicalNotOptions() + equal_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.LogicalNotOptions.LogicalNotOptionsStart(new_builder) + return tflite.LogicalNotOptions.LogicalNotOptionsEnd(new_builder) + + # UnpackOptions: + import tflite.UnpackOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().UnpackOptions: + + unpack_option = tflite.unpackOptions.unpackOptions() + unpack_option.Init(selected_builtin_option.Bytes, selected_builtin_option.Pos) + + tflite.unpackOptions.UnpackOptionsStart(new_builder) + tflite.unpackOptions.UnpackOptionsAddNum(new_builder, unpack_option.Num()) + tflite.PackOptions.UnpackOptionsAddAxis(new_builder, unpack_option.Axis()) + return tflite.UnpackOptions.UnpackOptionsEnd(new_builder) + + # FloorDivOptions: not supported + # SquareOptions: not supported + # ZerosLikeOptions: not supported + # FillOptions: not supported + + # LogicalAndOptions + import tflite.LogicalAndOptions + if builtin_option_type == tflite.BuiltinOptions.BuiltinOptions().LogicalAndOptions: + + logical_and_option = tflite.LogicalAndOptions.LogicalAndOptions() + logical_and_option.Init(selected_builtin_option.Bytes, + selected_builtin_option.Pos) + + tflite.LogicalAndOptions.LogicalAndOptionsStart(new_builder) + return tflite.LogicalAndOptions.LogicalAndOptionsEnd(new_builder) # Cannot handle builtin option type yet - return 0 + print("Cannot handle this option yet") + exit(1) def GenerateOperator(new_builder, selected_operator, used_tensors_dic, @@ -556,7 +916,7 @@ def GenerateSubgraph(new_builder, selected_subgraph, opcode_list, new_input_tens # Name subgraph_name = selected_subgraph.Name() have_name = False - if subgraph_name != "": + if subgraph_name and subgraph_name != "": have_name = True new_subgraph_name = new_builder.CreateString(subgraph_name) diff --git a/tools/tflitefile_tool/tensor_printer.py b/tools/tflitefile_tool/tensor_printer.py new file mode 100644 index 000000000..f566a6e10 --- /dev/null +++ b/tools/tflitefile_tool/tensor_printer.py @@ -0,0 +1,80 @@ +#!/usr/bin/python + +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tensor_wrapping import Tensor + +SYMBOLS = ['B', 'K', 'M', 'G', 'T'] + + +def ConvertBytesToHuman(n): + n = int(n) + if n < 0: + return 0 + + format_str = "%(val)3.1f%(symb)s" + prefix = {} + for i, s in enumerate(SYMBOLS[1:]): + prefix[s] = 1 << (i + 1) * 10 + + for symbol in reversed(SYMBOLS[1:]): + if n >= prefix[symbol]: + v = float(n) / prefix[symbol] + return format_str % dict(symb=symbol, val=v) + + return format_str % dict(symb=SYMBOLS[0], val=n) + + +class TensorPrinter(object): + def __init__(self, verbose, tensor): + self.verbose = verbose + self.tensor = tensor + + def PrintInfo(self, depth_str=""): + if (self.verbose < 1): + pass + + print_str = "" + if self.tensor.tensor_idx < 0: + print_str = "Tensor {0:4}".format(self.tensor.tensor_idx) + else: + buffer_idx = self.tensor.tf_tensor.Buffer() + isEmpty = "Filled" + if (self.tensor.tf_buffer.DataLength() == 0): + isEmpty = " Empty" + shape_str = self.GetShapeString() + type_name = self.tensor.type_name + + shape_name = "" + if self.tensor.tf_tensor.Name() != 0: + shape_name = self.tensor.tf_tensor.Name() + + memory_size = ConvertBytesToHuman(self.tensor.memory_size) + + print_str = "Tensor {0:4} : buffer {1:4} | {2} | {3:7} | Memory {4:6} | Shape {5} ({6})".format( + self.tensor.tensor_idx, buffer_idx, isEmpty, type_name, memory_size, + shape_str, shape_name) + print(depth_str + print_str) + + def GetShapeString(self): + if self.tensor.tf_tensor.ShapeLength() == 0: + return "Scalar" + return_string = "[" + for shape_idx in range(self.tensor.tf_tensor.ShapeLength()): + if (shape_idx != 0): + return_string += ", " + return_string += str(self.tensor.tf_tensor.Shape(shape_idx)) + return_string += "]" + return return_string diff --git a/tools/tflitefile_tool/tensor_wrapping.py b/tools/tflitefile_tool/tensor_wrapping.py index b1fba57d2..a32a573ce 100755 --- a/tools/tflitefile_tool/tensor_wrapping.py +++ b/tools/tflitefile_tool/tensor_wrapping.py @@ -1,5 +1,19 @@ #!/usr/bin/python +# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import tflite.Tensor import tflite.TensorType @@ -16,39 +30,47 @@ def SetTensorTypeStr(): TensorTypeList[fieldValue] = fieldName +TYPES = { + 'BOOL': 1, + 'COMPLEX64': 8, + 'FLOAT16': 2, + 'FLOAT32': 4, + 'INT16': 2, + 'INT32': 4, + 'INT64': 8, + 'UINT8': 1 +} + + +def GetTypeSize(type_name): + try: + return TYPES[type_name] + + except KeyError as error: + return 0 + + class Tensor(object): def __init__(self, tensor_idx, tf_tensor, tf_buffer): self.tensor_idx = tensor_idx self.tf_tensor = tf_tensor self.tf_buffer = tf_buffer + self.type_name = TensorTypeList[self.tf_tensor.Type()] + self.memory_size = self.GetMemorySize() + + def GetMemorySize(self): + type_size = GetTypeSize(self.type_name) + if type_size == 0: + return 0 + + # memory size in bytes + size = int(type_size) + shape_length = self.tf_tensor.ShapeLength() + if shape_length == 0: + return size + + for shape_idx in range(shape_length): + shape_size = int(self.tf_tensor.Shape(shape_idx)) + size *= shape_size - def PrintInfo(self, depth_str=""): - print_str = "" - if self.tensor_idx < 0: - print_str = "Tensor {0:4}".format(self.tensor_idx) - else: - buffer_idx = self.tf_tensor.Buffer() - isEmpty = "Filled" - if (self.tf_buffer.DataLength() == 0): - isEmpty = " Empty" - shape_str = self.GetShapeString() - type_name = TensorTypeList[self.tf_tensor.Type()] - - shape_name = "" - if self.tf_tensor.Name() != 0: - shape_name = self.tf_tensor.Name() - - print_str = "Tensor {0:4} : buffer {1:4} | {2} | {3:7} | Shape {4} ({5})".format( - self.tensor_idx, buffer_idx, isEmpty, type_name, shape_str, shape_name) - print(depth_str + print_str) - - def GetShapeString(self): - if self.tf_tensor.ShapeLength() == 0: - return "Scalar" - return_string = "[" - for shape_idx in range(self.tf_tensor.ShapeLength()): - if (shape_idx != 0): - return_string += ", " - return_string += str(self.tf_tensor.Shape(shape_idx)) - return_string += "]" - return return_string + return size diff --git a/tools/tflitefile_tool/tflite/BidirectionalSequenceRNNOptions.py b/tools/tflitefile_tool/tflite/BidirectionalSequenceRNNOptions.py index 5c057b6bf..474ee4ba2 100644 --- a/tools/tflitefile_tool/tflite/BidirectionalSequenceRNNOptions.py +++ b/tools/tflitefile_tool/tflite/BidirectionalSequenceRNNOptions.py @@ -23,8 +23,9 @@ class BidirectionalSequenceRNNOptions(object): def TimeMajor(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False # BidirectionalSequenceRNNOptions def FusedActivationFunction(self): diff --git a/tools/tflitefile_tool/tflite/BuiltinOperator.py b/tools/tflitefile_tool/tflite/BuiltinOperator.py index 2beda098e..8e2f9c680 100644 --- a/tools/tflitefile_tool/tflite/BuiltinOperator.py +++ b/tools/tflitefile_tool/tflite/BuiltinOperator.py @@ -84,3 +84,17 @@ class BuiltinOperator(object): POW = 78 ARG_MIN = 79 FAKE_QUANT = 80 + REDUCE_PROD = 81 + REDUCE_MAX = 82 + PACK = 83 + LOGICAL_OR = 84 + ONE_HOT = 85 + LOGICAL_AND = 86 + LOGICAL_NOT = 87 + UNPACK = 88 + REDUCE_MIN = 89 + FLOOR_DIV = 90 + REDUCE_ANY = 91 + SQUARE = 92 + ZEROS_LIKE = 93 + FILL = 94 diff --git a/tools/tflitefile_tool/tflite/BuiltinOptions.py b/tools/tflitefile_tool/tflite/BuiltinOptions.py index 5d3040839..7e1eb34ac 100644 --- a/tools/tflitefile_tool/tflite/BuiltinOptions.py +++ b/tools/tflitefile_tool/tflite/BuiltinOptions.py @@ -63,3 +63,13 @@ class BuiltinOptions(object): PowOptions = 56 ArgMinOptions = 57 FakeQuantOptions = 58 + PackOptions = 59 + LogicalOrOptions = 60 + OneHotOptions = 61 + LogicalAndOptions = 62 + LogicalNotOptions = 63 + UnpackOptions = 64 + FloorDivOptions = 65 + SquareOptions = 66 + ZerosLikeOptions = 67 + FillOptions = 68 diff --git a/tools/tflitefile_tool/tflite/DepthwiseConv2DOptions.py b/tools/tflitefile_tool/tflite/DepthwiseConv2DOptions.py index 9f0b3388f..786f7c53d 100644 --- a/tools/tflitefile_tool/tflite/DepthwiseConv2DOptions.py +++ b/tools/tflitefile_tool/tflite/DepthwiseConv2DOptions.py @@ -54,9 +54,23 @@ class DepthwiseConv2DOptions(object): return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) return 0 + # DepthwiseConv2DOptions + def DilationWFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + + # DepthwiseConv2DOptions + def DilationHFactor(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 1 + def DepthwiseConv2DOptionsStart(builder): - builder.StartObject(5) + builder.StartObject(7) def DepthwiseConv2DOptionsAddPadding(builder, padding): @@ -79,5 +93,13 @@ def DepthwiseConv2DOptionsAddFusedActivationFunction(builder, fusedActivationFun builder.PrependInt8Slot(4, fusedActivationFunction, 0) +def DepthwiseConv2DOptionsAddDilationWFactor(builder, dilationWFactor): + builder.PrependInt32Slot(5, dilationWFactor, 1) + + +def DepthwiseConv2DOptionsAddDilationHFactor(builder, dilationHFactor): + builder.PrependInt32Slot(6, dilationHFactor, 1) + + def DepthwiseConv2DOptionsEnd(builder): return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/FakeQuantOptions.py b/tools/tflitefile_tool/tflite/FakeQuantOptions.py index fc8023e60..c266bfc9d 100644 --- a/tools/tflitefile_tool/tflite/FakeQuantOptions.py +++ b/tools/tflitefile_tool/tflite/FakeQuantOptions.py @@ -44,8 +44,9 @@ class FakeQuantOptions(object): def NarrowRange(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def FakeQuantOptionsStart(builder): diff --git a/tools/tflitefile_tool/tflite/FillOptions.py b/tools/tflitefile_tool/tflite/FillOptions.py new file mode 100644 index 000000000..ee6273514 --- /dev/null +++ b/tools/tflitefile_tool/tflite/FillOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class FillOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFillOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FillOptions() + x.Init(buf, n + offset) + return x + + # FillOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def FillOptionsStart(builder): + builder.StartObject(0) + + +def FillOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/FloorDivOptions.py b/tools/tflitefile_tool/tflite/FloorDivOptions.py new file mode 100644 index 000000000..90b797112 --- /dev/null +++ b/tools/tflitefile_tool/tflite/FloorDivOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class FloorDivOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsFloorDivOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = FloorDivOptions() + x.Init(buf, n + offset) + return x + + # FloorDivOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def FloorDivOptionsStart(builder): + builder.StartObject(0) + + +def FloorDivOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/LogicalAndOptions.py b/tools/tflitefile_tool/tflite/LogicalAndOptions.py new file mode 100644 index 000000000..84cdfd92a --- /dev/null +++ b/tools/tflitefile_tool/tflite/LogicalAndOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class LogicalAndOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalAndOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalAndOptions() + x.Init(buf, n + offset) + return x + + # LogicalAndOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def LogicalAndOptionsStart(builder): + builder.StartObject(0) + + +def LogicalAndOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/LogicalNotOptions.py b/tools/tflitefile_tool/tflite/LogicalNotOptions.py new file mode 100644 index 000000000..966a419b7 --- /dev/null +++ b/tools/tflitefile_tool/tflite/LogicalNotOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class LogicalNotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalNotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalNotOptions() + x.Init(buf, n + offset) + return x + + # LogicalNotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def LogicalNotOptionsStart(builder): + builder.StartObject(0) + + +def LogicalNotOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/LogicalOrOptions.py b/tools/tflitefile_tool/tflite/LogicalOrOptions.py new file mode 100644 index 000000000..0a820cdaa --- /dev/null +++ b/tools/tflitefile_tool/tflite/LogicalOrOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class LogicalOrOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsLogicalOrOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = LogicalOrOptions() + x.Init(buf, n + offset) + return x + + # LogicalOrOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def LogicalOrOptionsStart(builder): + builder.StartObject(0) + + +def LogicalOrOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/Model.py b/tools/tflitefile_tool/tflite/Model.py index 4d1e01f44..b5072b171 100644 --- a/tools/tflitefile_tool/tflite/Model.py +++ b/tools/tflitefile_tool/tflite/Model.py @@ -71,7 +71,7 @@ class Model(object): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.String(o + self._tab.Pos) - return "" + return None # Model def Buffers(self, j): diff --git a/tools/tflitefile_tool/tflite/OneHotOptions.py b/tools/tflitefile_tool/tflite/OneHotOptions.py new file mode 100644 index 000000000..fba03f85e --- /dev/null +++ b/tools/tflitefile_tool/tflite/OneHotOptions.py @@ -0,0 +1,39 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class OneHotOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsOneHotOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = OneHotOptions() + x.Init(buf, n + offset) + return x + + # OneHotOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # OneHotOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + +def OneHotOptionsStart(builder): + builder.StartObject(1) + + +def OneHotOptionsAddAxis(builder, axis): + builder.PrependInt32Slot(0, axis, 0) + + +def OneHotOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/OperatorCode.py b/tools/tflitefile_tool/tflite/OperatorCode.py index 0f945b901..ca0b49ef3 100644 --- a/tools/tflitefile_tool/tflite/OperatorCode.py +++ b/tools/tflitefile_tool/tflite/OperatorCode.py @@ -31,7 +31,7 @@ class OperatorCode(object): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return self._tab.String(o + self._tab.Pos) - return "" + return None # OperatorCode def Version(self): diff --git a/tools/tflitefile_tool/tflite/PackOptions.py b/tools/tflitefile_tool/tflite/PackOptions.py new file mode 100644 index 000000000..c1d5579fd --- /dev/null +++ b/tools/tflitefile_tool/tflite/PackOptions.py @@ -0,0 +1,50 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class PackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsPackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = PackOptions() + x.Init(buf, n + offset) + return x + + # PackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # PackOptions + def ValuesCount(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # PackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + +def PackOptionsStart(builder): + builder.StartObject(2) + + +def PackOptionsAddValuesCount(builder, valuesCount): + builder.PrependInt32Slot(0, valuesCount, 0) + + +def PackOptionsAddAxis(builder, axis): + builder.PrependInt32Slot(1, axis, 0) + + +def PackOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/ReducerOptions.py b/tools/tflitefile_tool/tflite/ReducerOptions.py index 5b6fa1acf..1f1a1b173 100644 --- a/tools/tflitefile_tool/tflite/ReducerOptions.py +++ b/tools/tflitefile_tool/tflite/ReducerOptions.py @@ -23,8 +23,9 @@ class ReducerOptions(object): def KeepDims(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def ReducerOptionsStart(builder): diff --git a/tools/tflitefile_tool/tflite/ResizeBilinearOptions.py b/tools/tflitefile_tool/tflite/ResizeBilinearOptions.py index 66512bb1e..76948948e 100644 --- a/tools/tflitefile_tool/tflite/ResizeBilinearOptions.py +++ b/tools/tflitefile_tool/tflite/ResizeBilinearOptions.py @@ -23,8 +23,9 @@ class ResizeBilinearOptions(object): def AlignCorners(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def ResizeBilinearOptionsStart(builder): diff --git a/tools/tflitefile_tool/tflite/SequenceRNNOptions.py b/tools/tflitefile_tool/tflite/SequenceRNNOptions.py index bee7a0fc6..2681296bb 100644 --- a/tools/tflitefile_tool/tflite/SequenceRNNOptions.py +++ b/tools/tflitefile_tool/tflite/SequenceRNNOptions.py @@ -23,8 +23,9 @@ class SequenceRNNOptions(object): def TimeMajor(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False # SequenceRNNOptions def FusedActivationFunction(self): diff --git a/tools/tflitefile_tool/tflite/SkipGramOptions.py b/tools/tflitefile_tool/tflite/SkipGramOptions.py index 50738b924..9eb5059ea 100644 --- a/tools/tflitefile_tool/tflite/SkipGramOptions.py +++ b/tools/tflitefile_tool/tflite/SkipGramOptions.py @@ -37,8 +37,9 @@ class SkipGramOptions(object): def IncludeAllNgrams(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def SkipGramOptionsStart(builder): diff --git a/tools/tflitefile_tool/tflite/SparseToDenseOptions.py b/tools/tflitefile_tool/tflite/SparseToDenseOptions.py index 2782ae573..952d08fc1 100644 --- a/tools/tflitefile_tool/tflite/SparseToDenseOptions.py +++ b/tools/tflitefile_tool/tflite/SparseToDenseOptions.py @@ -23,8 +23,9 @@ class SparseToDenseOptions(object): def ValidateIndices(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def SparseToDenseOptionsStart(builder): diff --git a/tools/tflitefile_tool/tflite/SquareOptions.py b/tools/tflitefile_tool/tflite/SquareOptions.py new file mode 100644 index 000000000..0f9f5af9e --- /dev/null +++ b/tools/tflitefile_tool/tflite/SquareOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class SquareOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsSquareOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = SquareOptions() + x.Init(buf, n + offset) + return x + + # SquareOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def SquareOptionsStart(builder): + builder.StartObject(0) + + +def SquareOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/SubGraph.py b/tools/tflitefile_tool/tflite/SubGraph.py index c20880a36..df9acd8ce 100644 --- a/tools/tflitefile_tool/tflite/SubGraph.py +++ b/tools/tflitefile_tool/tflite/SubGraph.py @@ -112,7 +112,7 @@ class SubGraph(object): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: return self._tab.String(o + self._tab.Pos) - return "" + return None def SubGraphStart(builder): diff --git a/tools/tflitefile_tool/tflite/Tensor.py b/tools/tflitefile_tool/tflite/Tensor.py index 468b120f4..e5f13301c 100644 --- a/tools/tflitefile_tool/tflite/Tensor.py +++ b/tools/tflitefile_tool/tflite/Tensor.py @@ -62,7 +62,7 @@ class Tensor(object): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.String(o + self._tab.Pos) - return "" + return None # Tensor def Quantization(self): @@ -79,8 +79,9 @@ class Tensor(object): def IsVariable(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) if o != 0: - return self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos) - return 0 + return bool( + self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) + return False def TensorStart(builder): diff --git a/tools/tflitefile_tool/tflite/UnpackOptions.py b/tools/tflitefile_tool/tflite/UnpackOptions.py new file mode 100644 index 000000000..f580418e6 --- /dev/null +++ b/tools/tflitefile_tool/tflite/UnpackOptions.py @@ -0,0 +1,50 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class UnpackOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsUnpackOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = UnpackOptions() + x.Init(buf, n + offset) + return x + + # UnpackOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + # UnpackOptions + def Num(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + # UnpackOptions + def Axis(self): + o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) + if o != 0: + return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) + return 0 + + +def UnpackOptionsStart(builder): + builder.StartObject(2) + + +def UnpackOptionsAddNum(builder, num): + builder.PrependInt32Slot(0, num, 0) + + +def UnpackOptionsAddAxis(builder, axis): + builder.PrependInt32Slot(1, axis, 0) + + +def UnpackOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflitefile_tool/tflite/ZerosLikeOptions.py b/tools/tflitefile_tool/tflite/ZerosLikeOptions.py new file mode 100644 index 000000000..ca0880ab0 --- /dev/null +++ b/tools/tflitefile_tool/tflite/ZerosLikeOptions.py @@ -0,0 +1,28 @@ +# automatically generated by the FlatBuffers compiler, do not modify + +# namespace: tflite + +import flatbuffers + + +class ZerosLikeOptions(object): + __slots__ = ['_tab'] + + @classmethod + def GetRootAsZerosLikeOptions(cls, buf, offset): + n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) + x = ZerosLikeOptions() + x.Init(buf, n + offset) + return x + + # ZerosLikeOptions + def Init(self, buf, pos): + self._tab = flatbuffers.table.Table(buf, pos) + + +def ZerosLikeOptionsStart(builder): + builder.StartObject(0) + + +def ZerosLikeOptionsEnd(builder): + return builder.EndObject() diff --git a/tools/tflkit/README.md b/tools/tflkit/README.md new file mode 100644 index 000000000..805a6e35b --- /dev/null +++ b/tools/tflkit/README.md @@ -0,0 +1,366 @@ +# tflkit + +## Purpose + +There are a lot of tools related to TfLite. However, it is inconvenient to use the tools directly because there are many locations and parameters. The tflkit has been created to make it easier to run frequently used tools in scripts. The function provided in this directory uses existing tools rather than implementing them directly. So, additional modifications may occur depending on the TensorFlow version or other external factors. The function provided in this directory will be gradually expanded. + +## Prerequisites + +The scripts here use TensorFlow's tools, so you need an environment to build TensorFlow. +Running the scripts within this tutorial requires: +* [Install Bazel](https://docs.bazel.build/versions/master/install.html), the build tool used to compile TensorFlow. + +Initially, no external packages are installed on this project. Therefore, before running these scripts, you should install the associcated packages by running the following command once. +``` +make configure +``` + +## Summarize TF model + +### TensorFlow + +TensorFlow uses `summarize_graph` tool to inspect the model and provide guesses about likely input and output nodes, as well as other information that's useful for debugging. For more information, see [Inspecting Graphs](https://github.com/tensorflow/tensorflow/tree/9590c4c32dd4346ea5c35673336f5912c6072bf2/tensorflow/tools/graph_transforms#inspecting-graphs) page. + +Usage: +``` +$ bazel build tensorflow/tools/graph_transforms:summarize_graph +$ bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=<pb file> +``` + +### tflkit + +Usage: +``` +$ ./summarize_pb.sh <pb file> +``` + +The results shown below: +``` +$ ./summarize_pb.sh inception_v3.pb + + inception_v3.pb + +Inputs + name=input + type=float(1) + shape=[?,299,299,3] +Outputs + name=InceptionV3/Predictions/Reshape_1, op=Reshape +Op Types + 488 Const + 379 Identity + 95 Conv2D + 94 FusedBatchNorm + 94 Relu + 15 ConcatV2 + 10 AvgPool + 4 MaxPool + 2 Reshape + 1 BiasAdd + 1 Placeholder + 1 Shape + 1 Softmax + 1 Squeeze + + 14 Total +``` + +## Summarize TfLite model + +### tflkit + +Usage: +``` +$ ./summarize_tflite.sh <tflite file> +``` + +The results shown below: +``` +$ ./summarize_tflite.sh inception_v3.tflite +[Main model] + +Main model input tensors: [317] +Main model output tensors: [316] + +Operator 0: CONV_2D (instrs: 39,073,760, cycls: 39,073,760) + Fused Activation: RELU + Input Tensors[317, 0, 5] + Tensor 317 : buffer 183 | Empty | FLOAT32 | Shape [1, 299, 299, 3] (b'input') + Tensor 0 : buffer 205 | Filled | FLOAT32 | Shape [32, 3, 3, 3] (b'InceptionV3/Conv2d_1a_3x3/weights') + Tensor 5 : buffer 52 | Filled | FLOAT32 | Shape [32] (b'InceptionV3/InceptionV3/Conv2d_1a_3x3/Conv2D_bias') + Output Tensors[6] + Tensor 6 : buffer 285 | Empty | FLOAT32 | Shape [1, 149, 149, 32] (b'InceptionV3/InceptionV3/Conv2d_1a_3x3/Relu') + +[...] + +Operator 125: SOFTMAX (instrs: 4,003, cycls: 4,003) + Input Tensors[225] + Tensor 225 : buffer 142 | Empty | FLOAT32 | Shape [1, 1001] (b'InceptionV3/Logits/SpatialSqueeze') + Output Tensors[316] + Tensor 316 : buffer 53 | Empty | FLOAT32 | Shape [1, 1001] (b'InceptionV3/Predictions/Reshape_1') + + +Number of all operator types: 6 + CONV_2D : 95 (instrs: 11,435,404,777) + MAX_POOL_2D : 4 (instrs: 12,755,516) + AVERAGE_POOL_2D : 10 (instrs: 36,305,334) + CONCATENATION : 15 (instrs: 0) + RESHAPE : 1 (instrs: ???) + SOFTMAX : 1 (instrs: 4,003) +Number of all operators : 126 (total instrs: 11,484,469,630) +``` + +## Convert a TensorFlow model into TfLite model + +### TensorFlow + +TensorFlow provides some kinds of converting guideline. In Python, the [TFLiteConverter](https://www.tensorflow.org/api_docs/python/tf/contrib/lite/TFLiteConverter) class will help you to convert a TensorFlow GraphDef or SavedModel into `output_format` using TOCO. The `output_format` can be `TFLITE` or `GRAPHVIZ_DOT` format. The default `output_format` is `TFLITE`. And there is a Python command line interface for running TOCO, and its name is [tflite_convert](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/python/tflite_convert.py). This converts a TensorFlow GraphDef or SavedModel into `TFLITE` or `GRAPHVIZ_DOT` format like [TFLiteConverter](https://www.tensorflow.org/api_docs/python/tf/contrib/lite/TFLiteConverter). These two way also supports to convert a TensorFlow Keras model into `output_format`. Both functions are implemented using a tool called TOCO. + +### with tflkit + +The tflkit uses the [tflite_convert](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/python/tflite_convert.py) python command line interface to convert a TensorFlow model into TfLite model. It only supports to convert a TensorFlow GraphDef file into `TFLITE` format file. This tool supports the creation of individual `TFLITE` files for different input shapes. When converting to multiple `TFLITE` files, it needs to put a string called `NAME` in `TFLITE_PATH`. The string `NAME` will be replaced by what is listed in teh `NAME` environment. This tool requires an information file as a parameter. There is an [example file](info/convert.template) for a convert information. The `--tensorflow_path` and `--tensorflow_version` can change the TensorFlow location. By default, it uses `externals/tensorflow` directory. + +Convert information: + * GRAPHDEF_PATH : Full filepath of file containing frozen TensorFlow GraphDef. + * TFLITE_PATH : Full filepath of the output TfLite model. If `NAME` optional environment is used, it must include `NAME` string in the file name. (ex. `[...]/inception_v3_NAME.tflite`) + * INPUT : Names of the input arrays, comma-separated. + * INPUT_SHAPE : Shapes correspoding to `INPUT`, colon-separated. + For the creation of individual `TFLITE` files for different input shapes, space-separated. + * OUTPUT : Names of the output arrays, comma-seperated. + * NAME(Optional) : Names of the individual `TFLITE` files for different input shapes, space-seperated. + +Usage (for example, [InceptionV3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz)): +``` +$ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz +$ tar xzvf inception_v3_2018_04_27.tgz ./inception_v3.pb +$ cat > convert.info << END +GRAPHDEF_PATH="${PWD}/inception_v3.pb" +TFLITE_PATH="${PWD}/inception_v3.tflite" +INPUT="input" +INPUT_SHAPE="1,299,299,3" +OUTPUT="InceptionV3/Predictions/Reshape_1" +#NAME="" +END +$ ./tflite_convert.sh --info=./convert.info --tensorflow_version=1.12 +$ ls *.tflite +inception_v3.tflite +``` + +Usage (for example, multiple `TFLITE` files): +``` +$ cat > convert_multiple.info << END +GRAPHDEF_PATH="${PWD}/inception_v3.pb" +TFLITE_PATH="${PWD}/inception_v3_NAME.tflite" +INPUT="input" +INPUT_SHAPE="1,299,299,3 3,299,299,3" +OUTPUT="InceptionV3/Predictions/Reshape_1" +NAME="batch1 batch3" +END +$ ./tflite_convert.sh --info=./convert_multiple.info --tensorflow_version=1.12 +$ ls *.tflite +inception_v3_batch1.tflite +inception_v3_batch3.tflite +``` + +## Optimize a TensorFlow model for inference + +### TensorFlow + +This [optimize tool](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py) behaves like a transform tool. However, this tool is optimized to convert the trained TensorFlow graph for inference. This tool removes parts of a graph that are only needed for training. These include: + - Removing training-only operations like checkpoint saving. + - Stripping out parts of the graph that are never reached. + - Removing debug operations like CheckNumerics. + - Folding batch normalization ops into the pre-calculated weights. + - Fusing common operations into unified version. +The input and output file of this tool is a TensorFlow GraphDef file. + +### with tflkit + +The [optimize_for_inference.sh](optimize_for_inference.sh) file invokes the TensorFlow [optimize tool](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py). This tool requires a optimize information file as a parameter. Here is an [example file](info/optimize.template) for this tool. The information file needs `INPUT` and `OUTPUT` array names. The [summarize_pb.sh](summarize_pb.sh) file will help you to define the `INPUT` and `OUTPUT` array names. The `--tensorflow_path` can change the TensorFlow location. By default, it uses `externals/tensorflow` directory. + +Optimize information: + * GRAPHDEF_PATH : Full filepath of file containing frozen TensorFlow GraphDef. + * OPTIMIZE_PATH : Full filepath to save the output optimized graph. + * INPUT : Names of the input arrays, comma-separated. + * OUTPUT : Names of the output arrays, comma-seperated. + +Usage (for example, [InceptionV3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz)): +``` +$ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz +$ tar xzvf inception_v3_2018_04_27.tgz ./inception_v3.pb +$ cat > optimize.info << END +GRAPHDEF_PATH="${PWD}/inception_v3.pb" +OPTIMIZE_PATH="${PWD}/inception_v3.optimize.pb" +INPUT="input" +OUTPUT="InceptionV3/Predictions/Reshape_1" +END +$ ./optimize_for_inference.sh --info=./optimize.info +$ ls *.pb +inception_v3.optimize.pb inception_v3.pb +``` + +## Transform a TensorFlow graph + +### TensorFlow + +The trained TensorFlow model can be trasformed by some variants to deploy it in production. This [Graph Transform Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#graph-transform-tool) provides to support this behavior. There are so many transform options in this tool. For more information on transform options, please see [this page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#transform-reference). The input and output file of this tool is a TensorFlow GraphDef file. + +### with tflkit + +The [transform_graph.sh](transform_graph.sh) file supports to transform a TensorFlow GraphDef using various transform options. This tool requires a transform information file as a parameter and the transform options are described in the information file. There is an [example file](info/transform.template) for this tool. The information file needs `INPUT` and `OUTPUT` array names. The [summarize_pb.sh](summarize_pb.sh) file will help you to define the `INPUT` and `OUTPUT` array names. The `--tensorflow_path` can change the TensorFlow location. By default, it uses `externals/tensorflow` directory. + +Transform information: + * GRAPHDEF_PATH : Full filepath of file containing frozen TensorFlow GraphDef. + * TRANSFORM_PATH : Full filepath of the output TensorFlow GraphDef. + * INPUT : Names of the input arrays, comma-separated. + * OUTPUT : Names of the output arrays, comma-seperated. + * TRANSFORM_OPTIONS : Names of transform option, space-separated. + By default, it includes the following options. + * strip_unused_nodes : Removes all nodes not used in calculated the layer given in `OUTPUT` array, fed by `INPUT` array. + * remove_nodes : Removes the given name nodes from the graph. + * `Identity` is not necessary in inference graph. But if it needs in inference graph, this tool does not remove this node. + * `CheckNumerics` is useful during training but it is not necessary in inference graph. + * fold_constants : Replaces the sub-graps that always evaluate to constant expressions with those constants. This optimization is always executed at run-time after the graph is loaded, so it does'nt help latency, but it can simplify the graph and so make futher processing easier. + * fold_batch_norms : Scans the graph for any channel-wise multiplies immediately after convolutions, and multiplies the convolution's weights with the Mul instead so this can be omitted at inference time. It should be run after `fold_constants`. + +Usage (for example, [InceptionV3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz)): +``` +$ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz +$ tar xzvf inception_v3_2018_04_27.tgz ./inception_v3.pb +$ cat > transform.info << END +GRAPHDEF_PATH="${PWD}/inception_v3.pb" +TRANSFORM_PATH="${PWD}/inception_v3.transform.pb" +INPUT="input" +OUTPUT="InceptionV3/Predictions/Reshape_1" +TRANSFORM_OPTIONS="strip_unused_nodes(type=float, shape=\"1,299,299,3\") remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) fold_batch_norms" +END +$ ./transform_graph.sh --info=./transform.info +$ ls *.pb +inception_v3.pb inception_v3.transform.pb +``` + +## Freeze a TensorFlow model + +### TensorFlow + +TensorFlow provides methods to save and restore models. Each method stores related files in different ways. Here are two common ways to save the freeze stored models. + 1. Use [tf.train.Saver](https://www.tensorflow.org/guide/saved_model#save_and_restore_variables) + In this way, it creates a `MetaGraphDef` file and checkpoint files that contain the saved variables. Saving this way will result in the following files in the exported directory: + + ``` + $ ls /tmp/saver/ + checkpoint model.ckpt.data-00000-of-00001 model.ckpt.index model.ckpt.meta + ``` + + 2. Use [SavedModel](https://www.tensorflow.org/guide/saved_model#build_and_load_a_savedmodel) + It is the easiest way to create a saved model. Saving this way will result in the following files in the exported directory: + + ``` + $ ls /tmp/saved_model/ + saved_model.pb variables + $ tree /tmp/saved_model/ + /tmp/saved_model/ + ├── saved_model.pb + └── variables + ├── variables.data-00000-of-00001 + └── variables.index + ``` + +The [freeze_graph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) tool receives these files as input parameters and combines the stored variables and the standalone graphdef to generate a frozen graphdef file. + +### with tflkit + +The tflkit provides the simple way to create a frozen graph using [freeze_graph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) tool. This tool requires an information file as a parameter. There is an [example file](info/freeze.info) for a freeze tool. Either `SAVED_MODEL` or `META_GRAPH` must be declared. And `META_GRAPH` is always used with `CKPT_PATH`. The `--tensorflow_path` can change the TensorFlow location. By default, it uses `externals/tensorflow` directory. + +Freeze information: + * SAVED_MODEL : Full directory path with TensorFlow `SavedModel` file and variables. + * META_GRAPH : Full filepath of file containing TensorFlow `MetaGraphDef`. + * CKPT_PATH : Full filepath of file containing TensorFlow variables. (ex. [...]/*.ckpt) + * FROZEN_PATH : Full filepath to save the output frozen graph. + * OUTPUT : Names of the output arrays, comma-separated. + +Usage (for example, `tf.train.Saver`): +``` +$ cat > sample_saver.py << END +import tensorflow as tf + +# Create some variables. +v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer) +v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer) + +inc_v1 = v1.assign(v1+1) +dec_v2 = v2.assign(v2-1) + +# Add an op to initialize the variables. +init_op = tf.global_variables_initializer() + +# Add ops to save and restore all the variables. +saver = tf.train.Saver() + +# Later, launch the model, initialize the variables, do some work, and save the +# variables to disk. +with tf.Session() as sess: + sess.run(init_op) + # Do some work with the model. + inc_v1.op.run() + dec_v2.op.run() + # Save the variables to disk. + save_path = saver.save(sess, "/tmp/saver/model.ckpt") + print("Model saved in path: %s" % save_path) +END +$ python sample_saver.py +$ ls /tmp/saver/ +checkpoint model.ckpt.data-00000-of-00001 model.ckpt.index model.ckpt.meta +$ cat > freeze_saver.info << END +#SAVED_MODEL="" +META_GRAPH="/tmp/saver/model.ckpt.meta" +CKPT_PATH="/tmp/saver/model.ckpt" +FROZEN_PATH="/tmp/saver/model.frozen.pb" +OUTPUT="v2" +END +$ ./freeze_graph.sh --info=./freeze_saver.info +$ ls /tmp/saver/*.pb +/tmp/saver/model.frozen.pb +``` + +Usage (for example, `SavedModel`): +``` +$ cat > sample_saved_model.py << END +import tensorflow as tf + +# Create some variables. +v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer) +v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer) + +inc_v1 = v1.assign(v1+1) +dec_v2 = v2.assign(v2-1) + +# Add an op to initialize the variables. +init_op = tf.global_variables_initializer() + +# Later, launch the model, initialize the variables, do some work, and save the +# variables to disk. +with tf.Session() as sess: + sess.run(init_op) + # Do some work with the model. + inc_v1.op.run() + dec_v2.op.run() + # Save the variables to disk. + tf.saved_model.simple_save(sess, "/tmp/saved_model", inputs={'v1':v1}, outputs={'v2':v2}) +END +$ python sample_saved_model.py +$ ls /tmp/saved_model/ +saved_model.pb variables +$ cat > freeze_saved_model.info << END +SAVED_MODEL="/tmp/saved_model" +#META_GRAPH= +#CKPT_PATH= +FROZEN_PATH="/tmp/saved_model/model.frozen.pb" +OUTPUT="v2" +END +$ ./freeze_graph.sh --info=./info/freeze_saved_model.info +$ ls /tmp/saved_model/ +model.frozen.pb saved_model.pb variables +$ ls /tmp/saved_model/*.frozen.pb +/tmp/saved_model/model.frozen.pb +```
\ No newline at end of file diff --git a/tools/tflkit/convert.template b/tools/tflkit/convert.template new file mode 100644 index 000000000..88637de6f --- /dev/null +++ b/tools/tflkit/convert.template @@ -0,0 +1,6 @@ +GRAPHDEF_PATH= +TFLITE_PATH= +INPUT= +INPUT_SHAPE= +OUTPUT= +#NAME= diff --git a/tools/tflkit/freeze.template b/tools/tflkit/freeze.template new file mode 100644 index 000000000..076819c7f --- /dev/null +++ b/tools/tflkit/freeze.template @@ -0,0 +1,5 @@ +SAVED_MODEL= +META_GRAPH= +CKPT_PATH= +FROZEN_PATH= +OUTPUT= diff --git a/tools/tflkit/freeze_graph.sh b/tools/tflkit/freeze_graph.sh new file mode 100755 index 000000000..c491ba4d2 --- /dev/null +++ b/tools/tflkit/freeze_graph.sh @@ -0,0 +1,98 @@ +#!/bin/bash + +usage() +{ + echo "usage : $0" + echo " --info=Information file" + echo " --tensorflow_path=TensorFlow path (Use externals/tensorflow by default)" +} + +SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +TF_DIR="${SCRIPT_PATH}/../../externals/tensorflow" + +for i in "$@" +do + case $i in + --info=*) + INFO=${i#*=} + ;; + --tensorflow_path=*) + TF_DIR=${i#*=} + ;; + -h|--help) + usage + exit 0 + ;; + *) + usage + exit 1 + ;; + esac + shift +done + +if [ -z "$INFO" ]; then + echo "INFO is unset or set to the empty string" + usage + exit 1 +fi +if [ -z "$TF_DIR" ]; then + echo "tensorflow_path is unset or set to the empty string" + usage + exit 1 +fi + +if [ ! -x "$(command -v bazel)" ]; then + echo "Cannot find bazel. Please install bazel." + exit 1 +fi + +source $INFO + +if [ -z "$SAVED_MODEL" ] && [ -z "$META_GRAPH" ]; then + echo "SAVED_MODEL or META_GRAPH + CKPT_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ ! -z "$META_GRAPH" ] && [ -z "$CKPT_PATH" ]; then + echo "META_GRAPH is always used with CKPT_PATH" + echo "CKPT_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$FROZEN_PATH" ]; then + echo "FROZEN_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$OUTPUT" ]; then + echo "OUTPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi + +CUR_DIR=$(pwd) +{ + echo "Enter $TF_DIR" + pushd $TF_DIR > /dev/null + + if [ ! -z $SAVED_MODEL ]; then + bazel run tensorflow/python/tools:freeze_graph -- \ + --input_saved_model_dir="$SAVED_MODEL" \ + --input_binary=True \ + --output_node_names="$OUTPUT" \ + --output_graph="$FROZEN_PATH" + else + bazel run tensorflow/python/tools:freeze_graph -- \ + --input_meta_graph="$META_GRAPH" \ + --input_checkpoint="$CKPT_PATH" \ + --input_binary=True \ + --output_node_names="$OUTPUT" \ + --output_graph="$FROZEN_PATH" + fi + + popd + + echo "OUTPUT FILE : $FROZEN_PATH" +} diff --git a/tools/tflkit/optimize.template b/tools/tflkit/optimize.template new file mode 100644 index 000000000..5293a0319 --- /dev/null +++ b/tools/tflkit/optimize.template @@ -0,0 +1,4 @@ +GRAPHDEF_PATH= +OPTIMIZE_PATH= +INPUT= +OUTPUT= diff --git a/tools/tflkit/optimize_for_inference.sh b/tools/tflkit/optimize_for_inference.sh new file mode 100755 index 000000000..ef6e52912 --- /dev/null +++ b/tools/tflkit/optimize_for_inference.sh @@ -0,0 +1,90 @@ +#!/bin/bash + +usage() +{ + echo "usage : $0" + echo " --info=Information file" + echo " --tensorflow_path=TensorFlow path (Use externals/tensorflow by default)" +} + +SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +TF_DIR="${SCRIPT_PATH}/../../externals/tensorflow" + +for i in "$@" +do + case $i in + --info=*) + INFO=${i#*=} + ;; + --tensorflow_path=*) + TF_DIR=${i#*=} + ;; + -h|--help) + usage + exit 0 + ;; + *) + usage + exit 1 + ;; + esac + shift +done + +if [ -z "$INFO" ]; then + echo "INFO is unset or set to the empty string" + usage + exit 1 +fi +if [ -z "$TF_DIR" ]; then + echo "tensorflow_path is unset or set to the empty string" + usage + exit 1 +fi + +if [ ! -x "$(command -v bazel)" ]; then + echo "Cannot find bazel. Please install bazel." + exit 1 +fi + +source $INFO + +if [ -z "$GRAPHDEF_PATH" ]; then + echo "GRAPHDEF_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$OPTIMIZE_PATH" ]; then + echo "OPTIMIZE_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$INPUT" ]; then + echo "INPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$OUTPUT" ]; then + echo "OUTPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi + +CUR_DIR=$(pwd) +{ + echo "Enter $TF_DIR" + pushd $TF_DIR > /dev/null + + bazel run tensorflow/python/tools:optimize_for_inference -- \ + --input="$GRAPHDEF_PATH" \ + --output="$OPTIMIZE_PATH" \ + --frozen_graph=True \ + --input_names="$INPUT" \ + --output_names="$OUTPUT" \ + --toco_compatible=True + + popd + + echo "OUTPUT FILE : $OPTIMIZE_PATH" +} diff --git a/tools/tflkit/summarize_pb.py b/tools/tflkit/summarize_pb.py new file mode 100644 index 000000000..633804114 --- /dev/null +++ b/tools/tflkit/summarize_pb.py @@ -0,0 +1,111 @@ +import argparse +import os +import subprocess +import re + + +class cd: + """Context manager for changing the current working directory""" + + def __init__(self, newPath): + self.newPath = os.path.expanduser(newPath) + + def __enter__(self): + self.savedPath = os.getcwd() + os.chdir(self.newPath) + + def __exit__(self, etype, value, traceback): + os.chdir(self.savedPath) + + +def CheckExt(choices, name): + ext = os.path.splitext(name)[1][1:] + if ext not in choices: + parser.error("file does not end with one of {}".format(choices)) + return name + + +def PrintName(path): + print("") + print('\t' + os.path.basename(path)) + print("") + + +def PrintInput(data): + print("Inputs") + sub = data.split('(', 1)[1].rsplit(')', 1)[0] + for i in re.split(', ', sub): + print('\t' + i) + + +def PrintOutput(data): + print("Outputs") + sub = re.findall('\((.*?)\)', data) + for i in sub: + print('\t' + i) + + +def PrintOpType(data): + print("Op Types") + cnt = 0 + sub = data.rsplit(':', 1)[1].split(',') + for i in sub: + cnt = cnt + 1 + print('\t' + i.lstrip()) + print('\t{0} Total'.format(cnt)) + + +def BuildTensorFlowSummarizeGraph(tensorflow_path): + with cd(tensorflow_path): + subprocess.call( + ['bazel', 'build', 'tensorflow/tools/graph_transforms:summarize_graph']) + + +def SummarizeGraph(args): + if args.verbose is True: + vstr = "" + PrintName(args.input_file) + with cd(args.tensorflow_path): + proc = subprocess.Popen( + [ + 'bazel-bin/tensorflow/tools/graph_transforms/summarize_graph', + '--in_graph=' + args.input_file + ], + stdout=subprocess.PIPE) + while True: + line = proc.stdout.readline().decode() + if args.verbose: + vstr += line + if line != '': + if 'inputs:' in line: + PrintInput(line) + elif 'outputs:' in line: + PrintOutput(line) + elif 'Op types used:' in line: + PrintOpType(line) + else: + break + + if args.verbose: + print(vstr) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--input_file', + required=True, + type=lambda s: CheckExt((['pb']), s), + help='pb file to read') + parser.add_argument( + '--tensorflow_path', + default='../../externals/tensorflow', + help='TensorFlow git repository path') + parser.add_argument('--verbose', action='store_true') + args = parser.parse_args() + + # Build summarize_graph in external/tensorflow + BuildTensorFlowSummarizeGraph(args.tensorflow_path) + + # Summarize graph + SummarizeGraph(args) diff --git a/tools/tflkit/summarize_pb.sh b/tools/tflkit/summarize_pb.sh new file mode 100755 index 000000000..141f27bc3 --- /dev/null +++ b/tools/tflkit/summarize_pb.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +python summarize_pb.py --input_file=$1 $2 diff --git a/tools/tflkit/summarize_tflite.sh b/tools/tflkit/summarize_tflite.sh new file mode 100755 index 000000000..e15550b07 --- /dev/null +++ b/tools/tflkit/summarize_tflite.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +python "${SCRIPT_PATH}/../tflitefile_tool/model_parser.py" ${@} diff --git a/tools/tflkit/tflite_convert.sh b/tools/tflkit/tflite_convert.sh new file mode 100755 index 000000000..2056797ab --- /dev/null +++ b/tools/tflkit/tflite_convert.sh @@ -0,0 +1,136 @@ +#!/bin/bash + +usage() +{ + echo "usage : $0" + echo " --info=Information file" + echo " --tensorflow_path=TensorFlow path (Use externals/tensorflow by default)" + echo " --tensorflow_version=TensorFlow version (Must be entered)" +} + +SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +TF_DIR="${SCRIPT_PATH}/../../externals/tensorflow" + +for i in "$@" +do + case $i in + --info=*) + INFO=${i#*=} + ;; + --tensorflow_path=*) + TF_DIR=${i#*=} + ;; + --tensorflow_version=*) + TF_VERSION=${i#*=} + ;; + -h|--help) + usage + exit 0 + ;; + *) + usage + exit 1 + ;; + esac + shift +done + +if [ -z "$INFO" ]; then + echo "INFO is unset or set to the empty string" + usage + exit 1 +fi +if [ -z "$TF_DIR" ]; then + echo "tensorflow_path is unset or set to the empty string" + usage + exit 1 +fi +if [ -z "$TF_VERSION" ]; then + echo "tensorflow_version is unset or set to the empty string" + usage + exit 1 +fi + +if [ ! -x "$(command -v bazel)" ]; then + echo "Cannot find bazel. Please install bazel." + exit 1 +fi + +source $INFO + +if [ -z "$GRAPHDEF_PATH" ]; then + echo "GRAPHDEF_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$TFLITE_PATH" ]; then + echo "TFLITE_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$INPUT" ]; then + echo "INPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$INPUT_SHAPE" ]; then + echo "INPUT_SHAPE is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$OUTPUT" ]; then + echo "OUTPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi + +CUR_DIR=$(pwd) +{ + echo "Enter $TF_DIR" + pushd $TF_DIR > /dev/null + + NAME_LIST=() + INPUT_SHAPE_LIST=() + if [ -z "$NAME" ]; then + NAME_LIST+=("$TFLITE_PATH") + INPUT_SHAPE_LIST+=("$INPUT_SHAPE") + else + for name in $NAME; do + NAME_LIST[${#NAME_LIST[*]}]="${TFLITE_PATH/NAME/$name}" + done + for shape in $INPUT_SHAPE; do + INPUT_SHAPE_LIST[${#INPUT_SHAPE_LIST[*]}]="$shape" + done + if (( ${#NAME_LIST[*]} != ${#INPUT_SHAPE_LIST[*]} )); then + echo "The number of NAME and INPUT_SHAPE are different" + echo "Update the $INFO file" + exit 1 + fi + fi + + for (( i=0; i < ${#NAME_LIST[@]}; ++i )); do + if [ "${TF_VERSION%%.*}" = "2" ]; then + bazel run tensorflow/lite/python:tflite_convert -- \ + --output_file="${NAME_LIST[$i]}" \ + --graph_def_file="$GRAPHDEF_PATH" \ + --input_arrays="$INPUT" \ + --input_shapes="${INPUT_SHAPE_LIST[$i]}" \ + --output_arrays="$OUTPUT" \ + --allow_custom_ops=true + else + bazel run tensorflow/contrib/lite/python:tflite_convert -- \ + --output_file="${NAME_LIST[$i]}" \ + --graph_def_file="$GRAPHDEF_PATH" \ + --input_arrays="$INPUT" \ + --input_shapes="${INPUT_SHAPE_LIST[$i]}" \ + --output_arrays="$OUTPUT" \ + --allow_custom_ops + fi + done + popd + + for (( i=0; i < ${#NAME_LIST[@]}; ++i )); do + echo "OUTPUT FILE : ${NAME_LIST[$i]}" + done +} diff --git a/tools/tflkit/transform.template b/tools/tflkit/transform.template new file mode 100644 index 000000000..8a97f3da2 --- /dev/null +++ b/tools/tflkit/transform.template @@ -0,0 +1,5 @@ +GRAPHDEF_PATH= +TRANSFORM_PATH= +INPUT= +OUTPUT= +TRANSFORM_OPTIONS="strip_unused_nodes remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true) fold_batch_norms" diff --git a/tools/tflkit/transform_graph.sh b/tools/tflkit/transform_graph.sh new file mode 100755 index 000000000..828a36300 --- /dev/null +++ b/tools/tflkit/transform_graph.sh @@ -0,0 +1,94 @@ +#!/bin/bash + +usage() +{ + echo "usage : $0" + echo " --info=Information file" + echo " --tensorflow_path=TensorFlow path (Use externals/tensorflow by default)" +} + +SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +TF_DIR="${SCRIPT_PATH}/../../externals/tensorflow" + +for i in "$@" +do + case $i in + --info=*) + INFO=${i#*=} + ;; + --tensorflow_path=*) + TF_DIR=${i#*=} + ;; + -h|--help) + usage + exit 0 + ;; + *) + usage + exit 1 + ;; + esac + shift +done + +if [ -z "$INFO" ]; then + echo "INFO is unset or set to the empty string" + usage + exit 1 +fi +if [ -z "$TF_DIR" ]; then + echo "tensorflow_path is unset or set to the empty string" + usage + exit 1 +fi + +if [ ! -x "$(command -v bazel)" ]; then + echo "Cannot find bazel. Please install bazel." + exit 1 +fi + +source $INFO + +if [ -z "$GRAPHDEF_PATH" ]; then + echo "GRAPHDEF_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$TRANSFORM_PATH" ]; then + echo "TRANSFORM_PATH is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$INPUT" ]; then + echo "INPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$OUTPUT" ]; then + echo "OUTPUT is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi +if [ -z "$TRANSFORM_OPTIONS" ]; then + echo "TRANSFORM_OPTIONS is unset or set to the empty string" + echo "Update the $INFO file" + exit 1 +fi + +CUR_DIR=$(pwd) +{ + echo "Enter $TF_DIR" + pushd $TF_DIR > /dev/null + + bazel run tensorflow/tools/graph_transforms/transform_graph -- \ + --in_graph="$GRAPHDEF_PATH" \ + --out_graph="$TRANSFORM_PATH" \ + --inputs="$INPUT" \ + --outputs="$OUTPUT" \ + --transforms="$TRANSFORM_OPTIONS" + + popd + + echo "OUTPUT FILE : $TRANSFORM_PATH" +} |