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authorChunseok Lee <chunseok.lee@samsung.com>2019-01-08 08:36:34 (GMT)
committerChunseok Lee <chunseok.lee@samsung.com>2019-01-08 08:36:34 (GMT)
commitbd11b24234d7d43dfe05a81c520aa01ffad06e42 (patch)
tree57d0d4044977e4fa0e50cd9ba40b32006dff19eb
parent91f4ba45449f700a047a4aeea00b1a7c84e94c75 (diff)
downloadnnfw-upstream.zip
nnfw-upstream.tar.gz
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Imported Upstream version 0.3upstream/0.3upstream_bakupstream
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1988 files changed, 59129 insertions, 48291 deletions
diff --git a/.gitignore b/.gitignore
index 96188f8..da6e289 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 cc6da50..0000000
--- 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 fd9be5f..34824ff 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("${TARGET_OS}" STREQUAL "android")
diff --git a/Makefile b/Makefile
index 4c7b98e..fda5b0c 100644
--- a/Makefile
+++ b/Makefile
@@ -3,14 +3,13 @@ BUILD_TYPE?=Debug
CROSS_BUILD?=0
HOST_OS?=linux
TARGET_OS?=linux
-NPROCS:=1
PARALLEL_BUILD?=1
## TODO: fix obs build break
OBS_BUILD?=OFF
COVERAGE_BUILD?=0
BENCHMARK_ACL_BUILD?=0
OPTIONS?=
-UPDATE_MODULE?=1
+GENERATE_FILES?=1
# make TARGET and TYPE to lowercase
TARGET_ARCH_LC=$(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 e36a0ec..0000000
--- 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 3bebc27..0000000
--- 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 ecefdcb..0000000
--- 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 &&param_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 7bb31fc..0000000
--- 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 &&param_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 &&param_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 &&param_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 &&param_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 &&param_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>